librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-27 00:39:58	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 521 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-27 00:39:49	card stringlengths 11 1.01M
roa7n/gpt2-human_nontata_promoters-randomized_7_layers_0.0003_lr_2_e	roa7n	2023-09-28T22:06:22Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-28T22:06:19Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
pamelapaolacb/pruebaModeloTFM_Bert_es	pamelapaolacb	2023-09-28T21:45:53Z	123	0	transformers	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad_es", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-28T19:10:19Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - squad_es model-index: - name: pruebaModeloTFM_Bert_es results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pruebaModeloTFM_Bert_es This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_es dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
LarryAIDraw/maria_2	LarryAIDraw	2023-09-28T21:38:42Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-28T21:30:21Z	--- license: creativeml-openrail-m --- https://civitai.com/models/153259/maria-sarushima-yamada-kun-to-7-nin-no-majo
furkanbjk474/furkan	furkanbjk474	2023-09-28T21:37:10Z	0	1	espnet	[ "espnet", "image-to-image", "aa", "dataset:lmsys/lmsys-chat-1m", "dataset:fka/awesome-chatgpt-prompts", "dataset:uonlp/CulturaX", "license:apache-2.0", "region:us" ]	image-to-image	2023-09-28T21:34:49Z	--- license: apache-2.0 datasets: - lmsys/lmsys-chat-1m - fka/awesome-chatgpt-prompts - uonlp/CulturaX language: - aa metrics: - bleurt library_name: espnet pipeline_tag: image-to-image ---
LarryAIDraw/Aradia-10	LarryAIDraw	2023-09-28T21:35:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-28T21:30:42Z	--- license: creativeml-openrail-m --- https://civitai.com/models/153026/aradia-toaru-series
roa7n/gpt2-human_nontata_promoters-randomized_7_layers_0.003_lr_2_e	roa7n	2023-09-28T21:33:42Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T21:33:40Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
s3nh/Nexusflow-NexusRaven-13B-GGUF	s3nh	2023-09-28T21:29:45Z	0	1	transformers	[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T21:07:16Z	--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/Nexusflow/NexusRaven-13B). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card
kanishka/smolm-autoreg-bpe-babylm-base-1e-3	kanishka	2023-09-28T21:19:28Z	174	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T03:30:06Z	--- base_model: models/smolm-autoreg-bpe-babylm-base-1e-3/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-base-1e-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smolm-autoreg-bpe-babylm-base-1e-3 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-base-1e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-base-1e-3/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8078 - Accuracy: 0.4772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.2352 \| 1.0 \| 10468 \| 3.1040 \| 0.4394 \| \| 3.0865 \| 2.0 \| 20936 \| 3.0063 \| 0.4510 \| \| 3.0278 \| 3.0 \| 31404 \| 2.9597 \| 0.4567 \| \| 2.9932 \| 4.0 \| 41872 \| 2.9350 \| 0.4596 \| \| 2.9675 \| 5.0 \| 52340 \| 2.9148 \| 0.4622 \| \| 2.9492 \| 6.0 \| 62808 \| 2.9016 \| 0.4643 \| \| 2.9344 \| 7.0 \| 73276 \| 2.8918 \| 0.4652 \| \| 2.9213 \| 8.0 \| 83744 \| 2.8834 \| 0.4666 \| \| 2.912 \| 9.0 \| 94212 \| 2.8747 \| 0.4676 \| \| 2.9028 \| 10.0 \| 104680 \| 2.8695 \| 0.4684 \| \| 2.8964 \| 11.0 \| 115148 \| 2.8627 \| 0.4693 \| \| 2.8901 \| 12.0 \| 125616 \| 2.8589 \| 0.4698 \| \| 2.8839 \| 13.0 \| 136084 \| 2.8543 \| 0.4703 \| \| 2.8807 \| 14.0 \| 146552 \| 2.8507 \| 0.4709 \| \| 2.8738 \| 15.0 \| 157020 \| 2.8475 \| 0.4712 \| \| 2.8706 \| 16.0 \| 167488 \| 2.8422 \| 0.4720 \| \| 2.8645 \| 17.0 \| 177956 \| 2.8448 \| 0.4718 \| \| 2.8595 \| 18.0 \| 188424 \| 2.8365 \| 0.4728 \| \| 2.8542 \| 19.0 \| 198892 \| 2.8366 \| 0.4728 \| \| 2.8523 \| 20.0 \| 209360 \| 2.8318 \| 0.4731 \| \| 2.8479 \| 21.0 \| 219828 \| 2.8316 \| 0.4736 \| \| 2.849 \| 22.0 \| 230296 \| 2.8298 \| 0.4739 \| \| 2.8451 \| 23.0 \| 240764 \| 2.8291 \| 0.4739 \| \| 2.8436 \| 24.0 \| 251232 \| 2.8244 \| 0.4744 \| \| 2.8387 \| 25.0 \| 261700 \| 2.8247 \| 0.4745 \| \| 2.8354 \| 26.0 \| 272168 \| 2.8228 \| 0.4750 \| \| 2.8339 \| 27.0 \| 282636 \| 2.8213 \| 0.4751 \| \| 2.8307 \| 28.0 \| 293104 \| 2.8206 \| 0.4752 \| \| 2.8276 \| 29.0 \| 303572 \| 2.8176 \| 0.4756 \| \| 2.8244 \| 30.0 \| 314040 \| 2.8165 \| 0.4757 \| \| 2.8229 \| 31.0 \| 324508 \| 2.8165 \| 0.4757 \| \| 2.8209 \| 32.0 \| 334976 \| 2.8141 \| 0.4761 \| \| 2.8206 \| 33.0 \| 345444 \| 2.8119 \| 0.4763 \| \| 2.8178 \| 34.0 \| 355912 \| 2.8135 \| 0.4764 \| \| 2.8152 \| 35.0 \| 366380 \| 2.8119 \| 0.4766 \| \| 2.813 \| 36.0 \| 376848 \| 2.8106 \| 0.4767 \| \| 2.8107 \| 37.0 \| 387316 \| 2.8081 \| 0.4771 \| \| 2.8113 \| 38.0 \| 397784 \| 2.8091 \| 0.4769 \| \| 2.8073 \| 39.0 \| 408252 \| 2.8078 \| 0.4771 \| \| 2.8061 \| 40.0 \| 418720 \| 2.8078 \| 0.4772 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
badokorach/flan-t5-small-qa-10	badokorach	2023-09-28T21:01:39Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T18:42:47Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-qa-10 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 37.0091 \| 0.01 \| 10 \| 39.2389 \| \| 34.7317 \| 0.02 \| 20 \| 36.6257 \| \| 32.6533 \| 0.03 \| 30 \| 34.1553 \| \| 29.7792 \| 0.04 \| 40 \| 31.6802 \| \| 27.9268 \| 0.05 \| 50 \| 29.1925 \| \| 25.8475 \| 0.06 \| 60 \| 26.7287 \| \| 23.1826 \| 0.07 \| 70 \| 24.1478 \| \| 20.9714 \| 0.08 \| 80 \| 21.3731 \| \| 18.5839 \| 0.09 \| 90 \| 18.4583 \| \| 16.4034 \| 0.1 \| 100 \| 15.4845 \| \| 13.2378 \| 0.11 \| 110 \| 12.3754 \| \| 11.1189 \| 0.12 \| 120 \| 9.2924 \| \| 8.8691 \| 0.13 \| 130 \| 6.9111 \| \| 7.3894 \| 0.14 \| 140 \| 5.8619 \| \| 6.5278 \| 0.15 \| 150 \| 5.3589 \| \| 5.8773 \| 0.16 \| 160 \| 5.0272 \| \| 5.6232 \| 0.17 \| 170 \| 4.7799 \| \| 5.1812 \| 0.18 \| 180 \| 4.5834 \| \| 4.8379 \| 0.19 \| 190 \| 4.4145 \| \| 4.6718 \| 0.2 \| 200 \| 4.2705 \| \| 4.5679 \| 0.21 \| 210 \| 4.1402 \| \| 4.3725 \| 0.22 \| 220 \| 4.0193 \| \| 4.2319 \| 0.23 \| 230 \| 3.8986 \| \| 4.0162 \| 0.24 \| 240 \| 3.7799 \| \| 3.9175 \| 0.25 \| 250 \| 3.6602 \| \| 3.8803 \| 0.26 \| 260 \| 3.5376 \| \| 3.7403 \| 0.27 \| 270 \| 3.4149 \| \| 3.575 \| 0.28 \| 280 \| 3.2841 \| \| 3.5047 \| 0.29 \| 290 \| 3.1522 \| \| 3.3545 \| 0.3 \| 300 \| 3.0169 \| \| 3.2233 \| 0.31 \| 310 \| 2.8774 \| \| 3.1314 \| 0.32 \| 320 \| 2.7361 \| \| 2.9994 \| 0.33 \| 330 \| 2.5934 \| \| 2.901 \| 0.34 \| 340 \| 2.4511 \| \| 2.6969 \| 0.35 \| 350 \| 2.3144 \| \| 2.643 \| 0.36 \| 360 \| 2.1838 \| \| 2.5416 \| 0.37 \| 370 \| 2.0683 \| \| 2.3707 \| 0.38 \| 380 \| 1.9536 \| \| 2.3231 \| 0.39 \| 390 \| 1.8523 \| \| 2.2729 \| 0.4 \| 400 \| 1.7420 \| \| 2.0765 \| 0.41 \| 410 \| 1.6323 \| \| 2.0129 \| 0.42 \| 420 \| 1.5289 \| \| 1.8826 \| 0.43 \| 430 \| 1.4344 \| \| 1.878 \| 0.44 \| 440 \| 1.3528 \| \| 1.7105 \| 0.45 \| 450 \| 1.2652 \| \| 1.6877 \| 0.46 \| 460 \| 1.1842 \| \| 1.5966 \| 0.47 \| 470 \| 1.1044 \| \| 1.509 \| 0.48 \| 480 \| 1.0282 \| \| 1.4869 \| 0.49 \| 490 \| 0.9637 \| \| 1.3615 \| 0.5 \| 500 \| 0.9079 \| \| 1.3525 \| 0.51 \| 510 \| 0.8398 \| \| 1.2226 \| 0.52 \| 520 \| 0.7795 \| \| 1.1191 \| 0.53 \| 530 \| 0.7361 \| \| 1.0896 \| 0.54 \| 540 \| 0.6988 \| \| 1.0617 \| 0.55 \| 550 \| 0.6521 \| \| 0.9407 \| 0.56 \| 560 \| 0.6081 \| \| 0.9509 \| 0.57 \| 570 \| 0.5644 \| \| 0.941 \| 0.58 \| 580 \| 0.5292 \| \| 0.8058 \| 0.59 \| 590 \| 0.4883 \| \| 0.856 \| 0.6 \| 600 \| 0.4508 \| \| 0.7525 \| 0.61 \| 610 \| 0.4194 \| \| 0.684 \| 0.62 \| 620 \| 0.3900 \| \| 0.644 \| 0.63 \| 630 \| 0.3664 \| \| 0.6718 \| 0.64 \| 640 \| 0.3421 \| \| 0.6279 \| 0.65 \| 650 \| 0.3193 \| \| 0.561 \| 0.66 \| 660 \| 0.2956 \| \| 0.577 \| 0.67 \| 670 \| 0.2754 \| \| 0.5564 \| 0.68 \| 680 \| 0.2587 \| \| 0.5221 \| 0.69 \| 690 \| 0.2438 \| \| 0.4814 \| 0.7 \| 700 \| 0.2297 \| \| 0.4113 \| 0.71 \| 710 \| 0.2191 \| \| 0.4718 \| 0.72 \| 720 \| 0.2070 \| \| 0.3819 \| 0.73 \| 730 \| 0.1986 \| \| 0.3924 \| 0.74 \| 740 \| 0.1891 \| \| 0.4157 \| 0.75 \| 750 \| 0.1777 \| \| 0.3915 \| 0.76 \| 760 \| 0.1678 \| \| 0.3523 \| 0.77 \| 770 \| 0.1598 \| \| 0.4251 \| 0.78 \| 780 \| 0.1532 \| \| 0.2944 \| 0.79 \| 790 \| 0.1472 \| \| 0.3066 \| 0.8 \| 800 \| 0.1396 \| \| 0.3363 \| 0.81 \| 810 \| 0.1340 \| \| 0.2954 \| 0.82 \| 820 \| 0.1283 \| \| 0.2756 \| 0.83 \| 830 \| 0.1220 \| \| 0.2898 \| 0.84 \| 840 \| 0.1164 \| \| 0.2862 \| 0.85 \| 850 \| 0.1103 \| \| 0.264 \| 0.86 \| 860 \| 0.1056 \| \| 0.3028 \| 0.87 \| 870 \| 0.1004 \| \| 0.3009 \| 0.88 \| 880 \| 0.0937 \| \| 0.2909 \| 0.89 \| 890 \| 0.0881 \| \| 0.2922 \| 0.9 \| 900 \| 0.0826 \| \| 0.2493 \| 0.91 \| 910 \| 0.0773 \| \| 0.2524 \| 0.92 \| 920 \| 0.0749 \| \| 0.1929 \| 0.93 \| 930 \| 0.0727 \| \| 0.2461 \| 0.94 \| 940 \| 0.0679 \| \| 0.2471 \| 0.95 \| 950 \| 0.0642 \| \| 0.1956 \| 0.96 \| 960 \| 0.0628 \| \| 0.1839 \| 0.97 \| 970 \| 0.0617 \| \| 0.2151 \| 0.98 \| 980 \| 0.0599 \| \| 0.2182 \| 0.99 \| 990 \| 0.0580 \| \| 0.2473 \| 1.0 \| 1000 \| 0.0567 \| \| 0.2249 \| 1.01 \| 1010 \| 0.0562 \| \| 0.2807 \| 1.02 \| 1020 \| 0.0550 \| \| 0.2066 \| 1.03 \| 1030 \| 0.0543 \| \| 0.168 \| 1.04 \| 1040 \| 0.0550 \| \| 0.2199 \| 1.05 \| 1050 \| 0.0550 \| \| 0.2135 \| 1.06 \| 1060 \| 0.0543 \| \| 0.2316 \| 1.07 \| 1070 \| 0.0535 \| \| 0.196 \| 1.08 \| 1080 \| 0.0535 \| \| 0.163 \| 1.09 \| 1090 \| 0.0532 \| \| 0.115 \| 1.1 \| 1100 \| 0.0533 \| \| 0.1758 \| 1.11 \| 1110 \| 0.0532 \| \| 0.1862 \| 1.12 \| 1120 \| 0.0537 \| \| 0.2976 \| 1.13 \| 1130 \| 0.0535 \| \| 0.1683 \| 1.14 \| 1140 \| 0.0531 \| \| 0.1836 \| 1.15 \| 1150 \| 0.0533 \| \| 0.1932 \| 1.16 \| 1160 \| 0.0536 \| \| 0.2083 \| 1.17 \| 1170 \| 0.0524 \| \| 0.1482 \| 1.18 \| 1180 \| 0.0524 \| \| 0.1869 \| 1.19 \| 1190 \| 0.0523 \| \| 0.2302 \| 1.2 \| 1200 \| 0.0522 \| \| 0.1334 \| 1.21 \| 1210 \| 0.0522 \| \| 0.172 \| 1.22 \| 1220 \| 0.0519 \| \| 0.1492 \| 1.23 \| 1230 \| 0.0515 \| \| 0.18 \| 1.24 \| 1240 \| 0.0513 \| \| 0.2685 \| 1.25 \| 1250 \| 0.0517 \| \| 0.1621 \| 1.26 \| 1260 \| 0.0523 \| \| 0.172 \| 1.27 \| 1270 \| 0.0519 \| \| 0.2655 \| 1.28 \| 1280 \| 0.0516 \| \| 0.1625 \| 1.29 \| 1290 \| 0.0519 \| \| 0.251 \| 1.3 \| 1300 \| 0.0525 \| \| 0.1506 \| 1.31 \| 1310 \| 0.0528 \| \| 0.206 \| 1.32 \| 1320 \| 0.0530 \| \| 0.186 \| 1.33 \| 1330 \| 0.0526 \| \| 0.1778 \| 1.34 \| 1340 \| 0.0528 \| \| 0.167 \| 1.35 \| 1350 \| 0.0528 \| \| 0.1602 \| 1.36 \| 1360 \| 0.0528 \| \| 0.2509 \| 1.37 \| 1370 \| 0.0523 \| \| 0.1615 \| 1.38 \| 1380 \| 0.0522 \| \| 0.1988 \| 1.39 \| 1390 \| 0.0523 \| \| 0.1602 \| 1.4 \| 1400 \| 0.0525 \| \| 0.1637 \| 1.41 \| 1410 \| 0.0524 \| \| 0.1848 \| 1.42 \| 1420 \| 0.0523 \| \| 0.1662 \| 1.43 \| 1430 \| 0.0525 \| \| 0.1603 \| 1.44 \| 1440 \| 0.0525 \| \| 0.1723 \| 1.45 \| 1450 \| 0.0524 \| \| 0.15 \| 1.46 \| 1460 \| 0.0522 \| \| 0.1855 \| 1.47 \| 1470 \| 0.0519 \| \| 0.1828 \| 1.48 \| 1480 \| 0.0523 \| \| 0.1975 \| 1.49 \| 1490 \| 0.0523 \| \| 0.2105 \| 1.5 \| 1500 \| 0.0525 \| \| 0.1808 \| 1.51 \| 1510 \| 0.0530 \| \| 0.1714 \| 1.52 \| 1520 \| 0.0529 \| \| 0.1481 \| 1.53 \| 1530 \| 0.0533 \| \| 0.1579 \| 1.54 \| 1540 \| 0.0539 \| \| 0.194 \| 1.55 \| 1550 \| 0.0541 \| \| 0.1521 \| 1.56 \| 1560 \| 0.0540 \| \| 0.2194 \| 1.57 \| 1570 \| 0.0538 \| \| 0.1487 \| 1.58 \| 1580 \| 0.0536 \| \| 0.1511 \| 1.59 \| 1590 \| 0.0536 \| \| 0.1958 \| 1.6 \| 1600 \| 0.0537 \| \| 0.179 \| 1.61 \| 1610 \| 0.0536 \| \| 0.2006 \| 1.62 \| 1620 \| 0.0535 \| \| 0.1483 \| 1.63 \| 1630 \| 0.0537 \| \| 0.1321 \| 1.64 \| 1640 \| 0.0536 \| \| 0.1875 \| 1.65 \| 1650 \| 0.0534 \| \| 0.1777 \| 1.66 \| 1660 \| 0.0535 \| \| 0.1289 \| 1.67 \| 1670 \| 0.0534 \| \| 0.1534 \| 1.68 \| 1680 \| 0.0536 \| \| 0.1942 \| 1.69 \| 1690 \| 0.0535 \| \| 0.1434 \| 1.7 \| 1700 \| 0.0539 \| \| 0.1316 \| 1.71 \| 1710 \| 0.0543 \| \| 0.1205 \| 1.72 \| 1720 \| 0.0542 \| \| 0.1746 \| 1.73 \| 1730 \| 0.0538 \| \| 0.1505 \| 1.74 \| 1740 \| 0.0537 \| \| 0.1811 \| 1.75 \| 1750 \| 0.0541 \| \| 0.1292 \| 1.76 \| 1760 \| 0.0542 \| \| 0.1545 \| 1.77 \| 1770 \| 0.0541 \| \| 0.192 \| 1.78 \| 1780 \| 0.0540 \| \| 0.1223 \| 1.79 \| 1790 \| 0.0543 \| \| 0.2161 \| 1.8 \| 1800 \| 0.0546 \| \| 0.1408 \| 1.81 \| 1810 \| 0.0550 \| \| 0.1408 \| 1.82 \| 1820 \| 0.0554 \| \| 0.1239 \| 1.83 \| 1830 \| 0.0555 \| \| 0.2161 \| 1.84 \| 1840 \| 0.0555 \| \| 0.1122 \| 1.85 \| 1850 \| 0.0554 \| \| 0.1465 \| 1.86 \| 1860 \| 0.0549 \| \| 0.1385 \| 1.87 \| 1870 \| 0.0548 \| \| 0.1651 \| 1.88 \| 1880 \| 0.0550 \| \| 0.1716 \| 1.89 \| 1890 \| 0.0552 \| \| 0.1317 \| 1.9 \| 1900 \| 0.0550 \| \| 0.1228 \| 1.91 \| 1910 \| 0.0548 \| \| 0.1764 \| 1.92 \| 1920 \| 0.0553 \| \| 0.1694 \| 1.93 \| 1930 \| 0.0555 \| \| 0.1607 \| 1.94 \| 1940 \| 0.0555 \| \| 0.1989 \| 1.95 \| 1950 \| 0.0547 \| \| 0.1866 \| 1.96 \| 1960 \| 0.0545 \| \| 0.1006 \| 1.97 \| 1970 \| 0.0545 \| \| 0.1769 \| 1.98 \| 1980 \| 0.0547 \| \| 0.1266 \| 1.99 \| 1990 \| 0.0550 \| \| 0.1972 \| 2.0 \| 2000 \| 0.0547 \| \| 0.1404 \| 2.01 \| 2010 \| 0.0542 \| \| 0.2077 \| 2.02 \| 2020 \| 0.0543 \| \| 0.1607 \| 2.03 \| 2030 \| 0.0545 \| \| 0.157 \| 2.04 \| 2040 \| 0.0546 \| \| 0.1171 \| 2.05 \| 2050 \| 0.0549 \| \| 0.1419 \| 2.06 \| 2060 \| 0.0554 \| \| 0.2234 \| 2.07 \| 2070 \| 0.0554 \| \| 0.2123 \| 2.08 \| 2080 \| 0.0548 \| \| 0.1856 \| 2.09 \| 2090 \| 0.0544 \| \| 0.1061 \| 2.1 \| 2100 \| 0.0537 \| \| 0.2295 \| 2.11 \| 2110 \| 0.0537 \| \| 0.203 \| 2.12 \| 2120 \| 0.0543 \| \| 0.1858 \| 2.13 \| 2130 \| 0.0543 \| \| 0.1617 \| 2.14 \| 2140 \| 0.0546 \| \| 0.161 \| 2.15 \| 2150 \| 0.0550 \| \| 0.1707 \| 2.16 \| 2160 \| 0.0553 \| \| 0.2021 \| 2.17 \| 2170 \| 0.0556 \| \| 0.1121 \| 2.18 \| 2180 \| 0.0555 \| \| 0.1608 \| 2.19 \| 2190 \| 0.0557 \| \| 0.1782 \| 2.2 \| 2200 \| 0.0561 \| \| 0.1261 \| 2.21 \| 2210 \| 0.0563 \| \| 0.1748 \| 2.22 \| 2220 \| 0.0563 \| \| 0.254 \| 2.23 \| 2230 \| 0.0560 \| \| 0.1894 \| 2.24 \| 2240 \| 0.0560 \| \| 0.1825 \| 2.25 \| 2250 \| 0.0559 \| \| 0.1078 \| 2.26 \| 2260 \| 0.0555 \| \| 0.1718 \| 2.27 \| 2270 \| 0.0556 \| \| 0.1274 \| 2.28 \| 2280 \| 0.0556 \| \| 0.097 \| 2.29 \| 2290 \| 0.0557 \| \| 0.1887 \| 2.3 \| 2300 \| 0.0557 \| \| 0.1246 \| 2.31 \| 2310 \| 0.0552 \| \| 0.2024 \| 2.32 \| 2320 \| 0.0547 \| \| 0.1878 \| 2.33 \| 2330 \| 0.0542 \| \| 0.1661 \| 2.34 \| 2340 \| 0.0546 \| \| 0.142 \| 2.35 \| 2350 \| 0.0546 \| \| 0.1046 \| 2.36 \| 2360 \| 0.0546 \| \| 0.239 \| 2.37 \| 2370 \| 0.0546 \| \| 0.1454 \| 2.38 \| 2380 \| 0.0547 \| \| 0.144 \| 2.39 \| 2390 \| 0.0549 \| \| 0.1983 \| 2.4 \| 2400 \| 0.0548 \| \| 0.1583 \| 2.41 \| 2410 \| 0.0549 \| \| 0.1345 \| 2.42 \| 2420 \| 0.0547 \| \| 0.1838 \| 2.43 \| 2430 \| 0.0553 \| \| 0.1441 \| 2.44 \| 2440 \| 0.0557 \| \| 0.142 \| 2.45 \| 2450 \| 0.0556 \| \| 0.1743 \| 2.46 \| 2460 \| 0.0556 \| \| 0.152 \| 2.47 \| 2470 \| 0.0557 \| \| 0.1221 \| 2.48 \| 2480 \| 0.0556 \| \| 0.1611 \| 2.49 \| 2490 \| 0.0555 \| \| 0.128 \| 2.5 \| 2500 \| 0.0554 \| \| 0.1872 \| 2.51 \| 2510 \| 0.0554 \| \| 0.1425 \| 2.52 \| 2520 \| 0.0557 \| \| 0.1585 \| 2.53 \| 2530 \| 0.0561 \| \| 0.1257 \| 2.54 \| 2540 \| 0.0563 \| \| 0.1805 \| 2.55 \| 2550 \| 0.0560 \| \| 0.1431 \| 2.56 \| 2560 \| 0.0557 \| \| 0.1569 \| 2.57 \| 2570 \| 0.0558 \| \| 0.1703 \| 2.58 \| 2580 \| 0.0558 \| \| 0.1319 \| 2.59 \| 2590 \| 0.0555 \| \| 0.1608 \| 2.6 \| 2600 \| 0.0559 \| \| 0.1553 \| 2.61 \| 2610 \| 0.0560 \| \| 0.1855 \| 2.62 \| 2620 \| 0.0562 \| \| 0.1856 \| 2.63 \| 2630 \| 0.0562 \| \| 0.1715 \| 2.64 \| 2640 \| 0.0561 \| \| 0.1122 \| 2.65 \| 2650 \| 0.0562 \| \| 0.1671 \| 2.66 \| 2660 \| 0.0564 \| \| 0.1613 \| 2.67 \| 2670 \| 0.0567 \| \| 0.1648 \| 2.68 \| 2680 \| 0.0570 \| \| 0.15 \| 2.69 \| 2690 \| 0.0572 \| \| 0.1499 \| 2.7 \| 2700 \| 0.0571 \| \| 0.1598 \| 2.71 \| 2710 \| 0.0572 \| \| 0.1804 \| 2.72 \| 2720 \| 0.0572 \| \| 0.1721 \| 2.73 \| 2730 \| 0.0570 \| \| 0.131 \| 2.74 \| 2740 \| 0.0571 \| \| 0.2229 \| 2.75 \| 2750 \| 0.0570 \| \| 0.1337 \| 2.76 \| 2760 \| 0.0573 \| \| 0.1472 \| 2.77 \| 2770 \| 0.0575 \| \| 0.1202 \| 2.78 \| 2780 \| 0.0578 \| \| 0.1417 \| 2.79 \| 2790 \| 0.0582 \| \| 0.123 \| 2.8 \| 2800 \| 0.0584 \| \| 0.2054 \| 2.81 \| 2810 \| 0.0581 \| \| 0.1761 \| 2.82 \| 2820 \| 0.0573 \| \| 0.1034 \| 2.83 \| 2830 \| 0.0567 \| \| 0.1416 \| 2.84 \| 2840 \| 0.0564 \| \| 0.143 \| 2.85 \| 2850 \| 0.0565 \| \| 0.1295 \| 2.86 \| 2860 \| 0.0567 \| \| 0.1201 \| 2.87 \| 2870 \| 0.0565 \| \| 0.1168 \| 2.88 \| 2880 \| 0.0563 \| \| 0.1236 \| 2.89 \| 2890 \| 0.0559 \| \| 0.1664 \| 2.9 \| 2900 \| 0.0559 \| \| 0.1555 \| 2.91 \| 2910 \| 0.0562 \| \| 0.1657 \| 2.92 \| 2920 \| 0.0565 \| \| 0.1213 \| 2.93 \| 2930 \| 0.0568 \| \| 0.1557 \| 2.94 \| 2940 \| 0.0569 \| \| 0.1795 \| 2.95 \| 2950 \| 0.0570 \| \| 0.1655 \| 2.96 \| 2960 \| 0.0571 \| \| 0.2015 \| 2.97 \| 2970 \| 0.0571 \| \| 0.1956 \| 2.98 \| 2980 \| 0.0572 \| \| 0.1456 \| 2.99 \| 2990 \| 0.0575 \| \| 0.1298 \| 3.0 \| 3000 \| 0.0574 \| \| 0.1589 \| 3.01 \| 3010 \| 0.0574 \| \| 0.1367 \| 3.02 \| 3020 \| 0.0573 \| \| 0.1321 \| 3.03 \| 3030 \| 0.0573 \| \| 0.1451 \| 3.04 \| 3040 \| 0.0572 \| \| 0.174 \| 3.05 \| 3050 \| 0.0574 \| \| 0.1547 \| 3.06 \| 3060 \| 0.0577 \| \| 0.1229 \| 3.07 \| 3070 \| 0.0578 \| \| 0.1207 \| 3.08 \| 3080 \| 0.0584 \| \| 0.1308 \| 3.09 \| 3090 \| 0.0588 \| \| 0.1882 \| 3.1 \| 3100 \| 0.0588 \| \| 0.1647 \| 3.11 \| 3110 \| 0.0586 \| \| 0.1121 \| 3.12 \| 3120 \| 0.0587 \| \| 0.1656 \| 3.13 \| 3130 \| 0.0586 \| \| 0.1671 \| 3.14 \| 3140 \| 0.0588 \| \| 0.1399 \| 3.15 \| 3150 \| 0.0591 \| \| 0.1504 \| 3.16 \| 3160 \| 0.0594 \| \| 0.2393 \| 3.17 \| 3170 \| 0.0587 \| \| 0.1273 \| 3.18 \| 3180 \| 0.0583 \| \| 0.1365 \| 3.19 \| 3190 \| 0.0579 \| \| 0.1752 \| 3.2 \| 3200 \| 0.0582 \| \| 0.1526 \| 3.21 \| 3210 \| 0.0585 \| \| 0.1219 \| 3.22 \| 3220 \| 0.0582 \| \| 0.1416 \| 3.23 \| 3230 \| 0.0581 \| \| 0.1172 \| 3.24 \| 3240 \| 0.0577 \| \| 0.1205 \| 3.25 \| 3250 \| 0.0579 \| \| 0.1554 \| 3.26 \| 3260 \| 0.0582 \| \| 0.1442 \| 3.27 \| 3270 \| 0.0583 \| \| 0.195 \| 3.28 \| 3280 \| 0.0581 \| \| 0.1981 \| 3.29 \| 3290 \| 0.0577 \| \| 0.2147 \| 3.3 \| 3300 \| 0.0576 \| \| 0.1204 \| 3.31 \| 3310 \| 0.0578 \| \| 0.1628 \| 3.32 \| 3320 \| 0.0581 \| \| 0.3038 \| 3.33 \| 3330 \| 0.0583 \| \| 0.1759 \| 3.34 \| 3340 \| 0.0584 \| \| 0.1454 \| 3.35 \| 3350 \| 0.0585 \| \| 0.1269 \| 3.36 \| 3360 \| 0.0587 \| \| 0.1485 \| 3.37 \| 3370 \| 0.0591 \| \| 0.1197 \| 3.38 \| 3380 \| 0.0594 \| \| 0.1352 \| 3.39 \| 3390 \| 0.0597 \| \| 0.1679 \| 3.4 \| 3400 \| 0.0599 \| \| 0.1607 \| 3.41 \| 3410 \| 0.0601 \| \| 0.1519 \| 3.42 \| 3420 \| 0.0603 \| \| 0.1227 \| 3.43 \| 3430 \| 0.0605 \| \| 0.2099 \| 3.44 \| 3440 \| 0.0606 \| \| 0.1611 \| 3.45 \| 3450 \| 0.0605 \| \| 0.2014 \| 3.46 \| 3460 \| 0.0604 \| \| 0.1074 \| 3.47 \| 3470 \| 0.0601 \| \| 0.195 \| 3.48 \| 3480 \| 0.0600 \| \| 0.1559 \| 3.49 \| 3490 \| 0.0593 \| \| 0.1636 \| 3.5 \| 3500 \| 0.0588 \| \| 0.1503 \| 3.51 \| 3510 \| 0.0588 \| \| 0.1486 \| 3.52 \| 3520 \| 0.0585 \| \| 0.1763 \| 3.53 \| 3530 \| 0.0582 \| \| 0.1855 \| 3.54 \| 3540 \| 0.0582 \| \| 0.1641 \| 3.55 \| 3550 \| 0.0585 \| \| 0.1398 \| 3.56 \| 3560 \| 0.0586 \| \| 0.1719 \| 3.57 \| 3570 \| 0.0588 \| \| 0.109 \| 3.58 \| 3580 \| 0.0589 \| \| 0.1498 \| 3.59 \| 3590 \| 0.0589 \| \| 0.1012 \| 3.6 \| 3600 \| 0.0589 \| \| 0.1175 \| 3.61 \| 3610 \| 0.0591 \| \| 0.1434 \| 3.62 \| 3620 \| 0.0594 \| \| 0.1609 \| 3.63 \| 3630 \| 0.0596 \| \| 0.1448 \| 3.64 \| 3640 \| 0.0599 \| \| 0.2215 \| 3.65 \| 3650 \| 0.0599 \| \| 0.1529 \| 3.66 \| 3660 \| 0.0601 \| \| 0.1343 \| 3.67 \| 3670 \| 0.0603 \| \| 0.1914 \| 3.68 \| 3680 \| 0.0603 \| \| 0.1089 \| 3.69 \| 3690 \| 0.0602 \| \| 0.156 \| 3.7 \| 3700 \| 0.0603 \| \| 0.1274 \| 3.71 \| 3710 \| 0.0606 \| \| 0.1146 \| 3.72 \| 3720 \| 0.0609 \| \| 0.1378 \| 3.73 \| 3730 \| 0.0610 \| \| 0.101 \| 3.74 \| 3740 \| 0.0610 \| \| 0.1502 \| 3.75 \| 3750 \| 0.0610 \| \| 0.1323 \| 3.76 \| 3760 \| 0.0610 \| \| 0.2017 \| 3.77 \| 3770 \| 0.0611 \| \| 0.1167 \| 3.78 \| 3780 \| 0.0610 \| \| 0.2027 \| 3.79 \| 3790 \| 0.0611 \| \| 0.1739 \| 3.8 \| 3800 \| 0.0612 \| \| 0.1269 \| 3.81 \| 3810 \| 0.0610 \| \| 0.1642 \| 3.82 \| 3820 \| 0.0606 \| \| 0.141 \| 3.83 \| 3830 \| 0.0602 \| \| 0.1567 \| 3.84 \| 3840 \| 0.0598 \| \| 0.0849 \| 3.85 \| 3850 \| 0.0598 \| \| 0.1388 \| 3.86 \| 3860 \| 0.0601 \| \| 0.2029 \| 3.87 \| 3870 \| 0.0602 \| \| 0.1471 \| 3.88 \| 3880 \| 0.0600 \| \| 0.1307 \| 3.89 \| 3890 \| 0.0597 \| \| 0.1363 \| 3.9 \| 3900 \| 0.0596 \| \| 0.1566 \| 3.91 \| 3910 \| 0.0596 \| \| 0.1936 \| 3.92 \| 3920 \| 0.0594 \| \| 0.1063 \| 3.93 \| 3930 \| 0.0592 \| \| 0.1351 \| 3.94 \| 3940 \| 0.0592 \| \| 0.1886 \| 3.95 \| 3950 \| 0.0593 \| \| 0.1387 \| 3.96 \| 3960 \| 0.0597 \| \| 0.1532 \| 3.97 \| 3970 \| 0.0599 \| \| 0.0992 \| 3.98 \| 3980 \| 0.0599 \| \| 0.1444 \| 3.99 \| 3990 \| 0.0598 \| \| 0.1382 \| 4.0 \| 4000 \| 0.0599 \| \| 0.1647 \| 4.01 \| 4010 \| 0.0600 \| \| 0.1551 \| 4.02 \| 4020 \| 0.0604 \| \| 0.1777 \| 4.03 \| 4030 \| 0.0606 \| \| 0.1395 \| 4.04 \| 4040 \| 0.0608 \| \| 0.1312 \| 4.05 \| 4050 \| 0.0609 \| \| 0.143 \| 4.06 \| 4060 \| 0.0610 \| \| 0.106 \| 4.07 \| 4070 \| 0.0608 \| \| 0.168 \| 4.08 \| 4080 \| 0.0607 \| \| 0.0988 \| 4.09 \| 4090 \| 0.0604 \| \| 0.1718 \| 4.1 \| 4100 \| 0.0602 \| \| 0.1607 \| 4.11 \| 4110 \| 0.0602 \| \| 0.1428 \| 4.12 \| 4120 \| 0.0601 \| \| 0.1518 \| 4.13 \| 4130 \| 0.0599 \| \| 0.1941 \| 4.14 \| 4140 \| 0.0600 \| \| 0.1339 \| 4.15 \| 4150 \| 0.0599 \| \| 0.1379 \| 4.16 \| 4160 \| 0.0599 \| \| 0.218 \| 4.17 \| 4170 \| 0.0600 \| \| 0.1359 \| 4.18 \| 4180 \| 0.0602 \| \| 0.1941 \| 4.19 \| 4190 \| 0.0602 \| \| 0.1182 \| 4.2 \| 4200 \| 0.0602 \| \| 0.1398 \| 4.21 \| 4210 \| 0.0602 \| \| 0.1385 \| 4.22 \| 4220 \| 0.0601 \| \| 0.1652 \| 4.23 \| 4230 \| 0.0599 \| \| 0.0985 \| 4.24 \| 4240 \| 0.0599 \| \| 0.1342 \| 4.25 \| 4250 \| 0.0602 \| \| 0.1767 \| 4.26 \| 4260 \| 0.0606 \| \| 0.1621 \| 4.27 \| 4270 \| 0.0609 \| \| 0.1813 \| 4.28 \| 4280 \| 0.0612 \| \| 0.14 \| 4.29 \| 4290 \| 0.0613 \| \| 0.1726 \| 4.3 \| 4300 \| 0.0615 \| \| 0.1031 \| 4.31 \| 4310 \| 0.0617 \| \| 0.1429 \| 4.32 \| 4320 \| 0.0617 \| \| 0.1956 \| 4.33 \| 4330 \| 0.0615 \| \| 0.1515 \| 4.34 \| 4340 \| 0.0613 \| \| 0.1109 \| 4.35 \| 4350 \| 0.0614 \| \| 0.1102 \| 4.36 \| 4360 \| 0.0617 \| \| 0.2273 \| 4.37 \| 4370 \| 0.0617 \| \| 0.1179 \| 4.38 \| 4380 \| 0.0617 \| \| 0.1186 \| 4.39 \| 4390 \| 0.0617 \| \| 0.1259 \| 4.4 \| 4400 \| 0.0611 \| \| 0.1914 \| 4.41 \| 4410 \| 0.0610 \| \| 0.1552 \| 4.42 \| 4420 \| 0.0612 \| \| 0.1703 \| 4.43 \| 4430 \| 0.0614 \| \| 0.1549 \| 4.44 \| 4440 \| 0.0612 \| \| 0.1307 \| 4.45 \| 4450 \| 0.0611 \| \| 0.1853 \| 4.46 \| 4460 \| 0.0609 \| \| 0.1055 \| 4.47 \| 4470 \| 0.0606 \| \| 0.1342 \| 4.48 \| 4480 \| 0.0606 \| \| 0.0804 \| 4.49 \| 4490 \| 0.0609 \| \| 0.1566 \| 4.5 \| 4500 \| 0.0610 \| \| 0.1472 \| 4.51 \| 4510 \| 0.0613 \| \| 0.2179 \| 4.52 \| 4520 \| 0.0613 \| \| 0.1365 \| 4.53 \| 4530 \| 0.0614 \| \| 0.157 \| 4.54 \| 4540 \| 0.0610 \| \| 0.1515 \| 4.55 \| 4550 \| 0.0609 \| \| 0.1678 \| 4.56 \| 4560 \| 0.0611 \| \| 0.1569 \| 4.57 \| 4570 \| 0.0613 \| \| 0.1257 \| 4.58 \| 4580 \| 0.0614 \| \| 0.1452 \| 4.59 \| 4590 \| 0.0612 \| \| 0.1275 \| 4.6 \| 4600 \| 0.0612 \| \| 0.1446 \| 4.61 \| 4610 \| 0.0613 \| \| 0.1571 \| 4.62 \| 4620 \| 0.0613 \| \| 0.1371 \| 4.63 \| 4630 \| 0.0612 \| \| 0.1152 \| 4.64 \| 4640 \| 0.0612 \| \| 0.1797 \| 4.65 \| 4650 \| 0.0613 \| \| 0.0911 \| 4.66 \| 4660 \| 0.0613 \| \| 0.1463 \| 4.67 \| 4670 \| 0.0612 \| \| 0.1428 \| 4.68 \| 4680 \| 0.0610 \| \| 0.1489 \| 4.69 \| 4690 \| 0.0612 \| \| 0.1344 \| 4.7 \| 4700 \| 0.0615 \| \| 0.1493 \| 4.71 \| 4710 \| 0.0615 \| \| 0.147 \| 4.72 \| 4720 \| 0.0613 \| \| 0.2329 \| 4.73 \| 4730 \| 0.0606 \| \| 0.1679 \| 4.74 \| 4740 \| 0.0602 \| \| 0.0977 \| 4.75 \| 4750 \| 0.0602 \| \| 0.1292 \| 4.76 \| 4760 \| 0.0603 \| \| 0.1301 \| 4.77 \| 4770 \| 0.0606 \| \| 0.1855 \| 4.78 \| 4780 \| 0.0609 \| \| 0.1647 \| 4.79 \| 4790 \| 0.0607 \| \| 0.1395 \| 4.8 \| 4800 \| 0.0604 \| \| 0.1692 \| 4.81 \| 4810 \| 0.0604 \| \| 0.1135 \| 4.82 \| 4820 \| 0.0604 \| \| 0.1075 \| 4.83 \| 4830 \| 0.0605 \| \| 0.188 \| 4.84 \| 4840 \| 0.0604 \| \| 0.168 \| 4.85 \| 4850 \| 0.0605 \| \| 0.1356 \| 4.86 \| 4860 \| 0.0603 \| \| 0.134 \| 4.87 \| 4870 \| 0.0603 \| \| 0.1245 \| 4.88 \| 4880 \| 0.0604 \| \| 0.1816 \| 4.89 \| 4890 \| 0.0605 \| \| 0.1144 \| 4.9 \| 4900 \| 0.0607 \| \| 0.1651 \| 4.91 \| 4910 \| 0.0610 \| \| 0.1725 \| 4.92 \| 4920 \| 0.0610 \| \| 0.0947 \| 4.93 \| 4930 \| 0.0612 \| \| 0.118 \| 4.94 \| 4940 \| 0.0615 \| \| 0.1341 \| 4.95 \| 4950 \| 0.0615 \| \| 0.1324 \| 4.96 \| 4960 \| 0.0618 \| \| 0.1321 \| 4.97 \| 4970 \| 0.0622 \| \| 0.1485 \| 4.98 \| 4980 \| 0.0624 \| \| 0.1445 \| 4.99 \| 4990 \| 0.0626 \| \| 0.1793 \| 5.0 \| 5000 \| 0.0627 \| \| 0.1374 \| 5.01 \| 5010 \| 0.0623 \| \| 0.1726 \| 5.02 \| 5020 \| 0.0619 \| \| 0.178 \| 5.03 \| 5030 \| 0.0618 \| \| 0.1814 \| 5.04 \| 5040 \| 0.0623 \| \| 0.1533 \| 5.05 \| 5050 \| 0.0625 \| \| 0.1618 \| 5.06 \| 5060 \| 0.0627 \| \| 0.1502 \| 5.07 \| 5070 \| 0.0625 \| \| 0.1228 \| 5.08 \| 5080 \| 0.0622 \| \| 0.1544 \| 5.09 \| 5090 \| 0.0621 \| \| 0.1253 \| 5.1 \| 5100 \| 0.0620 \| \| 0.2073 \| 5.11 \| 5110 \| 0.0619 \| \| 0.13 \| 5.12 \| 5120 \| 0.0620 \| \| 0.1163 \| 5.13 \| 5130 \| 0.0623 \| \| 0.1154 \| 5.14 \| 5140 \| 0.0628 \| \| 0.1249 \| 5.15 \| 5150 \| 0.0631 \| \| 0.1783 \| 5.16 \| 5160 \| 0.0633 \| \| 0.1536 \| 5.17 \| 5170 \| 0.0633 \| \| 0.1679 \| 5.18 \| 5180 \| 0.0631 \| \| 0.1164 \| 5.19 \| 5190 \| 0.0629 \| \| 0.1213 \| 5.2 \| 5200 \| 0.0628 \| \| 0.1076 \| 5.21 \| 5210 \| 0.0626 \| \| 0.1332 \| 5.22 \| 5220 \| 0.0625 \| \| 0.1406 \| 5.23 \| 5230 \| 0.0626 \| \| 0.096 \| 5.24 \| 5240 \| 0.0628 \| \| 0.1556 \| 5.25 \| 5250 \| 0.0631 \| \| 0.1363 \| 5.26 \| 5260 \| 0.0629 \| \| 0.1223 \| 5.27 \| 5270 \| 0.0628 \| \| 0.226 \| 5.28 \| 5280 \| 0.0630 \| \| 0.1957 \| 5.29 \| 5290 \| 0.0634 \| \| 0.1303 \| 5.3 \| 5300 \| 0.0634 \| \| 0.1123 \| 5.31 \| 5310 \| 0.0633 \| \| 0.1753 \| 5.32 \| 5320 \| 0.0634 \| \| 0.1642 \| 5.33 \| 5330 \| 0.0634 \| \| 0.1412 \| 5.34 \| 5340 \| 0.0633 \| \| 0.1732 \| 5.35 \| 5350 \| 0.0631 \| \| 0.134 \| 5.36 \| 5360 \| 0.0629 \| \| 0.1596 \| 5.37 \| 5370 \| 0.0627 \| \| 0.1049 \| 5.38 \| 5380 \| 0.0622 \| \| 0.1108 \| 5.39 \| 5390 \| 0.0620 \| \| 0.1326 \| 5.4 \| 5400 \| 0.0622 \| \| 0.1676 \| 5.41 \| 5410 \| 0.0622 \| \| 0.1327 \| 5.42 \| 5420 \| 0.0621 \| \| 0.0943 \| 5.43 \| 5430 \| 0.0620 \| \| 0.0914 \| 5.44 \| 5440 \| 0.0620 \| \| 0.1201 \| 5.45 \| 5450 \| 0.0620 \| \| 0.1441 \| 5.46 \| 5460 \| 0.0619 \| \| 0.149 \| 5.47 \| 5470 \| 0.0621 \| \| 0.0949 \| 5.48 \| 5480 \| 0.0622 \| \| 0.1606 \| 5.49 \| 5490 \| 0.0623 \| \| 0.1151 \| 5.5 \| 5500 \| 0.0626 \| \| 0.1613 \| 5.51 \| 5510 \| 0.0627 \| \| 0.189 \| 5.52 \| 5520 \| 0.0629 \| \| 0.1084 \| 5.53 \| 5530 \| 0.0631 \| \| 0.1285 \| 5.54 \| 5540 \| 0.0632 \| \| 0.1509 \| 5.55 \| 5550 \| 0.0633 \| \| 0.1201 \| 5.56 \| 5560 \| 0.0637 \| \| 0.148 \| 5.57 \| 5570 \| 0.0636 \| \| 0.148 \| 5.58 \| 5580 \| 0.0634 \| \| 0.1019 \| 5.59 \| 5590 \| 0.0634 \| \| 0.1447 \| 5.6 \| 5600 \| 0.0634 \| \| 0.1521 \| 5.61 \| 5610 \| 0.0636 \| \| 0.19 \| 5.62 \| 5620 \| 0.0635 \| \| 0.1164 \| 5.63 \| 5630 \| 0.0633 \| \| 0.1488 \| 5.64 \| 5640 \| 0.0633 \| \| 0.1114 \| 5.65 \| 5650 \| 0.0631 \| \| 0.1373 \| 5.66 \| 5660 \| 0.0626 \| \| 0.0925 \| 5.67 \| 5670 \| 0.0624 \| \| 0.1138 \| 5.68 \| 5680 \| 0.0621 \| \| 0.1219 \| 5.69 \| 5690 \| 0.0620 \| \| 0.1692 \| 5.7 \| 5700 \| 0.0622 \| \| 0.1941 \| 5.71 \| 5710 \| 0.0626 \| \| 0.1725 \| 5.72 \| 5720 \| 0.0626 \| \| 0.1028 \| 5.73 \| 5730 \| 0.0627 \| \| 0.1359 \| 5.74 \| 5740 \| 0.0627 \| \| 0.1321 \| 5.75 \| 5750 \| 0.0629 \| \| 0.1093 \| 5.76 \| 5760 \| 0.0630 \| \| 0.1399 \| 5.77 \| 5770 \| 0.0631 \| \| 0.1117 \| 5.78 \| 5780 \| 0.0632 \| \| 0.154 \| 5.79 \| 5790 \| 0.0634 \| \| 0.1628 \| 5.8 \| 5800 \| 0.0637 \| \| 0.2267 \| 5.81 \| 5810 \| 0.0639 \| \| 0.1716 \| 5.82 \| 5820 \| 0.0639 \| \| 0.165 \| 5.83 \| 5830 \| 0.0640 \| \| 0.1013 \| 5.84 \| 5840 \| 0.0641 \| \| 0.1417 \| 5.85 \| 5850 \| 0.0641 \| \| 0.1607 \| 5.86 \| 5860 \| 0.0639 \| \| 0.1191 \| 5.87 \| 5870 \| 0.0638 \| \| 0.1549 \| 5.88 \| 5880 \| 0.0635 \| \| 0.1906 \| 5.89 \| 5890 \| 0.0635 \| \| 0.1307 \| 5.9 \| 5900 \| 0.0636 \| \| 0.123 \| 5.91 \| 5910 \| 0.0636 \| \| 0.1389 \| 5.92 \| 5920 \| 0.0636 \| \| 0.1152 \| 5.93 \| 5930 \| 0.0637 \| \| 0.1267 \| 5.94 \| 5940 \| 0.0638 \| \| 0.1301 \| 5.95 \| 5950 \| 0.0640 \| \| 0.1583 \| 5.96 \| 5960 \| 0.0642 \| \| 0.1958 \| 5.97 \| 5970 \| 0.0644 \| \| 0.1591 \| 5.98 \| 5980 \| 0.0644 \| \| 0.2638 \| 5.99 \| 5990 \| 0.0643 \| \| 0.1605 \| 6.0 \| 6000 \| 0.0644 \| \| 0.1227 \| 6.01 \| 6010 \| 0.0643 \| \| 0.1721 \| 6.02 \| 6020 \| 0.0642 \| \| 0.1828 \| 6.03 \| 6030 \| 0.0643 \| \| 0.0953 \| 6.04 \| 6040 \| 0.0643 \| \| 0.1538 \| 6.05 \| 6050 \| 0.0643 \| \| 0.1403 \| 6.06 \| 6060 \| 0.0643 \| \| 0.1094 \| 6.07 \| 6070 \| 0.0641 \| \| 0.1493 \| 6.08 \| 6080 \| 0.0645 \| \| 0.1313 \| 6.09 \| 6090 \| 0.0647 \| \| 0.158 \| 6.1 \| 6100 \| 0.0650 \| \| 0.1184 \| 6.11 \| 6110 \| 0.0650 \| \| 0.0781 \| 6.12 \| 6120 \| 0.0648 \| \| 0.121 \| 6.13 \| 6130 \| 0.0649 \| \| 0.1694 \| 6.14 \| 6140 \| 0.0649 \| \| 0.1687 \| 6.15 \| 6150 \| 0.0646 \| \| 0.1408 \| 6.16 \| 6160 \| 0.0645 \| \| 0.1807 \| 6.17 \| 6170 \| 0.0645 \| \| 0.109 \| 6.18 \| 6180 \| 0.0644 \| \| 0.1266 \| 6.19 \| 6190 \| 0.0644 \| \| 0.0925 \| 6.2 \| 6200 \| 0.0649 \| \| 0.1768 \| 6.21 \| 6210 \| 0.0649 \| \| 0.1434 \| 6.22 \| 6220 \| 0.0650 \| \| 0.1449 \| 6.23 \| 6230 \| 0.0645 \| \| 0.0938 \| 6.24 \| 6240 \| 0.0644 \| \| 0.1336 \| 6.25 \| 6250 \| 0.0641 \| \| 0.1798 \| 6.26 \| 6260 \| 0.0641 \| \| 0.1549 \| 6.27 \| 6270 \| 0.0643 \| \| 0.1151 \| 6.28 \| 6280 \| 0.0644 \| \| 0.1468 \| 6.29 \| 6290 \| 0.0645 \| \| 0.1169 \| 6.3 \| 6300 \| 0.0644 \| \| 0.197 \| 6.31 \| 6310 \| 0.0645 \| \| 0.1409 \| 6.32 \| 6320 \| 0.0646 \| \| 0.1861 \| 6.33 \| 6330 \| 0.0645 \| \| 0.1417 \| 6.34 \| 6340 \| 0.0645 \| \| 0.1526 \| 6.35 \| 6350 \| 0.0645 \| \| 0.1577 \| 6.36 \| 6360 \| 0.0646 \| \| 0.104 \| 6.37 \| 6370 \| 0.0645 \| \| 0.1371 \| 6.38 \| 6380 \| 0.0645 \| \| 0.1126 \| 6.39 \| 6390 \| 0.0646 \| \| 0.2212 \| 6.4 \| 6400 \| 0.0647 \| \| 0.1324 \| 6.41 \| 6410 \| 0.0648 \| \| 0.1478 \| 6.42 \| 6420 \| 0.0650 \| \| 0.1995 \| 6.43 \| 6430 \| 0.0647 \| \| 0.1495 \| 6.44 \| 6440 \| 0.0646 \| \| 0.108 \| 6.45 \| 6450 \| 0.0645 \| \| 0.1268 \| 6.46 \| 6460 \| 0.0641 \| \| 0.1233 \| 6.47 \| 6470 \| 0.0639 \| \| 0.1222 \| 6.48 \| 6480 \| 0.0634 \| \| 0.1324 \| 6.49 \| 6490 \| 0.0630 \| \| 0.1461 \| 6.5 \| 6500 \| 0.0627 \| \| 0.1541 \| 6.51 \| 6510 \| 0.0625 \| \| 0.1797 \| 6.52 \| 6520 \| 0.0623 \| \| 0.1712 \| 6.53 \| 6530 \| 0.0623 \| \| 0.2081 \| 6.54 \| 6540 \| 0.0627 \| \| 0.166 \| 6.55 \| 6550 \| 0.0628 \| \| 0.1498 \| 6.56 \| 6560 \| 0.0629 \| \| 0.1427 \| 6.57 \| 6570 \| 0.0628 \| \| 0.1548 \| 6.58 \| 6580 \| 0.0629 \| \| 0.13 \| 6.59 \| 6590 \| 0.0629 \| \| 0.1655 \| 6.6 \| 6600 \| 0.0628 \| \| 0.1734 \| 6.61 \| 6610 \| 0.0630 \| \| 0.1182 \| 6.62 \| 6620 \| 0.0631 \| \| 0.1494 \| 6.63 \| 6630 \| 0.0631 \| \| 0.1392 \| 6.64 \| 6640 \| 0.0630 \| \| 0.1464 \| 6.65 \| 6650 \| 0.0633 \| \| 0.1374 \| 6.66 \| 6660 \| 0.0634 \| \| 0.1406 \| 6.67 \| 6670 \| 0.0633 \| \| 0.166 \| 6.68 \| 6680 \| 0.0632 \| \| 0.089 \| 6.69 \| 6690 \| 0.0632 \| \| 0.1177 \| 6.7 \| 6700 \| 0.0634 \| \| 0.1074 \| 6.71 \| 6710 \| 0.0635 \| \| 0.1224 \| 6.72 \| 6720 \| 0.0636 \| \| 0.1754 \| 6.73 \| 6730 \| 0.0634 \| \| 0.1731 \| 6.74 \| 6740 \| 0.0632 \| \| 0.1566 \| 6.75 \| 6750 \| 0.0631 \| \| 0.1139 \| 6.76 \| 6760 \| 0.0633 \| \| 0.1255 \| 6.77 \| 6770 \| 0.0634 \| \| 0.166 \| 6.78 \| 6780 \| 0.0636 \| \| 0.1192 \| 6.79 \| 6790 \| 0.0638 \| \| 0.1203 \| 6.8 \| 6800 \| 0.0639 \| \| 0.1021 \| 6.81 \| 6810 \| 0.0641 \| \| 0.141 \| 6.82 \| 6820 \| 0.0641 \| \| 0.1272 \| 6.83 \| 6830 \| 0.0643 \| \| 0.1449 \| 6.84 \| 6840 \| 0.0645 \| \| 0.1459 \| 6.85 \| 6850 \| 0.0645 \| \| 0.094 \| 6.86 \| 6860 \| 0.0644 \| \| 0.1866 \| 6.87 \| 6870 \| 0.0644 \| \| 0.1521 \| 6.88 \| 6880 \| 0.0646 \| \| 0.1423 \| 6.89 \| 6890 \| 0.0647 \| \| 0.1465 \| 6.9 \| 6900 \| 0.0648 \| \| 0.1328 \| 6.91 \| 6910 \| 0.0651 \| \| 0.1 \| 6.92 \| 6920 \| 0.0653 \| \| 0.1292 \| 6.93 \| 6930 \| 0.0653 \| \| 0.1406 \| 6.94 \| 6940 \| 0.0650 \| \| 0.116 \| 6.95 \| 6950 \| 0.0648 \| \| 0.1355 \| 6.96 \| 6960 \| 0.0645 \| \| 0.1491 \| 6.97 \| 6970 \| 0.0643 \| \| 0.1359 \| 6.98 \| 6980 \| 0.0643 \| \| 0.1374 \| 6.99 \| 6990 \| 0.0644 \| \| 0.1129 \| 7.0 \| 7000 \| 0.0645 \| \| 0.1317 \| 7.01 \| 7010 \| 0.0645 \| \| 0.2238 \| 7.02 \| 7020 \| 0.0646 \| \| 0.1062 \| 7.03 \| 7030 \| 0.0645 \| \| 0.1742 \| 7.04 \| 7040 \| 0.0646 \| \| 0.1234 \| 7.05 \| 7050 \| 0.0649 \| \| 0.1861 \| 7.06 \| 7060 \| 0.0649 \| \| 0.1154 \| 7.07 \| 7070 \| 0.0647 \| \| 0.1185 \| 7.08 \| 7080 \| 0.0647 \| \| 0.1065 \| 7.09 \| 7090 \| 0.0648 \| \| 0.1047 \| 7.1 \| 7100 \| 0.0649 \| \| 0.1669 \| 7.11 \| 7110 \| 0.0647 \| \| 0.1304 \| 7.12 \| 7120 \| 0.0646 \| \| 0.0801 \| 7.13 \| 7130 \| 0.0646 \| \| 0.1261 \| 7.14 \| 7140 \| 0.0648 \| \| 0.171 \| 7.15 \| 7150 \| 0.0648 \| \| 0.1338 \| 7.16 \| 7160 \| 0.0647 \| \| 0.1074 \| 7.17 \| 7170 \| 0.0646 \| \| 0.1485 \| 7.18 \| 7180 \| 0.0645 \| \| 0.1314 \| 7.19 \| 7190 \| 0.0645 \| \| 0.1507 \| 7.2 \| 7200 \| 0.0646 \| \| 0.1734 \| 7.21 \| 7210 \| 0.0646 \| \| 0.1873 \| 7.22 \| 7220 \| 0.0647 \| \| 0.1544 \| 7.23 \| 7230 \| 0.0645 \| \| 0.1222 \| 7.24 \| 7240 \| 0.0646 \| \| 0.157 \| 7.25 \| 7250 \| 0.0644 \| \| 0.1391 \| 7.26 \| 7260 \| 0.0644 \| \| 0.162 \| 7.27 \| 7270 \| 0.0644 \| \| 0.1089 \| 7.28 \| 7280 \| 0.0644 \| \| 0.125 \| 7.29 \| 7290 \| 0.0643 \| \| 0.1363 \| 7.3 \| 7300 \| 0.0643 \| \| 0.1205 \| 7.31 \| 7310 \| 0.0645 \| \| 0.1116 \| 7.32 \| 7320 \| 0.0648 \| \| 0.1194 \| 7.33 \| 7330 \| 0.0648 \| \| 0.153 \| 7.34 \| 7340 \| 0.0649 \| \| 0.1262 \| 7.35 \| 7350 \| 0.0647 \| \| 0.1234 \| 7.36 \| 7360 \| 0.0646 \| \| 0.1381 \| 7.37 \| 7370 \| 0.0646 \| \| 0.1108 \| 7.38 \| 7380 \| 0.0647 \| \| 0.1719 \| 7.39 \| 7390 \| 0.0645 \| \| 0.1293 \| 7.4 \| 7400 \| 0.0644 \| \| 0.1562 \| 7.41 \| 7410 \| 0.0643 \| \| 0.149 \| 7.42 \| 7420 \| 0.0644 \| \| 0.0926 \| 7.43 \| 7430 \| 0.0641 \| \| 0.1603 \| 7.44 \| 7440 \| 0.0640 \| \| 0.1599 \| 7.45 \| 7450 \| 0.0639 \| \| 0.1281 \| 7.46 \| 7460 \| 0.0640 \| \| 0.146 \| 7.47 \| 7470 \| 0.0643 \| \| 0.1498 \| 7.48 \| 7480 \| 0.0643 \| \| 0.1699 \| 7.49 \| 7490 \| 0.0644 \| \| 0.1092 \| 7.5 \| 7500 \| 0.0646 \| \| 0.1338 \| 7.51 \| 7510 \| 0.0646 \| \| 0.1532 \| 7.52 \| 7520 \| 0.0643 \| \| 0.1016 \| 7.53 \| 7530 \| 0.0642 \| \| 0.1286 \| 7.54 \| 7540 \| 0.0643 \| \| 0.1242 \| 7.55 \| 7550 \| 0.0642 \| \| 0.1485 \| 7.56 \| 7560 \| 0.0641 \| \| 0.1754 \| 7.57 \| 7570 \| 0.0639 \| \| 0.1637 \| 7.58 \| 7580 \| 0.0639 \| \| 0.1075 \| 7.59 \| 7590 \| 0.0639 \| \| 0.1288 \| 7.6 \| 7600 \| 0.0640 \| \| 0.1639 \| 7.61 \| 7610 \| 0.0640 \| \| 0.1185 \| 7.62 \| 7620 \| 0.0640 \| \| 0.1321 \| 7.63 \| 7630 \| 0.0639 \| \| 0.1589 \| 7.64 \| 7640 \| 0.0636 \| \| 0.094 \| 7.65 \| 7650 \| 0.0634 \| \| 0.1115 \| 7.66 \| 7660 \| 0.0635 \| \| 0.1365 \| 7.67 \| 7670 \| 0.0635 \| \| 0.1567 \| 7.68 \| 7680 \| 0.0636 \| \| 0.182 \| 7.69 \| 7690 \| 0.0636 \| \| 0.1195 \| 7.7 \| 7700 \| 0.0636 \| \| 0.158 \| 7.71 \| 7710 \| 0.0637 \| \| 0.1354 \| 7.72 \| 7720 \| 0.0638 \| \| 0.1508 \| 7.73 \| 7730 \| 0.0637 \| \| 0.0876 \| 7.74 \| 7740 \| 0.0639 \| \| 0.1241 \| 7.75 \| 7750 \| 0.0640 \| \| 0.2049 \| 7.76 \| 7760 \| 0.0641 \| \| 0.1859 \| 7.77 \| 7770 \| 0.0645 \| \| 0.1036 \| 7.78 \| 7780 \| 0.0645 \| \| 0.1015 \| 7.79 \| 7790 \| 0.0643 \| \| 0.1235 \| 7.8 \| 7800 \| 0.0639 \| \| 0.1594 \| 7.81 \| 7810 \| 0.0640 \| \| 0.1295 \| 7.82 \| 7820 \| 0.0642 \| \| 0.152 \| 7.83 \| 7830 \| 0.0645 \| \| 0.1496 \| 7.84 \| 7840 \| 0.0647 \| \| 0.1353 \| 7.85 \| 7850 \| 0.0648 \| \| 0.1206 \| 7.86 \| 7860 \| 0.0649 \| \| 0.1627 \| 7.87 \| 7870 \| 0.0651 \| \| 0.1132 \| 7.88 \| 7880 \| 0.0653 \| \| 0.154 \| 7.89 \| 7890 \| 0.0652 \| \| 0.153 \| 7.9 \| 7900 \| 0.0649 \| \| 0.1225 \| 7.91 \| 7910 \| 0.0648 \| \| 0.1494 \| 7.92 \| 7920 \| 0.0648 \| \| 0.1358 \| 7.93 \| 7930 \| 0.0647 \| \| 0.1137 \| 7.94 \| 7940 \| 0.0648 \| \| 0.1546 \| 7.95 \| 7950 \| 0.0647 \| \| 0.114 \| 7.96 \| 7960 \| 0.0644 \| \| 0.1939 \| 7.97 \| 7970 \| 0.0644 \| \| 0.1276 \| 7.98 \| 7980 \| 0.0641 \| \| 0.1096 \| 7.99 \| 7990 \| 0.0639 \| \| 0.1764 \| 8.0 \| 8000 \| 0.0639 \| \| 0.1029 \| 8.01 \| 8010 \| 0.0642 \| \| 0.1344 \| 8.02 \| 8020 \| 0.0641 \| \| 0.1422 \| 8.03 \| 8030 \| 0.0643 \| \| 0.1055 \| 8.04 \| 8040 \| 0.0645 \| \| 0.1231 \| 8.05 \| 8050 \| 0.0646 \| \| 0.1303 \| 8.06 \| 8060 \| 0.0648 \| \| 0.1421 \| 8.07 \| 8070 \| 0.0651 \| \| 0.1325 \| 8.08 \| 8080 \| 0.0651 \| \| 0.0797 \| 8.09 \| 8090 \| 0.0649 \| \| 0.0961 \| 8.1 \| 8100 \| 0.0647 \| \| 0.1156 \| 8.11 \| 8110 \| 0.0647 \| \| 0.1529 \| 8.12 \| 8120 \| 0.0648 \| \| 0.1756 \| 8.13 \| 8130 \| 0.0648 \| \| 0.1158 \| 8.14 \| 8140 \| 0.0651 \| \| 0.1302 \| 8.15 \| 8150 \| 0.0654 \| \| 0.1404 \| 8.16 \| 8160 \| 0.0658 \| \| 0.1445 \| 8.17 \| 8170 \| 0.0662 \| \| 0.1499 \| 8.18 \| 8180 \| 0.0662 \| \| 0.1375 \| 8.19 \| 8190 \| 0.0663 \| \| 0.1663 \| 8.2 \| 8200 \| 0.0662 \| \| 0.1744 \| 8.21 \| 8210 \| 0.0661 \| \| 0.1414 \| 8.22 \| 8220 \| 0.0658 \| \| 0.0964 \| 8.23 \| 8230 \| 0.0658 \| \| 0.1214 \| 8.24 \| 8240 \| 0.0655 \| \| 0.1492 \| 8.25 \| 8250 \| 0.0653 \| \| 0.1534 \| 8.26 \| 8260 \| 0.0654 \| \| 0.1038 \| 8.27 \| 8270 \| 0.0655 \| \| 0.1779 \| 8.28 \| 8280 \| 0.0655 \| \| 0.1801 \| 8.29 \| 8290 \| 0.0657 \| \| 0.1314 \| 8.3 \| 8300 \| 0.0658 \| \| 0.1598 \| 8.31 \| 8310 \| 0.0657 \| \| 0.1078 \| 8.32 \| 8320 \| 0.0653 \| \| 0.1672 \| 8.33 \| 8330 \| 0.0649 \| \| 0.113 \| 8.34 \| 8340 \| 0.0649 \| \| 0.1046 \| 8.35 \| 8350 \| 0.0650 \| \| 0.1625 \| 8.36 \| 8360 \| 0.0650 \| \| 0.1112 \| 8.37 \| 8370 \| 0.0650 \| \| 0.1278 \| 8.38 \| 8380 \| 0.0649 \| \| 0.1684 \| 8.39 \| 8390 \| 0.0647 \| \| 0.2318 \| 8.4 \| 8400 \| 0.0647 \| \| 0.1687 \| 8.41 \| 8410 \| 0.0648 \| \| 0.1283 \| 8.42 \| 8420 \| 0.0649 \| \| 0.1107 \| 8.43 \| 8430 \| 0.0650 \| \| 0.1092 \| 8.44 \| 8440 \| 0.0652 \| \| 0.0851 \| 8.45 \| 8450 \| 0.0653 \| \| 0.1263 \| 8.46 \| 8460 \| 0.0656 \| \| 0.1936 \| 8.47 \| 8470 \| 0.0656 \| \| 0.1608 \| 8.48 \| 8480 \| 0.0656 \| \| 0.1023 \| 8.49 \| 8490 \| 0.0656 \| \| 0.1153 \| 8.5 \| 8500 \| 0.0658 \| \| 0.1682 \| 8.51 \| 8510 \| 0.0659 \| \| 0.1407 \| 8.52 \| 8520 \| 0.0660 \| \| 0.104 \| 8.53 \| 8530 \| 0.0660 \| \| 0.1371 \| 8.54 \| 8540 \| 0.0659 \| \| 0.1068 \| 8.55 \| 8550 \| 0.0659 \| \| 0.1418 \| 8.56 \| 8560 \| 0.0660 \| \| 0.1451 \| 8.57 \| 8570 \| 0.0660 \| \| 0.1379 \| 8.58 \| 8580 \| 0.0661 \| \| 0.1732 \| 8.59 \| 8590 \| 0.0660 \| \| 0.1287 \| 8.6 \| 8600 \| 0.0658 \| \| 0.1821 \| 8.61 \| 8610 \| 0.0657 \| \| 0.1184 \| 8.62 \| 8620 \| 0.0657 \| \| 0.1194 \| 8.63 \| 8630 \| 0.0658 \| \| 0.083 \| 8.64 \| 8640 \| 0.0660 \| \| 0.1724 \| 8.65 \| 8650 \| 0.0661 \| \| 0.1504 \| 8.66 \| 8660 \| 0.0659 \| \| 0.1338 \| 8.67 \| 8670 \| 0.0658 \| \| 0.1372 \| 8.68 \| 8680 \| 0.0658 \| \| 0.1367 \| 8.69 \| 8690 \| 0.0658 \| \| 0.1665 \| 8.7 \| 8700 \| 0.0659 \| \| 0.1389 \| 8.71 \| 8710 \| 0.0660 \| \| 0.1272 \| 8.72 \| 8720 \| 0.0660 \| \| 0.1595 \| 8.73 \| 8730 \| 0.0659 \| \| 0.1644 \| 8.74 \| 8740 \| 0.0658 \| \| 0.1249 \| 8.75 \| 8750 \| 0.0658 \| \| 0.1276 \| 8.76 \| 8760 \| 0.0658 \| \| 0.1103 \| 8.77 \| 8770 \| 0.0657 \| \| 0.1664 \| 8.78 \| 8780 \| 0.0658 \| \| 0.1832 \| 8.79 \| 8790 \| 0.0661 \| \| 0.1075 \| 8.8 \| 8800 \| 0.0662 \| \| 0.1526 \| 8.81 \| 8810 \| 0.0663 \| \| 0.1215 \| 8.82 \| 8820 \| 0.0666 \| \| 0.1317 \| 8.83 \| 8830 \| 0.0668 \| \| 0.1425 \| 8.84 \| 8840 \| 0.0668 \| \| 0.1572 \| 8.85 \| 8850 \| 0.0668 \| \| 0.1012 \| 8.86 \| 8860 \| 0.0667 \| \| 0.1222 \| 8.87 \| 8870 \| 0.0665 \| \| 0.1723 \| 8.88 \| 8880 \| 0.0665 \| \| 0.1139 \| 8.89 \| 8890 \| 0.0665 \| \| 0.1351 \| 8.9 \| 8900 \| 0.0665 \| \| 0.1258 \| 8.91 \| 8910 \| 0.0666 \| \| 0.1387 \| 8.92 \| 8920 \| 0.0667 \| \| 0.1554 \| 8.93 \| 8930 \| 0.0664 \| \| 0.1454 \| 8.94 \| 8940 \| 0.0662 \| \| 0.1066 \| 8.95 \| 8950 \| 0.0661 \| \| 0.1047 \| 8.96 \| 8960 \| 0.0659 \| \| 0.168 \| 8.97 \| 8970 \| 0.0658 \| \| 0.1162 \| 8.98 \| 8980 \| 0.0656 \| \| 0.109 \| 8.99 \| 8990 \| 0.0654 \| \| 0.1262 \| 9.0 \| 9000 \| 0.0653 \| \| 0.1563 \| 9.01 \| 9010 \| 0.0651 \| \| 0.13 \| 9.02 \| 9020 \| 0.0650 \| \| 0.1608 \| 9.03 \| 9030 \| 0.0651 \| \| 0.1369 \| 9.04 \| 9040 \| 0.0650 \| \| 0.1386 \| 9.05 \| 9050 \| 0.0646 \| \| 0.0843 \| 9.06 \| 9060 \| 0.0644 \| \| 0.0719 \| 9.07 \| 9070 \| 0.0643 \| \| 0.146 \| 9.08 \| 9080 \| 0.0641 \| \| 0.132 \| 9.09 \| 9090 \| 0.0640 \| \| 0.1425 \| 9.1 \| 9100 \| 0.0639 \| \| 0.1097 \| 9.11 \| 9110 \| 0.0640 \| \| 0.1684 \| 9.12 \| 9120 \| 0.0641 \| \| 0.1891 \| 9.13 \| 9130 \| 0.0640 \| \| 0.1083 \| 9.14 \| 9140 \| 0.0641 \| \| 0.1265 \| 9.15 \| 9150 \| 0.0643 \| \| 0.1183 \| 9.16 \| 9160 \| 0.0646 \| \| 0.0971 \| 9.17 \| 9170 \| 0.0647 \| \| 0.17 \| 9.18 \| 9180 \| 0.0645 \| \| 0.1074 \| 9.19 \| 9190 \| 0.0646 \| \| 0.1517 \| 9.2 \| 9200 \| 0.0647 \| \| 0.1763 \| 9.21 \| 9210 \| 0.0648 \| \| 0.1031 \| 9.22 \| 9220 \| 0.0647 \| \| 0.1419 \| 9.23 \| 9230 \| 0.0647 \| \| 0.1451 \| 9.24 \| 9240 \| 0.0649 \| \| 0.1657 \| 9.25 \| 9250 \| 0.0651 \| \| 0.1327 \| 9.26 \| 9260 \| 0.0652 \| \| 0.1279 \| 9.27 \| 9270 \| 0.0653 \| \| 0.111 \| 9.28 \| 9280 \| 0.0656 \| \| 0.1062 \| 9.29 \| 9290 \| 0.0658 \| \| 0.1185 \| 9.3 \| 9300 \| 0.0657 \| \| 0.1539 \| 9.31 \| 9310 \| 0.0658 \| \| 0.2525 \| 9.32 \| 9320 \| 0.0656 \| \| 0.0985 \| 9.33 \| 9330 \| 0.0655 \| \| 0.1161 \| 9.34 \| 9340 \| 0.0656 \| \| 0.1462 \| 9.35 \| 9350 \| 0.0655 \| \| 0.1229 \| 9.36 \| 9360 \| 0.0655 \| \| 0.102 \| 9.37 \| 9370 \| 0.0655 \| \| 0.155 \| 9.38 \| 9380 \| 0.0656 \| \| 0.1747 \| 9.39 \| 9390 \| 0.0657 \| \| 0.0887 \| 9.4 \| 9400 \| 0.0657 \| \| 0.1341 \| 9.41 \| 9410 \| 0.0654 \| \| 0.1598 \| 9.42 \| 9420 \| 0.0652 \| \| 0.1299 \| 9.43 \| 9430 \| 0.0654 \| \| 0.0918 \| 9.44 \| 9440 \| 0.0655 \| \| 0.1463 \| 9.45 \| 9450 \| 0.0654 \| \| 0.1867 \| 9.46 \| 9460 \| 0.0654 \| \| 0.1602 \| 9.47 \| 9470 \| 0.0653 \| \| 0.1077 \| 9.48 \| 9480 \| 0.0653 \| \| 0.1052 \| 9.49 \| 9490 \| 0.0653 \| \| 0.1283 \| 9.5 \| 9500 \| 0.0650 \| \| 0.1888 \| 9.51 \| 9510 \| 0.0647 \| \| 0.1381 \| 9.52 \| 9520 \| 0.0646 \| \| 0.0842 \| 9.53 \| 9530 \| 0.0645 \| \| 0.086 \| 9.54 \| 9540 \| 0.0644 \| \| 0.1155 \| 9.55 \| 9550 \| 0.0645 \| \| 0.114 \| 9.56 \| 9560 \| 0.0646 \| \| 0.1042 \| 9.57 \| 9570 \| 0.0649 \| \| 0.1629 \| 9.58 \| 9580 \| 0.0651 \| \| 0.1641 \| 9.59 \| 9590 \| 0.0652 \| \| 0.1158 \| 9.6 \| 9600 \| 0.0654 \| \| 0.0971 \| 9.61 \| 9610 \| 0.0656 \| \| 0.1908 \| 9.62 \| 9620 \| 0.0656 \| \| 0.1158 \| 9.63 \| 9630 \| 0.0656 \| \| 0.1543 \| 9.64 \| 9640 \| 0.0656 \| \| 0.1667 \| 9.65 \| 9650 \| 0.0656 \| \| 0.1439 \| 9.66 \| 9660 \| 0.0656 \| \| 0.1375 \| 9.67 \| 9670 \| 0.0658 \| \| 0.1048 \| 9.68 \| 9680 \| 0.0660 \| \| 0.1551 \| 9.69 \| 9690 \| 0.0659 \| \| 0.1096 \| 9.7 \| 9700 \| 0.0658 \| \| 0.1017 \| 9.71 \| 9710 \| 0.0657 \| \| 0.1198 \| 9.72 \| 9720 \| 0.0657 \| \| 0.1345 \| 9.73 \| 9730 \| 0.0657 \| \| 0.1268 \| 9.74 \| 9740 \| 0.0657 \| \| 0.124 \| 9.75 \| 9750 \| 0.0657 \| \| 0.1313 \| 9.76 \| 9760 \| 0.0658 \| \| 0.1956 \| 9.77 \| 9770 \| 0.0658 \| \| 0.0982 \| 9.78 \| 9780 \| 0.0658 \| \| 0.1091 \| 9.79 \| 9790 \| 0.0658 \| \| 0.1686 \| 9.8 \| 9800 \| 0.0658 \| \| 0.1185 \| 9.81 \| 9810 \| 0.0656 \| \| 0.1886 \| 9.82 \| 9820 \| 0.0654 \| \| 0.0923 \| 9.83 \| 9830 \| 0.0653 \| \| 0.1339 \| 9.84 \| 9840 \| 0.0651 \| \| 0.1813 \| 9.85 \| 9850 \| 0.0651 \| \| 0.1073 \| 9.86 \| 9860 \| 0.0651 \| \| 0.1452 \| 9.87 \| 9870 \| 0.0650 \| \| 0.138 \| 9.88 \| 9880 \| 0.0648 \| \| 0.1151 \| 9.89 \| 9890 \| 0.0648 \| \| 0.1364 \| 9.9 \| 9900 \| 0.0647 \| \| 0.1233 \| 9.91 \| 9910 \| 0.0646 \| \| 0.1475 \| 9.92 \| 9920 \| 0.0647 \| \| 0.157 \| 9.93 \| 9930 \| 0.0647 \| \| 0.0869 \| 9.94 \| 9940 \| 0.0649 \| \| 0.1193 \| 9.95 \| 9950 \| 0.0650 \| \| 0.1836 \| 9.96 \| 9960 \| 0.0650 \| \| 0.159 \| 9.97 \| 9970 \| 0.0649 \| \| 0.1575 \| 9.98 \| 9980 \| 0.0650 \| \| 0.141 \| 9.99 \| 9990 \| 0.0649 \| \| 0.1398 \| 10.0 \| 10000 \| 0.0651 \| \| 0.1547 \| 10.01 \| 10010 \| 0.0650 \| \| 0.1328 \| 10.02 \| 10020 \| 0.0650 \| \| 0.1164 \| 10.03 \| 10030 \| 0.0649 \| \| 0.1846 \| 10.04 \| 10040 \| 0.0650 \| \| 0.1184 \| 10.05 \| 10050 \| 0.0651 \| \| 0.1044 \| 10.06 \| 10060 \| 0.0651 \| \| 0.1384 \| 10.07 \| 10070 \| 0.0650 \| \| 0.1416 \| 10.08 \| 10080 \| 0.0650 \| \| 0.1547 \| 10.09 \| 10090 \| 0.0650 \| \| 0.0962 \| 10.1 \| 10100 \| 0.0650 \| \| 0.1004 \| 10.11 \| 10110 \| 0.0651 \| \| 0.155 \| 10.12 \| 10120 \| 0.0652 \| \| 0.1257 \| 10.13 \| 10130 \| 0.0653 \| \| 0.1257 \| 10.14 \| 10140 \| 0.0653 \| \| 0.0995 \| 10.15 \| 10150 \| 0.0653 \| \| 0.1006 \| 10.16 \| 10160 \| 0.0655 \| \| 0.0971 \| 10.17 \| 10170 \| 0.0655 \| \| 0.1214 \| 10.18 \| 10180 \| 0.0657 \| \| 0.1752 \| 10.19 \| 10190 \| 0.0659 \| \| 0.0797 \| 10.2 \| 10200 \| 0.0659 \| \| 0.1589 \| 10.21 \| 10210 \| 0.0660 \| \| 0.1616 \| 10.22 \| 10220 \| 0.0661 \| \| 0.086 \| 10.23 \| 10230 \| 0.0662 \| \| 0.1542 \| 10.24 \| 10240 \| 0.0662 \| \| 0.1493 \| 10.25 \| 10250 \| 0.0660 \| \| 0.1095 \| 10.26 \| 10260 \| 0.0658 \| \| 0.1545 \| 10.27 \| 10270 \| 0.0657 \| \| 0.1587 \| 10.28 \| 10280 \| 0.0655 \| \| 0.1758 \| 10.29 \| 10290 \| 0.0655 \| \| 0.1366 \| 10.3 \| 10300 \| 0.0655 \| \| 0.146 \| 10.31 \| 10310 \| 0.0657 \| \| 0.0977 \| 10.32 \| 10320 \| 0.0660 \| \| 0.085 \| 10.33 \| 10330 \| 0.0663 \| \| 0.1037 \| 10.34 \| 10340 \| 0.0664 \| \| 0.126 \| 10.35 \| 10350 \| 0.0664 \| \| 0.1065 \| 10.36 \| 10360 \| 0.0665 \| \| 0.0984 \| 10.37 \| 10370 \| 0.0663 \| \| 0.1078 \| 10.38 \| 10380 \| 0.0661 \| \| 0.17 \| 10.39 \| 10390 \| 0.0659 \| \| 0.1721 \| 10.4 \| 10400 \| 0.0659 \| \| 0.1347 \| 10.41 \| 10410 \| 0.0657 \| \| 0.2179 \| 10.42 \| 10420 \| 0.0656 \| \| 0.0688 \| 10.43 \| 10430 \| 0.0656 \| \| 0.1147 \| 10.44 \| 10440 \| 0.0656 \| \| 0.1239 \| 10.45 \| 10450 \| 0.0656 \| \| 0.1013 \| 10.46 \| 10460 \| 0.0658 \| \| 0.1596 \| 10.47 \| 10470 \| 0.0661 \| \| 0.101 \| 10.48 \| 10480 \| 0.0662 \| \| 0.1111 \| 10.49 \| 10490 \| 0.0662 \| \| 0.1179 \| 10.5 \| 10500 \| 0.0662 \| \| 0.1017 \| 10.51 \| 10510 \| 0.0663 \| \| 0.1477 \| 10.52 \| 10520 \| 0.0661 \| \| 0.1484 \| 10.53 \| 10530 \| 0.0658 \| \| 0.1598 \| 10.54 \| 10540 \| 0.0659 \| \| 0.1807 \| 10.55 \| 10550 \| 0.0660 \| \| 0.1616 \| 10.56 \| 10560 \| 0.0660 \| \| 0.1226 \| 10.57 \| 10570 \| 0.0659 \| \| 0.1563 \| 10.58 \| 10580 \| 0.0656 \| \| 0.1705 \| 10.59 \| 10590 \| 0.0654 \| \| 0.1488 \| 10.6 \| 10600 \| 0.0655 \| \| 0.1665 \| 10.61 \| 10610 \| 0.0655 \| \| 0.1165 \| 10.62 \| 10620 \| 0.0655 \| \| 0.1412 \| 10.63 \| 10630 \| 0.0656 \| \| 0.1515 \| 10.64 \| 10640 \| 0.0657 \| \| 0.1292 \| 10.65 \| 10650 \| 0.0658 \| \| 0.1175 \| 10.66 \| 10660 \| 0.0657 \| \| 0.1188 \| 10.67 \| 10670 \| 0.0658 \| \| 0.106 \| 10.68 \| 10680 \| 0.0659 \| \| 0.1239 \| 10.69 \| 10690 \| 0.0659 \| \| 0.1326 \| 10.7 \| 10700 \| 0.0659 \| \| 0.2108 \| 10.71 \| 10710 \| 0.0661 \| \| 0.1472 \| 10.72 \| 10720 \| 0.0662 \| \| 0.1237 \| 10.73 \| 10730 \| 0.0662 \| \| 0.1493 \| 10.74 \| 10740 \| 0.0663 \| \| 0.0985 \| 10.75 \| 10750 \| 0.0664 \| \| 0.1048 \| 10.76 \| 10760 \| 0.0664 \| \| 0.1236 \| 10.77 \| 10770 \| 0.0665 \| \| 0.108 \| 10.78 \| 10780 \| 0.0667 \| \| 0.1386 \| 10.79 \| 10790 \| 0.0665 \| \| 0.1501 \| 10.8 \| 10800 \| 0.0664 \| \| 0.1341 \| 10.81 \| 10810 \| 0.0664 \| \| 0.1171 \| 10.82 \| 10820 \| 0.0665 \| \| 0.1243 \| 10.83 \| 10830 \| 0.0664 \| \| 0.1225 \| 10.84 \| 10840 \| 0.0662 \| \| 0.2065 \| 10.85 \| 10850 \| 0.0661 \| \| 0.1032 \| 10.86 \| 10860 \| 0.0661 \| \| 0.1433 \| 10.87 \| 10870 \| 0.0661 \| \| 0.1933 \| 10.88 \| 10880 \| 0.0662 \| \| 0.1344 \| 10.89 \| 10890 \| 0.0661 \| \| 0.1046 \| 10.9 \| 10900 \| 0.0662 \| \| 0.1352 \| 10.91 \| 10910 \| 0.0662 \| \| 0.1302 \| 10.92 \| 10920 \| 0.0663 \| \| 0.1237 \| 10.93 \| 10930 \| 0.0663 \| \| 0.0985 \| 10.94 \| 10940 \| 0.0664 \| \| 0.1186 \| 10.95 \| 10950 \| 0.0664 \| \| 0.1929 \| 10.96 \| 10960 \| 0.0664 \| \| 0.1224 \| 10.97 \| 10970 \| 0.0664 \| \| 0.1753 \| 10.98 \| 10980 \| 0.0664 \| \| 0.1151 \| 10.99 \| 10990 \| 0.0663 \| \| 0.1213 \| 11.0 \| 11000 \| 0.0661 \| \| 0.148 \| 11.01 \| 11010 \| 0.0660 \| \| 0.1005 \| 11.02 \| 11020 \| 0.0660 \| \| 0.1579 \| 11.03 \| 11030 \| 0.0660 \| \| 0.1275 \| 11.04 \| 11040 \| 0.0661 \| \| 0.1498 \| 11.05 \| 11050 \| 0.0664 \| \| 0.1036 \| 11.06 \| 11060 \| 0.0666 \| \| 0.1002 \| 11.07 \| 11070 \| 0.0668 \| \| 0.1007 \| 11.08 \| 11080 \| 0.0669 \| \| 0.129 \| 11.09 \| 11090 \| 0.0667 \| \| 0.1988 \| 11.1 \| 11100 \| 0.0666 \| \| 0.1252 \| 11.11 \| 11110 \| 0.0667 \| \| 0.13 \| 11.12 \| 11120 \| 0.0667 \| \| 0.146 \| 11.13 \| 11130 \| 0.0669 \| \| 0.1384 \| 11.14 \| 11140 \| 0.0669 \| \| 0.1405 \| 11.15 \| 11150 \| 0.0667 \| \| 0.1023 \| 11.16 \| 11160 \| 0.0666 \| \| 0.156 \| 11.17 \| 11170 \| 0.0664 \| \| 0.0964 \| 11.18 \| 11180 \| 0.0663 \| \| 0.1173 \| 11.19 \| 11190 \| 0.0663 \| \| 0.084 \| 11.2 \| 11200 \| 0.0662 \| \| 0.1295 \| 11.21 \| 11210 \| 0.0662 \| \| 0.1377 \| 11.22 \| 11220 \| 0.0661 \| \| 0.1008 \| 11.23 \| 11230 \| 0.0660 \| \| 0.1132 \| 11.24 \| 11240 \| 0.0659 \| \| 0.1646 \| 11.25 \| 11250 \| 0.0658 \| \| 0.103 \| 11.26 \| 11260 \| 0.0659 \| \| 0.1331 \| 11.27 \| 11270 \| 0.0659 \| \| 0.1153 \| 11.28 \| 11280 \| 0.0659 \| \| 0.1457 \| 11.29 \| 11290 \| 0.0658 \| \| 0.1257 \| 11.3 \| 11300 \| 0.0658 \| \| 0.1541 \| 11.31 \| 11310 \| 0.0659 \| \| 0.1449 \| 11.32 \| 11320 \| 0.0660 \| \| 0.1419 \| 11.33 \| 11330 \| 0.0661 \| \| 0.118 \| 11.34 \| 11340 \| 0.0661 \| \| 0.1562 \| 11.35 \| 11350 \| 0.0660 \| \| 0.1441 \| 11.36 \| 11360 \| 0.0661 \| \| 0.1663 \| 11.37 \| 11370 \| 0.0661 \| \| 0.1754 \| 11.38 \| 11380 \| 0.0661 \| \| 0.0888 \| 11.39 \| 11390 \| 0.0662 \| \| 0.084 \| 11.4 \| 11400 \| 0.0661 \| \| 0.1095 \| 11.41 \| 11410 \| 0.0661 \| \| 0.1594 \| 11.42 \| 11420 \| 0.0663 \| \| 0.0926 \| 11.43 \| 11430 \| 0.0664 \| \| 0.1073 \| 11.44 \| 11440 \| 0.0664 \| \| 0.1468 \| 11.45 \| 11450 \| 0.0663 \| \| 0.1433 \| 11.46 \| 11460 \| 0.0661 \| \| 0.1353 \| 11.47 \| 11470 \| 0.0660 \| \| 0.1437 \| 11.48 \| 11480 \| 0.0659 \| \| 0.164 \| 11.49 \| 11490 \| 0.0657 \| \| 0.0949 \| 11.5 \| 11500 \| 0.0656 \| \| 0.0891 \| 11.51 \| 11510 \| 0.0656 \| \| 0.1316 \| 11.52 \| 11520 \| 0.0656 \| \| 0.1462 \| 11.53 \| 11530 \| 0.0656 \| \| 0.0974 \| 11.54 \| 11540 \| 0.0656 \| \| 0.121 \| 11.55 \| 11550 \| 0.0656 \| \| 0.1708 \| 11.56 \| 11560 \| 0.0657 \| \| 0.1312 \| 11.57 \| 11570 \| 0.0657 \| \| 0.1593 \| 11.58 \| 11580 \| 0.0658 \| \| 0.171 \| 11.59 \| 11590 \| 0.0659 \| \| 0.1354 \| 11.6 \| 11600 \| 0.0659 \| \| 0.1329 \| 11.61 \| 11610 \| 0.0661 \| \| 0.1405 \| 11.62 \| 11620 \| 0.0660 \| \| 0.1587 \| 11.63 \| 11630 \| 0.0657 \| \| 0.1106 \| 11.64 \| 11640 \| 0.0659 \| \| 0.1189 \| 11.65 \| 11650 \| 0.0661 \| \| 0.2006 \| 11.66 \| 11660 \| 0.0662 \| \| 0.1378 \| 11.67 \| 11670 \| 0.0662 \| \| 0.1274 \| 11.68 \| 11680 \| 0.0663 \| \| 0.1286 \| 11.69 \| 11690 \| 0.0663 \| \| 0.1211 \| 11.7 \| 11700 \| 0.0664 \| \| 0.1863 \| 11.71 \| 11710 \| 0.0665 \| \| 0.1428 \| 11.72 \| 11720 \| 0.0667 \| \| 0.1179 \| 11.73 \| 11730 \| 0.0666 \| \| 0.1265 \| 11.74 \| 11740 \| 0.0668 \| \| 0.1597 \| 11.75 \| 11750 \| 0.0668 \| \| 0.1148 \| 11.76 \| 11760 \| 0.0669 \| \| 0.14 \| 11.77 \| 11770 \| 0.0668 \| \| 0.1796 \| 11.78 \| 11780 \| 0.0669 \| \| 0.0774 \| 11.79 \| 11790 \| 0.0670 \| \| 0.1063 \| 11.8 \| 11800 \| 0.0671 \| \| 0.1279 \| 11.81 \| 11810 \| 0.0672 \| \| 0.1454 \| 11.82 \| 11820 \| 0.0672 \| \| 0.1495 \| 11.83 \| 11830 \| 0.0672 \| \| 0.1434 \| 11.84 \| 11840 \| 0.0671 \| \| 0.1312 \| 11.85 \| 11850 \| 0.0672 \| \| 0.1681 \| 11.86 \| 11860 \| 0.0672 \| \| 0.1374 \| 11.87 \| 11870 \| 0.0672 \| \| 0.0941 \| 11.88 \| 11880 \| 0.0672 \| \| 0.1178 \| 11.89 \| 11890 \| 0.0672 \| \| 0.099 \| 11.9 \| 11900 \| 0.0671 \| \| 0.1277 \| 11.91 \| 11910 \| 0.0670 \| \| 0.1463 \| 11.92 \| 11920 \| 0.0669 \| \| 0.124 \| 11.93 \| 11930 \| 0.0669 \| \| 0.1313 \| 11.94 \| 11940 \| 0.0669 \| \| 0.1254 \| 11.95 \| 11950 \| 0.0669 \| \| 0.1122 \| 11.96 \| 11960 \| 0.0669 \| \| 0.1844 \| 11.97 \| 11970 \| 0.0669 \| \| 0.099 \| 11.98 \| 11980 \| 0.0670 \| \| 0.1258 \| 11.99 \| 11990 \| 0.0671 \| \| 0.12 \| 12.0 \| 12000 \| 0.0671 \| \| 0.1278 \| 12.01 \| 12010 \| 0.0672 \| \| 0.1319 \| 12.02 \| 12020 \| 0.0673 \| \| 0.1171 \| 12.03 \| 12030 \| 0.0673 \| \| 0.1386 \| 12.04 \| 12040 \| 0.0672 \| \| 0.1322 \| 12.05 \| 12050 \| 0.0670 \| \| 0.1792 \| 12.06 \| 12060 \| 0.0669 \| \| 0.1276 \| 12.07 \| 12070 \| 0.0666 \| \| 0.1444 \| 12.08 \| 12080 \| 0.0666 \| \| 0.1319 \| 12.09 \| 12090 \| 0.0666 \| \| 0.1464 \| 12.1 \| 12100 \| 0.0667 \| \| 0.1341 \| 12.11 \| 12110 \| 0.0667 \| \| 0.1384 \| 12.12 \| 12120 \| 0.0667 \| \| 0.1442 \| 12.13 \| 12130 \| 0.0668 \| \| 0.1542 \| 12.14 \| 12140 \| 0.0669 \| \| 0.0987 \| 12.15 \| 12150 \| 0.0670 \| \| 0.1363 \| 12.16 \| 12160 \| 0.0670 \| \| 0.1716 \| 12.17 \| 12170 \| 0.0670 \| \| 0.1187 \| 12.18 \| 12180 \| 0.0670 \| \| 0.1646 \| 12.19 \| 12190 \| 0.0670 \| \| 0.0963 \| 12.2 \| 12200 \| 0.0669 \| \| 0.1797 \| 12.21 \| 12210 \| 0.0667 \| \| 0.1505 \| 12.22 \| 12220 \| 0.0668 \| \| 0.1135 \| 12.23 \| 12230 \| 0.0669 \| \| 0.1419 \| 12.24 \| 12240 \| 0.0669 \| \| 0.1102 \| 12.25 \| 12250 \| 0.0669 \| \| 0.0998 \| 12.26 \| 12260 \| 0.0669 \| \| 0.146 \| 12.27 \| 12270 \| 0.0670 \| \| 0.1438 \| 12.28 \| 12280 \| 0.0670 \| \| 0.1051 \| 12.29 \| 12290 \| 0.0670 \| \| 0.1023 \| 12.3 \| 12300 \| 0.0669 \| \| 0.0765 \| 12.31 \| 12310 \| 0.0670 \| \| 0.1629 \| 12.32 \| 12320 \| 0.0670 \| \| 0.1869 \| 12.33 \| 12330 \| 0.0669 \| \| 0.1629 \| 12.34 \| 12340 \| 0.0667 \| \| 0.0846 \| 12.35 \| 12350 \| 0.0667 \| \| 0.1336 \| 12.36 \| 12360 \| 0.0668 \| \| 0.0942 \| 12.37 \| 12370 \| 0.0669 \| \| 0.1513 \| 12.38 \| 12380 \| 0.0670 \| \| 0.1158 \| 12.39 \| 12390 \| 0.0670 \| \| 0.1026 \| 12.4 \| 12400 \| 0.0672 \| \| 0.1167 \| 12.41 \| 12410 \| 0.0673 \| \| 0.1051 \| 12.42 \| 12420 \| 0.0674 \| \| 0.1127 \| 12.43 \| 12430 \| 0.0675 \| \| 0.1346 \| 12.44 \| 12440 \| 0.0676 \| \| 0.1296 \| 12.45 \| 12450 \| 0.0676 \| \| 0.0994 \| 12.46 \| 12460 \| 0.0674 \| \| 0.0815 \| 12.47 \| 12470 \| 0.0673 \| \| 0.1575 \| 12.48 \| 12480 \| 0.0673 \| \| 0.1481 \| 12.49 \| 12490 \| 0.0672 \| \| 0.1694 \| 12.5 \| 12500 \| 0.0672 \| \| 0.1236 \| 12.51 \| 12510 \| 0.0671 \| \| 0.1346 \| 12.52 \| 12520 \| 0.0672 \| \| 0.1252 \| 12.53 \| 12530 \| 0.0674 \| \| 0.1392 \| 12.54 \| 12540 \| 0.0673 \| \| 0.0937 \| 12.55 \| 12550 \| 0.0671 \| \| 0.1199 \| 12.56 \| 12560 \| 0.0671 \| \| 0.1124 \| 12.57 \| 12570 \| 0.0671 \| \| 0.184 \| 12.58 \| 12580 \| 0.0672 \| \| 0.1092 \| 12.59 \| 12590 \| 0.0673 \| \| 0.1094 \| 12.6 \| 12600 \| 0.0671 \| \| 0.1447 \| 12.61 \| 12610 \| 0.0670 \| \| 0.1734 \| 12.62 \| 12620 \| 0.0670 \| \| 0.1351 \| 12.63 \| 12630 \| 0.0671 \| \| 0.1506 \| 12.64 \| 12640 \| 0.0672 \| \| 0.1695 \| 12.65 \| 12650 \| 0.0672 \| \| 0.0926 \| 12.66 \| 12660 \| 0.0670 \| \| 0.085 \| 12.67 \| 12670 \| 0.0669 \| \| 0.1957 \| 12.68 \| 12680 \| 0.0669 \| \| 0.1421 \| 12.69 \| 12690 \| 0.0669 \| \| 0.1538 \| 12.7 \| 12700 \| 0.0668 \| \| 0.0969 \| 12.71 \| 12710 \| 0.0667 \| \| 0.1072 \| 12.72 \| 12720 \| 0.0668 \| \| 0.1126 \| 12.73 \| 12730 \| 0.0670 \| \| 0.1013 \| 12.74 \| 12740 \| 0.0671 \| \| 0.1553 \| 12.75 \| 12750 \| 0.0672 \| \| 0.1494 \| 12.76 \| 12760 \| 0.0673 \| \| 0.0736 \| 12.77 \| 12770 \| 0.0673 \| \| 0.1924 \| 12.78 \| 12780 \| 0.0674 \| \| 0.1276 \| 12.79 \| 12790 \| 0.0674 \| \| 0.1599 \| 12.8 \| 12800 \| 0.0675 \| \| 0.122 \| 12.81 \| 12810 \| 0.0677 \| \| 0.1687 \| 12.82 \| 12820 \| 0.0678 \| \| 0.1597 \| 12.83 \| 12830 \| 0.0677 \| \| 0.1486 \| 12.84 \| 12840 \| 0.0675 \| \| 0.1001 \| 12.85 \| 12850 \| 0.0675 \| \| 0.0874 \| 12.86 \| 12860 \| 0.0675 \| \| 0.0952 \| 12.87 \| 12870 \| 0.0675 \| \| 0.1458 \| 12.88 \| 12880 \| 0.0675 \| \| 0.1472 \| 12.89 \| 12890 \| 0.0675 \| \| 0.1161 \| 12.9 \| 12900 \| 0.0674 \| \| 0.1455 \| 12.91 \| 12910 \| 0.0673 \| \| 0.1406 \| 12.92 \| 12920 \| 0.0672 \| \| 0.1108 \| 12.93 \| 12930 \| 0.0672 \| \| 0.1358 \| 12.94 \| 12940 \| 0.0672 \| \| 0.13 \| 12.95 \| 12950 \| 0.0673 \| \| 0.095 \| 12.96 \| 12960 \| 0.0674 \| \| 0.1302 \| 12.97 \| 12970 \| 0.0674 \| \| 0.1424 \| 12.98 \| 12980 \| 0.0674 \| \| 0.1343 \| 12.99 \| 12990 \| 0.0674 \| \| 0.0928 \| 13.0 \| 13000 \| 0.0674 \| \| 0.1126 \| 13.01 \| 13010 \| 0.0674 \| \| 0.1296 \| 13.02 \| 13020 \| 0.0673 \| \| 0.0952 \| 13.03 \| 13030 \| 0.0672 \| \| 0.1295 \| 13.04 \| 13040 \| 0.0673 \| \| 0.1826 \| 13.05 \| 13050 \| 0.0673 \| \| 0.1443 \| 13.06 \| 13060 \| 0.0673 \| \| 0.1527 \| 13.07 \| 13070 \| 0.0673 \| \| 0.142 \| 13.08 \| 13080 \| 0.0674 \| \| 0.0891 \| 13.09 \| 13090 \| 0.0675 \| \| 0.1165 \| 13.1 \| 13100 \| 0.0677 \| \| 0.0984 \| 13.11 \| 13110 \| 0.0678 \| \| 0.1501 \| 13.12 \| 13120 \| 0.0678 \| \| 0.2033 \| 13.13 \| 13130 \| 0.0678 \| \| 0.1264 \| 13.14 \| 13140 \| 0.0678 \| \| 0.1814 \| 13.15 \| 13150 \| 0.0678 \| \| 0.1828 \| 13.16 \| 13160 \| 0.0678 \| \| 0.0993 \| 13.17 \| 13170 \| 0.0678 \| \| 0.1141 \| 13.18 \| 13180 \| 0.0676 \| \| 0.1012 \| 13.19 \| 13190 \| 0.0675 \| \| 0.1036 \| 13.2 \| 13200 \| 0.0674 \| \| 0.1471 \| 13.21 \| 13210 \| 0.0674 \| \| 0.1388 \| 13.22 \| 13220 \| 0.0674 \| \| 0.106 \| 13.23 \| 13230 \| 0.0673 \| \| 0.1153 \| 13.24 \| 13240 \| 0.0673 \| \| 0.139 \| 13.25 \| 13250 \| 0.0672 \| \| 0.1298 \| 13.26 \| 13260 \| 0.0671 \| \| 0.1485 \| 13.27 \| 13270 \| 0.0672 \| \| 0.1664 \| 13.28 \| 13280 \| 0.0671 \| \| 0.13 \| 13.29 \| 13290 \| 0.0669 \| \| 0.1364 \| 13.3 \| 13300 \| 0.0668 \| \| 0.1537 \| 13.31 \| 13310 \| 0.0670 \| \| 0.2156 \| 13.32 \| 13320 \| 0.0670 \| \| 0.0895 \| 13.33 \| 13330 \| 0.0670 \| \| 0.138 \| 13.34 \| 13340 \| 0.0671 \| \| 0.1125 \| 13.35 \| 13350 \| 0.0671 \| \| 0.1168 \| 13.36 \| 13360 \| 0.0671 \| \| 0.1403 \| 13.37 \| 13370 \| 0.0670 \| \| 0.1249 \| 13.38 \| 13380 \| 0.0670 \| \| 0.1537 \| 13.39 \| 13390 \| 0.0671 \| \| 0.1391 \| 13.4 \| 13400 \| 0.0671 \| \| 0.1411 \| 13.41 \| 13410 \| 0.0673 \| \| 0.1082 \| 13.42 \| 13420 \| 0.0675 \| \| 0.1482 \| 13.43 \| 13430 \| 0.0675 \| \| 0.1452 \| 13.44 \| 13440 \| 0.0675 \| \| 0.1237 \| 13.45 \| 13450 \| 0.0674 \| \| 0.1095 \| 13.46 \| 13460 \| 0.0672 \| \| 0.1125 \| 13.47 \| 13470 \| 0.0671 \| \| 0.1165 \| 13.48 \| 13480 \| 0.0672 \| \| 0.1981 \| 13.49 \| 13490 \| 0.0671 \| \| 0.1634 \| 13.5 \| 13500 \| 0.0671 \| \| 0.161 \| 13.51 \| 13510 \| 0.0673 \| \| 0.1315 \| 13.52 \| 13520 \| 0.0673 \| \| 0.0916 \| 13.53 \| 13530 \| 0.0674 \| \| 0.1414 \| 13.54 \| 13540 \| 0.0675 \| \| 0.1313 \| 13.55 \| 13550 \| 0.0675 \| \| 0.0914 \| 13.56 \| 13560 \| 0.0675 \| \| 0.1081 \| 13.57 \| 13570 \| 0.0675 \| \| 0.1454 \| 13.58 \| 13580 \| 0.0675 \| \| 0.1472 \| 13.59 \| 13590 \| 0.0675 \| \| 0.099 \| 13.6 \| 13600 \| 0.0676 \| \| 0.1465 \| 13.61 \| 13610 \| 0.0676 \| \| 0.1276 \| 13.62 \| 13620 \| 0.0675 \| \| 0.109 \| 13.63 \| 13630 \| 0.0674 \| \| 0.1063 \| 13.64 \| 13640 \| 0.0673 \| \| 0.1552 \| 13.65 \| 13650 \| 0.0673 \| \| 0.105 \| 13.66 \| 13660 \| 0.0673 \| \| 0.1142 \| 13.67 \| 13670 \| 0.0675 \| \| 0.1519 \| 13.68 \| 13680 \| 0.0676 \| \| 0.1658 \| 13.69 \| 13690 \| 0.0677 \| \| 0.1445 \| 13.7 \| 13700 \| 0.0677 \| \| 0.1349 \| 13.71 \| 13710 \| 0.0678 \| \| 0.0989 \| 13.72 \| 13720 \| 0.0678 \| \| 0.1476 \| 13.73 \| 13730 \| 0.0678 \| \| 0.1408 \| 13.74 \| 13740 \| 0.0678 \| \| 0.0973 \| 13.75 \| 13750 \| 0.0678 \| \| 0.0911 \| 13.76 \| 13760 \| 0.0678 \| \| 0.1329 \| 13.77 \| 13770 \| 0.0679 \| \| 0.1466 \| 13.78 \| 13780 \| 0.0678 \| \| 0.1559 \| 13.79 \| 13790 \| 0.0678 \| \| 0.0985 \| 13.8 \| 13800 \| 0.0677 \| \| 0.1517 \| 13.81 \| 13810 \| 0.0677 \| \| 0.1126 \| 13.82 \| 13820 \| 0.0677 \| \| 0.1354 \| 13.83 \| 13830 \| 0.0677 \| \| 0.1257 \| 13.84 \| 13840 \| 0.0677 \| \| 0.1092 \| 13.85 \| 13850 \| 0.0677 \| \| 0.1596 \| 13.86 \| 13860 \| 0.0676 \| \| 0.1083 \| 13.87 \| 13870 \| 0.0676 \| \| 0.1004 \| 13.88 \| 13880 \| 0.0678 \| \| 0.1178 \| 13.89 \| 13890 \| 0.0679 \| \| 0.1418 \| 13.9 \| 13900 \| 0.0680 \| \| 0.1353 \| 13.91 \| 13910 \| 0.0681 \| \| 0.0727 \| 13.92 \| 13920 \| 0.0681 \| \| 0.1375 \| 13.93 \| 13930 \| 0.0682 \| \| 0.0958 \| 13.94 \| 13940 \| 0.0681 \| \| 0.1113 \| 13.95 \| 13950 \| 0.0681 \| \| 0.1047 \| 13.96 \| 13960 \| 0.0680 \| \| 0.0958 \| 13.97 \| 13970 \| 0.0679 \| \| 0.1237 \| 13.98 \| 13980 \| 0.0678 \| \| 0.1115 \| 13.99 \| 13990 \| 0.0676 \| \| 0.1609 \| 14.0 \| 14000 \| 0.0674 \| \| 0.1468 \| 14.01 \| 14010 \| 0.0674 \| \| 0.0906 \| 14.02 \| 14020 \| 0.0674 \| \| 0.0827 \| 14.03 \| 14030 \| 0.0675 \| \| 0.1283 \| 14.04 \| 14040 \| 0.0674 \| \| 0.1501 \| 14.05 \| 14050 \| 0.0674 \| \| 0.1385 \| 14.06 \| 14060 \| 0.0674 \| \| 0.1529 \| 14.07 \| 14070 \| 0.0674 \| \| 0.144 \| 14.08 \| 14080 \| 0.0673 \| \| 0.1779 \| 14.09 \| 14090 \| 0.0673 \| \| 0.1417 \| 14.1 \| 14100 \| 0.0672 \| \| 0.1297 \| 14.11 \| 14110 \| 0.0671 \| \| 0.1147 \| 14.12 \| 14120 \| 0.0671 \| \| 0.0785 \| 14.13 \| 14130 \| 0.0671 \| \| 0.1511 \| 14.14 \| 14140 \| 0.0671 \| \| 0.1242 \| 14.15 \| 14150 \| 0.0671 \| \| 0.1594 \| 14.16 \| 14160 \| 0.0669 \| \| 0.0934 \| 14.17 \| 14170 \| 0.0668 \| \| 0.1215 \| 14.18 \| 14180 \| 0.0669 \| \| 0.1903 \| 14.19 \| 14190 \| 0.0670 \| \| 0.1491 \| 14.2 \| 14200 \| 0.0671 \| \| 0.1379 \| 14.21 \| 14210 \| 0.0670 \| \| 0.1305 \| 14.22 \| 14220 \| 0.0669 \| \| 0.1399 \| 14.23 \| 14230 \| 0.0670 \| \| 0.1682 \| 14.24 \| 14240 \| 0.0671 \| \| 0.1143 \| 14.25 \| 14250 \| 0.0670 \| \| 0.0935 \| 14.26 \| 14260 \| 0.0669 \| \| 0.1032 \| 14.27 \| 14270 \| 0.0668 \| \| 0.1585 \| 14.28 \| 14280 \| 0.0667 \| \| 0.1342 \| 14.29 \| 14290 \| 0.0667 \| \| 0.1145 \| 14.3 \| 14300 \| 0.0665 \| \| 0.1574 \| 14.31 \| 14310 \| 0.0664 \| \| 0.1516 \| 14.32 \| 14320 \| 0.0663 \| \| 0.1492 \| 14.33 \| 14330 \| 0.0663 \| \| 0.1126 \| 14.34 \| 14340 \| 0.0663 \| \| 0.1084 \| 14.35 \| 14350 \| 0.0663 \| \| 0.1372 \| 14.36 \| 14360 \| 0.0663 \| \| 0.1479 \| 14.37 \| 14370 \| 0.0662 \| \| 0.111 \| 14.38 \| 14380 \| 0.0663 \| \| 0.1098 \| 14.39 \| 14390 \| 0.0664 \| \| 0.1803 \| 14.4 \| 14400 \| 0.0665 \| \| 0.161 \| 14.41 \| 14410 \| 0.0665 \| \| 0.1318 \| 14.42 \| 14420 \| 0.0666 \| \| 0.1728 \| 14.43 \| 14430 \| 0.0666 \| \| 0.1026 \| 14.44 \| 14440 \| 0.0666 \| \| 0.1062 \| 14.45 \| 14450 \| 0.0665 \| \| 0.1605 \| 14.46 \| 14460 \| 0.0665 \| \| 0.1153 \| 14.47 \| 14470 \| 0.0667 \| \| 0.1491 \| 14.48 \| 14480 \| 0.0667 \| \| 0.1702 \| 14.49 \| 14490 \| 0.0666 \| \| 0.1372 \| 14.5 \| 14500 \| 0.0666 \| \| 0.1517 \| 14.51 \| 14510 \| 0.0665 \| \| 0.1009 \| 14.52 \| 14520 \| 0.0664 \| \| 0.1384 \| 14.53 \| 14530 \| 0.0664 \| \| 0.0812 \| 14.54 \| 14540 \| 0.0665 \| \| 0.1405 \| 14.55 \| 14550 \| 0.0665 \| \| 0.1006 \| 14.56 \| 14560 \| 0.0666 \| \| 0.1215 \| 14.57 \| 14570 \| 0.0665 \| \| 0.1642 \| 14.58 \| 14580 \| 0.0665 \| \| 0.1275 \| 14.59 \| 14590 \| 0.0665 \| \| 0.1197 \| 14.6 \| 14600 \| 0.0665 \| \| 0.148 \| 14.61 \| 14610 \| 0.0665 \| \| 0.1127 \| 14.62 \| 14620 \| 0.0666 \| \| 0.0934 \| 14.63 \| 14630 \| 0.0666 \| \| 0.1453 \| 14.64 \| 14640 \| 0.0667 \| \| 0.1269 \| 14.65 \| 14650 \| 0.0667 \| \| 0.1501 \| 14.66 \| 14660 \| 0.0667 \| \| 0.1121 \| 14.67 \| 14670 \| 0.0667 \| \| 0.1112 \| 14.68 \| 14680 \| 0.0666 \| \| 0.1089 \| 14.69 \| 14690 \| 0.0665 \| \| 0.1169 \| 14.7 \| 14700 \| 0.0665 \| \| 0.1216 \| 14.71 \| 14710 \| 0.0665 \| \| 0.134 \| 14.72 \| 14720 \| 0.0665 \| \| 0.0875 \| 14.73 \| 14730 \| 0.0665 \| \| 0.1196 \| 14.74 \| 14740 \| 0.0665 \| \| 0.118 \| 14.75 \| 14750 \| 0.0664 \| \| 0.1044 \| 14.76 \| 14760 \| 0.0664 \| \| 0.0878 \| 14.77 \| 14770 \| 0.0664 \| \| 0.1422 \| 14.78 \| 14780 \| 0.0663 \| \| 0.1519 \| 14.79 \| 14790 \| 0.0662 \| \| 0.1263 \| 14.8 \| 14800 \| 0.0662 \| \| 0.0858 \| 14.81 \| 14810 \| 0.0663 \| \| 0.1215 \| 14.82 \| 14820 \| 0.0664 \| \| 0.1877 \| 14.83 \| 14830 \| 0.0665 \| \| 0.1656 \| 14.84 \| 14840 \| 0.0666 \| \| 0.1022 \| 14.85 \| 14850 \| 0.0666 \| \| 0.1478 \| 14.86 \| 14860 \| 0.0667 \| \| 0.1651 \| 14.87 \| 14870 \| 0.0668 \| \| 0.163 \| 14.88 \| 14880 \| 0.0669 \| \| 0.0913 \| 14.89 \| 14890 \| 0.0670 \| \| 0.1002 \| 14.9 \| 14900 \| 0.0671 \| \| 0.1255 \| 14.91 \| 14910 \| 0.0671 \| \| 0.1246 \| 14.92 \| 14920 \| 0.0671 \| \| 0.118 \| 14.93 \| 14930 \| 0.0671 \| \| 0.1068 \| 14.94 \| 14940 \| 0.0671 \| \| 0.1325 \| 14.95 \| 14950 \| 0.0671 \| \| 0.1164 \| 14.96 \| 14960 \| 0.0671 \| \| 0.1202 \| 14.97 \| 14970 \| 0.0670 \| \| 0.1139 \| 14.98 \| 14980 \| 0.0671 \| \| 0.1234 \| 14.99 \| 14990 \| 0.0671 \| \| 0.0874 \| 15.0 \| 15000 \| 0.0672 \| \| 0.0881 \| 15.01 \| 15010 \| 0.0672 \| \| 0.1028 \| 15.02 \| 15020 \| 0.0672 \| \| 0.1213 \| 15.03 \| 15030 \| 0.0673 \| \| 0.0887 \| 15.04 \| 15040 \| 0.0672 \| \| 0.1143 \| 15.05 \| 15050 \| 0.0672 \| \| 0.1755 \| 15.06 \| 15060 \| 0.0672 \| \| 0.11 \| 15.07 \| 15070 \| 0.0672 \| \| 0.1509 \| 15.08 \| 15080 \| 0.0673 \| \| 0.1111 \| 15.09 \| 15090 \| 0.0674 \| \| 0.129 \| 15.1 \| 15100 \| 0.0674 \| \| 0.1429 \| 15.11 \| 15110 \| 0.0674 \| \| 0.1164 \| 15.12 \| 15120 \| 0.0674 \| \| 0.1095 \| 15.13 \| 15130 \| 0.0674 \| \| 0.1083 \| 15.14 \| 15140 \| 0.0674 \| \| 0.1697 \| 15.15 \| 15150 \| 0.0675 \| \| 0.1123 \| 15.16 \| 15160 \| 0.0676 \| \| 0.1351 \| 15.17 \| 15170 \| 0.0676 \| \| 0.1246 \| 15.18 \| 15180 \| 0.0676 \| \| 0.1339 \| 15.19 \| 15190 \| 0.0677 \| \| 0.1753 \| 15.2 \| 15200 \| 0.0679 \| \| 0.1071 \| 15.21 \| 15210 \| 0.0680 \| \| 0.1145 \| 15.22 \| 15220 \| 0.0682 \| \| 0.1707 \| 15.23 \| 15230 \| 0.0683 \| \| 0.1358 \| 15.24 \| 15240 \| 0.0683 \| \| 0.1306 \| 15.25 \| 15250 \| 0.0682 \| \| 0.1291 \| 15.26 \| 15260 \| 0.0681 \| \| 0.0895 \| 15.27 \| 15270 \| 0.0680 \| \| 0.149 \| 15.28 \| 15280 \| 0.0679 \| \| 0.0662 \| 15.29 \| 15290 \| 0.0679 \| \| 0.162 \| 15.3 \| 15300 \| 0.0679 \| \| 0.0953 \| 15.31 \| 15310 \| 0.0679 \| \| 0.12 \| 15.32 \| 15320 \| 0.0680 \| \| 0.0858 \| 15.33 \| 15330 \| 0.0680 \| \| 0.1321 \| 15.34 \| 15340 \| 0.0681 \| \| 0.1988 \| 15.35 \| 15350 \| 0.0682 \| \| 0.1258 \| 15.36 \| 15360 \| 0.0682 \| \| 0.1262 \| 15.37 \| 15370 \| 0.0681 \| \| 0.134 \| 15.38 \| 15380 \| 0.0679 \| \| 0.1873 \| 15.39 \| 15390 \| 0.0678 \| \| 0.1302 \| 15.4 \| 15400 \| 0.0677 \| \| 0.103 \| 15.41 \| 15410 \| 0.0677 \| \| 0.1638 \| 15.42 \| 15420 \| 0.0676 \| \| 0.1407 \| 15.43 \| 15430 \| 0.0676 \| \| 0.1575 \| 15.44 \| 15440 \| 0.0675 \| \| 0.1308 \| 15.45 \| 15450 \| 0.0676 \| \| 0.0824 \| 15.46 \| 15460 \| 0.0676 \| \| 0.0911 \| 15.47 \| 15470 \| 0.0676 \| \| 0.1256 \| 15.48 \| 15480 \| 0.0676 \| \| 0.1219 \| 15.49 \| 15490 \| 0.0676 \| \| 0.1313 \| 15.5 \| 15500 \| 0.0676 \| \| 0.1056 \| 15.51 \| 15510 \| 0.0676 \| \| 0.1176 \| 15.52 \| 15520 \| 0.0676 \| \| 0.0867 \| 15.53 \| 15530 \| 0.0676 \| \| 0.1419 \| 15.54 \| 15540 \| 0.0676 \| \| 0.1312 \| 15.55 \| 15550 \| 0.0676 \| \| 0.1618 \| 15.56 \| 15560 \| 0.0677 \| \| 0.1562 \| 15.57 \| 15570 \| 0.0678 \| \| 0.1319 \| 15.58 \| 15580 \| 0.0679 \| \| 0.1625 \| 15.59 \| 15590 \| 0.0680 \| \| 0.1143 \| 15.6 \| 15600 \| 0.0679 \| \| 0.142 \| 15.61 \| 15610 \| 0.0679 \| \| 0.1458 \| 15.62 \| 15620 \| 0.0679 \| \| 0.1702 \| 15.63 \| 15630 \| 0.0679 \| \| 0.1282 \| 15.64 \| 15640 \| 0.0679 \| \| 0.1031 \| 15.65 \| 15650 \| 0.0679 \| \| 0.1013 \| 15.66 \| 15660 \| 0.0679 \| \| 0.1093 \| 15.67 \| 15670 \| 0.0679 \| \| 0.1258 \| 15.68 \| 15680 \| 0.0679 \| \| 0.0772 \| 15.69 \| 15690 \| 0.0678 \| \| 0.0824 \| 15.7 \| 15700 \| 0.0677 \| \| 0.1307 \| 15.71 \| 15710 \| 0.0677 \| \| 0.0898 \| 15.72 \| 15720 \| 0.0677 \| \| 0.1253 \| 15.73 \| 15730 \| 0.0678 \| \| 0.1511 \| 15.74 \| 15740 \| 0.0678 \| \| 0.1442 \| 15.75 \| 15750 \| 0.0677 \| \| 0.1784 \| 15.76 \| 15760 \| 0.0677 \| \| 0.0995 \| 15.77 \| 15770 \| 0.0676 \| \| 0.0988 \| 15.78 \| 15780 \| 0.0675 \| \| 0.1645 \| 15.79 \| 15790 \| 0.0674 \| \| 0.1588 \| 15.8 \| 15800 \| 0.0674 \| \| 0.1677 \| 15.81 \| 15810 \| 0.0673 \| \| 0.1472 \| 15.82 \| 15820 \| 0.0673 \| \| 0.1514 \| 15.83 \| 15830 \| 0.0674 \| \| 0.1079 \| 15.84 \| 15840 \| 0.0676 \| \| 0.1244 \| 15.85 \| 15850 \| 0.0676 \| \| 0.107 \| 15.86 \| 15860 \| 0.0676 \| \| 0.0886 \| 15.87 \| 15870 \| 0.0676 \| \| 0.1113 \| 15.88 \| 15880 \| 0.0676 \| \| 0.1499 \| 15.89 \| 15890 \| 0.0676 \| \| 0.128 \| 15.9 \| 15900 \| 0.0677 \| \| 0.1704 \| 15.91 \| 15910 \| 0.0677 \| \| 0.1385 \| 15.92 \| 15920 \| 0.0676 \| \| 0.1044 \| 15.93 \| 15930 \| 0.0676 \| \| 0.183 \| 15.94 \| 15940 \| 0.0676 \| \| 0.133 \| 15.95 \| 15950 \| 0.0676 \| \| 0.1186 \| 15.96 \| 15960 \| 0.0676 \| \| 0.112 \| 15.97 \| 15970 \| 0.0675 \| \| 0.1365 \| 15.98 \| 15980 \| 0.0675 \| \| 0.1066 \| 15.99 \| 15990 \| 0.0674 \| \| 0.1408 \| 16.0 \| 16000 \| 0.0674 \| \| 0.169 \| 16.01 \| 16010 \| 0.0674 \| \| 0.1469 \| 16.02 \| 16020 \| 0.0674 \| \| 0.1847 \| 16.03 \| 16030 \| 0.0675 \| \| 0.1483 \| 16.04 \| 16040 \| 0.0675 \| \| 0.1059 \| 16.05 \| 16050 \| 0.0675 \| \| 0.1334 \| 16.06 \| 16060 \| 0.0675 \| \| 0.1191 \| 16.07 \| 16070 \| 0.0675 \| \| 0.1206 \| 16.08 \| 16080 \| 0.0675 \| \| 0.1371 \| 16.09 \| 16090 \| 0.0675 \| \| 0.1313 \| 16.1 \| 16100 \| 0.0676 \| \| 0.1131 \| 16.11 \| 16110 \| 0.0676 \| \| 0.1578 \| 16.12 \| 16120 \| 0.0676 \| \| 0.0963 \| 16.13 \| 16130 \| 0.0676 \| \| 0.2233 \| 16.14 \| 16140 \| 0.0675 \| \| 0.1579 \| 16.15 \| 16150 \| 0.0675 \| \| 0.1269 \| 16.16 \| 16160 \| 0.0675 \| \| 0.1296 \| 16.17 \| 16170 \| 0.0675 \| \| 0.1473 \| 16.18 \| 16180 \| 0.0676 \| \| 0.1081 \| 16.19 \| 16190 \| 0.0676 \| \| 0.1054 \| 16.2 \| 16200 \| 0.0677 \| \| 0.1052 \| 16.21 \| 16210 \| 0.0677 \| \| 0.1317 \| 16.22 \| 16220 \| 0.0677 \| \| 0.1284 \| 16.23 \| 16230 \| 0.0677 \| \| 0.1332 \| 16.24 \| 16240 \| 0.0677 \| \| 0.1071 \| 16.25 \| 16250 \| 0.0677 \| \| 0.1343 \| 16.26 \| 16260 \| 0.0677 \| \| 0.1501 \| 16.27 \| 16270 \| 0.0677 \| \| 0.1277 \| 16.28 \| 16280 \| 0.0677 \| \| 0.0923 \| 16.29 \| 16290 \| 0.0678 \| \| 0.1248 \| 16.3 \| 16300 \| 0.0678 \| \| 0.1534 \| 16.31 \| 16310 \| 0.0678 \| \| 0.0914 \| 16.32 \| 16320 \| 0.0678 \| \| 0.2013 \| 16.33 \| 16330 \| 0.0679 \| \| 0.1221 \| 16.34 \| 16340 \| 0.0679 \| \| 0.1002 \| 16.35 \| 16350 \| 0.0679 \| \| 0.1697 \| 16.36 \| 16360 \| 0.0678 \| \| 0.2087 \| 16.37 \| 16370 \| 0.0677 \| \| 0.1306 \| 16.38 \| 16380 \| 0.0677 \| \| 0.1411 \| 16.39 \| 16390 \| 0.0677 \| \| 0.1174 \| 16.4 \| 16400 \| 0.0677 \| \| 0.1129 \| 16.41 \| 16410 \| 0.0677 \| \| 0.1288 \| 16.42 \| 16420 \| 0.0677 \| \| 0.1741 \| 16.43 \| 16430 \| 0.0677 \| \| 0.106 \| 16.44 \| 16440 \| 0.0678 \| \| 0.1714 \| 16.45 \| 16450 \| 0.0678 \| \| 0.1097 \| 16.46 \| 16460 \| 0.0678 \| \| 0.1027 \| 16.47 \| 16470 \| 0.0679 \| \| 0.146 \| 16.48 \| 16480 \| 0.0679 \| \| 0.123 \| 16.49 \| 16490 \| 0.0679 \| \| 0.1437 \| 16.5 \| 16500 \| 0.0679 \| \| 0.1062 \| 16.51 \| 16510 \| 0.0680 \| \| 0.1634 \| 16.52 \| 16520 \| 0.0679 \| \| 0.0851 \| 16.53 \| 16530 \| 0.0679 \| \| 0.0735 \| 16.54 \| 16540 \| 0.0679 \| \| 0.1056 \| 16.55 \| 16550 \| 0.0679 \| \| 0.1538 \| 16.56 \| 16560 \| 0.0680 \| \| 0.1675 \| 16.57 \| 16570 \| 0.0679 \| \| 0.123 \| 16.58 \| 16580 \| 0.0680 \| \| 0.1112 \| 16.59 \| 16590 \| 0.0680 \| \| 0.1596 \| 16.6 \| 16600 \| 0.0680 \| \| 0.1279 \| 16.61 \| 16610 \| 0.0680 \| \| 0.1091 \| 16.62 \| 16620 \| 0.0680 \| \| 0.0994 \| 16.63 \| 16630 \| 0.0680 \| \| 0.1385 \| 16.64 \| 16640 \| 0.0680 \| \| 0.0764 \| 16.65 \| 16650 \| 0.0680 \| \| 0.1032 \| 16.66 \| 16660 \| 0.0680 \| \| 0.1808 \| 16.67 \| 16670 \| 0.0679 \| \| 0.1235 \| 16.68 \| 16680 \| 0.0680 \| \| 0.1034 \| 16.69 \| 16690 \| 0.0679 \| \| 0.1096 \| 16.7 \| 16700 \| 0.0680 \| \| 0.1252 \| 16.71 \| 16710 \| 0.0680 \| \| 0.0921 \| 16.72 \| 16720 \| 0.0680 \| \| 0.1656 \| 16.73 \| 16730 \| 0.0679 \| \| 0.0974 \| 16.74 \| 16740 \| 0.0679 \| \| 0.1252 \| 16.75 \| 16750 \| 0.0679 \| \| 0.1263 \| 16.76 \| 16760 \| 0.0679 \| \| 0.1502 \| 16.77 \| 16770 \| 0.0679 \| \| 0.1424 \| 16.78 \| 16780 \| 0.0679 \| \| 0.11 \| 16.79 \| 16790 \| 0.0680 \| \| 0.1081 \| 16.8 \| 16800 \| 0.0680 \| \| 0.1256 \| 16.81 \| 16810 \| 0.0680 \| \| 0.0993 \| 16.82 \| 16820 \| 0.0680 \| \| 0.1148 \| 16.83 \| 16830 \| 0.0681 \| \| 0.1431 \| 16.84 \| 16840 \| 0.0681 \| \| 0.1085 \| 16.85 \| 16850 \| 0.0680 \| \| 0.1077 \| 16.86 \| 16860 \| 0.0679 \| \| 0.1247 \| 16.87 \| 16870 \| 0.0679 \| \| 0.087 \| 16.88 \| 16880 \| 0.0678 \| \| 0.1145 \| 16.89 \| 16890 \| 0.0678 \| \| 0.1615 \| 16.9 \| 16900 \| 0.0678 \| \| 0.1338 \| 16.91 \| 16910 \| 0.0677 \| \| 0.116 \| 16.92 \| 16920 \| 0.0677 \| \| 0.125 \| 16.93 \| 16930 \| 0.0677 \| \| 0.0954 \| 16.94 \| 16940 \| 0.0677 \| \| 0.1586 \| 16.95 \| 16950 \| 0.0677 \| \| 0.1027 \| 16.96 \| 16960 \| 0.0677 \| \| 0.097 \| 16.97 \| 16970 \| 0.0678 \| \| 0.1298 \| 16.98 \| 16980 \| 0.0678 \| \| 0.1255 \| 16.99 \| 16990 \| 0.0678 \| \| 0.0878 \| 17.0 \| 17000 \| 0.0678 \| \| 0.1538 \| 17.01 \| 17010 \| 0.0679 \| \| 0.1039 \| 17.02 \| 17020 \| 0.0679 \| \| 0.1264 \| 17.03 \| 17030 \| 0.0679 \| \| 0.1695 \| 17.04 \| 17040 \| 0.0679 \| \| 0.0961 \| 17.05 \| 17050 \| 0.0679 \| \| 0.1451 \| 17.06 \| 17060 \| 0.0679 \| \| 0.1206 \| 17.07 \| 17070 \| 0.0679 \| \| 0.1072 \| 17.08 \| 17080 \| 0.0679 \| \| 0.0849 \| 17.09 \| 17090 \| 0.0679 \| \| 0.1567 \| 17.1 \| 17100 \| 0.0679 \| \| 0.1017 \| 17.11 \| 17110 \| 0.0679 \| \| 0.1369 \| 17.12 \| 17120 \| 0.0679 \| \| 0.1033 \| 17.13 \| 17130 \| 0.0679 \| \| 0.1308 \| 17.14 \| 17140 \| 0.0679 \| \| 0.1366 \| 17.15 \| 17150 \| 0.0679 \| \| 0.1151 \| 17.16 \| 17160 \| 0.0679 \| \| 0.1282 \| 17.17 \| 17170 \| 0.0679 \| \| 0.1321 \| 17.18 \| 17180 \| 0.0679 \| \| 0.1374 \| 17.19 \| 17190 \| 0.0679 \| \| 0.1526 \| 17.2 \| 17200 \| 0.0679 \| \| 0.1288 \| 17.21 \| 17210 \| 0.0680 \| \| 0.1322 \| 17.22 \| 17220 \| 0.0680 \| \| 0.0959 \| 17.23 \| 17230 \| 0.0680 \| \| 0.1165 \| 17.24 \| 17240 \| 0.0680 \| \| 0.1482 \| 17.25 \| 17250 \| 0.0680 \| \| 0.1022 \| 17.26 \| 17260 \| 0.0679 \| \| 0.119 \| 17.27 \| 17270 \| 0.0679 \| \| 0.0637 \| 17.28 \| 17280 \| 0.0679 \| \| 0.0862 \| 17.29 \| 17290 \| 0.0679 \| \| 0.1719 \| 17.3 \| 17300 \| 0.0679 \| \| 0.1184 \| 17.31 \| 17310 \| 0.0679 \| \| 0.1071 \| 17.32 \| 17320 \| 0.0679 \| \| 0.0975 \| 17.33 \| 17330 \| 0.0679 \| \| 0.1764 \| 17.34 \| 17340 \| 0.0680 \| \| 0.1265 \| 17.35 \| 17350 \| 0.0680 \| \| 0.0872 \| 17.36 \| 17360 \| 0.0681 \| \| 0.0905 \| 17.37 \| 17370 \| 0.0681 \| \| 0.1804 \| 17.38 \| 17380 \| 0.0682 \| \| 0.1631 \| 17.39 \| 17390 \| 0.0682 \| \| 0.1525 \| 17.4 \| 17400 \| 0.0683 \| \| 0.2399 \| 17.41 \| 17410 \| 0.0683 \| \| 0.173 \| 17.42 \| 17420 \| 0.0682 \| \| 0.1068 \| 17.43 \| 17430 \| 0.0682 \| \| 0.1128 \| 17.44 \| 17440 \| 0.0682 \| \| 0.1486 \| 17.45 \| 17450 \| 0.0682 \| \| 0.1218 \| 17.46 \| 17460 \| 0.0682 \| \| 0.1763 \| 17.47 \| 17470 \| 0.0683 \| \| 0.2045 \| 17.48 \| 17480 \| 0.0683 \| \| 0.0808 \| 17.49 \| 17490 \| 0.0683 \| \| 0.0647 \| 17.5 \| 17500 \| 0.0683 \| \| 0.1647 \| 17.51 \| 17510 \| 0.0683 \| \| 0.1576 \| 17.52 \| 17520 \| 0.0683 \| \| 0.1091 \| 17.53 \| 17530 \| 0.0683 \| \| 0.1157 \| 17.54 \| 17540 \| 0.0682 \| \| 0.1159 \| 17.55 \| 17550 \| 0.0682 \| \| 0.1928 \| 17.56 \| 17560 \| 0.0682 \| \| 0.132 \| 17.57 \| 17570 \| 0.0682 \| \| 0.1011 \| 17.58 \| 17580 \| 0.0682 \| \| 0.1373 \| 17.59 \| 17590 \| 0.0682 \| \| 0.1239 \| 17.6 \| 17600 \| 0.0682 \| \| 0.1264 \| 17.61 \| 17610 \| 0.0681 \| \| 0.075 \| 17.62 \| 17620 \| 0.0681 \| \| 0.127 \| 17.63 \| 17630 \| 0.0680 \| \| 0.1296 \| 17.64 \| 17640 \| 0.0680 \| \| 0.0834 \| 17.65 \| 17650 \| 0.0680 \| \| 0.1357 \| 17.66 \| 17660 \| 0.0681 \| \| 0.1118 \| 17.67 \| 17670 \| 0.0681 \| \| 0.1179 \| 17.68 \| 17680 \| 0.0681 \| \| 0.1222 \| 17.69 \| 17690 \| 0.0681 \| \| 0.1234 \| 17.7 \| 17700 \| 0.0682 \| \| 0.0625 \| 17.71 \| 17710 \| 0.0681 \| \| 0.0986 \| 17.72 \| 17720 \| 0.0681 \| \| 0.2145 \| 17.73 \| 17730 \| 0.0681 \| \| 0.1318 \| 17.74 \| 17740 \| 0.0680 \| \| 0.1073 \| 17.75 \| 17750 \| 0.0680 \| \| 0.145 \| 17.76 \| 17760 \| 0.0680 \| \| 0.1493 \| 17.77 \| 17770 \| 0.0679 \| \| 0.0863 \| 17.78 \| 17780 \| 0.0679 \| \| 0.1664 \| 17.79 \| 17790 \| 0.0679 \| \| 0.1424 \| 17.8 \| 17800 \| 0.0679 \| \| 0.1012 \| 17.81 \| 17810 \| 0.0679 \| \| 0.1332 \| 17.82 \| 17820 \| 0.0678 \| \| 0.1487 \| 17.83 \| 17830 \| 0.0678 \| \| 0.1096 \| 17.84 \| 17840 \| 0.0679 \| \| 0.1239 \| 17.85 \| 17850 \| 0.0679 \| \| 0.1455 \| 17.86 \| 17860 \| 0.0679 \| \| 0.0981 \| 17.87 \| 17870 \| 0.0680 \| \| 0.1229 \| 17.88 \| 17880 \| 0.0680 \| \| 0.1192 \| 17.89 \| 17890 \| 0.0679 \| \| 0.1313 \| 17.9 \| 17900 \| 0.0679 \| \| 0.104 \| 17.91 \| 17910 \| 0.0679 \| \| 0.1355 \| 17.92 \| 17920 \| 0.0678 \| \| 0.0957 \| 17.93 \| 17930 \| 0.0678 \| \| 0.171 \| 17.94 \| 17940 \| 0.0678 \| \| 0.1029 \| 17.95 \| 17950 \| 0.0678 \| \| 0.1273 \| 17.96 \| 17960 \| 0.0678 \| \| 0.1102 \| 17.97 \| 17970 \| 0.0678 \| \| 0.1794 \| 17.98 \| 17980 \| 0.0678 \| \| 0.1301 \| 17.99 \| 17990 \| 0.0678 \| \| 0.1305 \| 18.0 \| 18000 \| 0.0678 \| \| 0.1745 \| 18.01 \| 18010 \| 0.0678 \| \| 0.1337 \| 18.02 \| 18020 \| 0.0678 \| \| 0.1219 \| 18.03 \| 18030 \| 0.0678 \| \| 0.1326 \| 18.04 \| 18040 \| 0.0678 \| \| 0.0912 \| 18.05 \| 18050 \| 0.0678 \| \| 0.115 \| 18.06 \| 18060 \| 0.0679 \| \| 0.1402 \| 18.07 \| 18070 \| 0.0679 \| \| 0.0903 \| 18.08 \| 18080 \| 0.0679 \| \| 0.1204 \| 18.09 \| 18090 \| 0.0679 \| \| 0.1296 \| 18.1 \| 18100 \| 0.0679 \| \| 0.1176 \| 18.11 \| 18110 \| 0.0678 \| \| 0.0926 \| 18.12 \| 18120 \| 0.0678 \| \| 0.1387 \| 18.13 \| 18130 \| 0.0678 \| \| 0.1098 \| 18.14 \| 18140 \| 0.0677 \| \| 0.1264 \| 18.15 \| 18150 \| 0.0677 \| \| 0.0864 \| 18.16 \| 18160 \| 0.0677 \| \| 0.2153 \| 18.17 \| 18170 \| 0.0677 \| \| 0.0984 \| 18.18 \| 18180 \| 0.0678 \| \| 0.1249 \| 18.19 \| 18190 \| 0.0678 \| \| 0.1411 \| 18.2 \| 18200 \| 0.0678 \| \| 0.1237 \| 18.21 \| 18210 \| 0.0678 \| \| 0.1076 \| 18.22 \| 18220 \| 0.0678 \| \| 0.1547 \| 18.23 \| 18230 \| 0.0678 \| \| 0.1031 \| 18.24 \| 18240 \| 0.0679 \| \| 0.1305 \| 18.25 \| 18250 \| 0.0678 \| \| 0.1385 \| 18.26 \| 18260 \| 0.0678 \| \| 0.1488 \| 18.27 \| 18270 \| 0.0678 \| \| 0.124 \| 18.28 \| 18280 \| 0.0678 \| \| 0.1043 \| 18.29 \| 18290 \| 0.0678 \| \| 0.1105 \| 18.3 \| 18300 \| 0.0679 \| \| 0.1424 \| 18.31 \| 18310 \| 0.0679 \| \| 0.109 \| 18.32 \| 18320 \| 0.0679 \| \| 0.0968 \| 18.33 \| 18330 \| 0.0679 \| \| 0.1356 \| 18.34 \| 18340 \| 0.0679 \| \| 0.1464 \| 18.35 \| 18350 \| 0.0678 \| \| 0.117 \| 18.36 \| 18360 \| 0.0679 \| \| 0.0959 \| 18.37 \| 18370 \| 0.0679 \| \| 0.162 \| 18.38 \| 18380 \| 0.0678 \| \| 0.1577 \| 18.39 \| 18390 \| 0.0679 \| \| 0.115 \| 18.4 \| 18400 \| 0.0679 \| \| 0.0833 \| 18.41 \| 18410 \| 0.0679 \| \| 0.1108 \| 18.42 \| 18420 \| 0.0679 \| \| 0.1653 \| 18.43 \| 18430 \| 0.0679 \| \| 0.1894 \| 18.44 \| 18440 \| 0.0679 \| \| 0.1565 \| 18.45 \| 18450 \| 0.0679 \| \| 0.1001 \| 18.46 \| 18460 \| 0.0679 \| \| 0.1084 \| 18.47 \| 18470 \| 0.0679 \| \| 0.164 \| 18.48 \| 18480 \| 0.0679 \| \| 0.1232 \| 18.49 \| 18490 \| 0.0679 \| \| 0.0927 \| 18.5 \| 18500 \| 0.0679 \| \| 0.1665 \| 18.51 \| 18510 \| 0.0679 \| \| 0.1389 \| 18.52 \| 18520 \| 0.0679 \| \| 0.121 \| 18.53 \| 18530 \| 0.0679 \| \| 0.1347 \| 18.54 \| 18540 \| 0.0679 \| \| 0.1238 \| 18.55 \| 18550 \| 0.0679 \| \| 0.0981 \| 18.56 \| 18560 \| 0.0679 \| \| 0.1181 \| 18.57 \| 18570 \| 0.0678 \| \| 0.1339 \| 18.58 \| 18580 \| 0.0678 \| \| 0.13 \| 18.59 \| 18590 \| 0.0678 \| \| 0.1145 \| 18.6 \| 18600 \| 0.0678 \| \| 0.1181 \| 18.61 \| 18610 \| 0.0678 \| \| 0.189 \| 18.62 \| 18620 \| 0.0679 \| \| 0.1237 \| 18.63 \| 18630 \| 0.0679 \| \| 0.1588 \| 18.64 \| 18640 \| 0.0679 \| \| 0.1375 \| 18.65 \| 18650 \| 0.0679 \| \| 0.1067 \| 18.66 \| 18660 \| 0.0679 \| \| 0.1511 \| 18.67 \| 18670 \| 0.0679 \| \| 0.1255 \| 18.68 \| 18680 \| 0.0679 \| \| 0.1328 \| 18.69 \| 18690 \| 0.0679 \| \| 0.1626 \| 18.7 \| 18700 \| 0.0679 \| \| 0.0661 \| 18.71 \| 18710 \| 0.0679 \| \| 0.0785 \| 18.72 \| 18720 \| 0.0679 \| \| 0.1707 \| 18.73 \| 18730 \| 0.0679 \| \| 0.1522 \| 18.74 \| 18740 \| 0.0679 \| \| 0.1449 \| 18.75 \| 18750 \| 0.0679 \| \| 0.083 \| 18.76 \| 18760 \| 0.0679 \| \| 0.0882 \| 18.77 \| 18770 \| 0.0679 \| \| 0.1285 \| 18.78 \| 18780 \| 0.0679 \| \| 0.0934 \| 18.79 \| 18790 \| 0.0678 \| \| 0.1788 \| 18.8 \| 18800 \| 0.0678 \| \| 0.0998 \| 18.81 \| 18810 \| 0.0678 \| \| 0.1165 \| 18.82 \| 18820 \| 0.0678 \| \| 0.1367 \| 18.83 \| 18830 \| 0.0678 \| \| 0.1192 \| 18.84 \| 18840 \| 0.0679 \| \| 0.14 \| 18.85 \| 18850 \| 0.0679 \| \| 0.1594 \| 18.86 \| 18860 \| 0.0678 \| \| 0.1493 \| 18.87 \| 18870 \| 0.0678 \| \| 0.1266 \| 18.88 \| 18880 \| 0.0679 \| \| 0.0926 \| 18.89 \| 18890 \| 0.0678 \| \| 0.1058 \| 18.9 \| 18900 \| 0.0679 \| \| 0.0981 \| 18.91 \| 18910 \| 0.0679 \| \| 0.0955 \| 18.92 \| 18920 \| 0.0679 \| \| 0.1762 \| 18.93 \| 18930 \| 0.0679 \| \| 0.1086 \| 18.94 \| 18940 \| 0.0678 \| \| 0.1509 \| 18.95 \| 18950 \| 0.0678 \| \| 0.1192 \| 18.96 \| 18960 \| 0.0678 \| \| 0.1253 \| 18.97 \| 18970 \| 0.0678 \| \| 0.1322 \| 18.98 \| 18980 \| 0.0678 \| \| 0.112 \| 18.99 \| 18990 \| 0.0679 \| \| 0.1392 \| 19.0 \| 19000 \| 0.0679 \| \| 0.1317 \| 19.01 \| 19010 \| 0.0679 \| \| 0.1201 \| 19.02 \| 19020 \| 0.0679 \| \| 0.1217 \| 19.03 \| 19030 \| 0.0679 \| \| 0.1015 \| 19.04 \| 19040 \| 0.0679 \| \| 0.0911 \| 19.05 \| 19050 \| 0.0679 \| \| 0.1095 \| 19.06 \| 19060 \| 0.0679 \| \| 0.1423 \| 19.07 \| 19070 \| 0.0679 \| \| 0.144 \| 19.08 \| 19080 \| 0.0679 \| \| 0.1376 \| 19.09 \| 19090 \| 0.0679 \| \| 0.0957 \| 19.1 \| 19100 \| 0.0679 \| \| 0.1214 \| 19.11 \| 19110 \| 0.0679 \| \| 0.1052 \| 19.12 \| 19120 \| 0.0679 \| \| 0.1113 \| 19.13 \| 19130 \| 0.0679 \| \| 0.1393 \| 19.14 \| 19140 \| 0.0679 \| \| 0.1622 \| 19.15 \| 19150 \| 0.0679 \| \| 0.1259 \| 19.16 \| 19160 \| 0.0679 \| \| 0.0982 \| 19.17 \| 19170 \| 0.0679 \| \| 0.1379 \| 19.18 \| 19180 \| 0.0679 \| \| 0.1301 \| 19.19 \| 19190 \| 0.0679 \| \| 0.1403 \| 19.2 \| 19200 \| 0.0679 \| \| 0.163 \| 19.21 \| 19210 \| 0.0679 \| \| 0.1091 \| 19.22 \| 19220 \| 0.0679 \| \| 0.1102 \| 19.23 \| 19230 \| 0.0679 \| \| 0.1017 \| 19.24 \| 19240 \| 0.0679 \| \| 0.0861 \| 19.25 \| 19250 \| 0.0679 \| \| 0.154 \| 19.26 \| 19260 \| 0.0679 \| \| 0.1095 \| 19.27 \| 19270 \| 0.0679 \| \| 0.1119 \| 19.28 \| 19280 \| 0.0679 \| \| 0.1117 \| 19.29 \| 19290 \| 0.0679 \| \| 0.1372 \| 19.3 \| 19300 \| 0.0679 \| \| 0.0972 \| 19.31 \| 19310 \| 0.0679 \| \| 0.1029 \| 19.32 \| 19320 \| 0.0679 \| \| 0.1148 \| 19.33 \| 19330 \| 0.0679 \| \| 0.1427 \| 19.34 \| 19340 \| 0.0679 \| \| 0.0774 \| 19.35 \| 19350 \| 0.0679 \| \| 0.0988 \| 19.36 \| 19360 \| 0.0679 \| \| 0.1259 \| 19.37 \| 19370 \| 0.0679 \| \| 0.1383 \| 19.38 \| 19380 \| 0.0679 \| \| 0.1604 \| 19.39 \| 19390 \| 0.0679 \| \| 0.1802 \| 19.4 \| 19400 \| 0.0679 \| \| 0.0756 \| 19.41 \| 19410 \| 0.0680 \| \| 0.1623 \| 19.42 \| 19420 \| 0.0680 \| \| 0.2077 \| 19.43 \| 19430 \| 0.0680 \| \| 0.1435 \| 19.44 \| 19440 \| 0.0680 \| \| 0.1506 \| 19.45 \| 19450 \| 0.0680 \| \| 0.101 \| 19.46 \| 19460 \| 0.0680 \| \| 0.1593 \| 19.47 \| 19470 \| 0.0680 \| \| 0.076 \| 19.48 \| 19480 \| 0.0680 \| \| 0.1329 \| 19.49 \| 19490 \| 0.0680 \| \| 0.1212 \| 19.5 \| 19500 \| 0.0680 \| \| 0.1458 \| 19.51 \| 19510 \| 0.0680 \| \| 0.1582 \| 19.52 \| 19520 \| 0.0680 \| \| 0.1203 \| 19.53 \| 19530 \| 0.0680 \| \| 0.1434 \| 19.54 \| 19540 \| 0.0680 \| \| 0.1366 \| 19.55 \| 19550 \| 0.0680 \| \| 0.1633 \| 19.56 \| 19560 \| 0.0680 \| \| 0.1484 \| 19.57 \| 19570 \| 0.0680 \| \| 0.1363 \| 19.58 \| 19580 \| 0.0680 \| \| 0.1428 \| 19.59 \| 19590 \| 0.0680 \| \| 0.1465 \| 19.6 \| 19600 \| 0.0680 \| \| 0.0957 \| 19.61 \| 19610 \| 0.0680 \| \| 0.1345 \| 19.62 \| 19620 \| 0.0680 \| \| 0.1382 \| 19.63 \| 19630 \| 0.0680 \| \| 0.1468 \| 19.64 \| 19640 \| 0.0680 \| \| 0.1237 \| 19.65 \| 19650 \| 0.0680 \| \| 0.1178 \| 19.66 \| 19660 \| 0.0680 \| \| 0.0848 \| 19.67 \| 19670 \| 0.0680 \| \| 0.1159 \| 19.68 \| 19680 \| 0.0680 \| \| 0.1639 \| 19.69 \| 19690 \| 0.0680 \| \| 0.1084 \| 19.7 \| 19700 \| 0.0680 \| \| 0.0811 \| 19.71 \| 19710 \| 0.0680 \| \| 0.0745 \| 19.72 \| 19720 \| 0.0680 \| \| 0.1026 \| 19.73 \| 19730 \| 0.0680 \| \| 0.0895 \| 19.74 \| 19740 \| 0.0680 \| \| 0.1719 \| 19.75 \| 19750 \| 0.0680 \| \| 0.1247 \| 19.76 \| 19760 \| 0.0680 \| \| 0.1174 \| 19.77 \| 19770 \| 0.0680 \| \| 0.1476 \| 19.78 \| 19780 \| 0.0680 \| \| 0.1718 \| 19.79 \| 19790 \| 0.0680 \| \| 0.104 \| 19.8 \| 19800 \| 0.0680 \| \| 0.166 \| 19.81 \| 19810 \| 0.0680 \| \| 0.1547 \| 19.82 \| 19820 \| 0.0680 \| \| 0.18 \| 19.83 \| 19830 \| 0.0680 \| \| 0.1405 \| 19.84 \| 19840 \| 0.0680 \| \| 0.1047 \| 19.85 \| 19850 \| 0.0680 \| \| 0.1585 \| 19.86 \| 19860 \| 0.0680 \| \| 0.1295 \| 19.87 \| 19870 \| 0.0680 \| \| 0.1498 \| 19.88 \| 19880 \| 0.0680 \| \| 0.0994 \| 19.89 \| 19890 \| 0.0680 \| \| 0.1122 \| 19.9 \| 19900 \| 0.0680 \| \| 0.0903 \| 19.91 \| 19910 \| 0.0680 \| \| 0.0906 \| 19.92 \| 19920 \| 0.0680 \| \| 0.1394 \| 19.93 \| 19930 \| 0.0680 \| \| 0.0912 \| 19.94 \| 19940 \| 0.0680 \| \| 0.1074 \| 19.95 \| 19950 \| 0.0680 \| \| 0.1219 \| 19.96 \| 19960 \| 0.0680 \| \| 0.1278 \| 19.97 \| 19970 \| 0.0680 \| \| 0.1234 \| 19.98 \| 19980 \| 0.0680 \| \| 0.1645 \| 19.99 \| 19990 \| 0.0680 \| \| 0.19 \| 20.0 \| 20000 \| 0.0680 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_3e-05_lr_8_e	roa7n	2023-09-28T21:01:12Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T21:01:10Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
sak-red/a2c-PandaReachDense-v3	sak-red	2023-09-28T20:37:26Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T20:30:58Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -11.78 +/- 3.46 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
wy2029/a2c-PandaReachDense-v3	wy2029	2023-09-28T20:35:54Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T20:29:58Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -11.51 +/- 8.88 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
KxSystems/mri_resnet_model	KxSystems	2023-09-28T20:33:41Z	0	0	keras	[ "keras", "tf-keras", "license:apache-2.0", "region:us" ]	null	2023-09-28T20:29:56Z	--- license: apache-2.0 library_name: keras ---
CyberHarem/tachibana_nina_citrus	CyberHarem	2023-09-28T20:19:28Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/tachibana_nina_citrus", "license:mit", "region:us" ]	text-to-image	2023-09-28T20:08:36Z	--- license: mit datasets: - CyberHarem/tachibana_nina_citrus pipeline_tag: text-to-image tags: - art --- # Lora of tachibana_nina_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/tachibana_nina_citrus.pt` as the embedding and `4760/tachibana_nina_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.970. The trigger words are: 1. `tachibana_nina_citrus` 2. `long_hair, grey_hair, hairband, blue_eyes, purple_eyes, bow, hair_between_eyes, hair_bow` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.952 \| [Download](5100/tachibana_nina_citrus.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.970 \| [Download](4760/tachibana_nina_citrus.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.969 \| [Download](4420/tachibana_nina_citrus.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.945 \| [Download](4080/tachibana_nina_citrus.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.949 \| [Download](3740/tachibana_nina_citrus.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.947 \| [Download](3400/tachibana_nina_citrus.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.950 \| [Download](3060/tachibana_nina_citrus.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.956 \| [Download](2720/tachibana_nina_citrus.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.919 \| [Download](2380/tachibana_nina_citrus.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.940 \| [Download](2040/tachibana_nina_citrus.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.933 \| [Download](1700/tachibana_nina_citrus.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.941 \| [Download](1360/tachibana_nina_citrus.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.839 \| [Download](1020/tachibana_nina_citrus.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.911 \| [Download](680/tachibana_nina_citrus.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.725 \| [Download](340/tachibana_nina_citrus.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Gurusha/sd-hand-model-lora-sdxl_text_encoder	Gurusha	2023-09-28T19:55:49Z	2	2	diffusers	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-28T13:43:40Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Gurusha/sd-hand-model-lora-sdxl_text_encoder These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
criceca/food_classifier	criceca	2023-09-28T19:54:44Z	63	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-28T19:50:36Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: criceca/food_classifier results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # criceca/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3562 - Validation Loss: 0.5212 - Train Accuracy: 1.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 10, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.7778 \| 0.6301 \| 1.0 \| 0 \| \| 0.5779 \| 0.5862 \| 1.0 \| 1 \| \| 0.4803 \| 0.5538 \| 1.0 \| 2 \| \| 0.4108 \| 0.5343 \| 1.0 \| 3 \| \| 0.3562 \| 0.5212 \| 1.0 \| 4 \| ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
gokuls/HBERTv1_emb_compress_48_L10_H128_A2	gokuls	2023-09-28T19:38:05Z	45	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-27T06:40:16Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_emb_compress_48_L10_H128_A2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.14372973477498716 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # HBERTv1_emb_compress_48_L10_H128_A2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.2119 - Accuracy: 0.1437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 80 - eval_batch_size: 80 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 7.7198 \| 0.14 \| 10000 \| 7.6446 \| 0.0636 \| \| 6.9424 \| 0.27 \| 20000 \| 6.9379 \| 0.0825 \| \| 6.7559 \| 0.41 \| 30000 \| 6.7492 \| 0.0974 \| \| 6.6513 \| 0.55 \| 40000 \| 6.6455 \| 0.1047 \| \| 6.5706 \| 0.68 \| 50000 \| 6.5734 \| 0.1113 \| \| 6.5236 \| 0.82 \| 60000 \| 6.5176 \| 0.1170 \| \| 6.479 \| 0.96 \| 70000 \| 6.4744 \| 0.1212 \| \| 6.4372 \| 1.09 \| 80000 \| 6.4382 \| 0.1255 \| \| 6.4069 \| 1.23 \| 90000 \| 6.4071 \| 0.1275 \| \| 6.3918 \| 1.37 \| 100000 \| 6.3806 \| 0.1302 \| \| 6.3625 \| 1.5 \| 110000 \| 6.3581 \| 0.1320 \| \| 6.339 \| 1.64 \| 120000 \| 6.3400 \| 0.1341 \| \| 6.3189 \| 1.78 \| 130000 \| 6.3237 \| 0.1350 \| \| 6.3115 \| 1.91 \| 140000 \| 6.3103 \| 0.1366 \| \| 6.298 \| 2.05 \| 150000 \| 6.2982 \| 0.1374 \| \| 6.2888 \| 2.18 \| 160000 \| 6.2878 \| 0.1382 \| \| 6.2786 \| 2.32 \| 170000 \| 6.2778 \| 0.1392 \| \| 6.2691 \| 2.46 \| 180000 \| 6.2703 \| 0.1398 \| \| 6.2675 \| 2.59 \| 190000 \| 6.2622 \| 0.1402 \| \| 6.255 \| 2.73 \| 200000 \| 6.2571 \| 0.1404 \| \| 6.2481 \| 2.87 \| 210000 \| 6.2517 \| 0.1407 \| \| 6.2465 \| 3.0 \| 220000 \| 6.2447 \| 0.1417 \| \| 6.2381 \| 3.14 \| 230000 \| 6.2399 \| 0.1419 \| \| 6.2332 \| 3.28 \| 240000 \| 6.2371 \| 0.1422 \| \| 6.2279 \| 3.41 \| 250000 \| 6.2321 \| 0.1424 \| \| 6.2275 \| 3.55 \| 260000 \| 6.2274 \| 0.1427 \| \| 6.2186 \| 3.69 \| 270000 \| 6.2249 \| 0.1429 \| \| 6.2192 \| 3.82 \| 280000 \| 6.2231 \| 0.1428 \| \| 6.2228 \| 3.96 \| 290000 \| 6.2208 \| 0.1433 \| \| 6.2154 \| 4.1 \| 300000 \| 6.2192 \| 0.1434 \| \| 6.2204 \| 4.23 \| 310000 \| 6.2177 \| 0.1433 \| \| 6.2123 \| 4.37 \| 320000 \| 6.2162 \| 0.1434 \| \| 6.2136 \| 4.51 \| 330000 \| 6.2134 \| 0.1437 \| \| 6.214 \| 4.64 \| 340000 \| 6.2131 \| 0.1439 \| \| 6.2139 \| 4.78 \| 350000 \| 6.2129 \| 0.1437 \| \| 6.211 \| 4.92 \| 360000 \| 6.2130 \| 0.1439 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/tachibana_sara_citrus	CyberHarem	2023-09-28T19:35:33Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/tachibana_sara_citrus", "license:mit", "region:us" ]	text-to-image	2023-09-28T19:24:37Z	--- license: mit datasets: - CyberHarem/tachibana_sara_citrus pipeline_tag: text-to-image tags: - art --- # Lora of tachibana_sara_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/tachibana_sara_citrus.pt` as the embedding and `4760/tachibana_sara_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.992. The trigger words are: 1. `tachibana_sara_citrus` 2. `long_hair, purple_eyes, serafuku, grey_hair, ahoge, blue_eyes, anime_coloring` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.953 \| [Download](5100/tachibana_sara_citrus.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.992 \| [Download](4760/tachibana_sara_citrus.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.983 \| [Download](4420/tachibana_sara_citrus.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.946 \| [Download](4080/tachibana_sara_citrus.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.896 \| [Download](3740/tachibana_sara_citrus.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.899 \| [Download](3400/tachibana_sara_citrus.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.899 \| [Download](3060/tachibana_sara_citrus.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.942 \| [Download](2720/tachibana_sara_citrus.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.929 \| [Download](2380/tachibana_sara_citrus.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.863 \| [Download](2040/tachibana_sara_citrus.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.792 \| [Download](1700/tachibana_sara_citrus.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.858 \| [Download](1360/tachibana_sara_citrus.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.844 \| [Download](1020/tachibana_sara_citrus.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.493 \| [Download](680/tachibana_sara_citrus.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.404 \| [Download](340/tachibana_sara_citrus.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Schadom/dqn-SpaceInvadersNoFrameskip-v4-v6	Schadom	2023-09-28T19:29:52Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T19:29:08Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 796.00 +/- 319.83 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Schadom -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Schadom -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Schadom ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
vineetsharma/databricks-dolly-15k-distilgpt2-v1	vineetsharma	2023-09-28T19:23:57Z	141	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T18:13:40Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: databricks-dolly-15k-distilgpt2-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # databricks-dolly-15k-distilgpt2-v1 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 3.3381 - eval_runtime: 25.854 - eval_samples_per_second: 58.057 - eval_steps_per_second: 7.272 - epoch: 5.01 - step: 7523 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
shikari2917/papapic	shikari2917	2023-09-28T19:23:04Z	5	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-28T19:22:57Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks atique tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.0003_lr_8_e	roa7n	2023-09-28T19:12:02Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T19:12:00Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
asmaa1/videomae-base-groub7-finetuned-SLT-subset	asmaa1	2023-09-28T18:53:22Z	64	0	transformers	[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-09-28T05:42:23Z	--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-groub7-finetuned-SLT-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-groub7-finetuned-SLT-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4002 - Accuracy: 0.1190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 80 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.8439 \| 0.14 \| 11 \| 3.6563 \| 0.0714 \| \| 3.8227 \| 1.14 \| 22 \| 3.6419 \| 0.0476 \| \| 3.7396 \| 2.14 \| 33 \| 3.5863 \| 0.0952 \| \| 3.6939 \| 3.14 \| 44 \| 3.5431 \| 0.0952 \| \| 3.5364 \| 4.14 \| 55 \| 3.4718 \| 0.1190 \| \| 3.508 \| 5.14 \| 66 \| 3.4199 \| 0.1190 \| \| 3.5206 \| 6.14 \| 77 \| 3.4016 \| 0.1190 \| \| 3.425 \| 7.04 \| 80 \| 3.4002 \| 0.1190 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3
platzi/platzi-vit-model-Carlos-Moreno	platzi	2023-09-28T18:48:35Z	213	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-28T18:44:17Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: platzi-vit-model-Carlos-Moreno results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9849624060150376 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # platzi-vit-model-Carlos-Moreno This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0368 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.144 \| 3.85 \| 500 \| 0.0368 \| 0.9850 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
fp16-guy/CuriousMerge_2.5D_fp16_cleaned	fp16-guy	2023-09-28T18:32:22Z	0	0	null	[ "text-to-image", "region:us" ]	text-to-image	2023-09-28T17:46:17Z	--- pipeline_tag: text-to-image --- CuriousMerge 2.5D, but fp16/cleaned - smaller size, same result. ======== /// [original checkpoint link](https://civitai.com/models/79070/curiousmerge-25d) (all rights to the model belong to Cur1ous) --- [grid 01](https://huggingface.co/datasets/fp16-guy/grids/blob/main/curiousmerge25D_v50V4V2%2001%2020230928205844-111-curiousmerge25D_v50V4V2-Euler%20a-6.png) (1.99gb version)* [grid 02](https://huggingface.co/datasets/fp16-guy/grids/blob/main/curiousmerge25D_v50V4V2%2002%2020230928205938-111-curiousmerge25D_v50V4V2-Euler%20a-6.png) (1.83gb version - no vae)* [grid 03](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/curiousmerge25D_v50V4V2%20inp%2001%2020230928184336-111-curiousmerge25D_v50V4V2-Euler%20a-8.png) (1.99gb inpainting version)* [grid 04](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/curiousmerge25D_v50V4V2%20inp%2002%2020230928184150-111-curiousmerge25D_v50V4V2-Euler%20a-8.png) (1.83gb inpainting version - no vae)*
Sooyung/ppo-LunarLander-v2	Sooyung	2023-09-28T18:26:38Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T18:26:22Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 297.29 +/- 8.93 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
deutsche-welle/t5_large_peft_wnc_debiaser	deutsche-welle	2023-09-28T18:18:39Z	115	1	peft	[ "peft", "media", "text2text-generation", "en", "base_model:google-t5/t5-large", "base_model:adapter:google-t5/t5-large", "region:us" ]	text2text-generation	2023-07-27T13:43:48Z	--- language: - en library_name: peft tags: - media metrics: - rouge pipeline_tag: text2text-generation base_model: t5-large --- The model is trained on [Wiki Neutrality Corpus (WNC)](https://ojs.aaai.org/index.php/AAAI/article/view/5385). Big kudos to the authors of WNC. We uploaded an example Python script (main.py), you can refer it if you want to host it via FastAPI.
deutsche-welle/bias_classifier_roberta_base_peft	deutsche-welle	2023-09-28T18:18:27Z	2	1	peft	[ "peft", "roberta", "text-classification", "en", "dataset:mediabiasgroup/BABE", "arxiv:1910.09700", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "region:us" ]	text-classification	2023-07-31T13:02:47Z	--- language: - en library_name: peft tags: - roberta datasets: - mediabiasgroup/BABE metrics: - f1 pipeline_tag: text-classification base_model: roberta-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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ArturGadek/LunarLander-v2	ArturGadek	2023-09-28T18:14:02Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T18:13:03Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 287.00 +/- 13.87 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
kadriu/shqip-mms-3	kadriu	2023-09-28T18:01:48Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-08-17T22:33:52Z	--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: shqip-mms-3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # shqip-mms-3 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4195 - Wer: 0.3447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.5717 \| 0.31 \| 300 \| 0.9548 \| 0.7373 \| \| 0.7823 \| 0.63 \| 600 \| 0.7372 \| 0.5886 \| \| 0.6863 \| 0.94 \| 900 \| 0.6122 \| 0.5261 \| \| 0.5908 \| 1.25 \| 1200 \| 0.5215 \| 0.4440 \| \| 0.5237 \| 1.57 \| 1500 \| 0.4693 \| 0.3987 \| \| 0.4662 \| 1.88 \| 1800 \| 0.4195 \| 0.3447 \| ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.1.0.dev20230810 - Datasets 2.14.3 - Tokenizers 0.13.3
infCapital/viet-llama2-ft	infCapital	2023-09-28T17:57:21Z	1,447	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "vi", "dataset:tatsu-lab/alpaca", "dataset:ewof/alpaca-instruct-unfiltered", "dataset:databricks/databricks-dolly-15k", "dataset:teknium/GPTeacher-General-Instruct", "dataset:garage-bAInd/Open-Platypus", "dataset:Honkware/oasst1-alpaca-json", "dataset:GAIR/lima", "dataset:infCapital/viet-llama2-ft-tiny", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T16:45:15Z	--- datasets: - tatsu-lab/alpaca - ewof/alpaca-instruct-unfiltered - databricks/databricks-dolly-15k - teknium/GPTeacher-General-Instruct - garage-bAInd/Open-Platypus - Honkware/oasst1-alpaca-json - GAIR/lima - infCapital/viet-llama2-ft-tiny language: - vi --- + LLaMa2 - 7B Chat models, extend vocab size to 44800 for Vietnamese understanding. + Continual Pre-Train with 2B Vietnames Tokens aligned from VnNews Corpus, 10K vnthuquan books, wikipedia_vi + Fine-Tuning with infCapital/viet-llama2-ft-tiny dataset, the combination of vaious dataset then translated into Vietnamese using OpenAI GPT-3 + For more information: email me at duyhunghd6@gmail.com \| http://fb.com/hungbui2013
Yntec/nuipenimix2	Yntec	2023-09-28T17:39:27Z	12,747	4	diffusers	[ "diffusers", "safetensors", "Anime", "Cute", "Animals", "McSionnaigh", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T16:58:20Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - Cute - Animals - McSionnaigh - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # nuipenimix 2 fp16 no-ema version of this model. Original page: https://civitai.com/models/81937?modelVersionId=139841 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/SoiwQEhZqvAW9JdXn5Abu.png) ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/MIbzralOKww2sWggu9lFW.png) icon of cute little red panda, round frame, blue glow, purple background
gokuls/HBERTv1_emb_compress_48_L12_H64_A2	gokuls	2023-09-28T17:39:14Z	46	0	transformers	[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-27T07:12:38Z	--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_emb_compress_48_L12_H64_A2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.12850906143802152 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # HBERTv1_emb_compress_48_L12_H64_A2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.4079 - Accuracy: 0.1285 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 8.6554 \| 0.16 \| 10000 \| 8.5846 \| 0.0483 \| \| 7.2331 \| 0.33 \| 20000 \| 7.2280 \| 0.0542 \| \| 7.0014 \| 0.49 \| 30000 \| 6.9927 \| 0.0677 \| \| 6.8699 \| 0.66 \| 40000 \| 6.8637 \| 0.0856 \| \| 6.7777 \| 0.82 \| 50000 \| 6.7726 \| 0.0922 \| \| 6.7091 \| 0.98 \| 60000 \| 6.7101 \| 0.0974 \| \| 6.6626 \| 1.15 \| 70000 \| 6.6620 \| 0.1015 \| \| 6.6279 \| 1.31 \| 80000 \| 6.6255 \| 0.1040 \| \| 6.5917 \| 1.47 \| 90000 \| 6.5948 \| 0.1068 \| \| 6.5691 \| 1.64 \| 100000 \| 6.5695 \| 0.1094 \| \| 6.5486 \| 1.8 \| 110000 \| 6.5460 \| 0.1122 \| \| 6.5246 \| 1.97 \| 120000 \| 6.5275 \| 0.1144 \| \| 6.5069 \| 2.13 \| 130000 \| 6.5115 \| 0.1162 \| \| 6.5001 \| 2.29 \| 140000 \| 6.4962 \| 0.1180 \| \| 6.4785 \| 2.46 \| 150000 \| 6.4822 \| 0.1197 \| \| 6.4706 \| 2.62 \| 160000 \| 6.4714 \| 0.1212 \| \| 6.4612 \| 2.79 \| 170000 \| 6.4610 \| 0.1225 \| \| 6.4485 \| 2.95 \| 180000 \| 6.4530 \| 0.1233 \| \| 6.4477 \| 3.11 \| 190000 \| 6.4441 \| 0.1243 \| \| 6.4373 \| 3.28 \| 200000 \| 6.4395 \| 0.1251 \| \| 6.4351 \| 3.44 \| 210000 \| 6.4322 \| 0.1259 \| \| 6.4273 \| 3.6 \| 220000 \| 6.4264 \| 0.1262 \| \| 6.4153 \| 3.77 \| 230000 \| 6.4219 \| 0.1269 \| \| 6.4188 \| 3.93 \| 240000 \| 6.4182 \| 0.1274 \| \| 6.4128 \| 4.1 \| 250000 \| 6.4150 \| 0.1278 \| \| 6.4189 \| 4.26 \| 260000 \| 6.4121 \| 0.1280 \| \| 6.4102 \| 4.42 \| 270000 \| 6.4112 \| 0.1282 \| \| 6.4105 \| 4.59 \| 280000 \| 6.4087 \| 0.1285 \| \| 6.4065 \| 4.75 \| 290000 \| 6.4067 \| 0.1287 \| \| 6.4082 \| 4.92 \| 300000 \| 6.4070 \| 0.1285 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.13.3
thewiz/kd-bertBase-bertTiny	thewiz	2023-09-28T17:13:49Z	120	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-28T16:36:52Z	--- license: apache-2.0 base_model: google/bert_uncased_L-2_H-128_A-2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: kd-bertBase-bertTiny results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8268348623853211 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kd-bertBase-bertTiny This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0591 - Accuracy: 0.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4776 \| 1.0 \| 527 \| 1.1240 \| 0.8062 \| \| 0.8442 \| 2.0 \| 1054 \| 1.0475 \| 0.8165 \| \| 0.6568 \| 3.0 \| 1581 \| 1.0529 \| 0.8131 \| \| 0.5623 \| 4.0 \| 2108 \| 1.0503 \| 0.8188 \| \| 0.5066 \| 5.0 \| 2635 \| 1.0471 \| 0.8303 \| \| 0.4736 \| 6.0 \| 3162 \| 1.0711 \| 0.8280 \| \| 0.4603 \| 7.0 \| 3689 \| 1.0591 \| 0.8268 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
afaranda/my_awesome_billsum_model	afaranda	2023-09-28T16:58:13Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T14:03:18Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1398 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4899 - Rouge1: 0.1398 - Rouge2: 0.0512 - Rougel: 0.117 - Rougelsum: 0.117 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.7815 \| 0.1245 \| 0.0363 \| 0.1044 \| 0.1044 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.5728 \| 0.133 \| 0.0452 \| 0.1112 \| 0.1114 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5064 \| 0.1382 \| 0.0514 \| 0.1163 \| 0.1164 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.4899 \| 0.1398 \| 0.0512 \| 0.117 \| 0.117 \| 19.0 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
vineetsharma/databricks-dolly-15k-pythia-70m-deduped-v1	vineetsharma	2023-09-28T16:55:16Z	156	0	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-24T17:01:45Z	--- license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - generated_from_trainer model-index: - name: databricks-dolly-15k-pythia-70m-deduped-v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # databricks-dolly-15k-pythia-70m-deduped-v1 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3063 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 5.8656 \| 1.0 \| 1501 \| 4.6547 \| \| 4.609 \| 2.0 \| 3002 \| 4.4454 \| \| 4.1706 \| 3.0 \| 4503 \| 4.3141 \| \| 3.8124 \| 4.0 \| 6004 \| 4.3063 \| \| 3.4925 \| 5.0 \| 7505 \| 4.3213 \| \| 3.1927 \| 6.0 \| 9006 \| 4.3806 \| \| 2.9257 \| 7.0 \| 10507 \| 4.4425 \| \| 2.6838 \| 8.0 \| 12008 \| 4.5654 \| \| 2.4619 \| 9.0 \| 13509 \| 4.7032 \| \| 2.2826 \| 10.0 \| 15010 \| 4.8537 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
maulairfani/autocomplete_model	maulairfani	2023-09-28T16:51:34Z	180	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-28T16:11:58Z	--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: autocomplete_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # autocomplete_model This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 17 \| 3.2168 \| \| No log \| 2.0 \| 34 \| 3.1874 \| \| No log \| 3.0 \| 51 \| 3.2537 \| \| No log \| 4.0 \| 68 \| 3.2260 \| \| No log \| 5.0 \| 85 \| 3.1759 \| \| 3.4421 \| 6.0 \| 102 \| 3.1777 \| \| 3.4421 \| 7.0 \| 119 \| 3.2093 \| \| 3.4421 \| 8.0 \| 136 \| 3.2277 \| \| 3.4421 \| 9.0 \| 153 \| 3.1694 \| \| 3.4421 \| 10.0 \| 170 \| 3.1333 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
erkam/sg2im-256-bs-16x2	erkam	2023-09-28T16:50:24Z	2	0	diffusers	[ "diffusers", "sg-to-image", "scene-graph", "stable-diffusion", "stable-diffusion-diffusers", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]	null	2023-09-23T01:13:28Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 tags: - sg-to-image - scene-graph - stable-diffusion - stable-diffusion-diffusers - diffusers - lora inference: true --- # LoRA text2image fine-tuning - erkam/sg2im-256-bs-16x2 These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.
AmirH98/Reinforce-CartPole-v1	AmirH98	2023-09-28T16:48:12Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T16:48:03Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
facebook/mbart-large-50-many-to-many-mmt	facebook	2023-09-28T16:42:59Z	295,669	336	transformers	[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "mbart", "text2text-generation", "mbart-50", "translation", "multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "ur", "xh", "gl", "sl", "arxiv:2008.00401", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- language: - multilingual - ar - cs - de - en - es - et - fi - fr - gu - hi - it - ja - kk - ko - lt - lv - my - ne - nl - ro - ru - si - tr - vi - zh - af - az - bn - fa - he - hr - id - ka - km - mk - ml - mn - mr - pl - ps - pt - sv - sw - ta - te - th - tl - uk - ur - xh - gl - sl tags: - mbart-50 pipeline_tag: translation --- # mBART-50 many to many multilingual machine translation This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. ```python from transformers import MBartForConditionalGeneration, MBart50TokenizerFast article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है" article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا." model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") # translate Hindi to French tokenizer.src_lang = "hi_IN" encoded_hi = tokenizer(article_hi, return_tensors="pt") generated_tokens = model.generate( encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire dans la Syrie." # translate Arabic to English tokenizer.src_lang = "ar_AR" encoded_ar = tokenizer(article_ar, return_tensors="pt") generated_tokens = model.generate( encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "The Secretary-General of the United Nations says there is no military solution in Syria." ``` See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions. ## Languages covered Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
CyberHarem/kurumi_lycorisrecoil	CyberHarem	2023-09-28T16:38:36Z	0	1	null	[ "art", "text-to-image", "dataset:CyberHarem/kurumi_lycorisrecoil", "license:mit", "region:us" ]	text-to-image	2023-09-28T16:26:50Z	--- license: mit datasets: - CyberHarem/kurumi_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of kurumi_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/kurumi_lycorisrecoil.pt` as the embedding and `5100/kurumi_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 5100, with the score of 0.996. The trigger words are: 1. `kurumi_lycorisrecoil` 2. `blonde_hair, long_hair, hairband, black_hairband, forehead, hoodie, black_ribbon, blue_eyes, ribbon, closed_mouth, hair_ribbon, white_hoodie, indoors` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.996 \| [Download](5100/kurumi_lycorisrecoil.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.993 \| [Download](4760/kurumi_lycorisrecoil.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.989 \| [Download](4420/kurumi_lycorisrecoil.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.991 \| [Download](4080/kurumi_lycorisrecoil.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.989 \| [Download](3740/kurumi_lycorisrecoil.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.985 \| [Download](3400/kurumi_lycorisrecoil.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.994 \| [Download](3060/kurumi_lycorisrecoil.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.917 \| [Download](2720/kurumi_lycorisrecoil.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.991 \| [Download](2380/kurumi_lycorisrecoil.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.980 \| [Download](2040/kurumi_lycorisrecoil.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.991 \| [Download](1700/kurumi_lycorisrecoil.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.986 \| [Download](1360/kurumi_lycorisrecoil.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.987 \| [Download](1020/kurumi_lycorisrecoil.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.924 \| [Download](680/kurumi_lycorisrecoil.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.989 \| [Download](340/kurumi_lycorisrecoil.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Crataco/Pygmalion-6B-GGML	Crataco	2023-09-28T16:37:54Z	0	1	null	[ "text-generation", "conversational", "gpt-j", "ggml", "en", "license:creativeml-openrail-m", "region:us" ]	text-generation	2023-09-25T18:52:41Z	--- license: creativeml-openrail-m language: - en thumbnail: tags: - text-generation - conversational - gpt-j - ggml inference: false --- # Pygmalion 6B GGML ### This repository contains quantized conversions of the current Pygmalion 6B checkpoints. For use with frontends that support GGML quantized GPT-J models, such as KoboldCpp and Oobabooga (with the CTransformers loader). Last updated on 2023-09-26. Description: - The `pygmalion-6b-main` files are quantized from the main branch of Pygmalion 6B. Also known as "experiment 2", released on January 13th. - The `pygmalion-6b-dev` files are quantized from the dev branch of Pygmalion 6B. Also known as "part 4/10 of experiment 7", released on March 12th. - The motivation behind these quantizations was to have one repository for both the main and dev versions of Pygmalion, as well as all quantization formats available. Some users may prefer the prose and creativity of Pygmalion 6B (and its lack of synthetic GPT-4 data) over newer models, or find 6B's requirements more affordable than 7B. For a modern alternative, [Pygmalion 2 7B](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF) is worth investigating. RAM usage: Model \| Startup RAM usage (KoboldCpp) \| Startup RAM usage (Oobabooga) :--:\|:--:\|:--: pygmalion-6b-dev.q4_0.bin \| 3.7 GiB \| 3.7 GiB pygmalion-6b-dev.q4_1.bin \| 4.1 GiB \| 4.1 GiB pygmalion-6b-dev.q5_0.bin \| 4.4 GiB \| 4.4 GiB pygmalion-6b-dev.q5_1.bin \| 4.8 GiB \| 4.8 GiB pygmalion-6b-dev.q8_0.bin \| 6.5 GiB \| 6.6 GiB Notes: - Tested with these SillyTavern settings: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6251b9851842c08ef3111c4f/6Zo7uCqPY3ku96PymqrKo.png) - ggerganov/ggml [[8ca2c19]](https://github.com/ggerganov/ggml/tree/8ca2c19a3bb8622954d858fbf6383522684eaf34)'s gpt-j conversion script was used for conversion and quantization. First they were converted to f16 ggml files, then quantized. The original model can be found [here](https://huggingface.co/PygmalionAI/pygmalion-6b), and the original model card can be found below. * * * # Pygmalion 6B ## Model description Pymalion 6B is a proof-of-concept dialogue model based on EleutherAI's [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B). Warning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances. ## Training data The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations. ## Training procedure Model weights were initialized from the `uft-6b` ConvoGPT model made available in [this commit](https://huggingface.co/hakurei/convogpt/tree/41b67bfddb6cd97070ffddf708e9720c9cb8d224/6b-uft). The model was then further fine-tuned on ~48.5 million tokens for ~5k steps on 4 NVIDIA A40s using DeepSpeed. ## Intended use ### The easy way We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found [here](https://github.com/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb). ### The manual way The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format: ``` [CHARACTER]'s Persona: [A few sentences about the character you want the model to play] <START> [DIALOGUE HISTORY] You: [Your input message here] [CHARACTER]: ``` Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like: ``` [CHARACTER]: [some dialogue here] You: [your response to the dialogue above] ``` Apart from chat history, you can also just add example conversations in `[DIALOGUE HISTORY]` to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition. ## Known issues We haven't played around with the model enough to enumerate them. Feel free to give us some feedback!
TheBloke/leo-hessianai-7B-GGUF	TheBloke	2023-09-28T16:28:04Z	239	3	transformers	[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:oscar-corpus/OSCAR-2301", "dataset:wikipedia", "dataset:bjoernp/tagesschau-2018-2023", "base_model:LeoLM/leo-hessianai-7b", "base_model:quantized:LeoLM/leo-hessianai-7b", "license:llama2", "region:us" ]	text-generation	2023-09-28T16:23:50Z	--- base_model: LeoLM/leo-hessianai-7b datasets: - oscar-corpus/OSCAR-2301 - wikipedia - bjoernp/tagesschau-2018-2023 inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [leo-hessianai-7b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q2_K.gguf) \| Q2_K \| 2 \| 2.83 GB\| 5.33 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [leo-hessianai-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| very small, high quality loss \| \| [leo-hessianai-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| 5.80 GB \| very small, high quality loss \| \| [leo-hessianai-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| small, substantial quality loss \| \| [leo-hessianai-7b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| 6.33 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [leo-hessianai-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| 6.36 GB \| small, greater quality loss \| \| [leo-hessianai-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| medium, balanced quality - recommended \| \| [leo-hessianai-7b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| 7.15 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [leo-hessianai-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| large, low quality loss - recommended \| \| [leo-hessianai-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| large, very low quality loss - recommended \| \| [leo-hessianai-7b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| 8.03 GB \| very large, extremely low quality loss \| \| [leo-hessianai-7b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-7b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-GGUF", model_file="leo-hessianai-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: LAION LeoLM's Leo Hessianai 7B # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## Model Details - Finetuned from: [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) - Model type: Causal decoder-only transformer language model - Language: English and German - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="LeoLM/leo-hessianai-7b", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True # True for flash-attn2 else False ) ``` ## Training parameters ![training_parameters](imgs/training_params.png "Training Hyperparameters") ## Benchmarks ![benchmarks](imgs/benchmarks.png "Benchmark Scores") <!-- original-model-card end -->
LicensedViber/Lora	LicensedViber	2023-09-28T16:06:58Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-09-28T16:06:58Z	--- license: creativeml-openrail-m ---
CyberHarem/nakahara_mizuki_lycorisrecoil	CyberHarem	2023-09-28T15:55:34Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/nakahara_mizuki_lycorisrecoil", "license:mit", "region:us" ]	text-to-image	2023-09-28T15:44:35Z	--- license: mit datasets: - CyberHarem/nakahara_mizuki_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of nakahara_mizuki_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/nakahara_mizuki_lycorisrecoil.pt` as the embedding and `4420/nakahara_mizuki_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4420, with the score of 0.997. The trigger words are: 1. `nakahara_mizuki_lycorisrecoil` 2. `brown_hair, long_hair, glasses, red-framed_eyewear, hair_between_eyes, brown_eyes, semi-rimless_eyewear` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.996 \| [Download](5100/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.993 \| [Download](4760/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.997 \| [Download](4420/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.996 \| [Download](4080/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.987 \| [Download](3740/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.994 \| [Download](3400/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.994 \| [Download](3060/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.994 \| [Download](2720/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.993 \| [Download](2380/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.994 \| [Download](2040/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.992 \| [Download](1700/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.990 \| [Download](1360/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.982 \| [Download](1020/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.982 \| [Download](680/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.975 \| [Download](340/nakahara_mizuki_lycorisrecoil.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
gsl22/testando	gsl22	2023-09-28T15:52:59Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-28T14:21:31Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: testando results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # testando This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3
CyberHarem/aihara_yuzu_citrus	CyberHarem	2023-09-28T15:45:15Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/aihara_yuzu_citrus", "license:mit", "region:us" ]	text-to-image	2023-09-28T15:26:55Z	--- license: mit datasets: - CyberHarem/aihara_yuzu_citrus pipeline_tag: text-to-image tags: - art --- # Lora of aihara_yuzu_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 9000, you need to download `9000/aihara_yuzu_citrus.pt` as the embedding and `9000/aihara_yuzu_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 9000, with the score of 0.603. The trigger words are: 1. `aihara_yuzu_citrus` 2. `blonde_hair, green_eyes, long_hair, jewelry, earrings, brown_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| pattern_14 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 9000 \| 0.603 \| [Download](9000/aihara_yuzu_citrus.zip) \| ![pattern_1-9000](9000/previews/pattern_1.png) \| ![pattern_2-9000](9000/previews/pattern_2.png) \| ![pattern_3-9000](9000/previews/pattern_3.png) \| ![pattern_4-9000](9000/previews/pattern_4.png) \| ![pattern_5-9000](9000/previews/pattern_5.png) \| ![pattern_6-9000](9000/previews/pattern_6.png) \| ![pattern_7-9000](9000/previews/pattern_7.png) \| ![pattern_8-9000](9000/previews/pattern_8.png) \| ![pattern_9-9000](9000/previews/pattern_9.png) \| ![pattern_10-9000](9000/previews/pattern_10.png) \| [<NSFW, click to see>](9000/previews/pattern_11.png) \| ![pattern_12-9000](9000/previews/pattern_12.png) \| ![pattern_13-9000](9000/previews/pattern_13.png) \| ![pattern_14-9000](9000/previews/pattern_14.png) \| ![bikini-9000](9000/previews/bikini.png) \| [<NSFW, click to see>](9000/previews/bondage.png) \| ![free-9000](9000/previews/free.png) \| ![maid-9000](9000/previews/maid.png) \| ![miko-9000](9000/previews/miko.png) \| [<NSFW, click to see>](9000/previews/nude.png) \| [<NSFW, click to see>](9000/previews/nude2.png) \| ![suit-9000](9000/previews/suit.png) \| ![yukata-9000](9000/previews/yukata.png) \| \| 8400 \| 0.592 \| [Download](8400/aihara_yuzu_citrus.zip) \| ![pattern_1-8400](8400/previews/pattern_1.png) \| ![pattern_2-8400](8400/previews/pattern_2.png) \| ![pattern_3-8400](8400/previews/pattern_3.png) \| ![pattern_4-8400](8400/previews/pattern_4.png) \| ![pattern_5-8400](8400/previews/pattern_5.png) \| ![pattern_6-8400](8400/previews/pattern_6.png) \| ![pattern_7-8400](8400/previews/pattern_7.png) \| ![pattern_8-8400](8400/previews/pattern_8.png) \| ![pattern_9-8400](8400/previews/pattern_9.png) \| ![pattern_10-8400](8400/previews/pattern_10.png) \| [<NSFW, click to see>](8400/previews/pattern_11.png) \| ![pattern_12-8400](8400/previews/pattern_12.png) \| ![pattern_13-8400](8400/previews/pattern_13.png) \| ![pattern_14-8400](8400/previews/pattern_14.png) \| ![bikini-8400](8400/previews/bikini.png) \| [<NSFW, click to see>](8400/previews/bondage.png) \| ![free-8400](8400/previews/free.png) \| ![maid-8400](8400/previews/maid.png) \| ![miko-8400](8400/previews/miko.png) \| [<NSFW, click to see>](8400/previews/nude.png) \| [<NSFW, click to see>](8400/previews/nude2.png) \| ![suit-8400](8400/previews/suit.png) \| ![yukata-8400](8400/previews/yukata.png) \| \| 7800 \| 0.539 \| [Download](7800/aihara_yuzu_citrus.zip) \| ![pattern_1-7800](7800/previews/pattern_1.png) \| ![pattern_2-7800](7800/previews/pattern_2.png) \| ![pattern_3-7800](7800/previews/pattern_3.png) \| ![pattern_4-7800](7800/previews/pattern_4.png) \| ![pattern_5-7800](7800/previews/pattern_5.png) \| ![pattern_6-7800](7800/previews/pattern_6.png) \| ![pattern_7-7800](7800/previews/pattern_7.png) \| ![pattern_8-7800](7800/previews/pattern_8.png) \| ![pattern_9-7800](7800/previews/pattern_9.png) \| ![pattern_10-7800](7800/previews/pattern_10.png) \| [<NSFW, click to see>](7800/previews/pattern_11.png) \| ![pattern_12-7800](7800/previews/pattern_12.png) \| ![pattern_13-7800](7800/previews/pattern_13.png) \| ![pattern_14-7800](7800/previews/pattern_14.png) \| ![bikini-7800](7800/previews/bikini.png) \| [<NSFW, click to see>](7800/previews/bondage.png) \| ![free-7800](7800/previews/free.png) \| ![maid-7800](7800/previews/maid.png) \| ![miko-7800](7800/previews/miko.png) \| [<NSFW, click to see>](7800/previews/nude.png) \| [<NSFW, click to see>](7800/previews/nude2.png) \| ![suit-7800](7800/previews/suit.png) \| ![yukata-7800](7800/previews/yukata.png) \| \| 7200 \| 0.560 \| [Download](7200/aihara_yuzu_citrus.zip) \| ![pattern_1-7200](7200/previews/pattern_1.png) \| ![pattern_2-7200](7200/previews/pattern_2.png) \| ![pattern_3-7200](7200/previews/pattern_3.png) \| ![pattern_4-7200](7200/previews/pattern_4.png) \| ![pattern_5-7200](7200/previews/pattern_5.png) \| ![pattern_6-7200](7200/previews/pattern_6.png) \| ![pattern_7-7200](7200/previews/pattern_7.png) \| ![pattern_8-7200](7200/previews/pattern_8.png) \| ![pattern_9-7200](7200/previews/pattern_9.png) \| ![pattern_10-7200](7200/previews/pattern_10.png) \| [<NSFW, click to see>](7200/previews/pattern_11.png) \| ![pattern_12-7200](7200/previews/pattern_12.png) \| ![pattern_13-7200](7200/previews/pattern_13.png) \| ![pattern_14-7200](7200/previews/pattern_14.png) \| ![bikini-7200](7200/previews/bikini.png) \| [<NSFW, click to see>](7200/previews/bondage.png) \| ![free-7200](7200/previews/free.png) \| ![maid-7200](7200/previews/maid.png) \| ![miko-7200](7200/previews/miko.png) \| [<NSFW, click to see>](7200/previews/nude.png) \| [<NSFW, click to see>](7200/previews/nude2.png) \| ![suit-7200](7200/previews/suit.png) \| ![yukata-7200](7200/previews/yukata.png) \| \| 6600 \| 0.538 \| [Download](6600/aihara_yuzu_citrus.zip) \| ![pattern_1-6600](6600/previews/pattern_1.png) \| ![pattern_2-6600](6600/previews/pattern_2.png) \| ![pattern_3-6600](6600/previews/pattern_3.png) \| ![pattern_4-6600](6600/previews/pattern_4.png) \| ![pattern_5-6600](6600/previews/pattern_5.png) \| ![pattern_6-6600](6600/previews/pattern_6.png) \| ![pattern_7-6600](6600/previews/pattern_7.png) \| ![pattern_8-6600](6600/previews/pattern_8.png) \| ![pattern_9-6600](6600/previews/pattern_9.png) \| ![pattern_10-6600](6600/previews/pattern_10.png) \| [<NSFW, click to see>](6600/previews/pattern_11.png) \| ![pattern_12-6600](6600/previews/pattern_12.png) \| ![pattern_13-6600](6600/previews/pattern_13.png) \| ![pattern_14-6600](6600/previews/pattern_14.png) \| ![bikini-6600](6600/previews/bikini.png) \| [<NSFW, click to see>](6600/previews/bondage.png) \| ![free-6600](6600/previews/free.png) \| ![maid-6600](6600/previews/maid.png) \| ![miko-6600](6600/previews/miko.png) \| [<NSFW, click to see>](6600/previews/nude.png) \| [<NSFW, click to see>](6600/previews/nude2.png) \| ![suit-6600](6600/previews/suit.png) \| ![yukata-6600](6600/previews/yukata.png) \| \| 6000 \| 0.526 \| [Download](6000/aihara_yuzu_citrus.zip) \| ![pattern_1-6000](6000/previews/pattern_1.png) \| ![pattern_2-6000](6000/previews/pattern_2.png) \| ![pattern_3-6000](6000/previews/pattern_3.png) \| ![pattern_4-6000](6000/previews/pattern_4.png) \| ![pattern_5-6000](6000/previews/pattern_5.png) \| ![pattern_6-6000](6000/previews/pattern_6.png) \| ![pattern_7-6000](6000/previews/pattern_7.png) \| ![pattern_8-6000](6000/previews/pattern_8.png) \| ![pattern_9-6000](6000/previews/pattern_9.png) \| ![pattern_10-6000](6000/previews/pattern_10.png) \| [<NSFW, click to see>](6000/previews/pattern_11.png) \| ![pattern_12-6000](6000/previews/pattern_12.png) \| ![pattern_13-6000](6000/previews/pattern_13.png) \| ![pattern_14-6000](6000/previews/pattern_14.png) \| ![bikini-6000](6000/previews/bikini.png) \| [<NSFW, click to see>](6000/previews/bondage.png) \| ![free-6000](6000/previews/free.png) \| ![maid-6000](6000/previews/maid.png) \| ![miko-6000](6000/previews/miko.png) \| [<NSFW, click to see>](6000/previews/nude.png) \| [<NSFW, click to see>](6000/previews/nude2.png) \| ![suit-6000](6000/previews/suit.png) \| ![yukata-6000](6000/previews/yukata.png) \| \| 5400 \| 0.514 \| [Download](5400/aihara_yuzu_citrus.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| ![pattern_3-5400](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| ![pattern_6-5400](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![pattern_9-5400](5400/previews/pattern_9.png) \| ![pattern_10-5400](5400/previews/pattern_10.png) \| [<NSFW, click to see>](5400/previews/pattern_11.png) \| ![pattern_12-5400](5400/previews/pattern_12.png) \| ![pattern_13-5400](5400/previews/pattern_13.png) \| ![pattern_14-5400](5400/previews/pattern_14.png) \| ![bikini-5400](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4800 \| 0.533 \| [Download](4800/aihara_yuzu_citrus.zip) \| ![pattern_1-4800](4800/previews/pattern_1.png) \| ![pattern_2-4800](4800/previews/pattern_2.png) \| ![pattern_3-4800](4800/previews/pattern_3.png) \| ![pattern_4-4800](4800/previews/pattern_4.png) \| ![pattern_5-4800](4800/previews/pattern_5.png) \| ![pattern_6-4800](4800/previews/pattern_6.png) \| ![pattern_7-4800](4800/previews/pattern_7.png) \| ![pattern_8-4800](4800/previews/pattern_8.png) \| ![pattern_9-4800](4800/previews/pattern_9.png) \| ![pattern_10-4800](4800/previews/pattern_10.png) \| [<NSFW, click to see>](4800/previews/pattern_11.png) \| ![pattern_12-4800](4800/previews/pattern_12.png) \| ![pattern_13-4800](4800/previews/pattern_13.png) \| ![pattern_14-4800](4800/previews/pattern_14.png) \| ![bikini-4800](4800/previews/bikini.png) \| [<NSFW, click to see>](4800/previews/bondage.png) \| ![free-4800](4800/previews/free.png) \| ![maid-4800](4800/previews/maid.png) \| ![miko-4800](4800/previews/miko.png) \| [<NSFW, click to see>](4800/previews/nude.png) \| [<NSFW, click to see>](4800/previews/nude2.png) \| ![suit-4800](4800/previews/suit.png) \| ![yukata-4800](4800/previews/yukata.png) \| \| 4200 \| 0.557 \| [Download](4200/aihara_yuzu_citrus.zip) \| ![pattern_1-4200](4200/previews/pattern_1.png) \| ![pattern_2-4200](4200/previews/pattern_2.png) \| ![pattern_3-4200](4200/previews/pattern_3.png) \| ![pattern_4-4200](4200/previews/pattern_4.png) \| ![pattern_5-4200](4200/previews/pattern_5.png) \| ![pattern_6-4200](4200/previews/pattern_6.png) \| ![pattern_7-4200](4200/previews/pattern_7.png) \| ![pattern_8-4200](4200/previews/pattern_8.png) \| ![pattern_9-4200](4200/previews/pattern_9.png) \| ![pattern_10-4200](4200/previews/pattern_10.png) \| [<NSFW, click to see>](4200/previews/pattern_11.png) \| ![pattern_12-4200](4200/previews/pattern_12.png) \| ![pattern_13-4200](4200/previews/pattern_13.png) \| ![pattern_14-4200](4200/previews/pattern_14.png) \| ![bikini-4200](4200/previews/bikini.png) \| [<NSFW, click to see>](4200/previews/bondage.png) \| ![free-4200](4200/previews/free.png) \| ![maid-4200](4200/previews/maid.png) \| ![miko-4200](4200/previews/miko.png) \| [<NSFW, click to see>](4200/previews/nude.png) \| [<NSFW, click to see>](4200/previews/nude2.png) \| ![suit-4200](4200/previews/suit.png) \| ![yukata-4200](4200/previews/yukata.png) \| \| 3600 \| 0.460 \| [Download](3600/aihara_yuzu_citrus.zip) \| ![pattern_1-3600](3600/previews/pattern_1.png) \| ![pattern_2-3600](3600/previews/pattern_2.png) \| ![pattern_3-3600](3600/previews/pattern_3.png) \| ![pattern_4-3600](3600/previews/pattern_4.png) \| ![pattern_5-3600](3600/previews/pattern_5.png) \| ![pattern_6-3600](3600/previews/pattern_6.png) \| ![pattern_7-3600](3600/previews/pattern_7.png) \| ![pattern_8-3600](3600/previews/pattern_8.png) \| ![pattern_9-3600](3600/previews/pattern_9.png) \| ![pattern_10-3600](3600/previews/pattern_10.png) \| [<NSFW, click to see>](3600/previews/pattern_11.png) \| ![pattern_12-3600](3600/previews/pattern_12.png) \| ![pattern_13-3600](3600/previews/pattern_13.png) \| ![pattern_14-3600](3600/previews/pattern_14.png) \| ![bikini-3600](3600/previews/bikini.png) \| [<NSFW, click to see>](3600/previews/bondage.png) \| ![free-3600](3600/previews/free.png) \| ![maid-3600](3600/previews/maid.png) \| ![miko-3600](3600/previews/miko.png) \| [<NSFW, click to see>](3600/previews/nude.png) \| [<NSFW, click to see>](3600/previews/nude2.png) \| ![suit-3600](3600/previews/suit.png) \| ![yukata-3600](3600/previews/yukata.png) \| \| 3000 \| 0.328 \| [Download](3000/aihara_yuzu_citrus.zip) \| ![pattern_1-3000](3000/previews/pattern_1.png) \| ![pattern_2-3000](3000/previews/pattern_2.png) \| ![pattern_3-3000](3000/previews/pattern_3.png) \| ![pattern_4-3000](3000/previews/pattern_4.png) \| ![pattern_5-3000](3000/previews/pattern_5.png) \| ![pattern_6-3000](3000/previews/pattern_6.png) \| ![pattern_7-3000](3000/previews/pattern_7.png) \| ![pattern_8-3000](3000/previews/pattern_8.png) \| ![pattern_9-3000](3000/previews/pattern_9.png) \| ![pattern_10-3000](3000/previews/pattern_10.png) \| [<NSFW, click to see>](3000/previews/pattern_11.png) \| ![pattern_12-3000](3000/previews/pattern_12.png) \| ![pattern_13-3000](3000/previews/pattern_13.png) \| ![pattern_14-3000](3000/previews/pattern_14.png) \| ![bikini-3000](3000/previews/bikini.png) \| [<NSFW, click to see>](3000/previews/bondage.png) \| ![free-3000](3000/previews/free.png) \| ![maid-3000](3000/previews/maid.png) \| ![miko-3000](3000/previews/miko.png) \| [<NSFW, click to see>](3000/previews/nude.png) \| [<NSFW, click to see>](3000/previews/nude2.png) \| ![suit-3000](3000/previews/suit.png) \| ![yukata-3000](3000/previews/yukata.png) \| \| 2400 \| 0.427 \| [Download](2400/aihara_yuzu_citrus.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![pattern_4-2400](2400/previews/pattern_4.png) \| ![pattern_5-2400](2400/previews/pattern_5.png) \| ![pattern_6-2400](2400/previews/pattern_6.png) \| ![pattern_7-2400](2400/previews/pattern_7.png) \| ![pattern_8-2400](2400/previews/pattern_8.png) \| ![pattern_9-2400](2400/previews/pattern_9.png) \| ![pattern_10-2400](2400/previews/pattern_10.png) \| [<NSFW, click to see>](2400/previews/pattern_11.png) \| ![pattern_12-2400](2400/previews/pattern_12.png) \| ![pattern_13-2400](2400/previews/pattern_13.png) \| ![pattern_14-2400](2400/previews/pattern_14.png) \| ![bikini-2400](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 1800 \| 0.395 \| [Download](1800/aihara_yuzu_citrus.zip) \| ![pattern_1-1800](1800/previews/pattern_1.png) \| ![pattern_2-1800](1800/previews/pattern_2.png) \| ![pattern_3-1800](1800/previews/pattern_3.png) \| ![pattern_4-1800](1800/previews/pattern_4.png) \| ![pattern_5-1800](1800/previews/pattern_5.png) \| ![pattern_6-1800](1800/previews/pattern_6.png) \| ![pattern_7-1800](1800/previews/pattern_7.png) \| ![pattern_8-1800](1800/previews/pattern_8.png) \| ![pattern_9-1800](1800/previews/pattern_9.png) \| ![pattern_10-1800](1800/previews/pattern_10.png) \| [<NSFW, click to see>](1800/previews/pattern_11.png) \| ![pattern_12-1800](1800/previews/pattern_12.png) \| ![pattern_13-1800](1800/previews/pattern_13.png) \| ![pattern_14-1800](1800/previews/pattern_14.png) \| ![bikini-1800](1800/previews/bikini.png) \| [<NSFW, click to see>](1800/previews/bondage.png) \| ![free-1800](1800/previews/free.png) \| ![maid-1800](1800/previews/maid.png) \| ![miko-1800](1800/previews/miko.png) \| [<NSFW, click to see>](1800/previews/nude.png) \| [<NSFW, click to see>](1800/previews/nude2.png) \| ![suit-1800](1800/previews/suit.png) \| ![yukata-1800](1800/previews/yukata.png) \| \| 1200 \| 0.288 \| [Download](1200/aihara_yuzu_citrus.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![pattern_4-1200](1200/previews/pattern_4.png) \| ![pattern_5-1200](1200/previews/pattern_5.png) \| ![pattern_6-1200](1200/previews/pattern_6.png) \| ![pattern_7-1200](1200/previews/pattern_7.png) \| ![pattern_8-1200](1200/previews/pattern_8.png) \| ![pattern_9-1200](1200/previews/pattern_9.png) \| ![pattern_10-1200](1200/previews/pattern_10.png) \| [<NSFW, click to see>](1200/previews/pattern_11.png) \| ![pattern_12-1200](1200/previews/pattern_12.png) \| ![pattern_13-1200](1200/previews/pattern_13.png) \| ![pattern_14-1200](1200/previews/pattern_14.png) \| ![bikini-1200](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 600 \| 0.221 \| [Download](600/aihara_yuzu_citrus.zip) \| ![pattern_1-600](600/previews/pattern_1.png) \| ![pattern_2-600](600/previews/pattern_2.png) \| ![pattern_3-600](600/previews/pattern_3.png) \| ![pattern_4-600](600/previews/pattern_4.png) \| ![pattern_5-600](600/previews/pattern_5.png) \| ![pattern_6-600](600/previews/pattern_6.png) \| ![pattern_7-600](600/previews/pattern_7.png) \| ![pattern_8-600](600/previews/pattern_8.png) \| ![pattern_9-600](600/previews/pattern_9.png) \| ![pattern_10-600](600/previews/pattern_10.png) \| [<NSFW, click to see>](600/previews/pattern_11.png) \| ![pattern_12-600](600/previews/pattern_12.png) \| ![pattern_13-600](600/previews/pattern_13.png) \| ![pattern_14-600](600/previews/pattern_14.png) \| ![bikini-600](600/previews/bikini.png) \| [<NSFW, click to see>](600/previews/bondage.png) \| ![free-600](600/previews/free.png) \| ![maid-600](600/previews/maid.png) \| ![miko-600](600/previews/miko.png) \| [<NSFW, click to see>](600/previews/nude.png) \| [<NSFW, click to see>](600/previews/nude2.png) \| ![suit-600](600/previews/suit.png) \| ![yukata-600](600/previews/yukata.png) \|
vaibhav9/hangman-bert-large	vaibhav9	2023-09-28T15:39:27Z	196	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-28T09:19:00Z	--- tags: - generated_from_trainer model-index: - name: hangman-bert-large results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hangman-bert-large This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.8263 \| 1.0 \| 2609 \| 1.8458 \| \| 1.8227 \| 2.0 \| 5218 \| 1.8441 \| \| 1.8206 \| 3.0 \| 7827 \| 1.8376 \| \| 1.8146 \| 4.0 \| 10436 \| 1.8349 \| \| 1.8108 \| 5.0 \| 13045 \| 1.8323 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
joachimsallstrom/aether-glitch-lora-for-sdxl	joachimsallstrom	2023-09-28T15:35:34Z	910	17	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "style", "vhs glitch", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]	text-to-image	2023-09-28T15:35:20Z	--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - style - vhs glitch base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: vhs glitch widget: - text: " vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic" - text: " vhs glitch man on a mountain looking at the sunset, cinematic, black background" - text: " vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background" - text: " vhs glitch award-winning photo of kung fury battling a monster, cinematic horror" - text: " vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain" - text: " vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora" - text: " vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background" - text: " vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background" --- # Aether Glitch - LoRA for SDXL ![Image 0](2680627.jpeg) > vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic <p><strong><span style="color:rgb(230, 73, 128)">MAKE SURE TO CHECK THE IMAGES ON THIS PAGE FOR PROMPTS AND SETTINGS TO GET GOOD RESULTS.</span></strong><br /><br />This is <strong><span style="color:rgb(230, 73, 128)">A</span><span style="color:rgb(190, 75, 219)">e</span><span style="color:rgb(121, 80, 242)">t</span><span style="color:rgb(76, 110, 245)">h</span><span style="color:rgb(34, 139, 230)">e</span><span style="color:rgb(21, 170, 191)">r</span></strong> <strong><span style="color:rgb(21, 170, 191)">G</span><span style="color:rgb(64, 192, 87)">l</span><span style="color:rgb(130, 201, 30)">it</span><span style="color:rgb(250, 176, 5)">c</span><span style="color:rgb(253, 126, 20)">h</span></strong>, an SDXL LoRA that simulates vhs tape glitches over subjects. Make sure to trigger the LoRA with <strong><em>vhs glitch</em></strong>, and play with weights to get more or less balanced glitch or crt tv looks. Looks cool on both photoreal and animated stuff. Gaming, movies and horror works well, and of course synthwave and such. Overall a great mood setter!<br /><br />Thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> who sponsored the finetuning of this LoRA. <span style="color:rgb(219, 222, 225)">It was made on Lastben’s SDXL LoRA trainer through RunDiffusion which will soon be made public for everyone. Aether Glitch is of course available on their platform to try it out.</span></p><p></p><p><span style="color:rgb(219, 222, 225)">Also thanks to Masslevel, Warlock and Adam D for contributing with awesome images.</span></p> ## Image examples for the model: ![Image 1](2675921.jpeg) > vhs glitch man on a mountain looking at the sunset, cinematic, black background ![Image 2](2675899.jpeg) > vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background ![Image 3](2680127.jpeg) > vhs glitch award-winning photo of kung fury battling a monster, cinematic horror ![Image 4](2665925.jpeg) > vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background ![Image 5](2680711.jpeg) > vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain ![Image 6](2665924.jpeg) > vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background ![Image 7](2675806.jpeg) > vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora ![Image 8](2675809.jpeg) > vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background ![Image 9](2675807.jpeg) > vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background
TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ	TheBloke	2023-09-28T15:34:24Z	33	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:bjoernp/oasst25-08-23-filtered", "base_model:LeoLM/leo-hessianai-7b-chat-bilingual", "base_model:quantized:LeoLM/leo-hessianai-7b-chat-bilingual", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T14:52:03Z	--- base_model: LeoLM/leo-hessianai-7b-chat-bilingual datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_70k - bjoernp/oasst25-08-23-filtered inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat Bilingual model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 7B Chat Bilingual - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 3.90 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.28 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.01 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.16 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.62 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 4.03 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-7B-chat-bilingual-GPTQ`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-7B-chat-bilingual-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 7B Chat Bilingual # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores: ``` { "first_turn": 5.64375, "second_turn": 4.075, "categories": { "writing": 5.925, "roleplay": 5.25, "reasoning": 3.1, "math": 1.8, "coding": 3.4, "extraction": 5, "stem": 6.5, "humanities": 7.9 }, "average": 4.859375 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen. In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 233275 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| \| Weight decay \| 0.001 \| ## Dataset Details ``` ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%)) ----------------- Accepted: 24427/24427 (100.0%) Accepted tokens: 9549043 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5054 Avg tokens per sample: 390.9216440823679 ----------------- ## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%)) ----------------- Accepted: 68600/68600 (100.0%) Accepted tokens: 33045040 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 481.7061224489796 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%)) ----------------- Accepted: 3646/3646 (100.0%) Accepted tokens: 2338738 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 641.4530992868897 ----------------- ## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%)) ----------------- Accepted: 8922/8922 (100.0%) Accepted tokens: 4526427 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5407 Avg tokens per sample: 507.3332212508406 ----------------- ## Stats for 'total' (235632 samples (100.0%)) ----------------- Accepted: 235632/235632 (100.0%) Accepted tokens: 115862397 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 491.70909299246284 ----------------- ```
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_3e-05_lr_2_e	roa7n	2023-09-28T15:33:46Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T15:33:43Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
MerziaAdamjee/codellama2-finetuned-sqldata	MerziaAdamjee	2023-09-28T15:18:32Z	4	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T13:32:27Z	--- license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - generated_from_trainer model-index: - name: codellama2-finetuned-sqldata results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codellama2-finetuned-sqldata This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0
Ori/lama-2-13b-peft-no-ret-seed-1	Ori	2023-09-28T15:17:14Z	3	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-09-28T15:16:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
CyberHarem/inoue_takina_lycorisrecoil	CyberHarem	2023-09-28T15:13:39Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/inoue_takina_lycorisrecoil", "license:mit", "region:us" ]	text-to-image	2023-09-28T15:00:07Z	--- license: mit datasets: - CyberHarem/inoue_takina_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of inoue_takina_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7020, you need to download `7020/inoue_takina_lycorisrecoil.pt` as the embedding and `7020/inoue_takina_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 7020, with the score of 0.986. The trigger words are: 1. `inoue_takina_lycorisrecoil` 2. `black_hair, long_hair, bangs, purple_eyes, closed_mouth, ribbon, green_ribbon, neck_ribbon` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 8100 \| 0.875 \| [Download](8100/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-8100](8100/previews/pattern_1.png) \| ![pattern_2-8100](8100/previews/pattern_2.png) \| ![pattern_3-8100](8100/previews/pattern_3.png) \| ![pattern_4-8100](8100/previews/pattern_4.png) \| ![pattern_5-8100](8100/previews/pattern_5.png) \| ![pattern_6-8100](8100/previews/pattern_6.png) \| ![pattern_7-8100](8100/previews/pattern_7.png) \| ![pattern_8-8100](8100/previews/pattern_8.png) \| ![bikini-8100](8100/previews/bikini.png) \| [<NSFW, click to see>](8100/previews/bondage.png) \| ![free-8100](8100/previews/free.png) \| ![maid-8100](8100/previews/maid.png) \| ![miko-8100](8100/previews/miko.png) \| [<NSFW, click to see>](8100/previews/nude.png) \| [<NSFW, click to see>](8100/previews/nude2.png) \| ![suit-8100](8100/previews/suit.png) \| ![yukata-8100](8100/previews/yukata.png) \| \| 7560 \| 0.974 \| [Download](7560/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-7560](7560/previews/pattern_1.png) \| ![pattern_2-7560](7560/previews/pattern_2.png) \| ![pattern_3-7560](7560/previews/pattern_3.png) \| ![pattern_4-7560](7560/previews/pattern_4.png) \| ![pattern_5-7560](7560/previews/pattern_5.png) \| ![pattern_6-7560](7560/previews/pattern_6.png) \| ![pattern_7-7560](7560/previews/pattern_7.png) \| ![pattern_8-7560](7560/previews/pattern_8.png) \| ![bikini-7560](7560/previews/bikini.png) \| [<NSFW, click to see>](7560/previews/bondage.png) \| ![free-7560](7560/previews/free.png) \| ![maid-7560](7560/previews/maid.png) \| ![miko-7560](7560/previews/miko.png) \| [<NSFW, click to see>](7560/previews/nude.png) \| [<NSFW, click to see>](7560/previews/nude2.png) \| ![suit-7560](7560/previews/suit.png) \| ![yukata-7560](7560/previews/yukata.png) \| \| 7020 \| 0.986 \| [Download](7020/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-7020](7020/previews/pattern_1.png) \| ![pattern_2-7020](7020/previews/pattern_2.png) \| ![pattern_3-7020](7020/previews/pattern_3.png) \| ![pattern_4-7020](7020/previews/pattern_4.png) \| ![pattern_5-7020](7020/previews/pattern_5.png) \| ![pattern_6-7020](7020/previews/pattern_6.png) \| ![pattern_7-7020](7020/previews/pattern_7.png) \| ![pattern_8-7020](7020/previews/pattern_8.png) \| ![bikini-7020](7020/previews/bikini.png) \| [<NSFW, click to see>](7020/previews/bondage.png) \| ![free-7020](7020/previews/free.png) \| ![maid-7020](7020/previews/maid.png) \| ![miko-7020](7020/previews/miko.png) \| [<NSFW, click to see>](7020/previews/nude.png) \| [<NSFW, click to see>](7020/previews/nude2.png) \| ![suit-7020](7020/previews/suit.png) \| ![yukata-7020](7020/previews/yukata.png) \| \| 6480 \| 0.979 \| [Download](6480/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-6480](6480/previews/pattern_1.png) \| ![pattern_2-6480](6480/previews/pattern_2.png) \| ![pattern_3-6480](6480/previews/pattern_3.png) \| ![pattern_4-6480](6480/previews/pattern_4.png) \| ![pattern_5-6480](6480/previews/pattern_5.png) \| ![pattern_6-6480](6480/previews/pattern_6.png) \| ![pattern_7-6480](6480/previews/pattern_7.png) \| ![pattern_8-6480](6480/previews/pattern_8.png) \| ![bikini-6480](6480/previews/bikini.png) \| [<NSFW, click to see>](6480/previews/bondage.png) \| ![free-6480](6480/previews/free.png) \| ![maid-6480](6480/previews/maid.png) \| ![miko-6480](6480/previews/miko.png) \| [<NSFW, click to see>](6480/previews/nude.png) \| [<NSFW, click to see>](6480/previews/nude2.png) \| ![suit-6480](6480/previews/suit.png) \| ![yukata-6480](6480/previews/yukata.png) \| \| 5940 \| 0.972 \| [Download](5940/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-5940](5940/previews/pattern_1.png) \| ![pattern_2-5940](5940/previews/pattern_2.png) \| ![pattern_3-5940](5940/previews/pattern_3.png) \| ![pattern_4-5940](5940/previews/pattern_4.png) \| ![pattern_5-5940](5940/previews/pattern_5.png) \| ![pattern_6-5940](5940/previews/pattern_6.png) \| ![pattern_7-5940](5940/previews/pattern_7.png) \| ![pattern_8-5940](5940/previews/pattern_8.png) \| ![bikini-5940](5940/previews/bikini.png) \| [<NSFW, click to see>](5940/previews/bondage.png) \| ![free-5940](5940/previews/free.png) \| ![maid-5940](5940/previews/maid.png) \| ![miko-5940](5940/previews/miko.png) \| [<NSFW, click to see>](5940/previews/nude.png) \| [<NSFW, click to see>](5940/previews/nude2.png) \| ![suit-5940](5940/previews/suit.png) \| ![yukata-5940](5940/previews/yukata.png) \| \| 5400 \| 0.921 \| [Download](5400/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-5400](5400/previews/pattern_1.png) \| ![pattern_2-5400](5400/previews/pattern_2.png) \| ![pattern_3-5400](5400/previews/pattern_3.png) \| ![pattern_4-5400](5400/previews/pattern_4.png) \| ![pattern_5-5400](5400/previews/pattern_5.png) \| ![pattern_6-5400](5400/previews/pattern_6.png) \| ![pattern_7-5400](5400/previews/pattern_7.png) \| ![pattern_8-5400](5400/previews/pattern_8.png) \| ![bikini-5400](5400/previews/bikini.png) \| [<NSFW, click to see>](5400/previews/bondage.png) \| ![free-5400](5400/previews/free.png) \| ![maid-5400](5400/previews/maid.png) \| ![miko-5400](5400/previews/miko.png) \| [<NSFW, click to see>](5400/previews/nude.png) \| [<NSFW, click to see>](5400/previews/nude2.png) \| ![suit-5400](5400/previews/suit.png) \| ![yukata-5400](5400/previews/yukata.png) \| \| 4860 \| 0.982 \| [Download](4860/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-4860](4860/previews/pattern_1.png) \| ![pattern_2-4860](4860/previews/pattern_2.png) \| ![pattern_3-4860](4860/previews/pattern_3.png) \| ![pattern_4-4860](4860/previews/pattern_4.png) \| ![pattern_5-4860](4860/previews/pattern_5.png) \| ![pattern_6-4860](4860/previews/pattern_6.png) \| ![pattern_7-4860](4860/previews/pattern_7.png) \| ![pattern_8-4860](4860/previews/pattern_8.png) \| ![bikini-4860](4860/previews/bikini.png) \| [<NSFW, click to see>](4860/previews/bondage.png) \| ![free-4860](4860/previews/free.png) \| ![maid-4860](4860/previews/maid.png) \| ![miko-4860](4860/previews/miko.png) \| [<NSFW, click to see>](4860/previews/nude.png) \| [<NSFW, click to see>](4860/previews/nude2.png) \| ![suit-4860](4860/previews/suit.png) \| ![yukata-4860](4860/previews/yukata.png) \| \| 4320 \| 0.982 \| [Download](4320/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-4320](4320/previews/pattern_1.png) \| ![pattern_2-4320](4320/previews/pattern_2.png) \| ![pattern_3-4320](4320/previews/pattern_3.png) \| ![pattern_4-4320](4320/previews/pattern_4.png) \| ![pattern_5-4320](4320/previews/pattern_5.png) \| ![pattern_6-4320](4320/previews/pattern_6.png) \| ![pattern_7-4320](4320/previews/pattern_7.png) \| ![pattern_8-4320](4320/previews/pattern_8.png) \| ![bikini-4320](4320/previews/bikini.png) \| [<NSFW, click to see>](4320/previews/bondage.png) \| ![free-4320](4320/previews/free.png) \| ![maid-4320](4320/previews/maid.png) \| ![miko-4320](4320/previews/miko.png) \| [<NSFW, click to see>](4320/previews/nude.png) \| [<NSFW, click to see>](4320/previews/nude2.png) \| ![suit-4320](4320/previews/suit.png) \| ![yukata-4320](4320/previews/yukata.png) \| \| 3780 \| 0.982 \| [Download](3780/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-3780](3780/previews/pattern_1.png) \| ![pattern_2-3780](3780/previews/pattern_2.png) \| ![pattern_3-3780](3780/previews/pattern_3.png) \| ![pattern_4-3780](3780/previews/pattern_4.png) \| ![pattern_5-3780](3780/previews/pattern_5.png) \| ![pattern_6-3780](3780/previews/pattern_6.png) \| ![pattern_7-3780](3780/previews/pattern_7.png) \| ![pattern_8-3780](3780/previews/pattern_8.png) \| ![bikini-3780](3780/previews/bikini.png) \| [<NSFW, click to see>](3780/previews/bondage.png) \| ![free-3780](3780/previews/free.png) \| ![maid-3780](3780/previews/maid.png) \| ![miko-3780](3780/previews/miko.png) \| [<NSFW, click to see>](3780/previews/nude.png) \| [<NSFW, click to see>](3780/previews/nude2.png) \| ![suit-3780](3780/previews/suit.png) \| ![yukata-3780](3780/previews/yukata.png) \| \| 3240 \| 0.967 \| [Download](3240/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-3240](3240/previews/pattern_1.png) \| ![pattern_2-3240](3240/previews/pattern_2.png) \| ![pattern_3-3240](3240/previews/pattern_3.png) \| ![pattern_4-3240](3240/previews/pattern_4.png) \| ![pattern_5-3240](3240/previews/pattern_5.png) \| ![pattern_6-3240](3240/previews/pattern_6.png) \| ![pattern_7-3240](3240/previews/pattern_7.png) \| ![pattern_8-3240](3240/previews/pattern_8.png) \| ![bikini-3240](3240/previews/bikini.png) \| [<NSFW, click to see>](3240/previews/bondage.png) \| ![free-3240](3240/previews/free.png) \| ![maid-3240](3240/previews/maid.png) \| ![miko-3240](3240/previews/miko.png) \| [<NSFW, click to see>](3240/previews/nude.png) \| [<NSFW, click to see>](3240/previews/nude2.png) \| ![suit-3240](3240/previews/suit.png) \| ![yukata-3240](3240/previews/yukata.png) \| \| 2700 \| 0.908 \| [Download](2700/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-2700](2700/previews/pattern_1.png) \| ![pattern_2-2700](2700/previews/pattern_2.png) \| ![pattern_3-2700](2700/previews/pattern_3.png) \| ![pattern_4-2700](2700/previews/pattern_4.png) \| ![pattern_5-2700](2700/previews/pattern_5.png) \| ![pattern_6-2700](2700/previews/pattern_6.png) \| ![pattern_7-2700](2700/previews/pattern_7.png) \| ![pattern_8-2700](2700/previews/pattern_8.png) \| ![bikini-2700](2700/previews/bikini.png) \| [<NSFW, click to see>](2700/previews/bondage.png) \| ![free-2700](2700/previews/free.png) \| ![maid-2700](2700/previews/maid.png) \| ![miko-2700](2700/previews/miko.png) \| [<NSFW, click to see>](2700/previews/nude.png) \| [<NSFW, click to see>](2700/previews/nude2.png) \| ![suit-2700](2700/previews/suit.png) \| ![yukata-2700](2700/previews/yukata.png) \| \| 2160 \| 0.885 \| [Download](2160/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-2160](2160/previews/pattern_1.png) \| ![pattern_2-2160](2160/previews/pattern_2.png) \| ![pattern_3-2160](2160/previews/pattern_3.png) \| ![pattern_4-2160](2160/previews/pattern_4.png) \| ![pattern_5-2160](2160/previews/pattern_5.png) \| ![pattern_6-2160](2160/previews/pattern_6.png) \| ![pattern_7-2160](2160/previews/pattern_7.png) \| ![pattern_8-2160](2160/previews/pattern_8.png) \| ![bikini-2160](2160/previews/bikini.png) \| [<NSFW, click to see>](2160/previews/bondage.png) \| ![free-2160](2160/previews/free.png) \| ![maid-2160](2160/previews/maid.png) \| ![miko-2160](2160/previews/miko.png) \| [<NSFW, click to see>](2160/previews/nude.png) \| [<NSFW, click to see>](2160/previews/nude2.png) \| ![suit-2160](2160/previews/suit.png) \| ![yukata-2160](2160/previews/yukata.png) \| \| 1620 \| 0.941 \| [Download](1620/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-1620](1620/previews/pattern_1.png) \| ![pattern_2-1620](1620/previews/pattern_2.png) \| ![pattern_3-1620](1620/previews/pattern_3.png) \| ![pattern_4-1620](1620/previews/pattern_4.png) \| ![pattern_5-1620](1620/previews/pattern_5.png) \| ![pattern_6-1620](1620/previews/pattern_6.png) \| ![pattern_7-1620](1620/previews/pattern_7.png) \| ![pattern_8-1620](1620/previews/pattern_8.png) \| ![bikini-1620](1620/previews/bikini.png) \| [<NSFW, click to see>](1620/previews/bondage.png) \| ![free-1620](1620/previews/free.png) \| ![maid-1620](1620/previews/maid.png) \| ![miko-1620](1620/previews/miko.png) \| [<NSFW, click to see>](1620/previews/nude.png) \| [<NSFW, click to see>](1620/previews/nude2.png) \| ![suit-1620](1620/previews/suit.png) \| ![yukata-1620](1620/previews/yukata.png) \| \| 1080 \| 0.960 \| [Download](1080/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-1080](1080/previews/pattern_1.png) \| ![pattern_2-1080](1080/previews/pattern_2.png) \| ![pattern_3-1080](1080/previews/pattern_3.png) \| ![pattern_4-1080](1080/previews/pattern_4.png) \| ![pattern_5-1080](1080/previews/pattern_5.png) \| ![pattern_6-1080](1080/previews/pattern_6.png) \| ![pattern_7-1080](1080/previews/pattern_7.png) \| ![pattern_8-1080](1080/previews/pattern_8.png) \| ![bikini-1080](1080/previews/bikini.png) \| [<NSFW, click to see>](1080/previews/bondage.png) \| ![free-1080](1080/previews/free.png) \| ![maid-1080](1080/previews/maid.png) \| ![miko-1080](1080/previews/miko.png) \| [<NSFW, click to see>](1080/previews/nude.png) \| [<NSFW, click to see>](1080/previews/nude2.png) \| ![suit-1080](1080/previews/suit.png) \| ![yukata-1080](1080/previews/yukata.png) \| \| 540 \| 0.952 \| [Download](540/inoue_takina_lycorisrecoil.zip) \| ![pattern_1-540](540/previews/pattern_1.png) \| ![pattern_2-540](540/previews/pattern_2.png) \| ![pattern_3-540](540/previews/pattern_3.png) \| ![pattern_4-540](540/previews/pattern_4.png) \| ![pattern_5-540](540/previews/pattern_5.png) \| ![pattern_6-540](540/previews/pattern_6.png) \| ![pattern_7-540](540/previews/pattern_7.png) \| ![pattern_8-540](540/previews/pattern_8.png) \| ![bikini-540](540/previews/bikini.png) \| [<NSFW, click to see>](540/previews/bondage.png) \| ![free-540](540/previews/free.png) \| ![maid-540](540/previews/maid.png) \| ![miko-540](540/previews/miko.png) \| [<NSFW, click to see>](540/previews/nude.png) \| [<NSFW, click to see>](540/previews/nude2.png) \| ![suit-540](540/previews/suit.png) \| ![yukata-540](540/previews/yukata.png) \|
Ori/lama-2-13b-peft-mh-at-1-v2-seed-1	Ori	2023-09-28T15:13:15Z	1	0	peft	[ "peft", "safetensors", "region:us" ]	null	2023-09-28T15:12:10Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
panfengfreedom/ModelTv	panfengfreedom	2023-09-28T15:08:21Z	0	0	adapter-transformers	[ "adapter-transformers", "art", "aa", "dataset:fka/awesome-chatgpt-prompts", "license:apache-2.0", "region:us" ]	null	2023-09-28T15:07:13Z	--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - aa metrics: - accuracy library_name: adapter-transformers tags: - art ---
TinyLlama/tinyLlama-intermediate-checkpoints	TinyLlama	2023-09-28T14:53:27Z	51	12	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-09-28T14:27:51Z	--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata language: - en --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. <div align="center"> <img src="./TinyLlama_logo.png" width="300"/> </div> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is an intermediate checkpoint with 50K steps and 105B tokens. #### Releases Schedule We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison. \| Date \| HF Checkpoint \| Tokens \| Step \| HellaSwag Acc_norm \| \|------------\|-------------------------------------------------\|--------\|------\|---------------------\| \| Baseline \| [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)\| 800B \| -- \| 38.31 \| \| Baseline \| [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) \| 105B \| 50k \| 42.04 \| \| Baseline \| [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) \| 300B \| 143k \| 47.16 \| \| 2023-09-04 \| [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) \| 105B \| 50k \| 43.50 \| \| 2023-09-16 \| -- \| 500B \| -- \| -- \| \| 2023-10-01 \| -- \| 1T \| -- \| -- \| \| 2023-10-16 \| -- \| 1.5T \| -- \| -- \| \| 2023-10-31 \| -- \| 2T \| -- \| -- \| \| 2023-11-15 \| -- \| 2.5T \| -- \| -- \| \| 2023-12-01 \| -- \| 3T \| -- \| -- \| #### How to use You will need the transformers>=4.31 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ``` from transformers import AutoTokenizer import transformers import torch model = "PY007/TinyLlama-1.1B-step-50K-105b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1.5, eos_token_id=tokenizer.eos_token_id, max_length=500, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
anders0204/pyramids	anders0204	2023-09-28T14:38:24Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-09-28T14:38:22Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: anders0204/pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
badokorach/flan-t5-small-qa-91	badokorach	2023-09-28T14:35:47Z	8	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T12:33:04Z	--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-91 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-qa-91 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.1207 \| 1.0 \| 500 \| 0.0685 \| \| 0.1176 \| 2.0 \| 1000 \| 0.0693 \| \| 0.116 \| 3.0 \| 1500 \| 0.0696 \| \| 0.1152 \| 4.0 \| 2000 \| 0.0709 \| \| 0.1138 \| 5.0 \| 2500 \| 0.0703 \| \| 0.1134 \| 6.0 \| 3000 \| 0.0717 \| \| 0.1125 \| 7.0 \| 3500 \| 0.0707 \| \| 0.1119 \| 8.0 \| 4000 \| 0.0710 \| \| 0.1115 \| 9.0 \| 4500 \| 0.0717 \| \| 0.1113 \| 10.0 \| 5000 \| 0.0713 \| \| 0.111 \| 11.0 \| 5500 \| 0.0721 \| \| 0.1107 \| 12.0 \| 6000 \| 0.0726 \| \| 0.1106 \| 13.0 \| 6500 \| 0.0721 \| \| 0.111 \| 14.0 \| 7000 \| 0.0720 \| \| 0.1108 \| 15.0 \| 7500 \| 0.0721 \| \| 0.1106 \| 16.0 \| 8000 \| 0.0722 \| \| 0.1104 \| 17.0 \| 8500 \| 0.0729 \| \| 0.1102 \| 18.0 \| 9000 \| 0.0727 \| \| 0.1101 \| 19.0 \| 9500 \| 0.0727 \| \| 0.11 \| 20.0 \| 10000 \| 0.0727 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
albusdumbledore/llama-trial	albusdumbledore	2023-09-28T14:29:48Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-26T19:16:22Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0
yyjun/yyjun.KoAlpaca_mingu_ver	yyjun	2023-09-28T14:28:35Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-28T14:28:32Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.003_lr_2_e	roa7n	2023-09-28T14:28:15Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-28T14:28:13Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
anuragrawal/my-awesome-setfit-model	anuragrawal	2023-09-28T14:27:30Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-28T14:26:41Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # anuragrawal/my-awesome-setfit-model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("anuragrawal/my-awesome-setfit-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
santiviquez/amazon-reviews-sentiment-distilbert-base-uncased-6000-samples	santiviquez	2023-09-28T14:16:42Z	112	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-28T13:48:30Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: amazon-reviews-sentiment-distilbert-base-uncased-6000-samples results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: en split: validation args: en metrics: - name: Accuracy type: accuracy value: 0.7355 - name: F1 type: f1 value: 0.6586935295304587 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amazon-reviews-sentiment-distilbert-base-uncased-6000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.6126 - Accuracy: 0.7355 - F1: 0.6587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 188 \| 0.6172 \| 0.7335 \| 0.6516 \| \| No log \| 2.0 \| 376 \| 0.6126 \| 0.7355 \| 0.6587 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
skaliy/spine-segmentation	skaliy	2023-09-28T14:16:33Z	0	6	fastMONAI	[ "fastMONAI", "medical", "image-segmentation", "en", "region:us" ]	image-segmentation	2022-11-08T15:19:16Z	--- language: - en pipeline_tag: image-segmentation tags: - medical library_name: fastMONAI --- # Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research ## Requirements Last checked and validated with fastMONAI version 0.3.2. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model. ![Suggested pipeline](pipeline.png) ## Data ### Haukeland University Hospital (HUH): 15 T2-weighted MRI scans - [Study](https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-2306-8) #### Scanner Specifications - Type: Siemens MAGNETOM Avanto 1.5 T - Slice Thickness: 4 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x269 - Interslice Gap: 0.4 mm - Repetition Time: 3700 ms - Echo Time: 87 ms - Number of Acquisitions: 2 ### Sahlgren-ska University Hospital (SUH): 10 T2-weighted MRI scans - [Study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789539/) #### Scanner Specifications - Type: Siemens MAGNETOM Aera 1.5 T - Slice Thickness: 3.5 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x384 - Interslice Gap: 0.7 mm - Repetition Time: 3500 ms - Echo Time: 95 ms - Number of Acquisitions: 1 ### Data Annotation - Process: Manual segmentation of vertebrae - Experts: Conducted by an MRI expert with consensus from a senior radiologist - Software: ITK-SNAP - Scope: Segmented vertebrae in all slices, including bone marrow but excluding vertebral cortex ### External Data Evaluation - Source: [Annotated T2-weighted MR images of the Lower Spine](https://zenodo.org/record/22304) - Scan Specifications: 23 scans, 1.5 T in sagittal orientation - Segments: Seven vertebrae are manually segmented on each image (Th11–L5) - Modification: Th11 manually removed for consistency with the annotation for SUH and HUH.
Gman/sd-class-butterflies-32	Gman	2023-09-28T14:12:59Z	48	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-09-28T14:11:38Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Gman/sd-class-butterflies-32') image = pipeline().images[0] image ```
herve76/bbhf2	herve76	2023-09-28T14:07:28Z	2	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:herve76/bbhf2", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-28T13:21:53Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: bbhf2 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - herve76/bbhf2 --- # LoRA DreamBooth - herve76/bbhf2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: ``` bbhf2 ``` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.to("cuda") # This is where you load your trained weights pipe.load_lora_weights('herve76/bbhf2') prompt = "A majestic bbhf2 jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```
ArnaudHureaux/Llama-2-70b-chat-hf-miniguanaco	ArnaudHureaux	2023-09-28T14:03:35Z	13	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-09-26T10:42:23Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
VuongQuoc/checkpoints_28_9_microsoft_deberta_V4	VuongQuoc	2023-09-28T13:50:03Z	69	0	transformers	[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2023-09-28T02:49:56Z	--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: checkpoints_28_9_microsoft_deberta_V4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # checkpoints_28_9_microsoft_deberta_V4 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2854 - Map@3: 0.5483 - Accuracy: 0.435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map@3 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:--------:\| \| 1.2365 \| 0.11 \| 100 \| 1.0631 \| 0.7583 \| 0.64 \| \| 0.8608 \| 0.21 \| 200 \| 0.7329 \| 0.8383 \| 0.75 \| \| 0.8527 \| 0.32 \| 300 \| 0.6985 \| 0.8575 \| 0.78 \| \| 0.744 \| 0.43 \| 400 \| 0.6498 \| 0.8625 \| 0.785 \| \| 0.7686 \| 0.53 \| 500 \| 0.7450 \| 0.8575 \| 0.765 \| \| 1.4098 \| 0.64 \| 600 \| 1.3030 \| 0.5575 \| 0.4 \| \| 1.4246 \| 0.75 \| 700 \| 1.3018 \| 0.5575 \| 0.435 \| \| 1.3987 \| 0.85 \| 800 \| 1.2906 \| 0.5450 \| 0.41 \| \| 1.4121 \| 0.96 \| 900 \| 1.2854 \| 0.5483 \| 0.435 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.0 - Datasets 2.9.0 - Tokenizers 0.13.3
CyberHarem/kodama_miyako_yagatekimininaru	CyberHarem	2023-09-28T13:49:16Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/kodama_miyako_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T13:39:51Z	--- license: mit datasets: - CyberHarem/kodama_miyako_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of kodama_miyako_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/kodama_miyako_yagatekimininaru.pt` as the embedding and `2380/kodama_miyako_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 2380, with the score of 1.000. The trigger words are: 1. `kodama_miyako_yagatekimininaru` 2. `brown_hair, short_hair, indoors, bangs, collarbone, blurry` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:--------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.900 \| [Download](5100/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.996 \| [Download](4760/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 1.000 \| [Download](4420/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.800 \| [Download](4080/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.990 \| [Download](3740/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 1.000 \| [Download](3400/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 1.000 \| [Download](3060/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.964 \| [Download](2720/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 1.000 \| [Download](2380/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.960 \| [Download](2040/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.867 \| [Download](1700/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.788 \| [Download](1360/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.749 \| [Download](1020/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.497 \| [Download](680/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.715 \| [Download](340/kodama_miyako_yagatekimininaru.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
drhyrum/bert-tiny-torch-picklebomb	drhyrum	2023-09-28T13:46:25Z	122	0	transformers	[ "transformers", "pytorch", "bert", "BERT", "MNLI", "NLI", "transformer", "pre-training", "en", "arxiv:1908.08962", "arxiv:2110.01518", "license:mit", "endpoints_compatible", "region:us" ]	null	2023-09-28T13:27:51Z	--- language: - en license: - mit tags: - BERT - MNLI - NLI - transformer - pre-training --- DISCLAIMER: This repo demonstrates a picklebomb payload in pytorch that may go undetected by superficial scanning. The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) Other models to check out: - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
anders0204/snowball	anders0204	2023-09-28T13:46:01Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-09-28T13:45:57Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: anders0204/snowball 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
pejho/bloom_prompt_tuning_1695907859.055798	pejho	2023-09-28T13:39:03Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-28T13:39:01Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
joemaatouk/donut-base-sroie	joemaatouk	2023-09-28T13:36:07Z	9	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:ez_pass-dataset", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-09-11T17:56:59Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - ez_pass-dataset model-index: - name: donut-base-sroie results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the ez_pass-dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
TheBloke/leo-hessianai-13B-chat-GPTQ	TheBloke	2023-09-28T13:34:46Z	41	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "base_model:LeoLM/leo-hessianai-13b-chat", "base_model:quantized:LeoLM/leo-hessianai-13b-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-28T12:22:36Z	--- base_model: LeoLM/leo-hessianai-13b-chat datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B Chat model_type: llama pipeline_tag: text-generation prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Leo Hessianai 13B Chat - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat) <!-- description start --> ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b-chat) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 8.01 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 13.66 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 14.55 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [German Quad](https://huggingface.co/datasets/deepset/germanquad) \| 8192 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-13B-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-13B-chat-GPTQ`: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-13B-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/leo-hessianai-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `leo-hessianai-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: LAION LeoLM's Leo Hessianai 13B Chat # LAION LeoLM: Linguistically Enhanced Open Language Model Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer 42, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI. ## LeoLM Chat `LeoLM/leo-hessianai-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores: ``` { "first_turn": 6.525, "second_turn": 5.15, "categories": { "writing": 6.925, "roleplay": 6.7, "reasoning": 4.55, "math": 3.25, "coding": 3.45, "extraction": 5.4, "stem": 7.55, "humanities": 8.875 }, "average": 5.8375 } ``` ## Model Details - Finetuned from: [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b) - Model type: Causal decoder-only transformer language model - Language: English and German - Demo: [Web Demo]() - License: [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - Contact: [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<\|im_start\|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<\|im_end\|> """ prompt_format = "<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren." ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <\|im_start\|>user {prompt 1}<\|im_end\|> <\|im_start\|>assistant {reply 1}<\|im_end\|> <\|im_start\|>user {prompt 2}<\|im_end\|> <\|im_start\|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-13b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-13b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details \| Hyperparameter \| Value \| \|---\|---\| \| Num epochs \| 3 \| \| Examples per epoch \| 131214 \| \| Global batch size \| 256 \| \| Learning rate \| 3e-5 \| \| Warmup steps \| 100 \| \| LR scheduler \| Cosine \| \| Adam betas \| (0.9, 0.95) \| ## Dataset Details ``` ## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%)) ----------------- Accepted: 3534/3534 (100.0%) Accepted tokens: 2259302 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 639.3044708545557 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'total' (132540 samples (100.0%)) ----------------- Accepted: 132540/132540 (100.0%) Accepted tokens: 67530728 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 5507 Avg tokens per sample: 509.51205673758864 ----------------- ```
Aliki/Llama-2-13b-chat-german-areto26-test1	Aliki	2023-09-28T13:32:14Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-28T13:32:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
EvinFrancis/my-pet-dog-xzr	EvinFrancis	2023-09-28T13:26:46Z	4	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T13:22:47Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog-xzr Dreambooth model trained by EvinFrancis following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoXJITD9 Sample pictures of this concept: ![0](https://huggingface.co/EvinFrancis/my-pet-dog-xzr/resolve/main/sample_images/download_(1).png) ![1](https://huggingface.co/EvinFrancis/my-pet-dog-xzr/resolve/main/sample_images/download.png)
joejoequte/test	joejoequte	2023-09-28T12:58:28Z	1	0	transformers	[ "transformers", "endpoints_compatible", "region:us" ]	null	2023-09-28T12:57:18Z	# v2ray_for_Huggingface 在Huggingface搭建v2ray节点配置，Caddy+V2ray实现需修改密码见config.json
shahzain/whisper-sm-urdu-finetuned	shahzain	2023-09-28T12:52:20Z	113	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-19T04:07:53Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-sm-urdu-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-sm-urdu-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [Common Voice 13.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/viewer/ur/train) dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Wer: 36.0220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.4017 \| 0.18 \| 1000 \| 0.2591 \| 65.3388 \| \| 0.3963 \| 0.36 \| 2000 \| 0.2135 \| 43.6355 \| \| 0.3162 \| 0.54 \| 3000 \| 0.1880 \| 35.8573 \| \| 0.3479 \| 0.72 \| 4000 \| 0.1777 \| 36.0220 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3
sxandie/autotrain-air-sea-services-28092023-91915144626	sxandie	2023-09-28T12:40:34Z	108	0	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "autotrain", "unk", "dataset:sxandie/autotrain-data-air-sea-services-28092023", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-28T12:38:08Z	--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain" datasets: - sxandie/autotrain-data-air-sea-services-28092023 co2_eq_emissions: emissions: 0.8518554225909252 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 91915144626 - CO2 Emissions (in grams): 0.8519 ## Validation Metrics - Loss: 0.921 - Accuracy: 0.759 - Precision: 0.159 - Recall: 0.098 - F1: 0.122 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sxandie/autotrain-air-sea-services-28092023-91915144626 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
shikari2917/mypic4	shikari2917	2023-09-28T12:40:17Z	5	1	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-28T12:40:15Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks rafeh tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
mHossain/bangla_idiom_generation_v3	mHossain	2023-09-28T12:36:51Z	103	1	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T12:20:38Z	--- tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla_idiom_generation_v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bangla_idiom_generation_v3 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.5201 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 10.8938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 226 \| 4.7622 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.5398 \| \| No log \| 2.0 \| 452 \| 4.7514 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.6018 \| \| 5.1485 \| 3.0 \| 678 \| 4.7347 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.2478 \| \| 5.1485 \| 4.0 \| 904 \| 4.7101 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.9912 \| \| 5.0936 \| 5.0 \| 1130 \| 4.6840 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 12.0841 \| \| 5.0936 \| 6.0 \| 1356 \| 4.6527 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 12.1991 \| \| 4.9965 \| 7.0 \| 1582 \| 4.6199 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 12.5044 \| \| 4.9965 \| 8.0 \| 1808 \| 4.5845 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.8761 \| \| 4.8494 \| 9.0 \| 2034 \| 4.5543 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.5398 \| \| 4.8494 \| 10.0 \| 2260 \| 4.5201 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.8938 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
badokorach/flan-t5-small-qa-9-qa-91	badokorach	2023-09-28T12:28:24Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:badokorach/flan-t5-small-qa-9", "base_model:finetune:badokorach/flan-t5-small-qa-9", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T09:51:48Z	--- license: apache-2.0 base_model: badokorach/flan-t5-small-qa-9 tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-9-qa-91 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-small-qa-9-qa-91 This model is a fine-tuned version of [badokorach/flan-t5-small-qa-9](https://huggingface.co/badokorach/flan-t5-small-qa-9) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 343 \| 0.1041 \| \| 0.0207 \| 2.0 \| 686 \| 0.1041 \| \| 0.0196 \| 3.0 \| 1029 \| 0.1057 \| \| 0.0196 \| 4.0 \| 1372 \| 0.1052 \| \| 0.0186 \| 5.0 \| 1715 \| 0.1043 \| \| 0.0173 \| 6.0 \| 2058 \| 0.1050 \| \| 0.0173 \| 7.0 \| 2401 \| 0.1079 \| \| 0.0165 \| 8.0 \| 2744 \| 0.1068 \| \| 0.0161 \| 9.0 \| 3087 \| 0.1081 \| \| 0.0161 \| 10.0 \| 3430 \| 0.1084 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3
CyberHarem/kanou_koyomi_yagatekimininaru	CyberHarem	2023-09-28T12:21:10Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/kanou_koyomi_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T12:09:24Z	--- license: mit datasets: - CyberHarem/kanou_koyomi_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of kanou_koyomi_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/kanou_koyomi_yagatekimininaru.pt` as the embedding and `4420/kanou_koyomi_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4420, with the score of 0.882. The trigger words are: 1. `kanou_koyomi_yagatekimininaru` 2. `short_hair, black_hair, closed_mouth, indoors, brown_hair, blue_eyes, black_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.815 \| [Download](5100/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.719 \| [Download](4760/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.882 \| [Download](4420/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.746 \| [Download](4080/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.794 \| [Download](3740/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.755 \| [Download](3400/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.700 \| [Download](3060/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.664 \| [Download](2720/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.799 \| [Download](2380/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.666 \| [Download](2040/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.819 \| [Download](1700/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.724 \| [Download](1360/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.675 \| [Download](1020/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.607 \| [Download](680/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.528 \| [Download](340/kanou_koyomi_yagatekimininaru.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
CyberHarem/aoyama_midori_istheorderarabbit	CyberHarem	2023-09-28T12:17:17Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/aoyama_midori_istheorderarabbit", "license:mit", "region:us" ]	text-to-image	2023-09-28T11:48:41Z	--- license: mit datasets: - CyberHarem/aoyama_midori_istheorderarabbit pipeline_tag: text-to-image tags: - art --- # Lora of aoyama_midori_istheorderarabbit This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4800, you need to download `4800/aoyama_midori_istheorderarabbit.pt` as the embedding and `4800/aoyama_midori_istheorderarabbit.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4800, with the score of 0.872. The trigger words are: 1. `aoyama_midori_istheorderarabbit` 2. `blush, blonde_hair, hair_between_eyes, smile, blue_eyes, bangs, long_hair, closed_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:---------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 6000 \| 0.822 \| [Download](6000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-6000](6000/previews/pattern_1.png) \| ![pattern_2-6000](6000/previews/pattern_2.png) \| ![pattern_3-6000](6000/previews/pattern_3.png) \| ![pattern_4-6000](6000/previews/pattern_4.png) \| ![pattern_5-6000](6000/previews/pattern_5.png) \| ![pattern_6-6000](6000/previews/pattern_6.png) \| ![pattern_7-6000](6000/previews/pattern_7.png) \| ![pattern_8-6000](6000/previews/pattern_8.png) \| ![pattern_9-6000](6000/previews/pattern_9.png) \| ![pattern_10-6000](6000/previews/pattern_10.png) \| ![pattern_11-6000](6000/previews/pattern_11.png) \| ![bikini-6000](6000/previews/bikini.png) \| [<NSFW, click to see>](6000/previews/bondage.png) \| ![free-6000](6000/previews/free.png) \| ![maid-6000](6000/previews/maid.png) \| ![miko-6000](6000/previews/miko.png) \| [<NSFW, click to see>](6000/previews/nude.png) \| [<NSFW, click to see>](6000/previews/nude2.png) \| ![suit-6000](6000/previews/suit.png) \| ![yukata-6000](6000/previews/yukata.png) \| \| 5600 \| 0.838 \| [Download](5600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-5600](5600/previews/pattern_1.png) \| ![pattern_2-5600](5600/previews/pattern_2.png) \| ![pattern_3-5600](5600/previews/pattern_3.png) \| ![pattern_4-5600](5600/previews/pattern_4.png) \| ![pattern_5-5600](5600/previews/pattern_5.png) \| ![pattern_6-5600](5600/previews/pattern_6.png) \| ![pattern_7-5600](5600/previews/pattern_7.png) \| ![pattern_8-5600](5600/previews/pattern_8.png) \| ![pattern_9-5600](5600/previews/pattern_9.png) \| ![pattern_10-5600](5600/previews/pattern_10.png) \| ![pattern_11-5600](5600/previews/pattern_11.png) \| ![bikini-5600](5600/previews/bikini.png) \| [<NSFW, click to see>](5600/previews/bondage.png) \| ![free-5600](5600/previews/free.png) \| ![maid-5600](5600/previews/maid.png) \| ![miko-5600](5600/previews/miko.png) \| [<NSFW, click to see>](5600/previews/nude.png) \| [<NSFW, click to see>](5600/previews/nude2.png) \| ![suit-5600](5600/previews/suit.png) \| ![yukata-5600](5600/previews/yukata.png) \| \| 5200 \| 0.833 \| [Download](5200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-5200](5200/previews/pattern_1.png) \| ![pattern_2-5200](5200/previews/pattern_2.png) \| ![pattern_3-5200](5200/previews/pattern_3.png) \| ![pattern_4-5200](5200/previews/pattern_4.png) \| ![pattern_5-5200](5200/previews/pattern_5.png) \| ![pattern_6-5200](5200/previews/pattern_6.png) \| ![pattern_7-5200](5200/previews/pattern_7.png) \| ![pattern_8-5200](5200/previews/pattern_8.png) \| ![pattern_9-5200](5200/previews/pattern_9.png) \| ![pattern_10-5200](5200/previews/pattern_10.png) \| ![pattern_11-5200](5200/previews/pattern_11.png) \| ![bikini-5200](5200/previews/bikini.png) \| [<NSFW, click to see>](5200/previews/bondage.png) \| ![free-5200](5200/previews/free.png) \| ![maid-5200](5200/previews/maid.png) \| ![miko-5200](5200/previews/miko.png) \| [<NSFW, click to see>](5200/previews/nude.png) \| [<NSFW, click to see>](5200/previews/nude2.png) \| ![suit-5200](5200/previews/suit.png) \| ![yukata-5200](5200/previews/yukata.png) \| \| 4800 \| 0.872 \| [Download](4800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4800](4800/previews/pattern_1.png) \| ![pattern_2-4800](4800/previews/pattern_2.png) \| ![pattern_3-4800](4800/previews/pattern_3.png) \| ![pattern_4-4800](4800/previews/pattern_4.png) \| ![pattern_5-4800](4800/previews/pattern_5.png) \| ![pattern_6-4800](4800/previews/pattern_6.png) \| ![pattern_7-4800](4800/previews/pattern_7.png) \| ![pattern_8-4800](4800/previews/pattern_8.png) \| ![pattern_9-4800](4800/previews/pattern_9.png) \| ![pattern_10-4800](4800/previews/pattern_10.png) \| ![pattern_11-4800](4800/previews/pattern_11.png) \| ![bikini-4800](4800/previews/bikini.png) \| [<NSFW, click to see>](4800/previews/bondage.png) \| ![free-4800](4800/previews/free.png) \| ![maid-4800](4800/previews/maid.png) \| ![miko-4800](4800/previews/miko.png) \| [<NSFW, click to see>](4800/previews/nude.png) \| [<NSFW, click to see>](4800/previews/nude2.png) \| ![suit-4800](4800/previews/suit.png) \| ![yukata-4800](4800/previews/yukata.png) \| \| 4400 \| 0.786 \| [Download](4400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4400](4400/previews/pattern_1.png) \| ![pattern_2-4400](4400/previews/pattern_2.png) \| ![pattern_3-4400](4400/previews/pattern_3.png) \| ![pattern_4-4400](4400/previews/pattern_4.png) \| ![pattern_5-4400](4400/previews/pattern_5.png) \| ![pattern_6-4400](4400/previews/pattern_6.png) \| ![pattern_7-4400](4400/previews/pattern_7.png) \| ![pattern_8-4400](4400/previews/pattern_8.png) \| ![pattern_9-4400](4400/previews/pattern_9.png) \| ![pattern_10-4400](4400/previews/pattern_10.png) \| ![pattern_11-4400](4400/previews/pattern_11.png) \| ![bikini-4400](4400/previews/bikini.png) \| [<NSFW, click to see>](4400/previews/bondage.png) \| ![free-4400](4400/previews/free.png) \| ![maid-4400](4400/previews/maid.png) \| ![miko-4400](4400/previews/miko.png) \| [<NSFW, click to see>](4400/previews/nude.png) \| [<NSFW, click to see>](4400/previews/nude2.png) \| ![suit-4400](4400/previews/suit.png) \| ![yukata-4400](4400/previews/yukata.png) \| \| 4000 \| 0.834 \| [Download](4000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-4000](4000/previews/pattern_1.png) \| ![pattern_2-4000](4000/previews/pattern_2.png) \| ![pattern_3-4000](4000/previews/pattern_3.png) \| ![pattern_4-4000](4000/previews/pattern_4.png) \| ![pattern_5-4000](4000/previews/pattern_5.png) \| ![pattern_6-4000](4000/previews/pattern_6.png) \| ![pattern_7-4000](4000/previews/pattern_7.png) \| ![pattern_8-4000](4000/previews/pattern_8.png) \| ![pattern_9-4000](4000/previews/pattern_9.png) \| ![pattern_10-4000](4000/previews/pattern_10.png) \| ![pattern_11-4000](4000/previews/pattern_11.png) \| ![bikini-4000](4000/previews/bikini.png) \| [<NSFW, click to see>](4000/previews/bondage.png) \| ![free-4000](4000/previews/free.png) \| ![maid-4000](4000/previews/maid.png) \| ![miko-4000](4000/previews/miko.png) \| [<NSFW, click to see>](4000/previews/nude.png) \| [<NSFW, click to see>](4000/previews/nude2.png) \| ![suit-4000](4000/previews/suit.png) \| ![yukata-4000](4000/previews/yukata.png) \| \| 3600 \| 0.787 \| [Download](3600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-3600](3600/previews/pattern_1.png) \| ![pattern_2-3600](3600/previews/pattern_2.png) \| ![pattern_3-3600](3600/previews/pattern_3.png) \| ![pattern_4-3600](3600/previews/pattern_4.png) \| ![pattern_5-3600](3600/previews/pattern_5.png) \| ![pattern_6-3600](3600/previews/pattern_6.png) \| ![pattern_7-3600](3600/previews/pattern_7.png) \| ![pattern_8-3600](3600/previews/pattern_8.png) \| ![pattern_9-3600](3600/previews/pattern_9.png) \| ![pattern_10-3600](3600/previews/pattern_10.png) \| ![pattern_11-3600](3600/previews/pattern_11.png) \| ![bikini-3600](3600/previews/bikini.png) \| [<NSFW, click to see>](3600/previews/bondage.png) \| ![free-3600](3600/previews/free.png) \| ![maid-3600](3600/previews/maid.png) \| ![miko-3600](3600/previews/miko.png) \| [<NSFW, click to see>](3600/previews/nude.png) \| [<NSFW, click to see>](3600/previews/nude2.png) \| ![suit-3600](3600/previews/suit.png) \| ![yukata-3600](3600/previews/yukata.png) \| \| 3200 \| 0.829 \| [Download](3200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-3200](3200/previews/pattern_1.png) \| ![pattern_2-3200](3200/previews/pattern_2.png) \| ![pattern_3-3200](3200/previews/pattern_3.png) \| ![pattern_4-3200](3200/previews/pattern_4.png) \| ![pattern_5-3200](3200/previews/pattern_5.png) \| ![pattern_6-3200](3200/previews/pattern_6.png) \| ![pattern_7-3200](3200/previews/pattern_7.png) \| ![pattern_8-3200](3200/previews/pattern_8.png) \| ![pattern_9-3200](3200/previews/pattern_9.png) \| ![pattern_10-3200](3200/previews/pattern_10.png) \| ![pattern_11-3200](3200/previews/pattern_11.png) \| ![bikini-3200](3200/previews/bikini.png) \| [<NSFW, click to see>](3200/previews/bondage.png) \| ![free-3200](3200/previews/free.png) \| ![maid-3200](3200/previews/maid.png) \| ![miko-3200](3200/previews/miko.png) \| [<NSFW, click to see>](3200/previews/nude.png) \| [<NSFW, click to see>](3200/previews/nude2.png) \| ![suit-3200](3200/previews/suit.png) \| ![yukata-3200](3200/previews/yukata.png) \| \| 2800 \| 0.778 \| [Download](2800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2800](2800/previews/pattern_1.png) \| ![pattern_2-2800](2800/previews/pattern_2.png) \| ![pattern_3-2800](2800/previews/pattern_3.png) \| ![pattern_4-2800](2800/previews/pattern_4.png) \| ![pattern_5-2800](2800/previews/pattern_5.png) \| ![pattern_6-2800](2800/previews/pattern_6.png) \| ![pattern_7-2800](2800/previews/pattern_7.png) \| ![pattern_8-2800](2800/previews/pattern_8.png) \| ![pattern_9-2800](2800/previews/pattern_9.png) \| ![pattern_10-2800](2800/previews/pattern_10.png) \| ![pattern_11-2800](2800/previews/pattern_11.png) \| ![bikini-2800](2800/previews/bikini.png) \| [<NSFW, click to see>](2800/previews/bondage.png) \| ![free-2800](2800/previews/free.png) \| ![maid-2800](2800/previews/maid.png) \| ![miko-2800](2800/previews/miko.png) \| [<NSFW, click to see>](2800/previews/nude.png) \| [<NSFW, click to see>](2800/previews/nude2.png) \| ![suit-2800](2800/previews/suit.png) \| ![yukata-2800](2800/previews/yukata.png) \| \| 2400 \| 0.749 \| [Download](2400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2400](2400/previews/pattern_1.png) \| ![pattern_2-2400](2400/previews/pattern_2.png) \| ![pattern_3-2400](2400/previews/pattern_3.png) \| ![pattern_4-2400](2400/previews/pattern_4.png) \| ![pattern_5-2400](2400/previews/pattern_5.png) \| ![pattern_6-2400](2400/previews/pattern_6.png) \| ![pattern_7-2400](2400/previews/pattern_7.png) \| ![pattern_8-2400](2400/previews/pattern_8.png) \| ![pattern_9-2400](2400/previews/pattern_9.png) \| ![pattern_10-2400](2400/previews/pattern_10.png) \| ![pattern_11-2400](2400/previews/pattern_11.png) \| ![bikini-2400](2400/previews/bikini.png) \| [<NSFW, click to see>](2400/previews/bondage.png) \| ![free-2400](2400/previews/free.png) \| ![maid-2400](2400/previews/maid.png) \| ![miko-2400](2400/previews/miko.png) \| [<NSFW, click to see>](2400/previews/nude.png) \| [<NSFW, click to see>](2400/previews/nude2.png) \| ![suit-2400](2400/previews/suit.png) \| ![yukata-2400](2400/previews/yukata.png) \| \| 2000 \| 0.806 \| [Download](2000/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-2000](2000/previews/pattern_1.png) \| ![pattern_2-2000](2000/previews/pattern_2.png) \| ![pattern_3-2000](2000/previews/pattern_3.png) \| ![pattern_4-2000](2000/previews/pattern_4.png) \| ![pattern_5-2000](2000/previews/pattern_5.png) \| ![pattern_6-2000](2000/previews/pattern_6.png) \| ![pattern_7-2000](2000/previews/pattern_7.png) \| ![pattern_8-2000](2000/previews/pattern_8.png) \| ![pattern_9-2000](2000/previews/pattern_9.png) \| ![pattern_10-2000](2000/previews/pattern_10.png) \| ![pattern_11-2000](2000/previews/pattern_11.png) \| ![bikini-2000](2000/previews/bikini.png) \| [<NSFW, click to see>](2000/previews/bondage.png) \| ![free-2000](2000/previews/free.png) \| ![maid-2000](2000/previews/maid.png) \| ![miko-2000](2000/previews/miko.png) \| [<NSFW, click to see>](2000/previews/nude.png) \| [<NSFW, click to see>](2000/previews/nude2.png) \| ![suit-2000](2000/previews/suit.png) \| ![yukata-2000](2000/previews/yukata.png) \| \| 1600 \| 0.687 \| [Download](1600/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-1600](1600/previews/pattern_1.png) \| ![pattern_2-1600](1600/previews/pattern_2.png) \| ![pattern_3-1600](1600/previews/pattern_3.png) \| ![pattern_4-1600](1600/previews/pattern_4.png) \| ![pattern_5-1600](1600/previews/pattern_5.png) \| ![pattern_6-1600](1600/previews/pattern_6.png) \| ![pattern_7-1600](1600/previews/pattern_7.png) \| ![pattern_8-1600](1600/previews/pattern_8.png) \| ![pattern_9-1600](1600/previews/pattern_9.png) \| ![pattern_10-1600](1600/previews/pattern_10.png) \| ![pattern_11-1600](1600/previews/pattern_11.png) \| ![bikini-1600](1600/previews/bikini.png) \| [<NSFW, click to see>](1600/previews/bondage.png) \| ![free-1600](1600/previews/free.png) \| ![maid-1600](1600/previews/maid.png) \| ![miko-1600](1600/previews/miko.png) \| [<NSFW, click to see>](1600/previews/nude.png) \| [<NSFW, click to see>](1600/previews/nude2.png) \| ![suit-1600](1600/previews/suit.png) \| ![yukata-1600](1600/previews/yukata.png) \| \| 1200 \| 0.718 \| [Download](1200/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-1200](1200/previews/pattern_1.png) \| ![pattern_2-1200](1200/previews/pattern_2.png) \| ![pattern_3-1200](1200/previews/pattern_3.png) \| ![pattern_4-1200](1200/previews/pattern_4.png) \| ![pattern_5-1200](1200/previews/pattern_5.png) \| ![pattern_6-1200](1200/previews/pattern_6.png) \| ![pattern_7-1200](1200/previews/pattern_7.png) \| ![pattern_8-1200](1200/previews/pattern_8.png) \| ![pattern_9-1200](1200/previews/pattern_9.png) \| ![pattern_10-1200](1200/previews/pattern_10.png) \| ![pattern_11-1200](1200/previews/pattern_11.png) \| ![bikini-1200](1200/previews/bikini.png) \| [<NSFW, click to see>](1200/previews/bondage.png) \| ![free-1200](1200/previews/free.png) \| ![maid-1200](1200/previews/maid.png) \| ![miko-1200](1200/previews/miko.png) \| [<NSFW, click to see>](1200/previews/nude.png) \| [<NSFW, click to see>](1200/previews/nude2.png) \| ![suit-1200](1200/previews/suit.png) \| ![yukata-1200](1200/previews/yukata.png) \| \| 800 \| 0.673 \| [Download](800/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-800](800/previews/pattern_1.png) \| ![pattern_2-800](800/previews/pattern_2.png) \| ![pattern_3-800](800/previews/pattern_3.png) \| ![pattern_4-800](800/previews/pattern_4.png) \| ![pattern_5-800](800/previews/pattern_5.png) \| ![pattern_6-800](800/previews/pattern_6.png) \| ![pattern_7-800](800/previews/pattern_7.png) \| ![pattern_8-800](800/previews/pattern_8.png) \| ![pattern_9-800](800/previews/pattern_9.png) \| ![pattern_10-800](800/previews/pattern_10.png) \| ![pattern_11-800](800/previews/pattern_11.png) \| ![bikini-800](800/previews/bikini.png) \| [<NSFW, click to see>](800/previews/bondage.png) \| ![free-800](800/previews/free.png) \| ![maid-800](800/previews/maid.png) \| ![miko-800](800/previews/miko.png) \| [<NSFW, click to see>](800/previews/nude.png) \| [<NSFW, click to see>](800/previews/nude2.png) \| ![suit-800](800/previews/suit.png) \| ![yukata-800](800/previews/yukata.png) \| \| 400 \| 0.435 \| [Download](400/aoyama_midori_istheorderarabbit.zip) \| ![pattern_1-400](400/previews/pattern_1.png) \| ![pattern_2-400](400/previews/pattern_2.png) \| ![pattern_3-400](400/previews/pattern_3.png) \| ![pattern_4-400](400/previews/pattern_4.png) \| ![pattern_5-400](400/previews/pattern_5.png) \| ![pattern_6-400](400/previews/pattern_6.png) \| ![pattern_7-400](400/previews/pattern_7.png) \| ![pattern_8-400](400/previews/pattern_8.png) \| ![pattern_9-400](400/previews/pattern_9.png) \| ![pattern_10-400](400/previews/pattern_10.png) \| ![pattern_11-400](400/previews/pattern_11.png) \| ![bikini-400](400/previews/bikini.png) \| [<NSFW, click to see>](400/previews/bondage.png) \| ![free-400](400/previews/free.png) \| ![maid-400](400/previews/maid.png) \| ![miko-400](400/previews/miko.png) \| [<NSFW, click to see>](400/previews/nude.png) \| [<NSFW, click to see>](400/previews/nude2.png) \| ![suit-400](400/previews/suit.png) \| ![yukata-400](400/previews/yukata.png) \|
mHossain/bangla_idiom_generation_v2	mHossain	2023-09-28T12:11:45Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:mHossain/bangla-para-v3-500000", "base_model:finetune:mHossain/bangla-para-v3-500000", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-28T11:29:55Z	--- base_model: mHossain/bangla-para-v3-500000 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla_idiom_generation_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bangla_idiom_generation_v2 This model is a fine-tuned version of [mHossain/bangla-para-v3-500000](https://huggingface.co/mHossain/bangla-para-v3-500000) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.7655 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 10.4292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 226 \| 7.4650 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 11.1195 \| \| No log \| 2.0 \| 452 \| 7.1989 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.677 \| \| 7.7649 \| 3.0 \| 678 \| 6.8146 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.8938 \| \| 7.7649 \| 4.0 \| 904 \| 6.3650 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.3805 \| \| 7.0991 \| 5.0 \| 1130 \| 5.8781 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.2478 \| \| 7.0991 \| 6.0 \| 1356 \| 5.4460 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.0487 \| \| 6.1699 \| 7.0 \| 1582 \| 5.1824 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.3363 \| \| 6.1699 \| 8.0 \| 1808 \| 4.9930 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.2478 \| \| 5.5395 \| 9.0 \| 2034 \| 4.8596 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.5575 \| \| 5.5395 \| 10.0 \| 2260 \| 4.7655 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.4292 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
yudiwbs/absa_elektronik_aspek_v2	yudiwbs	2023-09-28T11:52:36Z	0	0	flair	[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]	token-classification	2023-09-24T13:59:16Z	--- tags: - flair - token-classification - sequence-tagger-model widget: - text: "Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus keyboard jp jg. Thanks gan." --- Klasifikasi aspek dalam review laptop ### Demo: How to use in Flair Requires: - [Flair](https://github.com/flairNLP/flair/) (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("yudiwbs/absa_elektronik_aspek_v2") sentence = Sentence('Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus jg. Thanks gan.') # predict model.predict(sentence) print(sentence.to_tagged_string()) # iterate over entities and print for entity in sentence.get_spans('aspek'): print(entity) ```
facebook/regnet-y-640-seer	facebook	2023-09-28T11:51:06Z	162	0	transformers	[ "transformers", "pytorch", "regnet", "image-feature-extraction", "vision", "arxiv:2202.08360", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-feature-extraction	2022-03-18T15:56:16Z	--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNetModel RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetModel >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1088, 7, 7] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
facebook/regnet-y-320-seer	facebook	2023-09-28T11:50:27Z	213	2	transformers	[ "transformers", "pytorch", "tf", "regnet", "image-feature-extraction", "vision", "arxiv:2202.08360", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-feature-extraction	2022-03-18T13:51:03Z	--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNetModel RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetModel >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1088, 7, 7] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).
hmxiong/ScanNet_Finetune_global_guidance_10epoch_bs64	hmxiong	2023-09-28T11:47:16Z	0	0	null	[ "region:us" ]	null	2023-09-28T02:29:07Z	每个场景使用的是全部的18个类别信息
hmxiong/ScanNet_Finetune_no_guidance_10epoch_bs64	hmxiong	2023-09-28T11:45:57Z	0	0	null	[ "region:us" ]	null	2023-09-28T02:30:25Z	训练阶段不加入任何的类别guidance
CyberHarem/saeki_sayaka_yagatekimininaru	CyberHarem	2023-09-28T11:36:35Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/saeki_sayaka_yagatekimininaru", "license:mit", "region:us" ]	text-to-image	2023-09-28T11:23:30Z	--- license: mit datasets: - CyberHarem/saeki_sayaka_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of saeki_sayaka_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/saeki_sayaka_yagatekimininaru.pt` as the embedding and `4760/saeki_sayaka_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.932. The trigger words are: 1. `saeki_sayaka_yagatekimininaru` 2. `long_hair, brown_hair, braid, green_eyes, bow` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.889 \| [Download](5100/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![pattern_8-5100](5100/previews/pattern_8.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.932 \| [Download](4760/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![pattern_8-4760](4760/previews/pattern_8.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.827 \| [Download](4420/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![pattern_8-4420](4420/previews/pattern_8.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.920 \| [Download](4080/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![pattern_8-4080](4080/previews/pattern_8.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.851 \| [Download](3740/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![pattern_8-3740](3740/previews/pattern_8.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.835 \| [Download](3400/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![pattern_8-3400](3400/previews/pattern_8.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.832 \| [Download](3060/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![pattern_8-3060](3060/previews/pattern_8.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.857 \| [Download](2720/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![pattern_8-2720](2720/previews/pattern_8.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.805 \| [Download](2380/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![pattern_8-2380](2380/previews/pattern_8.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.798 \| [Download](2040/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![pattern_8-2040](2040/previews/pattern_8.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.769 \| [Download](1700/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![pattern_8-1700](1700/previews/pattern_8.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.829 \| [Download](1360/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![pattern_8-1360](1360/previews/pattern_8.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.711 \| [Download](1020/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![pattern_6-1020](1020/previews/pattern_6.png) \| ![pattern_7-1020](1020/previews/pattern_7.png) \| ![pattern_8-1020](1020/previews/pattern_8.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.705 \| [Download](680/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![pattern_8-680](680/previews/pattern_8.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.580 \| [Download](340/saeki_sayaka_yagatekimininaru.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![pattern_8-340](340/previews/pattern_8.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Udayy26/my-pet-cat-xzg	Udayy26	2023-09-28T11:29:15Z	1	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-28T11:22:58Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-xzg Dreambooth model trained by Udayy26 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CBIT50 Sample pictures of this concept: ![0](https://huggingface.co/Udayy26/my-pet-cat-xzg/resolve/main/sample_images/xzg.jpg)
colab086/roberta-finetuned-subjqa-movies_2	colab086	2023-09-28T11:13:44Z	122	0	transformers	[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-09-28T10:57:30Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-finetuned-subjqa-movies_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
SamuelGalaxys/iTEMX-bloxerBETA	SamuelGalaxys	2023-09-28T11:12:03Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-09-28T10:15:08Z	--- license: mit --- # iTEMX-bloxerBETA ### 재고관리 앱 bloxer5의 초기 버전 [![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger) Bloxer는 재고관리 앱 하이브리드 소프트웨어로, 당신이 어느 OS를 사용하던 손쉽게 접근할 수 있습니다. ## Features 기능들 - HTML, csv, xlsx등 정형 데이터 테이블 삽입 지원 - JSON(반정형 데이터)지원 - 정형 데이터로 Excel 추출 - AI를 통해 대시보드에서 테이블의 전체적인 프로세스 확인 - 환율 계산 및 결과 반영 Markdown is a lightweight markup language based on the formatting conventions that people naturally use in email. As [John Gruber] writes on the [Markdown site][df1] > Bloxer는 소규모-중규모 사업장이 사용하기 좋습니다. > 원활한 입출고 관리, 여러 유형의 데이터를 적용하고 > 그리고 우리의 강력한 인공지능이 제공해주는 대시보드에서 > 데이터 테이블의 현재 상황을 한눈에 보세요. > 대한민국에 거주하는 자영업자 및 소상공인(월 ₩5,000,000 이하) > 이라면 15개월간 50% 할인된 가격에 사용할 수 있습니다. 만약 마음에 들지 않는다면 언제든지 취소하세요. 우리는 부담되지 않은 가격에 모두에게 제공할 것입니다. ## Tech Bloxer uses a number of open source projects to work properly: - [electron] - Hybrid for Web or Windows, MAC, Linux! - [BUN.js] - awesome backgrounder - [Next.js] - awesome backender - [BootstrapUI] - great UI boilerplate for modern web apps - [node.js] - Database with AWS or MongoDB - [Firebase] - Database built by Google Cloud - [Python] - Analyze table - [jQuery] - Convert JS code tracks renderer process - - [Hahaha](http://samuelgalaxys.github.io) - SamuelGalaxys And of course Dillinger itself is open source with a [public repository][dill] on GitHub. ## Installation iTEMX-Bloxer requires [Node.js](https://nodejs.org/) v10+ to run. Install the dependencies and devDependencies and start the server. ```sh npm install electron npm i node app ``` 셀레니움 설치 ```sh npm install --selenium NODE_ENV=production node app ```
SophiaPu/RLunit2	SophiaPu	2023-09-28T11:07:00Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T11:06:56Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RLunit2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="SophiaPu/RLunit2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
santiviquez/amazon-reviews-finetuning-distilbert-base-uncased	santiviquez	2023-09-28T11:04:26Z	133	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "base_model:nlptown/bert-base-multilingual-uncased-sentiment", "base_model:finetune:nlptown/bert-base-multilingual-uncased-sentiment", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-27T12:41:22Z	--- license: mit base_model: nlptown/bert-base-multilingual-uncased-sentiment tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: amazon-reviews-finetuning-distilbert-base-uncased results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: en split: validation args: en metrics: - name: Accuracy type: accuracy value: 0.7703180212014135 - name: F1 type: f1 value: 0.7271375381543915 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # amazon-reviews-finetuning-distilbert-base-uncased This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5859 - Accuracy: 0.7703 - F1: 0.7271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 188 \| 0.5587 \| 0.7756 \| 0.7297 \| \| No log \| 2.0 \| 376 \| 0.5859 \| 0.7703 \| 0.7271 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3
poisson-fish/Marcoroni-70B-v1-AWQ	poisson-fish	2023-09-28T10:59:20Z	14	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "lmdeploy", "awq", "marcoroni", "70b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-25T02:23:56Z	--- tags: - lmdeploy - awq - marcoroni - 70b --- This is [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) quantized to LMDeploy 4bit AWQ with the following config: ```bash python3 -m lmdeploy.lite.apis.auto_awq \ --model ./Marcoroni-70B-v1 \ --w_bits 4 \ --w_group_size 128 \ --work_dir ./quant ``` # Original Model Card: # Marcoroni-70B # Model Details * Trained by: trained by AIDC AI-Business. * Model type: Marcoroni-70B is an auto-regressive language model based on the Llama 2 transformer architecture. * Language(s): English * License for Marcoroni-70B base weights: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) # Prompting ## Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ```
SophiaPu/q-FrozenLake-v1-4x4-noSlippery	SophiaPu	2023-09-28T10:56:43Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-28T10:53:00Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="SophiaPu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
dss107/news5	dss107	2023-09-28T10:39:29Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-09-28T10:38:23Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/news5 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/news5") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

roa7n/gpt2-human_nontata_promoters-randomized_7_layers_0.0003_lr_2_e

roa7n

2023-09-28T22:06:22Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-28T22:06:19Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

pamelapaolacb/pruebaModeloTFM_Bert_es

pamelapaolacb

2023-09-28T21:45:53Z

123

0

transformers

[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad_es", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-09-28T19:10:19Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - squad_es model-index: - name: pruebaModeloTFM_Bert_es results: [] ---  # pruebaModeloTFM_Bert_es This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad_es dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 0 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

LarryAIDraw/maria_2

LarryAIDraw

2023-09-28T21:38:42Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-28T21:30:21Z

--- license: creativeml-openrail-m --- https://civitai.com/models/153259/maria-sarushima-yamada-kun-to-7-nin-no-majo

furkanbjk474/furkan

furkanbjk474

2023-09-28T21:37:10Z

0

1

espnet

[ "espnet", "image-to-image", "aa", "dataset:lmsys/lmsys-chat-1m", "dataset:fka/awesome-chatgpt-prompts", "dataset:uonlp/CulturaX", "license:apache-2.0", "region:us" ]

image-to-image

2023-09-28T21:34:49Z

--- license: apache-2.0 datasets: - lmsys/lmsys-chat-1m - fka/awesome-chatgpt-prompts - uonlp/CulturaX language: - aa metrics: - bleurt library_name: espnet pipeline_tag: image-to-image ---

LarryAIDraw/Aradia-10

LarryAIDraw

2023-09-28T21:35:35Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-28T21:30:42Z

--- license: creativeml-openrail-m --- https://civitai.com/models/153026/aradia-toaru-series

roa7n/gpt2-human_nontata_promoters-randomized_7_layers_0.003_lr_2_e

roa7n

2023-09-28T21:33:42Z

0

peft

[ "peft", "region:us" ]

null

2023-09-28T21:33:40Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

s3nh/Nexusflow-NexusRaven-13B-GGUF

s3nh

2023-09-28T21:29:45Z

0

1

transformers

[ "transformers", "gguf", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T21:07:16Z

--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/Nexusflow/NexusRaven-13B). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### Perplexity params Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16 7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066 13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543 ### inference TODO # Original model card

kanishka/smolm-autoreg-bpe-babylm-base-1e-3

kanishka

2023-09-28T21:19:28Z

174

0

transformers

[ "transformers", "pytorch", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T03:30:06Z

--- base_model: models/smolm-autoreg-bpe-babylm-base-1e-3/config.json tags: - generated_from_trainer metrics: - accuracy model-index: - name: smolm-autoreg-bpe-babylm-base-1e-3 results: [] ---  # smolm-autoreg-bpe-babylm-base-1e-3 This model is a fine-tuned version of [models/smolm-autoreg-bpe-babylm-base-1e-3/config.json](https://huggingface.co/models/smolm-autoreg-bpe-babylm-base-1e-3/config.json) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8078 - Accuracy: 0.4772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 3.2352 | 1.0 | 10468 | 3.1040 | 0.4394 | | 3.0865 | 2.0 | 20936 | 3.0063 | 0.4510 | | 3.0278 | 3.0 | 31404 | 2.9597 | 0.4567 | | 2.9932 | 4.0 | 41872 | 2.9350 | 0.4596 | | 2.9675 | 5.0 | 52340 | 2.9148 | 0.4622 | | 2.9492 | 6.0 | 62808 | 2.9016 | 0.4643 | | 2.9344 | 7.0 | 73276 | 2.8918 | 0.4652 | | 2.9213 | 8.0 | 83744 | 2.8834 | 0.4666 | | 2.912 | 9.0 | 94212 | 2.8747 | 0.4676 | | 2.9028 | 10.0 | 104680 | 2.8695 | 0.4684 | | 2.8964 | 11.0 | 115148 | 2.8627 | 0.4693 | | 2.8901 | 12.0 | 125616 | 2.8589 | 0.4698 | | 2.8839 | 13.0 | 136084 | 2.8543 | 0.4703 | | 2.8807 | 14.0 | 146552 | 2.8507 | 0.4709 | | 2.8738 | 15.0 | 157020 | 2.8475 | 0.4712 | | 2.8706 | 16.0 | 167488 | 2.8422 | 0.4720 | | 2.8645 | 17.0 | 177956 | 2.8448 | 0.4718 | | 2.8595 | 18.0 | 188424 | 2.8365 | 0.4728 | | 2.8542 | 19.0 | 198892 | 2.8366 | 0.4728 | | 2.8523 | 20.0 | 209360 | 2.8318 | 0.4731 | | 2.8479 | 21.0 | 219828 | 2.8316 | 0.4736 | | 2.849 | 22.0 | 230296 | 2.8298 | 0.4739 | | 2.8451 | 23.0 | 240764 | 2.8291 | 0.4739 | | 2.8436 | 24.0 | 251232 | 2.8244 | 0.4744 | | 2.8387 | 25.0 | 261700 | 2.8247 | 0.4745 | | 2.8354 | 26.0 | 272168 | 2.8228 | 0.4750 | | 2.8339 | 27.0 | 282636 | 2.8213 | 0.4751 | | 2.8307 | 28.0 | 293104 | 2.8206 | 0.4752 | | 2.8276 | 29.0 | 303572 | 2.8176 | 0.4756 | | 2.8244 | 30.0 | 314040 | 2.8165 | 0.4757 | | 2.8229 | 31.0 | 324508 | 2.8165 | 0.4757 | | 2.8209 | 32.0 | 334976 | 2.8141 | 0.4761 | | 2.8206 | 33.0 | 345444 | 2.8119 | 0.4763 | | 2.8178 | 34.0 | 355912 | 2.8135 | 0.4764 | | 2.8152 | 35.0 | 366380 | 2.8119 | 0.4766 | | 2.813 | 36.0 | 376848 | 2.8106 | 0.4767 | | 2.8107 | 37.0 | 387316 | 2.8081 | 0.4771 | | 2.8113 | 38.0 | 397784 | 2.8091 | 0.4769 | | 2.8073 | 39.0 | 408252 | 2.8078 | 0.4771 | | 2.8061 | 40.0 | 418720 | 2.8078 | 0.4772 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

badokorach/flan-t5-small-qa-10

badokorach

2023-09-28T21:01:39Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T18:42:47Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-10 results: [] ---  # flan-t5-small-qa-10 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0680 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 37.0091 | 0.01 | 10 | 39.2389 | | 34.7317 | 0.02 | 20 | 36.6257 | | 32.6533 | 0.03 | 30 | 34.1553 | | 29.7792 | 0.04 | 40 | 31.6802 | | 27.9268 | 0.05 | 50 | 29.1925 | | 25.8475 | 0.06 | 60 | 26.7287 | | 23.1826 | 0.07 | 70 | 24.1478 | | 20.9714 | 0.08 | 80 | 21.3731 | | 18.5839 | 0.09 | 90 | 18.4583 | | 16.4034 | 0.1 | 100 | 15.4845 | | 13.2378 | 0.11 | 110 | 12.3754 | | 11.1189 | 0.12 | 120 | 9.2924 | | 8.8691 | 0.13 | 130 | 6.9111 | | 7.3894 | 0.14 | 140 | 5.8619 | | 6.5278 | 0.15 | 150 | 5.3589 | | 5.8773 | 0.16 | 160 | 5.0272 | | 5.6232 | 0.17 | 170 | 4.7799 | | 5.1812 | 0.18 | 180 | 4.5834 | | 4.8379 | 0.19 | 190 | 4.4145 | | 4.6718 | 0.2 | 200 | 4.2705 | | 4.5679 | 0.21 | 210 | 4.1402 | | 4.3725 | 0.22 | 220 | 4.0193 | | 4.2319 | 0.23 | 230 | 3.8986 | | 4.0162 | 0.24 | 240 | 3.7799 | | 3.9175 | 0.25 | 250 | 3.6602 | | 3.8803 | 0.26 | 260 | 3.5376 | | 3.7403 | 0.27 | 270 | 3.4149 | | 3.575 | 0.28 | 280 | 3.2841 | | 3.5047 | 0.29 | 290 | 3.1522 | | 3.3545 | 0.3 | 300 | 3.0169 | | 3.2233 | 0.31 | 310 | 2.8774 | | 3.1314 | 0.32 | 320 | 2.7361 | | 2.9994 | 0.33 | 330 | 2.5934 | | 2.901 | 0.34 | 340 | 2.4511 | | 2.6969 | 0.35 | 350 | 2.3144 | | 2.643 | 0.36 | 360 | 2.1838 | | 2.5416 | 0.37 | 370 | 2.0683 | | 2.3707 | 0.38 | 380 | 1.9536 | | 2.3231 | 0.39 | 390 | 1.8523 | | 2.2729 | 0.4 | 400 | 1.7420 | | 2.0765 | 0.41 | 410 | 1.6323 | | 2.0129 | 0.42 | 420 | 1.5289 | | 1.8826 | 0.43 | 430 | 1.4344 | | 1.878 | 0.44 | 440 | 1.3528 | | 1.7105 | 0.45 | 450 | 1.2652 | | 1.6877 | 0.46 | 460 | 1.1842 | | 1.5966 | 0.47 | 470 | 1.1044 | | 1.509 | 0.48 | 480 | 1.0282 | | 1.4869 | 0.49 | 490 | 0.9637 | | 1.3615 | 0.5 | 500 | 0.9079 | | 1.3525 | 0.51 | 510 | 0.8398 | | 1.2226 | 0.52 | 520 | 0.7795 | | 1.1191 | 0.53 | 530 | 0.7361 | | 1.0896 | 0.54 | 540 | 0.6988 | | 1.0617 | 0.55 | 550 | 0.6521 | | 0.9407 | 0.56 | 560 | 0.6081 | | 0.9509 | 0.57 | 570 | 0.5644 | | 0.941 | 0.58 | 580 | 0.5292 | | 0.8058 | 0.59 | 590 | 0.4883 | | 0.856 | 0.6 | 600 | 0.4508 | | 0.7525 | 0.61 | 610 | 0.4194 | | 0.684 | 0.62 | 620 | 0.3900 | | 0.644 | 0.63 | 630 | 0.3664 | | 0.6718 | 0.64 | 640 | 0.3421 | | 0.6279 | 0.65 | 650 | 0.3193 | | 0.561 | 0.66 | 660 | 0.2956 | | 0.577 | 0.67 | 670 | 0.2754 | | 0.5564 | 0.68 | 680 | 0.2587 | | 0.5221 | 0.69 | 690 | 0.2438 | | 0.4814 | 0.7 | 700 | 0.2297 | | 0.4113 | 0.71 | 710 | 0.2191 | | 0.4718 | 0.72 | 720 | 0.2070 | | 0.3819 | 0.73 | 730 | 0.1986 | | 0.3924 | 0.74 | 740 | 0.1891 | | 0.4157 | 0.75 | 750 | 0.1777 | | 0.3915 | 0.76 | 760 | 0.1678 | | 0.3523 | 0.77 | 770 | 0.1598 | | 0.4251 | 0.78 | 780 | 0.1532 | | 0.2944 | 0.79 | 790 | 0.1472 | | 0.3066 | 0.8 | 800 | 0.1396 | | 0.3363 | 0.81 | 810 | 0.1340 | | 0.2954 | 0.82 | 820 | 0.1283 | | 0.2756 | 0.83 | 830 | 0.1220 | | 0.2898 | 0.84 | 840 | 0.1164 | | 0.2862 | 0.85 | 850 | 0.1103 | | 0.264 | 0.86 | 860 | 0.1056 | | 0.3028 | 0.87 | 870 | 0.1004 | | 0.3009 | 0.88 | 880 | 0.0937 | | 0.2909 | 0.89 | 890 | 0.0881 | | 0.2922 | 0.9 | 900 | 0.0826 | | 0.2493 | 0.91 | 910 | 0.0773 | | 0.2524 | 0.92 | 920 | 0.0749 | | 0.1929 | 0.93 | 930 | 0.0727 | | 0.2461 | 0.94 | 940 | 0.0679 | | 0.2471 | 0.95 | 950 | 0.0642 | | 0.1956 | 0.96 | 960 | 0.0628 | | 0.1839 | 0.97 | 970 | 0.0617 | | 0.2151 | 0.98 | 980 | 0.0599 | | 0.2182 | 0.99 | 990 | 0.0580 | | 0.2473 | 1.0 | 1000 | 0.0567 | | 0.2249 | 1.01 | 1010 | 0.0562 | | 0.2807 | 1.02 | 1020 | 0.0550 | | 0.2066 | 1.03 | 1030 | 0.0543 | | 0.168 | 1.04 | 1040 | 0.0550 | | 0.2199 | 1.05 | 1050 | 0.0550 | | 0.2135 | 1.06 | 1060 | 0.0543 | | 0.2316 | 1.07 | 1070 | 0.0535 | | 0.196 | 1.08 | 1080 | 0.0535 | | 0.163 | 1.09 | 1090 | 0.0532 | | 0.115 | 1.1 | 1100 | 0.0533 | | 0.1758 | 1.11 | 1110 | 0.0532 | | 0.1862 | 1.12 | 1120 | 0.0537 | | 0.2976 | 1.13 | 1130 | 0.0535 | | 0.1683 | 1.14 | 1140 | 0.0531 | | 0.1836 | 1.15 | 1150 | 0.0533 | | 0.1932 | 1.16 | 1160 | 0.0536 | | 0.2083 | 1.17 | 1170 | 0.0524 | | 0.1482 | 1.18 | 1180 | 0.0524 | | 0.1869 | 1.19 | 1190 | 0.0523 | | 0.2302 | 1.2 | 1200 | 0.0522 | | 0.1334 | 1.21 | 1210 | 0.0522 | | 0.172 | 1.22 | 1220 | 0.0519 | | 0.1492 | 1.23 | 1230 | 0.0515 | | 0.18 | 1.24 | 1240 | 0.0513 | | 0.2685 | 1.25 | 1250 | 0.0517 | | 0.1621 | 1.26 | 1260 | 0.0523 | | 0.172 | 1.27 | 1270 | 0.0519 | | 0.2655 | 1.28 | 1280 | 0.0516 | | 0.1625 | 1.29 | 1290 | 0.0519 | | 0.251 | 1.3 | 1300 | 0.0525 | | 0.1506 | 1.31 | 1310 | 0.0528 | | 0.206 | 1.32 | 1320 | 0.0530 | | 0.186 | 1.33 | 1330 | 0.0526 | | 0.1778 | 1.34 | 1340 | 0.0528 | | 0.167 | 1.35 | 1350 | 0.0528 | | 0.1602 | 1.36 | 1360 | 0.0528 | | 0.2509 | 1.37 | 1370 | 0.0523 | | 0.1615 | 1.38 | 1380 | 0.0522 | | 0.1988 | 1.39 | 1390 | 0.0523 | | 0.1602 | 1.4 | 1400 | 0.0525 | | 0.1637 | 1.41 | 1410 | 0.0524 | | 0.1848 | 1.42 | 1420 | 0.0523 | | 0.1662 | 1.43 | 1430 | 0.0525 | | 0.1603 | 1.44 | 1440 | 0.0525 | | 0.1723 | 1.45 | 1450 | 0.0524 | | 0.15 | 1.46 | 1460 | 0.0522 | | 0.1855 | 1.47 | 1470 | 0.0519 | | 0.1828 | 1.48 | 1480 | 0.0523 | | 0.1975 | 1.49 | 1490 | 0.0523 | | 0.2105 | 1.5 | 1500 | 0.0525 | | 0.1808 | 1.51 | 1510 | 0.0530 | | 0.1714 | 1.52 | 1520 | 0.0529 | | 0.1481 | 1.53 | 1530 | 0.0533 | | 0.1579 | 1.54 | 1540 | 0.0539 | | 0.194 | 1.55 | 1550 | 0.0541 | | 0.1521 | 1.56 | 1560 | 0.0540 | | 0.2194 | 1.57 | 1570 | 0.0538 | | 0.1487 | 1.58 | 1580 | 0.0536 | | 0.1511 | 1.59 | 1590 | 0.0536 | | 0.1958 | 1.6 | 1600 | 0.0537 | | 0.179 | 1.61 | 1610 | 0.0536 | | 0.2006 | 1.62 | 1620 | 0.0535 | | 0.1483 | 1.63 | 1630 | 0.0537 | | 0.1321 | 1.64 | 1640 | 0.0536 | | 0.1875 | 1.65 | 1650 | 0.0534 | | 0.1777 | 1.66 | 1660 | 0.0535 | | 0.1289 | 1.67 | 1670 | 0.0534 | | 0.1534 | 1.68 | 1680 | 0.0536 | | 0.1942 | 1.69 | 1690 | 0.0535 | | 0.1434 | 1.7 | 1700 | 0.0539 | | 0.1316 | 1.71 | 1710 | 0.0543 | | 0.1205 | 1.72 | 1720 | 0.0542 | | 0.1746 | 1.73 | 1730 | 0.0538 | | 0.1505 | 1.74 | 1740 | 0.0537 | | 0.1811 | 1.75 | 1750 | 0.0541 | | 0.1292 | 1.76 | 1760 | 0.0542 | | 0.1545 | 1.77 | 1770 | 0.0541 | | 0.192 | 1.78 | 1780 | 0.0540 | | 0.1223 | 1.79 | 1790 | 0.0543 | | 0.2161 | 1.8 | 1800 | 0.0546 | | 0.1408 | 1.81 | 1810 | 0.0550 | | 0.1408 | 1.82 | 1820 | 0.0554 | | 0.1239 | 1.83 | 1830 | 0.0555 | | 0.2161 | 1.84 | 1840 | 0.0555 | | 0.1122 | 1.85 | 1850 | 0.0554 | | 0.1465 | 1.86 | 1860 | 0.0549 | | 0.1385 | 1.87 | 1870 | 0.0548 | | 0.1651 | 1.88 | 1880 | 0.0550 | | 0.1716 | 1.89 | 1890 | 0.0552 | | 0.1317 | 1.9 | 1900 | 0.0550 | | 0.1228 | 1.91 | 1910 | 0.0548 | | 0.1764 | 1.92 | 1920 | 0.0553 | | 0.1694 | 1.93 | 1930 | 0.0555 | | 0.1607 | 1.94 | 1940 | 0.0555 | | 0.1989 | 1.95 | 1950 | 0.0547 | | 0.1866 | 1.96 | 1960 | 0.0545 | | 0.1006 | 1.97 | 1970 | 0.0545 | | 0.1769 | 1.98 | 1980 | 0.0547 | | 0.1266 | 1.99 | 1990 | 0.0550 | | 0.1972 | 2.0 | 2000 | 0.0547 | | 0.1404 | 2.01 | 2010 | 0.0542 | | 0.2077 | 2.02 | 2020 | 0.0543 | | 0.1607 | 2.03 | 2030 | 0.0545 | | 0.157 | 2.04 | 2040 | 0.0546 | | 0.1171 | 2.05 | 2050 | 0.0549 | | 0.1419 | 2.06 | 2060 | 0.0554 | | 0.2234 | 2.07 | 2070 | 0.0554 | | 0.2123 | 2.08 | 2080 | 0.0548 | | 0.1856 | 2.09 | 2090 | 0.0544 | | 0.1061 | 2.1 | 2100 | 0.0537 | | 0.2295 | 2.11 | 2110 | 0.0537 | | 0.203 | 2.12 | 2120 | 0.0543 | | 0.1858 | 2.13 | 2130 | 0.0543 | | 0.1617 | 2.14 | 2140 | 0.0546 | | 0.161 | 2.15 | 2150 | 0.0550 | | 0.1707 | 2.16 | 2160 | 0.0553 | | 0.2021 | 2.17 | 2170 | 0.0556 | | 0.1121 | 2.18 | 2180 | 0.0555 | | 0.1608 | 2.19 | 2190 | 0.0557 | | 0.1782 | 2.2 | 2200 | 0.0561 | | 0.1261 | 2.21 | 2210 | 0.0563 | | 0.1748 | 2.22 | 2220 | 0.0563 | | 0.254 | 2.23 | 2230 | 0.0560 | | 0.1894 | 2.24 | 2240 | 0.0560 | | 0.1825 | 2.25 | 2250 | 0.0559 | | 0.1078 | 2.26 | 2260 | 0.0555 | | 0.1718 | 2.27 | 2270 | 0.0556 | | 0.1274 | 2.28 | 2280 | 0.0556 | | 0.097 | 2.29 | 2290 | 0.0557 | | 0.1887 | 2.3 | 2300 | 0.0557 | | 0.1246 | 2.31 | 2310 | 0.0552 | | 0.2024 | 2.32 | 2320 | 0.0547 | | 0.1878 | 2.33 | 2330 | 0.0542 | | 0.1661 | 2.34 | 2340 | 0.0546 | | 0.142 | 2.35 | 2350 | 0.0546 | | 0.1046 | 2.36 | 2360 | 0.0546 | | 0.239 | 2.37 | 2370 | 0.0546 | | 0.1454 | 2.38 | 2380 | 0.0547 | | 0.144 | 2.39 | 2390 | 0.0549 | | 0.1983 | 2.4 | 2400 | 0.0548 | | 0.1583 | 2.41 | 2410 | 0.0549 | | 0.1345 | 2.42 | 2420 | 0.0547 | | 0.1838 | 2.43 | 2430 | 0.0553 | | 0.1441 | 2.44 | 2440 | 0.0557 | | 0.142 | 2.45 | 2450 | 0.0556 | | 0.1743 | 2.46 | 2460 | 0.0556 | | 0.152 | 2.47 | 2470 | 0.0557 | | 0.1221 | 2.48 | 2480 | 0.0556 | | 0.1611 | 2.49 | 2490 | 0.0555 | | 0.128 | 2.5 | 2500 | 0.0554 | | 0.1872 | 2.51 | 2510 | 0.0554 | | 0.1425 | 2.52 | 2520 | 0.0557 | | 0.1585 | 2.53 | 2530 | 0.0561 | | 0.1257 | 2.54 | 2540 | 0.0563 | | 0.1805 | 2.55 | 2550 | 0.0560 | | 0.1431 | 2.56 | 2560 | 0.0557 | | 0.1569 | 2.57 | 2570 | 0.0558 | | 0.1703 | 2.58 | 2580 | 0.0558 | | 0.1319 | 2.59 | 2590 | 0.0555 | | 0.1608 | 2.6 | 2600 | 0.0559 | | 0.1553 | 2.61 | 2610 | 0.0560 | | 0.1855 | 2.62 | 2620 | 0.0562 | | 0.1856 | 2.63 | 2630 | 0.0562 | | 0.1715 | 2.64 | 2640 | 0.0561 | | 0.1122 | 2.65 | 2650 | 0.0562 | | 0.1671 | 2.66 | 2660 | 0.0564 | | 0.1613 | 2.67 | 2670 | 0.0567 | | 0.1648 | 2.68 | 2680 | 0.0570 | | 0.15 | 2.69 | 2690 | 0.0572 | | 0.1499 | 2.7 | 2700 | 0.0571 | | 0.1598 | 2.71 | 2710 | 0.0572 | | 0.1804 | 2.72 | 2720 | 0.0572 | | 0.1721 | 2.73 | 2730 | 0.0570 | | 0.131 | 2.74 | 2740 | 0.0571 | | 0.2229 | 2.75 | 2750 | 0.0570 | | 0.1337 | 2.76 | 2760 | 0.0573 | | 0.1472 | 2.77 | 2770 | 0.0575 | | 0.1202 | 2.78 | 2780 | 0.0578 | | 0.1417 | 2.79 | 2790 | 0.0582 | | 0.123 | 2.8 | 2800 | 0.0584 | | 0.2054 | 2.81 | 2810 | 0.0581 | | 0.1761 | 2.82 | 2820 | 0.0573 | | 0.1034 | 2.83 | 2830 | 0.0567 | | 0.1416 | 2.84 | 2840 | 0.0564 | | 0.143 | 2.85 | 2850 | 0.0565 | | 0.1295 | 2.86 | 2860 | 0.0567 | | 0.1201 | 2.87 | 2870 | 0.0565 | | 0.1168 | 2.88 | 2880 | 0.0563 | | 0.1236 | 2.89 | 2890 | 0.0559 | | 0.1664 | 2.9 | 2900 | 0.0559 | | 0.1555 | 2.91 | 2910 | 0.0562 | | 0.1657 | 2.92 | 2920 | 0.0565 | | 0.1213 | 2.93 | 2930 | 0.0568 | | 0.1557 | 2.94 | 2940 | 0.0569 | | 0.1795 | 2.95 | 2950 | 0.0570 | | 0.1655 | 2.96 | 2960 | 0.0571 | | 0.2015 | 2.97 | 2970 | 0.0571 | | 0.1956 | 2.98 | 2980 | 0.0572 | | 0.1456 | 2.99 | 2990 | 0.0575 | | 0.1298 | 3.0 | 3000 | 0.0574 | | 0.1589 | 3.01 | 3010 | 0.0574 | | 0.1367 | 3.02 | 3020 | 0.0573 | | 0.1321 | 3.03 | 3030 | 0.0573 | | 0.1451 | 3.04 | 3040 | 0.0572 | | 0.174 | 3.05 | 3050 | 0.0574 | | 0.1547 | 3.06 | 3060 | 0.0577 | | 0.1229 | 3.07 | 3070 | 0.0578 | | 0.1207 | 3.08 | 3080 | 0.0584 | | 0.1308 | 3.09 | 3090 | 0.0588 | | 0.1882 | 3.1 | 3100 | 0.0588 | | 0.1647 | 3.11 | 3110 | 0.0586 | | 0.1121 | 3.12 | 3120 | 0.0587 | | 0.1656 | 3.13 | 3130 | 0.0586 | | 0.1671 | 3.14 | 3140 | 0.0588 | | 0.1399 | 3.15 | 3150 | 0.0591 | | 0.1504 | 3.16 | 3160 | 0.0594 | | 0.2393 | 3.17 | 3170 | 0.0587 | | 0.1273 | 3.18 | 3180 | 0.0583 | | 0.1365 | 3.19 | 3190 | 0.0579 | | 0.1752 | 3.2 | 3200 | 0.0582 | | 0.1526 | 3.21 | 3210 | 0.0585 | | 0.1219 | 3.22 | 3220 | 0.0582 | | 0.1416 | 3.23 | 3230 | 0.0581 | | 0.1172 | 3.24 | 3240 | 0.0577 | | 0.1205 | 3.25 | 3250 | 0.0579 | | 0.1554 | 3.26 | 3260 | 0.0582 | | 0.1442 | 3.27 | 3270 | 0.0583 | | 0.195 | 3.28 | 3280 | 0.0581 | | 0.1981 | 3.29 | 3290 | 0.0577 | | 0.2147 | 3.3 | 3300 | 0.0576 | | 0.1204 | 3.31 | 3310 | 0.0578 | | 0.1628 | 3.32 | 3320 | 0.0581 | | 0.3038 | 3.33 | 3330 | 0.0583 | | 0.1759 | 3.34 | 3340 | 0.0584 | | 0.1454 | 3.35 | 3350 | 0.0585 | | 0.1269 | 3.36 | 3360 | 0.0587 | | 0.1485 | 3.37 | 3370 | 0.0591 | | 0.1197 | 3.38 | 3380 | 0.0594 | | 0.1352 | 3.39 | 3390 | 0.0597 | | 0.1679 | 3.4 | 3400 | 0.0599 | | 0.1607 | 3.41 | 3410 | 0.0601 | | 0.1519 | 3.42 | 3420 | 0.0603 | | 0.1227 | 3.43 | 3430 | 0.0605 | | 0.2099 | 3.44 | 3440 | 0.0606 | | 0.1611 | 3.45 | 3450 | 0.0605 | | 0.2014 | 3.46 | 3460 | 0.0604 | | 0.1074 | 3.47 | 3470 | 0.0601 | | 0.195 | 3.48 | 3480 | 0.0600 | | 0.1559 | 3.49 | 3490 | 0.0593 | | 0.1636 | 3.5 | 3500 | 0.0588 | | 0.1503 | 3.51 | 3510 | 0.0588 | | 0.1486 | 3.52 | 3520 | 0.0585 | | 0.1763 | 3.53 | 3530 | 0.0582 | | 0.1855 | 3.54 | 3540 | 0.0582 | | 0.1641 | 3.55 | 3550 | 0.0585 | | 0.1398 | 3.56 | 3560 | 0.0586 | | 0.1719 | 3.57 | 3570 | 0.0588 | | 0.109 | 3.58 | 3580 | 0.0589 | | 0.1498 | 3.59 | 3590 | 0.0589 | | 0.1012 | 3.6 | 3600 | 0.0589 | | 0.1175 | 3.61 | 3610 | 0.0591 | | 0.1434 | 3.62 | 3620 | 0.0594 | | 0.1609 | 3.63 | 3630 | 0.0596 | | 0.1448 | 3.64 | 3640 | 0.0599 | | 0.2215 | 3.65 | 3650 | 0.0599 | | 0.1529 | 3.66 | 3660 | 0.0601 | | 0.1343 | 3.67 | 3670 | 0.0603 | | 0.1914 | 3.68 | 3680 | 0.0603 | | 0.1089 | 3.69 | 3690 | 0.0602 | | 0.156 | 3.7 | 3700 | 0.0603 | | 0.1274 | 3.71 | 3710 | 0.0606 | | 0.1146 | 3.72 | 3720 | 0.0609 | | 0.1378 | 3.73 | 3730 | 0.0610 | | 0.101 | 3.74 | 3740 | 0.0610 | | 0.1502 | 3.75 | 3750 | 0.0610 | | 0.1323 | 3.76 | 3760 | 0.0610 | | 0.2017 | 3.77 | 3770 | 0.0611 | | 0.1167 | 3.78 | 3780 | 0.0610 | | 0.2027 | 3.79 | 3790 | 0.0611 | | 0.1739 | 3.8 | 3800 | 0.0612 | | 0.1269 | 3.81 | 3810 | 0.0610 | | 0.1642 | 3.82 | 3820 | 0.0606 | | 0.141 | 3.83 | 3830 | 0.0602 | | 0.1567 | 3.84 | 3840 | 0.0598 | | 0.0849 | 3.85 | 3850 | 0.0598 | | 0.1388 | 3.86 | 3860 | 0.0601 | | 0.2029 | 3.87 | 3870 | 0.0602 | | 0.1471 | 3.88 | 3880 | 0.0600 | | 0.1307 | 3.89 | 3890 | 0.0597 | | 0.1363 | 3.9 | 3900 | 0.0596 | | 0.1566 | 3.91 | 3910 | 0.0596 | | 0.1936 | 3.92 | 3920 | 0.0594 | | 0.1063 | 3.93 | 3930 | 0.0592 | | 0.1351 | 3.94 | 3940 | 0.0592 | | 0.1886 | 3.95 | 3950 | 0.0593 | | 0.1387 | 3.96 | 3960 | 0.0597 | | 0.1532 | 3.97 | 3970 | 0.0599 | | 0.0992 | 3.98 | 3980 | 0.0599 | | 0.1444 | 3.99 | 3990 | 0.0598 | | 0.1382 | 4.0 | 4000 | 0.0599 | | 0.1647 | 4.01 | 4010 | 0.0600 | | 0.1551 | 4.02 | 4020 | 0.0604 | | 0.1777 | 4.03 | 4030 | 0.0606 | | 0.1395 | 4.04 | 4040 | 0.0608 | | 0.1312 | 4.05 | 4050 | 0.0609 | | 0.143 | 4.06 | 4060 | 0.0610 | | 0.106 | 4.07 | 4070 | 0.0608 | | 0.168 | 4.08 | 4080 | 0.0607 | | 0.0988 | 4.09 | 4090 | 0.0604 | | 0.1718 | 4.1 | 4100 | 0.0602 | | 0.1607 | 4.11 | 4110 | 0.0602 | | 0.1428 | 4.12 | 4120 | 0.0601 | | 0.1518 | 4.13 | 4130 | 0.0599 | | 0.1941 | 4.14 | 4140 | 0.0600 | | 0.1339 | 4.15 | 4150 | 0.0599 | | 0.1379 | 4.16 | 4160 | 0.0599 | | 0.218 | 4.17 | 4170 | 0.0600 | | 0.1359 | 4.18 | 4180 | 0.0602 | | 0.1941 | 4.19 | 4190 | 0.0602 | | 0.1182 | 4.2 | 4200 | 0.0602 | | 0.1398 | 4.21 | 4210 | 0.0602 | | 0.1385 | 4.22 | 4220 | 0.0601 | | 0.1652 | 4.23 | 4230 | 0.0599 | | 0.0985 | 4.24 | 4240 | 0.0599 | | 0.1342 | 4.25 | 4250 | 0.0602 | | 0.1767 | 4.26 | 4260 | 0.0606 | | 0.1621 | 4.27 | 4270 | 0.0609 | | 0.1813 | 4.28 | 4280 | 0.0612 | | 0.14 | 4.29 | 4290 | 0.0613 | | 0.1726 | 4.3 | 4300 | 0.0615 | | 0.1031 | 4.31 | 4310 | 0.0617 | | 0.1429 | 4.32 | 4320 | 0.0617 | | 0.1956 | 4.33 | 4330 | 0.0615 | | 0.1515 | 4.34 | 4340 | 0.0613 | | 0.1109 | 4.35 | 4350 | 0.0614 | | 0.1102 | 4.36 | 4360 | 0.0617 | | 0.2273 | 4.37 | 4370 | 0.0617 | | 0.1179 | 4.38 | 4380 | 0.0617 | | 0.1186 | 4.39 | 4390 | 0.0617 | | 0.1259 | 4.4 | 4400 | 0.0611 | | 0.1914 | 4.41 | 4410 | 0.0610 | | 0.1552 | 4.42 | 4420 | 0.0612 | | 0.1703 | 4.43 | 4430 | 0.0614 | | 0.1549 | 4.44 | 4440 | 0.0612 | | 0.1307 | 4.45 | 4450 | 0.0611 | | 0.1853 | 4.46 | 4460 | 0.0609 | | 0.1055 | 4.47 | 4470 | 0.0606 | | 0.1342 | 4.48 | 4480 | 0.0606 | | 0.0804 | 4.49 | 4490 | 0.0609 | | 0.1566 | 4.5 | 4500 | 0.0610 | | 0.1472 | 4.51 | 4510 | 0.0613 | | 0.2179 | 4.52 | 4520 | 0.0613 | | 0.1365 | 4.53 | 4530 | 0.0614 | | 0.157 | 4.54 | 4540 | 0.0610 | | 0.1515 | 4.55 | 4550 | 0.0609 | | 0.1678 | 4.56 | 4560 | 0.0611 | | 0.1569 | 4.57 | 4570 | 0.0613 | | 0.1257 | 4.58 | 4580 | 0.0614 | | 0.1452 | 4.59 | 4590 | 0.0612 | | 0.1275 | 4.6 | 4600 | 0.0612 | | 0.1446 | 4.61 | 4610 | 0.0613 | | 0.1571 | 4.62 | 4620 | 0.0613 | | 0.1371 | 4.63 | 4630 | 0.0612 | | 0.1152 | 4.64 | 4640 | 0.0612 | | 0.1797 | 4.65 | 4650 | 0.0613 | | 0.0911 | 4.66 | 4660 | 0.0613 | | 0.1463 | 4.67 | 4670 | 0.0612 | | 0.1428 | 4.68 | 4680 | 0.0610 | | 0.1489 | 4.69 | 4690 | 0.0612 | | 0.1344 | 4.7 | 4700 | 0.0615 | | 0.1493 | 4.71 | 4710 | 0.0615 | | 0.147 | 4.72 | 4720 | 0.0613 | | 0.2329 | 4.73 | 4730 | 0.0606 | | 0.1679 | 4.74 | 4740 | 0.0602 | | 0.0977 | 4.75 | 4750 | 0.0602 | | 0.1292 | 4.76 | 4760 | 0.0603 | | 0.1301 | 4.77 | 4770 | 0.0606 | | 0.1855 | 4.78 | 4780 | 0.0609 | | 0.1647 | 4.79 | 4790 | 0.0607 | | 0.1395 | 4.8 | 4800 | 0.0604 | | 0.1692 | 4.81 | 4810 | 0.0604 | | 0.1135 | 4.82 | 4820 | 0.0604 | | 0.1075 | 4.83 | 4830 | 0.0605 | | 0.188 | 4.84 | 4840 | 0.0604 | | 0.168 | 4.85 | 4850 | 0.0605 | | 0.1356 | 4.86 | 4860 | 0.0603 | | 0.134 | 4.87 | 4870 | 0.0603 | | 0.1245 | 4.88 | 4880 | 0.0604 | | 0.1816 | 4.89 | 4890 | 0.0605 | | 0.1144 | 4.9 | 4900 | 0.0607 | | 0.1651 | 4.91 | 4910 | 0.0610 | | 0.1725 | 4.92 | 4920 | 0.0610 | | 0.0947 | 4.93 | 4930 | 0.0612 | | 0.118 | 4.94 | 4940 | 0.0615 | | 0.1341 | 4.95 | 4950 | 0.0615 | | 0.1324 | 4.96 | 4960 | 0.0618 | | 0.1321 | 4.97 | 4970 | 0.0622 | | 0.1485 | 4.98 | 4980 | 0.0624 | | 0.1445 | 4.99 | 4990 | 0.0626 | | 0.1793 | 5.0 | 5000 | 0.0627 | | 0.1374 | 5.01 | 5010 | 0.0623 | | 0.1726 | 5.02 | 5020 | 0.0619 | | 0.178 | 5.03 | 5030 | 0.0618 | | 0.1814 | 5.04 | 5040 | 0.0623 | | 0.1533 | 5.05 | 5050 | 0.0625 | | 0.1618 | 5.06 | 5060 | 0.0627 | | 0.1502 | 5.07 | 5070 | 0.0625 | | 0.1228 | 5.08 | 5080 | 0.0622 | | 0.1544 | 5.09 | 5090 | 0.0621 | | 0.1253 | 5.1 | 5100 | 0.0620 | | 0.2073 | 5.11 | 5110 | 0.0619 | | 0.13 | 5.12 | 5120 | 0.0620 | | 0.1163 | 5.13 | 5130 | 0.0623 | | 0.1154 | 5.14 | 5140 | 0.0628 | | 0.1249 | 5.15 | 5150 | 0.0631 | | 0.1783 | 5.16 | 5160 | 0.0633 | | 0.1536 | 5.17 | 5170 | 0.0633 | | 0.1679 | 5.18 | 5180 | 0.0631 | | 0.1164 | 5.19 | 5190 | 0.0629 | | 0.1213 | 5.2 | 5200 | 0.0628 | | 0.1076 | 5.21 | 5210 | 0.0626 | | 0.1332 | 5.22 | 5220 | 0.0625 | | 0.1406 | 5.23 | 5230 | 0.0626 | | 0.096 | 5.24 | 5240 | 0.0628 | | 0.1556 | 5.25 | 5250 | 0.0631 | | 0.1363 | 5.26 | 5260 | 0.0629 | | 0.1223 | 5.27 | 5270 | 0.0628 | | 0.226 | 5.28 | 5280 | 0.0630 | | 0.1957 | 5.29 | 5290 | 0.0634 | | 0.1303 | 5.3 | 5300 | 0.0634 | | 0.1123 | 5.31 | 5310 | 0.0633 | | 0.1753 | 5.32 | 5320 | 0.0634 | | 0.1642 | 5.33 | 5330 | 0.0634 | | 0.1412 | 5.34 | 5340 | 0.0633 | | 0.1732 | 5.35 | 5350 | 0.0631 | | 0.134 | 5.36 | 5360 | 0.0629 | | 0.1596 | 5.37 | 5370 | 0.0627 | | 0.1049 | 5.38 | 5380 | 0.0622 | | 0.1108 | 5.39 | 5390 | 0.0620 | | 0.1326 | 5.4 | 5400 | 0.0622 | | 0.1676 | 5.41 | 5410 | 0.0622 | | 0.1327 | 5.42 | 5420 | 0.0621 | | 0.0943 | 5.43 | 5430 | 0.0620 | | 0.0914 | 5.44 | 5440 | 0.0620 | | 0.1201 | 5.45 | 5450 | 0.0620 | | 0.1441 | 5.46 | 5460 | 0.0619 | | 0.149 | 5.47 | 5470 | 0.0621 | | 0.0949 | 5.48 | 5480 | 0.0622 | | 0.1606 | 5.49 | 5490 | 0.0623 | | 0.1151 | 5.5 | 5500 | 0.0626 | | 0.1613 | 5.51 | 5510 | 0.0627 | | 0.189 | 5.52 | 5520 | 0.0629 | | 0.1084 | 5.53 | 5530 | 0.0631 | | 0.1285 | 5.54 | 5540 | 0.0632 | | 0.1509 | 5.55 | 5550 | 0.0633 | | 0.1201 | 5.56 | 5560 | 0.0637 | | 0.148 | 5.57 | 5570 | 0.0636 | | 0.148 | 5.58 | 5580 | 0.0634 | | 0.1019 | 5.59 | 5590 | 0.0634 | | 0.1447 | 5.6 | 5600 | 0.0634 | | 0.1521 | 5.61 | 5610 | 0.0636 | | 0.19 | 5.62 | 5620 | 0.0635 | | 0.1164 | 5.63 | 5630 | 0.0633 | | 0.1488 | 5.64 | 5640 | 0.0633 | | 0.1114 | 5.65 | 5650 | 0.0631 | | 0.1373 | 5.66 | 5660 | 0.0626 | | 0.0925 | 5.67 | 5670 | 0.0624 | | 0.1138 | 5.68 | 5680 | 0.0621 | | 0.1219 | 5.69 | 5690 | 0.0620 | | 0.1692 | 5.7 | 5700 | 0.0622 | | 0.1941 | 5.71 | 5710 | 0.0626 | | 0.1725 | 5.72 | 5720 | 0.0626 | | 0.1028 | 5.73 | 5730 | 0.0627 | | 0.1359 | 5.74 | 5740 | 0.0627 | | 0.1321 | 5.75 | 5750 | 0.0629 | | 0.1093 | 5.76 | 5760 | 0.0630 | | 0.1399 | 5.77 | 5770 | 0.0631 | | 0.1117 | 5.78 | 5780 | 0.0632 | | 0.154 | 5.79 | 5790 | 0.0634 | | 0.1628 | 5.8 | 5800 | 0.0637 | | 0.2267 | 5.81 | 5810 | 0.0639 | | 0.1716 | 5.82 | 5820 | 0.0639 | | 0.165 | 5.83 | 5830 | 0.0640 | | 0.1013 | 5.84 | 5840 | 0.0641 | | 0.1417 | 5.85 | 5850 | 0.0641 | | 0.1607 | 5.86 | 5860 | 0.0639 | | 0.1191 | 5.87 | 5870 | 0.0638 | | 0.1549 | 5.88 | 5880 | 0.0635 | | 0.1906 | 5.89 | 5890 | 0.0635 | | 0.1307 | 5.9 | 5900 | 0.0636 | | 0.123 | 5.91 | 5910 | 0.0636 | | 0.1389 | 5.92 | 5920 | 0.0636 | | 0.1152 | 5.93 | 5930 | 0.0637 | | 0.1267 | 5.94 | 5940 | 0.0638 | | 0.1301 | 5.95 | 5950 | 0.0640 | | 0.1583 | 5.96 | 5960 | 0.0642 | | 0.1958 | 5.97 | 5970 | 0.0644 | | 0.1591 | 5.98 | 5980 | 0.0644 | | 0.2638 | 5.99 | 5990 | 0.0643 | | 0.1605 | 6.0 | 6000 | 0.0644 | | 0.1227 | 6.01 | 6010 | 0.0643 | | 0.1721 | 6.02 | 6020 | 0.0642 | | 0.1828 | 6.03 | 6030 | 0.0643 | | 0.0953 | 6.04 | 6040 | 0.0643 | | 0.1538 | 6.05 | 6050 | 0.0643 | | 0.1403 | 6.06 | 6060 | 0.0643 | | 0.1094 | 6.07 | 6070 | 0.0641 | | 0.1493 | 6.08 | 6080 | 0.0645 | | 0.1313 | 6.09 | 6090 | 0.0647 | | 0.158 | 6.1 | 6100 | 0.0650 | | 0.1184 | 6.11 | 6110 | 0.0650 | | 0.0781 | 6.12 | 6120 | 0.0648 | | 0.121 | 6.13 | 6130 | 0.0649 | | 0.1694 | 6.14 | 6140 | 0.0649 | | 0.1687 | 6.15 | 6150 | 0.0646 | | 0.1408 | 6.16 | 6160 | 0.0645 | | 0.1807 | 6.17 | 6170 | 0.0645 | | 0.109 | 6.18 | 6180 | 0.0644 | | 0.1266 | 6.19 | 6190 | 0.0644 | | 0.0925 | 6.2 | 6200 | 0.0649 | | 0.1768 | 6.21 | 6210 | 0.0649 | | 0.1434 | 6.22 | 6220 | 0.0650 | | 0.1449 | 6.23 | 6230 | 0.0645 | | 0.0938 | 6.24 | 6240 | 0.0644 | | 0.1336 | 6.25 | 6250 | 0.0641 | | 0.1798 | 6.26 | 6260 | 0.0641 | | 0.1549 | 6.27 | 6270 | 0.0643 | | 0.1151 | 6.28 | 6280 | 0.0644 | | 0.1468 | 6.29 | 6290 | 0.0645 | | 0.1169 | 6.3 | 6300 | 0.0644 | | 0.197 | 6.31 | 6310 | 0.0645 | | 0.1409 | 6.32 | 6320 | 0.0646 | | 0.1861 | 6.33 | 6330 | 0.0645 | | 0.1417 | 6.34 | 6340 | 0.0645 | | 0.1526 | 6.35 | 6350 | 0.0645 | | 0.1577 | 6.36 | 6360 | 0.0646 | | 0.104 | 6.37 | 6370 | 0.0645 | | 0.1371 | 6.38 | 6380 | 0.0645 | | 0.1126 | 6.39 | 6390 | 0.0646 | | 0.2212 | 6.4 | 6400 | 0.0647 | | 0.1324 | 6.41 | 6410 | 0.0648 | | 0.1478 | 6.42 | 6420 | 0.0650 | | 0.1995 | 6.43 | 6430 | 0.0647 | | 0.1495 | 6.44 | 6440 | 0.0646 | | 0.108 | 6.45 | 6450 | 0.0645 | | 0.1268 | 6.46 | 6460 | 0.0641 | | 0.1233 | 6.47 | 6470 | 0.0639 | | 0.1222 | 6.48 | 6480 | 0.0634 | | 0.1324 | 6.49 | 6490 | 0.0630 | | 0.1461 | 6.5 | 6500 | 0.0627 | | 0.1541 | 6.51 | 6510 | 0.0625 | | 0.1797 | 6.52 | 6520 | 0.0623 | | 0.1712 | 6.53 | 6530 | 0.0623 | | 0.2081 | 6.54 | 6540 | 0.0627 | | 0.166 | 6.55 | 6550 | 0.0628 | | 0.1498 | 6.56 | 6560 | 0.0629 | | 0.1427 | 6.57 | 6570 | 0.0628 | | 0.1548 | 6.58 | 6580 | 0.0629 | | 0.13 | 6.59 | 6590 | 0.0629 | | 0.1655 | 6.6 | 6600 | 0.0628 | | 0.1734 | 6.61 | 6610 | 0.0630 | | 0.1182 | 6.62 | 6620 | 0.0631 | | 0.1494 | 6.63 | 6630 | 0.0631 | | 0.1392 | 6.64 | 6640 | 0.0630 | | 0.1464 | 6.65 | 6650 | 0.0633 | | 0.1374 | 6.66 | 6660 | 0.0634 | | 0.1406 | 6.67 | 6670 | 0.0633 | | 0.166 | 6.68 | 6680 | 0.0632 | | 0.089 | 6.69 | 6690 | 0.0632 | | 0.1177 | 6.7 | 6700 | 0.0634 | | 0.1074 | 6.71 | 6710 | 0.0635 | | 0.1224 | 6.72 | 6720 | 0.0636 | | 0.1754 | 6.73 | 6730 | 0.0634 | | 0.1731 | 6.74 | 6740 | 0.0632 | | 0.1566 | 6.75 | 6750 | 0.0631 | | 0.1139 | 6.76 | 6760 | 0.0633 | | 0.1255 | 6.77 | 6770 | 0.0634 | | 0.166 | 6.78 | 6780 | 0.0636 | | 0.1192 | 6.79 | 6790 | 0.0638 | | 0.1203 | 6.8 | 6800 | 0.0639 | | 0.1021 | 6.81 | 6810 | 0.0641 | | 0.141 | 6.82 | 6820 | 0.0641 | | 0.1272 | 6.83 | 6830 | 0.0643 | | 0.1449 | 6.84 | 6840 | 0.0645 | | 0.1459 | 6.85 | 6850 | 0.0645 | | 0.094 | 6.86 | 6860 | 0.0644 | | 0.1866 | 6.87 | 6870 | 0.0644 | | 0.1521 | 6.88 | 6880 | 0.0646 | | 0.1423 | 6.89 | 6890 | 0.0647 | | 0.1465 | 6.9 | 6900 | 0.0648 | | 0.1328 | 6.91 | 6910 | 0.0651 | | 0.1 | 6.92 | 6920 | 0.0653 | | 0.1292 | 6.93 | 6930 | 0.0653 | | 0.1406 | 6.94 | 6940 | 0.0650 | | 0.116 | 6.95 | 6950 | 0.0648 | | 0.1355 | 6.96 | 6960 | 0.0645 | | 0.1491 | 6.97 | 6970 | 0.0643 | | 0.1359 | 6.98 | 6980 | 0.0643 | | 0.1374 | 6.99 | 6990 | 0.0644 | | 0.1129 | 7.0 | 7000 | 0.0645 | | 0.1317 | 7.01 | 7010 | 0.0645 | | 0.2238 | 7.02 | 7020 | 0.0646 | | 0.1062 | 7.03 | 7030 | 0.0645 | | 0.1742 | 7.04 | 7040 | 0.0646 | | 0.1234 | 7.05 | 7050 | 0.0649 | | 0.1861 | 7.06 | 7060 | 0.0649 | | 0.1154 | 7.07 | 7070 | 0.0647 | | 0.1185 | 7.08 | 7080 | 0.0647 | | 0.1065 | 7.09 | 7090 | 0.0648 | | 0.1047 | 7.1 | 7100 | 0.0649 | | 0.1669 | 7.11 | 7110 | 0.0647 | | 0.1304 | 7.12 | 7120 | 0.0646 | | 0.0801 | 7.13 | 7130 | 0.0646 | | 0.1261 | 7.14 | 7140 | 0.0648 | | 0.171 | 7.15 | 7150 | 0.0648 | | 0.1338 | 7.16 | 7160 | 0.0647 | | 0.1074 | 7.17 | 7170 | 0.0646 | | 0.1485 | 7.18 | 7180 | 0.0645 | | 0.1314 | 7.19 | 7190 | 0.0645 | | 0.1507 | 7.2 | 7200 | 0.0646 | | 0.1734 | 7.21 | 7210 | 0.0646 | | 0.1873 | 7.22 | 7220 | 0.0647 | | 0.1544 | 7.23 | 7230 | 0.0645 | | 0.1222 | 7.24 | 7240 | 0.0646 | | 0.157 | 7.25 | 7250 | 0.0644 | | 0.1391 | 7.26 | 7260 | 0.0644 | | 0.162 | 7.27 | 7270 | 0.0644 | | 0.1089 | 7.28 | 7280 | 0.0644 | | 0.125 | 7.29 | 7290 | 0.0643 | | 0.1363 | 7.3 | 7300 | 0.0643 | | 0.1205 | 7.31 | 7310 | 0.0645 | | 0.1116 | 7.32 | 7320 | 0.0648 | | 0.1194 | 7.33 | 7330 | 0.0648 | | 0.153 | 7.34 | 7340 | 0.0649 | | 0.1262 | 7.35 | 7350 | 0.0647 | | 0.1234 | 7.36 | 7360 | 0.0646 | | 0.1381 | 7.37 | 7370 | 0.0646 | | 0.1108 | 7.38 | 7380 | 0.0647 | | 0.1719 | 7.39 | 7390 | 0.0645 | | 0.1293 | 7.4 | 7400 | 0.0644 | | 0.1562 | 7.41 | 7410 | 0.0643 | | 0.149 | 7.42 | 7420 | 0.0644 | | 0.0926 | 7.43 | 7430 | 0.0641 | | 0.1603 | 7.44 | 7440 | 0.0640 | | 0.1599 | 7.45 | 7450 | 0.0639 | | 0.1281 | 7.46 | 7460 | 0.0640 | | 0.146 | 7.47 | 7470 | 0.0643 | | 0.1498 | 7.48 | 7480 | 0.0643 | | 0.1699 | 7.49 | 7490 | 0.0644 | | 0.1092 | 7.5 | 7500 | 0.0646 | | 0.1338 | 7.51 | 7510 | 0.0646 | | 0.1532 | 7.52 | 7520 | 0.0643 | | 0.1016 | 7.53 | 7530 | 0.0642 | | 0.1286 | 7.54 | 7540 | 0.0643 | | 0.1242 | 7.55 | 7550 | 0.0642 | | 0.1485 | 7.56 | 7560 | 0.0641 | | 0.1754 | 7.57 | 7570 | 0.0639 | | 0.1637 | 7.58 | 7580 | 0.0639 | | 0.1075 | 7.59 | 7590 | 0.0639 | | 0.1288 | 7.6 | 7600 | 0.0640 | | 0.1639 | 7.61 | 7610 | 0.0640 | | 0.1185 | 7.62 | 7620 | 0.0640 | | 0.1321 | 7.63 | 7630 | 0.0639 | | 0.1589 | 7.64 | 7640 | 0.0636 | | 0.094 | 7.65 | 7650 | 0.0634 | | 0.1115 | 7.66 | 7660 | 0.0635 | | 0.1365 | 7.67 | 7670 | 0.0635 | | 0.1567 | 7.68 | 7680 | 0.0636 | | 0.182 | 7.69 | 7690 | 0.0636 | | 0.1195 | 7.7 | 7700 | 0.0636 | | 0.158 | 7.71 | 7710 | 0.0637 | | 0.1354 | 7.72 | 7720 | 0.0638 | | 0.1508 | 7.73 | 7730 | 0.0637 | | 0.0876 | 7.74 | 7740 | 0.0639 | | 0.1241 | 7.75 | 7750 | 0.0640 | | 0.2049 | 7.76 | 7760 | 0.0641 | | 0.1859 | 7.77 | 7770 | 0.0645 | | 0.1036 | 7.78 | 7780 | 0.0645 | | 0.1015 | 7.79 | 7790 | 0.0643 | | 0.1235 | 7.8 | 7800 | 0.0639 | | 0.1594 | 7.81 | 7810 | 0.0640 | | 0.1295 | 7.82 | 7820 | 0.0642 | | 0.152 | 7.83 | 7830 | 0.0645 | | 0.1496 | 7.84 | 7840 | 0.0647 | | 0.1353 | 7.85 | 7850 | 0.0648 | | 0.1206 | 7.86 | 7860 | 0.0649 | | 0.1627 | 7.87 | 7870 | 0.0651 | | 0.1132 | 7.88 | 7880 | 0.0653 | | 0.154 | 7.89 | 7890 | 0.0652 | | 0.153 | 7.9 | 7900 | 0.0649 | | 0.1225 | 7.91 | 7910 | 0.0648 | | 0.1494 | 7.92 | 7920 | 0.0648 | | 0.1358 | 7.93 | 7930 | 0.0647 | | 0.1137 | 7.94 | 7940 | 0.0648 | | 0.1546 | 7.95 | 7950 | 0.0647 | | 0.114 | 7.96 | 7960 | 0.0644 | | 0.1939 | 7.97 | 7970 | 0.0644 | | 0.1276 | 7.98 | 7980 | 0.0641 | | 0.1096 | 7.99 | 7990 | 0.0639 | | 0.1764 | 8.0 | 8000 | 0.0639 | | 0.1029 | 8.01 | 8010 | 0.0642 | | 0.1344 | 8.02 | 8020 | 0.0641 | | 0.1422 | 8.03 | 8030 | 0.0643 | | 0.1055 | 8.04 | 8040 | 0.0645 | | 0.1231 | 8.05 | 8050 | 0.0646 | | 0.1303 | 8.06 | 8060 | 0.0648 | | 0.1421 | 8.07 | 8070 | 0.0651 | | 0.1325 | 8.08 | 8080 | 0.0651 | | 0.0797 | 8.09 | 8090 | 0.0649 | | 0.0961 | 8.1 | 8100 | 0.0647 | | 0.1156 | 8.11 | 8110 | 0.0647 | | 0.1529 | 8.12 | 8120 | 0.0648 | | 0.1756 | 8.13 | 8130 | 0.0648 | | 0.1158 | 8.14 | 8140 | 0.0651 | | 0.1302 | 8.15 | 8150 | 0.0654 | | 0.1404 | 8.16 | 8160 | 0.0658 | | 0.1445 | 8.17 | 8170 | 0.0662 | | 0.1499 | 8.18 | 8180 | 0.0662 | | 0.1375 | 8.19 | 8190 | 0.0663 | | 0.1663 | 8.2 | 8200 | 0.0662 | | 0.1744 | 8.21 | 8210 | 0.0661 | | 0.1414 | 8.22 | 8220 | 0.0658 | | 0.0964 | 8.23 | 8230 | 0.0658 | | 0.1214 | 8.24 | 8240 | 0.0655 | | 0.1492 | 8.25 | 8250 | 0.0653 | | 0.1534 | 8.26 | 8260 | 0.0654 | | 0.1038 | 8.27 | 8270 | 0.0655 | | 0.1779 | 8.28 | 8280 | 0.0655 | | 0.1801 | 8.29 | 8290 | 0.0657 | | 0.1314 | 8.3 | 8300 | 0.0658 | | 0.1598 | 8.31 | 8310 | 0.0657 | | 0.1078 | 8.32 | 8320 | 0.0653 | | 0.1672 | 8.33 | 8330 | 0.0649 | | 0.113 | 8.34 | 8340 | 0.0649 | | 0.1046 | 8.35 | 8350 | 0.0650 | | 0.1625 | 8.36 | 8360 | 0.0650 | | 0.1112 | 8.37 | 8370 | 0.0650 | | 0.1278 | 8.38 | 8380 | 0.0649 | | 0.1684 | 8.39 | 8390 | 0.0647 | | 0.2318 | 8.4 | 8400 | 0.0647 | | 0.1687 | 8.41 | 8410 | 0.0648 | | 0.1283 | 8.42 | 8420 | 0.0649 | | 0.1107 | 8.43 | 8430 | 0.0650 | | 0.1092 | 8.44 | 8440 | 0.0652 | | 0.0851 | 8.45 | 8450 | 0.0653 | | 0.1263 | 8.46 | 8460 | 0.0656 | | 0.1936 | 8.47 | 8470 | 0.0656 | | 0.1608 | 8.48 | 8480 | 0.0656 | | 0.1023 | 8.49 | 8490 | 0.0656 | | 0.1153 | 8.5 | 8500 | 0.0658 | | 0.1682 | 8.51 | 8510 | 0.0659 | | 0.1407 | 8.52 | 8520 | 0.0660 | | 0.104 | 8.53 | 8530 | 0.0660 | | 0.1371 | 8.54 | 8540 | 0.0659 | | 0.1068 | 8.55 | 8550 | 0.0659 | | 0.1418 | 8.56 | 8560 | 0.0660 | | 0.1451 | 8.57 | 8570 | 0.0660 | | 0.1379 | 8.58 | 8580 | 0.0661 | | 0.1732 | 8.59 | 8590 | 0.0660 | | 0.1287 | 8.6 | 8600 | 0.0658 | | 0.1821 | 8.61 | 8610 | 0.0657 | | 0.1184 | 8.62 | 8620 | 0.0657 | | 0.1194 | 8.63 | 8630 | 0.0658 | | 0.083 | 8.64 | 8640 | 0.0660 | | 0.1724 | 8.65 | 8650 | 0.0661 | | 0.1504 | 8.66 | 8660 | 0.0659 | | 0.1338 | 8.67 | 8670 | 0.0658 | | 0.1372 | 8.68 | 8680 | 0.0658 | | 0.1367 | 8.69 | 8690 | 0.0658 | | 0.1665 | 8.7 | 8700 | 0.0659 | | 0.1389 | 8.71 | 8710 | 0.0660 | | 0.1272 | 8.72 | 8720 | 0.0660 | | 0.1595 | 8.73 | 8730 | 0.0659 | | 0.1644 | 8.74 | 8740 | 0.0658 | | 0.1249 | 8.75 | 8750 | 0.0658 | | 0.1276 | 8.76 | 8760 | 0.0658 | | 0.1103 | 8.77 | 8770 | 0.0657 | | 0.1664 | 8.78 | 8780 | 0.0658 | | 0.1832 | 8.79 | 8790 | 0.0661 | | 0.1075 | 8.8 | 8800 | 0.0662 | | 0.1526 | 8.81 | 8810 | 0.0663 | | 0.1215 | 8.82 | 8820 | 0.0666 | | 0.1317 | 8.83 | 8830 | 0.0668 | | 0.1425 | 8.84 | 8840 | 0.0668 | | 0.1572 | 8.85 | 8850 | 0.0668 | | 0.1012 | 8.86 | 8860 | 0.0667 | | 0.1222 | 8.87 | 8870 | 0.0665 | | 0.1723 | 8.88 | 8880 | 0.0665 | | 0.1139 | 8.89 | 8890 | 0.0665 | | 0.1351 | 8.9 | 8900 | 0.0665 | | 0.1258 | 8.91 | 8910 | 0.0666 | | 0.1387 | 8.92 | 8920 | 0.0667 | | 0.1554 | 8.93 | 8930 | 0.0664 | | 0.1454 | 8.94 | 8940 | 0.0662 | | 0.1066 | 8.95 | 8950 | 0.0661 | | 0.1047 | 8.96 | 8960 | 0.0659 | | 0.168 | 8.97 | 8970 | 0.0658 | | 0.1162 | 8.98 | 8980 | 0.0656 | | 0.109 | 8.99 | 8990 | 0.0654 | | 0.1262 | 9.0 | 9000 | 0.0653 | | 0.1563 | 9.01 | 9010 | 0.0651 | | 0.13 | 9.02 | 9020 | 0.0650 | | 0.1608 | 9.03 | 9030 | 0.0651 | | 0.1369 | 9.04 | 9040 | 0.0650 | | 0.1386 | 9.05 | 9050 | 0.0646 | | 0.0843 | 9.06 | 9060 | 0.0644 | | 0.0719 | 9.07 | 9070 | 0.0643 | | 0.146 | 9.08 | 9080 | 0.0641 | | 0.132 | 9.09 | 9090 | 0.0640 | | 0.1425 | 9.1 | 9100 | 0.0639 | | 0.1097 | 9.11 | 9110 | 0.0640 | | 0.1684 | 9.12 | 9120 | 0.0641 | | 0.1891 | 9.13 | 9130 | 0.0640 | | 0.1083 | 9.14 | 9140 | 0.0641 | | 0.1265 | 9.15 | 9150 | 0.0643 | | 0.1183 | 9.16 | 9160 | 0.0646 | | 0.0971 | 9.17 | 9170 | 0.0647 | | 0.17 | 9.18 | 9180 | 0.0645 | | 0.1074 | 9.19 | 9190 | 0.0646 | | 0.1517 | 9.2 | 9200 | 0.0647 | | 0.1763 | 9.21 | 9210 | 0.0648 | | 0.1031 | 9.22 | 9220 | 0.0647 | | 0.1419 | 9.23 | 9230 | 0.0647 | | 0.1451 | 9.24 | 9240 | 0.0649 | | 0.1657 | 9.25 | 9250 | 0.0651 | | 0.1327 | 9.26 | 9260 | 0.0652 | | 0.1279 | 9.27 | 9270 | 0.0653 | | 0.111 | 9.28 | 9280 | 0.0656 | | 0.1062 | 9.29 | 9290 | 0.0658 | | 0.1185 | 9.3 | 9300 | 0.0657 | | 0.1539 | 9.31 | 9310 | 0.0658 | | 0.2525 | 9.32 | 9320 | 0.0656 | | 0.0985 | 9.33 | 9330 | 0.0655 | | 0.1161 | 9.34 | 9340 | 0.0656 | | 0.1462 | 9.35 | 9350 | 0.0655 | | 0.1229 | 9.36 | 9360 | 0.0655 | | 0.102 | 9.37 | 9370 | 0.0655 | | 0.155 | 9.38 | 9380 | 0.0656 | | 0.1747 | 9.39 | 9390 | 0.0657 | | 0.0887 | 9.4 | 9400 | 0.0657 | | 0.1341 | 9.41 | 9410 | 0.0654 | | 0.1598 | 9.42 | 9420 | 0.0652 | | 0.1299 | 9.43 | 9430 | 0.0654 | | 0.0918 | 9.44 | 9440 | 0.0655 | | 0.1463 | 9.45 | 9450 | 0.0654 | | 0.1867 | 9.46 | 9460 | 0.0654 | | 0.1602 | 9.47 | 9470 | 0.0653 | | 0.1077 | 9.48 | 9480 | 0.0653 | | 0.1052 | 9.49 | 9490 | 0.0653 | | 0.1283 | 9.5 | 9500 | 0.0650 | | 0.1888 | 9.51 | 9510 | 0.0647 | | 0.1381 | 9.52 | 9520 | 0.0646 | | 0.0842 | 9.53 | 9530 | 0.0645 | | 0.086 | 9.54 | 9540 | 0.0644 | | 0.1155 | 9.55 | 9550 | 0.0645 | | 0.114 | 9.56 | 9560 | 0.0646 | | 0.1042 | 9.57 | 9570 | 0.0649 | | 0.1629 | 9.58 | 9580 | 0.0651 | | 0.1641 | 9.59 | 9590 | 0.0652 | | 0.1158 | 9.6 | 9600 | 0.0654 | | 0.0971 | 9.61 | 9610 | 0.0656 | | 0.1908 | 9.62 | 9620 | 0.0656 | | 0.1158 | 9.63 | 9630 | 0.0656 | | 0.1543 | 9.64 | 9640 | 0.0656 | | 0.1667 | 9.65 | 9650 | 0.0656 | | 0.1439 | 9.66 | 9660 | 0.0656 | | 0.1375 | 9.67 | 9670 | 0.0658 | | 0.1048 | 9.68 | 9680 | 0.0660 | | 0.1551 | 9.69 | 9690 | 0.0659 | | 0.1096 | 9.7 | 9700 | 0.0658 | | 0.1017 | 9.71 | 9710 | 0.0657 | | 0.1198 | 9.72 | 9720 | 0.0657 | | 0.1345 | 9.73 | 9730 | 0.0657 | | 0.1268 | 9.74 | 9740 | 0.0657 | | 0.124 | 9.75 | 9750 | 0.0657 | | 0.1313 | 9.76 | 9760 | 0.0658 | | 0.1956 | 9.77 | 9770 | 0.0658 | | 0.0982 | 9.78 | 9780 | 0.0658 | | 0.1091 | 9.79 | 9790 | 0.0658 | | 0.1686 | 9.8 | 9800 | 0.0658 | | 0.1185 | 9.81 | 9810 | 0.0656 | | 0.1886 | 9.82 | 9820 | 0.0654 | | 0.0923 | 9.83 | 9830 | 0.0653 | | 0.1339 | 9.84 | 9840 | 0.0651 | | 0.1813 | 9.85 | 9850 | 0.0651 | | 0.1073 | 9.86 | 9860 | 0.0651 | | 0.1452 | 9.87 | 9870 | 0.0650 | | 0.138 | 9.88 | 9880 | 0.0648 | | 0.1151 | 9.89 | 9890 | 0.0648 | | 0.1364 | 9.9 | 9900 | 0.0647 | | 0.1233 | 9.91 | 9910 | 0.0646 | | 0.1475 | 9.92 | 9920 | 0.0647 | | 0.157 | 9.93 | 9930 | 0.0647 | | 0.0869 | 9.94 | 9940 | 0.0649 | | 0.1193 | 9.95 | 9950 | 0.0650 | | 0.1836 | 9.96 | 9960 | 0.0650 | | 0.159 | 9.97 | 9970 | 0.0649 | | 0.1575 | 9.98 | 9980 | 0.0650 | | 0.141 | 9.99 | 9990 | 0.0649 | | 0.1398 | 10.0 | 10000 | 0.0651 | | 0.1547 | 10.01 | 10010 | 0.0650 | | 0.1328 | 10.02 | 10020 | 0.0650 | | 0.1164 | 10.03 | 10030 | 0.0649 | | 0.1846 | 10.04 | 10040 | 0.0650 | | 0.1184 | 10.05 | 10050 | 0.0651 | | 0.1044 | 10.06 | 10060 | 0.0651 | | 0.1384 | 10.07 | 10070 | 0.0650 | | 0.1416 | 10.08 | 10080 | 0.0650 | | 0.1547 | 10.09 | 10090 | 0.0650 | | 0.0962 | 10.1 | 10100 | 0.0650 | | 0.1004 | 10.11 | 10110 | 0.0651 | | 0.155 | 10.12 | 10120 | 0.0652 | | 0.1257 | 10.13 | 10130 | 0.0653 | | 0.1257 | 10.14 | 10140 | 0.0653 | | 0.0995 | 10.15 | 10150 | 0.0653 | | 0.1006 | 10.16 | 10160 | 0.0655 | | 0.0971 | 10.17 | 10170 | 0.0655 | | 0.1214 | 10.18 | 10180 | 0.0657 | | 0.1752 | 10.19 | 10190 | 0.0659 | | 0.0797 | 10.2 | 10200 | 0.0659 | | 0.1589 | 10.21 | 10210 | 0.0660 | | 0.1616 | 10.22 | 10220 | 0.0661 | | 0.086 | 10.23 | 10230 | 0.0662 | | 0.1542 | 10.24 | 10240 | 0.0662 | | 0.1493 | 10.25 | 10250 | 0.0660 | | 0.1095 | 10.26 | 10260 | 0.0658 | | 0.1545 | 10.27 | 10270 | 0.0657 | | 0.1587 | 10.28 | 10280 | 0.0655 | | 0.1758 | 10.29 | 10290 | 0.0655 | | 0.1366 | 10.3 | 10300 | 0.0655 | | 0.146 | 10.31 | 10310 | 0.0657 | | 0.0977 | 10.32 | 10320 | 0.0660 | | 0.085 | 10.33 | 10330 | 0.0663 | | 0.1037 | 10.34 | 10340 | 0.0664 | | 0.126 | 10.35 | 10350 | 0.0664 | | 0.1065 | 10.36 | 10360 | 0.0665 | | 0.0984 | 10.37 | 10370 | 0.0663 | | 0.1078 | 10.38 | 10380 | 0.0661 | | 0.17 | 10.39 | 10390 | 0.0659 | | 0.1721 | 10.4 | 10400 | 0.0659 | | 0.1347 | 10.41 | 10410 | 0.0657 | | 0.2179 | 10.42 | 10420 | 0.0656 | | 0.0688 | 10.43 | 10430 | 0.0656 | | 0.1147 | 10.44 | 10440 | 0.0656 | | 0.1239 | 10.45 | 10450 | 0.0656 | | 0.1013 | 10.46 | 10460 | 0.0658 | | 0.1596 | 10.47 | 10470 | 0.0661 | | 0.101 | 10.48 | 10480 | 0.0662 | | 0.1111 | 10.49 | 10490 | 0.0662 | | 0.1179 | 10.5 | 10500 | 0.0662 | | 0.1017 | 10.51 | 10510 | 0.0663 | | 0.1477 | 10.52 | 10520 | 0.0661 | | 0.1484 | 10.53 | 10530 | 0.0658 | | 0.1598 | 10.54 | 10540 | 0.0659 | | 0.1807 | 10.55 | 10550 | 0.0660 | | 0.1616 | 10.56 | 10560 | 0.0660 | | 0.1226 | 10.57 | 10570 | 0.0659 | | 0.1563 | 10.58 | 10580 | 0.0656 | | 0.1705 | 10.59 | 10590 | 0.0654 | | 0.1488 | 10.6 | 10600 | 0.0655 | | 0.1665 | 10.61 | 10610 | 0.0655 | | 0.1165 | 10.62 | 10620 | 0.0655 | | 0.1412 | 10.63 | 10630 | 0.0656 | | 0.1515 | 10.64 | 10640 | 0.0657 | | 0.1292 | 10.65 | 10650 | 0.0658 | | 0.1175 | 10.66 | 10660 | 0.0657 | | 0.1188 | 10.67 | 10670 | 0.0658 | | 0.106 | 10.68 | 10680 | 0.0659 | | 0.1239 | 10.69 | 10690 | 0.0659 | | 0.1326 | 10.7 | 10700 | 0.0659 | | 0.2108 | 10.71 | 10710 | 0.0661 | | 0.1472 | 10.72 | 10720 | 0.0662 | | 0.1237 | 10.73 | 10730 | 0.0662 | | 0.1493 | 10.74 | 10740 | 0.0663 | | 0.0985 | 10.75 | 10750 | 0.0664 | | 0.1048 | 10.76 | 10760 | 0.0664 | | 0.1236 | 10.77 | 10770 | 0.0665 | | 0.108 | 10.78 | 10780 | 0.0667 | | 0.1386 | 10.79 | 10790 | 0.0665 | | 0.1501 | 10.8 | 10800 | 0.0664 | | 0.1341 | 10.81 | 10810 | 0.0664 | | 0.1171 | 10.82 | 10820 | 0.0665 | | 0.1243 | 10.83 | 10830 | 0.0664 | | 0.1225 | 10.84 | 10840 | 0.0662 | | 0.2065 | 10.85 | 10850 | 0.0661 | | 0.1032 | 10.86 | 10860 | 0.0661 | | 0.1433 | 10.87 | 10870 | 0.0661 | | 0.1933 | 10.88 | 10880 | 0.0662 | | 0.1344 | 10.89 | 10890 | 0.0661 | | 0.1046 | 10.9 | 10900 | 0.0662 | | 0.1352 | 10.91 | 10910 | 0.0662 | | 0.1302 | 10.92 | 10920 | 0.0663 | | 0.1237 | 10.93 | 10930 | 0.0663 | | 0.0985 | 10.94 | 10940 | 0.0664 | | 0.1186 | 10.95 | 10950 | 0.0664 | | 0.1929 | 10.96 | 10960 | 0.0664 | | 0.1224 | 10.97 | 10970 | 0.0664 | | 0.1753 | 10.98 | 10980 | 0.0664 | | 0.1151 | 10.99 | 10990 | 0.0663 | | 0.1213 | 11.0 | 11000 | 0.0661 | | 0.148 | 11.01 | 11010 | 0.0660 | | 0.1005 | 11.02 | 11020 | 0.0660 | | 0.1579 | 11.03 | 11030 | 0.0660 | | 0.1275 | 11.04 | 11040 | 0.0661 | | 0.1498 | 11.05 | 11050 | 0.0664 | | 0.1036 | 11.06 | 11060 | 0.0666 | | 0.1002 | 11.07 | 11070 | 0.0668 | | 0.1007 | 11.08 | 11080 | 0.0669 | | 0.129 | 11.09 | 11090 | 0.0667 | | 0.1988 | 11.1 | 11100 | 0.0666 | | 0.1252 | 11.11 | 11110 | 0.0667 | | 0.13 | 11.12 | 11120 | 0.0667 | | 0.146 | 11.13 | 11130 | 0.0669 | | 0.1384 | 11.14 | 11140 | 0.0669 | | 0.1405 | 11.15 | 11150 | 0.0667 | | 0.1023 | 11.16 | 11160 | 0.0666 | | 0.156 | 11.17 | 11170 | 0.0664 | | 0.0964 | 11.18 | 11180 | 0.0663 | | 0.1173 | 11.19 | 11190 | 0.0663 | | 0.084 | 11.2 | 11200 | 0.0662 | | 0.1295 | 11.21 | 11210 | 0.0662 | | 0.1377 | 11.22 | 11220 | 0.0661 | | 0.1008 | 11.23 | 11230 | 0.0660 | | 0.1132 | 11.24 | 11240 | 0.0659 | | 0.1646 | 11.25 | 11250 | 0.0658 | | 0.103 | 11.26 | 11260 | 0.0659 | | 0.1331 | 11.27 | 11270 | 0.0659 | | 0.1153 | 11.28 | 11280 | 0.0659 | | 0.1457 | 11.29 | 11290 | 0.0658 | | 0.1257 | 11.3 | 11300 | 0.0658 | | 0.1541 | 11.31 | 11310 | 0.0659 | | 0.1449 | 11.32 | 11320 | 0.0660 | | 0.1419 | 11.33 | 11330 | 0.0661 | | 0.118 | 11.34 | 11340 | 0.0661 | | 0.1562 | 11.35 | 11350 | 0.0660 | | 0.1441 | 11.36 | 11360 | 0.0661 | | 0.1663 | 11.37 | 11370 | 0.0661 | | 0.1754 | 11.38 | 11380 | 0.0661 | | 0.0888 | 11.39 | 11390 | 0.0662 | | 0.084 | 11.4 | 11400 | 0.0661 | | 0.1095 | 11.41 | 11410 | 0.0661 | | 0.1594 | 11.42 | 11420 | 0.0663 | | 0.0926 | 11.43 | 11430 | 0.0664 | | 0.1073 | 11.44 | 11440 | 0.0664 | | 0.1468 | 11.45 | 11450 | 0.0663 | | 0.1433 | 11.46 | 11460 | 0.0661 | | 0.1353 | 11.47 | 11470 | 0.0660 | | 0.1437 | 11.48 | 11480 | 0.0659 | | 0.164 | 11.49 | 11490 | 0.0657 | | 0.0949 | 11.5 | 11500 | 0.0656 | | 0.0891 | 11.51 | 11510 | 0.0656 | | 0.1316 | 11.52 | 11520 | 0.0656 | | 0.1462 | 11.53 | 11530 | 0.0656 | | 0.0974 | 11.54 | 11540 | 0.0656 | | 0.121 | 11.55 | 11550 | 0.0656 | | 0.1708 | 11.56 | 11560 | 0.0657 | | 0.1312 | 11.57 | 11570 | 0.0657 | | 0.1593 | 11.58 | 11580 | 0.0658 | | 0.171 | 11.59 | 11590 | 0.0659 | | 0.1354 | 11.6 | 11600 | 0.0659 | | 0.1329 | 11.61 | 11610 | 0.0661 | | 0.1405 | 11.62 | 11620 | 0.0660 | | 0.1587 | 11.63 | 11630 | 0.0657 | | 0.1106 | 11.64 | 11640 | 0.0659 | | 0.1189 | 11.65 | 11650 | 0.0661 | | 0.2006 | 11.66 | 11660 | 0.0662 | | 0.1378 | 11.67 | 11670 | 0.0662 | | 0.1274 | 11.68 | 11680 | 0.0663 | | 0.1286 | 11.69 | 11690 | 0.0663 | | 0.1211 | 11.7 | 11700 | 0.0664 | | 0.1863 | 11.71 | 11710 | 0.0665 | | 0.1428 | 11.72 | 11720 | 0.0667 | | 0.1179 | 11.73 | 11730 | 0.0666 | | 0.1265 | 11.74 | 11740 | 0.0668 | | 0.1597 | 11.75 | 11750 | 0.0668 | | 0.1148 | 11.76 | 11760 | 0.0669 | | 0.14 | 11.77 | 11770 | 0.0668 | | 0.1796 | 11.78 | 11780 | 0.0669 | | 0.0774 | 11.79 | 11790 | 0.0670 | | 0.1063 | 11.8 | 11800 | 0.0671 | | 0.1279 | 11.81 | 11810 | 0.0672 | | 0.1454 | 11.82 | 11820 | 0.0672 | | 0.1495 | 11.83 | 11830 | 0.0672 | | 0.1434 | 11.84 | 11840 | 0.0671 | | 0.1312 | 11.85 | 11850 | 0.0672 | | 0.1681 | 11.86 | 11860 | 0.0672 | | 0.1374 | 11.87 | 11870 | 0.0672 | | 0.0941 | 11.88 | 11880 | 0.0672 | | 0.1178 | 11.89 | 11890 | 0.0672 | | 0.099 | 11.9 | 11900 | 0.0671 | | 0.1277 | 11.91 | 11910 | 0.0670 | | 0.1463 | 11.92 | 11920 | 0.0669 | | 0.124 | 11.93 | 11930 | 0.0669 | | 0.1313 | 11.94 | 11940 | 0.0669 | | 0.1254 | 11.95 | 11950 | 0.0669 | | 0.1122 | 11.96 | 11960 | 0.0669 | | 0.1844 | 11.97 | 11970 | 0.0669 | | 0.099 | 11.98 | 11980 | 0.0670 | | 0.1258 | 11.99 | 11990 | 0.0671 | | 0.12 | 12.0 | 12000 | 0.0671 | | 0.1278 | 12.01 | 12010 | 0.0672 | | 0.1319 | 12.02 | 12020 | 0.0673 | | 0.1171 | 12.03 | 12030 | 0.0673 | | 0.1386 | 12.04 | 12040 | 0.0672 | | 0.1322 | 12.05 | 12050 | 0.0670 | | 0.1792 | 12.06 | 12060 | 0.0669 | | 0.1276 | 12.07 | 12070 | 0.0666 | | 0.1444 | 12.08 | 12080 | 0.0666 | | 0.1319 | 12.09 | 12090 | 0.0666 | | 0.1464 | 12.1 | 12100 | 0.0667 | | 0.1341 | 12.11 | 12110 | 0.0667 | | 0.1384 | 12.12 | 12120 | 0.0667 | | 0.1442 | 12.13 | 12130 | 0.0668 | | 0.1542 | 12.14 | 12140 | 0.0669 | | 0.0987 | 12.15 | 12150 | 0.0670 | | 0.1363 | 12.16 | 12160 | 0.0670 | | 0.1716 | 12.17 | 12170 | 0.0670 | | 0.1187 | 12.18 | 12180 | 0.0670 | | 0.1646 | 12.19 | 12190 | 0.0670 | | 0.0963 | 12.2 | 12200 | 0.0669 | | 0.1797 | 12.21 | 12210 | 0.0667 | | 0.1505 | 12.22 | 12220 | 0.0668 | | 0.1135 | 12.23 | 12230 | 0.0669 | | 0.1419 | 12.24 | 12240 | 0.0669 | | 0.1102 | 12.25 | 12250 | 0.0669 | | 0.0998 | 12.26 | 12260 | 0.0669 | | 0.146 | 12.27 | 12270 | 0.0670 | | 0.1438 | 12.28 | 12280 | 0.0670 | | 0.1051 | 12.29 | 12290 | 0.0670 | | 0.1023 | 12.3 | 12300 | 0.0669 | | 0.0765 | 12.31 | 12310 | 0.0670 | | 0.1629 | 12.32 | 12320 | 0.0670 | | 0.1869 | 12.33 | 12330 | 0.0669 | | 0.1629 | 12.34 | 12340 | 0.0667 | | 0.0846 | 12.35 | 12350 | 0.0667 | | 0.1336 | 12.36 | 12360 | 0.0668 | | 0.0942 | 12.37 | 12370 | 0.0669 | | 0.1513 | 12.38 | 12380 | 0.0670 | | 0.1158 | 12.39 | 12390 | 0.0670 | | 0.1026 | 12.4 | 12400 | 0.0672 | | 0.1167 | 12.41 | 12410 | 0.0673 | | 0.1051 | 12.42 | 12420 | 0.0674 | | 0.1127 | 12.43 | 12430 | 0.0675 | | 0.1346 | 12.44 | 12440 | 0.0676 | | 0.1296 | 12.45 | 12450 | 0.0676 | | 0.0994 | 12.46 | 12460 | 0.0674 | | 0.0815 | 12.47 | 12470 | 0.0673 | | 0.1575 | 12.48 | 12480 | 0.0673 | | 0.1481 | 12.49 | 12490 | 0.0672 | | 0.1694 | 12.5 | 12500 | 0.0672 | | 0.1236 | 12.51 | 12510 | 0.0671 | | 0.1346 | 12.52 | 12520 | 0.0672 | | 0.1252 | 12.53 | 12530 | 0.0674 | | 0.1392 | 12.54 | 12540 | 0.0673 | | 0.0937 | 12.55 | 12550 | 0.0671 | | 0.1199 | 12.56 | 12560 | 0.0671 | | 0.1124 | 12.57 | 12570 | 0.0671 | | 0.184 | 12.58 | 12580 | 0.0672 | | 0.1092 | 12.59 | 12590 | 0.0673 | | 0.1094 | 12.6 | 12600 | 0.0671 | | 0.1447 | 12.61 | 12610 | 0.0670 | | 0.1734 | 12.62 | 12620 | 0.0670 | | 0.1351 | 12.63 | 12630 | 0.0671 | | 0.1506 | 12.64 | 12640 | 0.0672 | | 0.1695 | 12.65 | 12650 | 0.0672 | | 0.0926 | 12.66 | 12660 | 0.0670 | | 0.085 | 12.67 | 12670 | 0.0669 | | 0.1957 | 12.68 | 12680 | 0.0669 | | 0.1421 | 12.69 | 12690 | 0.0669 | | 0.1538 | 12.7 | 12700 | 0.0668 | | 0.0969 | 12.71 | 12710 | 0.0667 | | 0.1072 | 12.72 | 12720 | 0.0668 | | 0.1126 | 12.73 | 12730 | 0.0670 | | 0.1013 | 12.74 | 12740 | 0.0671 | | 0.1553 | 12.75 | 12750 | 0.0672 | | 0.1494 | 12.76 | 12760 | 0.0673 | | 0.0736 | 12.77 | 12770 | 0.0673 | | 0.1924 | 12.78 | 12780 | 0.0674 | | 0.1276 | 12.79 | 12790 | 0.0674 | | 0.1599 | 12.8 | 12800 | 0.0675 | | 0.122 | 12.81 | 12810 | 0.0677 | | 0.1687 | 12.82 | 12820 | 0.0678 | | 0.1597 | 12.83 | 12830 | 0.0677 | | 0.1486 | 12.84 | 12840 | 0.0675 | | 0.1001 | 12.85 | 12850 | 0.0675 | | 0.0874 | 12.86 | 12860 | 0.0675 | | 0.0952 | 12.87 | 12870 | 0.0675 | | 0.1458 | 12.88 | 12880 | 0.0675 | | 0.1472 | 12.89 | 12890 | 0.0675 | | 0.1161 | 12.9 | 12900 | 0.0674 | | 0.1455 | 12.91 | 12910 | 0.0673 | | 0.1406 | 12.92 | 12920 | 0.0672 | | 0.1108 | 12.93 | 12930 | 0.0672 | | 0.1358 | 12.94 | 12940 | 0.0672 | | 0.13 | 12.95 | 12950 | 0.0673 | | 0.095 | 12.96 | 12960 | 0.0674 | | 0.1302 | 12.97 | 12970 | 0.0674 | | 0.1424 | 12.98 | 12980 | 0.0674 | | 0.1343 | 12.99 | 12990 | 0.0674 | | 0.0928 | 13.0 | 13000 | 0.0674 | | 0.1126 | 13.01 | 13010 | 0.0674 | | 0.1296 | 13.02 | 13020 | 0.0673 | | 0.0952 | 13.03 | 13030 | 0.0672 | | 0.1295 | 13.04 | 13040 | 0.0673 | | 0.1826 | 13.05 | 13050 | 0.0673 | | 0.1443 | 13.06 | 13060 | 0.0673 | | 0.1527 | 13.07 | 13070 | 0.0673 | | 0.142 | 13.08 | 13080 | 0.0674 | | 0.0891 | 13.09 | 13090 | 0.0675 | | 0.1165 | 13.1 | 13100 | 0.0677 | | 0.0984 | 13.11 | 13110 | 0.0678 | | 0.1501 | 13.12 | 13120 | 0.0678 | | 0.2033 | 13.13 | 13130 | 0.0678 | | 0.1264 | 13.14 | 13140 | 0.0678 | | 0.1814 | 13.15 | 13150 | 0.0678 | | 0.1828 | 13.16 | 13160 | 0.0678 | | 0.0993 | 13.17 | 13170 | 0.0678 | | 0.1141 | 13.18 | 13180 | 0.0676 | | 0.1012 | 13.19 | 13190 | 0.0675 | | 0.1036 | 13.2 | 13200 | 0.0674 | | 0.1471 | 13.21 | 13210 | 0.0674 | | 0.1388 | 13.22 | 13220 | 0.0674 | | 0.106 | 13.23 | 13230 | 0.0673 | | 0.1153 | 13.24 | 13240 | 0.0673 | | 0.139 | 13.25 | 13250 | 0.0672 | | 0.1298 | 13.26 | 13260 | 0.0671 | | 0.1485 | 13.27 | 13270 | 0.0672 | | 0.1664 | 13.28 | 13280 | 0.0671 | | 0.13 | 13.29 | 13290 | 0.0669 | | 0.1364 | 13.3 | 13300 | 0.0668 | | 0.1537 | 13.31 | 13310 | 0.0670 | | 0.2156 | 13.32 | 13320 | 0.0670 | | 0.0895 | 13.33 | 13330 | 0.0670 | | 0.138 | 13.34 | 13340 | 0.0671 | | 0.1125 | 13.35 | 13350 | 0.0671 | | 0.1168 | 13.36 | 13360 | 0.0671 | | 0.1403 | 13.37 | 13370 | 0.0670 | | 0.1249 | 13.38 | 13380 | 0.0670 | | 0.1537 | 13.39 | 13390 | 0.0671 | | 0.1391 | 13.4 | 13400 | 0.0671 | | 0.1411 | 13.41 | 13410 | 0.0673 | | 0.1082 | 13.42 | 13420 | 0.0675 | | 0.1482 | 13.43 | 13430 | 0.0675 | | 0.1452 | 13.44 | 13440 | 0.0675 | | 0.1237 | 13.45 | 13450 | 0.0674 | | 0.1095 | 13.46 | 13460 | 0.0672 | | 0.1125 | 13.47 | 13470 | 0.0671 | | 0.1165 | 13.48 | 13480 | 0.0672 | | 0.1981 | 13.49 | 13490 | 0.0671 | | 0.1634 | 13.5 | 13500 | 0.0671 | | 0.161 | 13.51 | 13510 | 0.0673 | | 0.1315 | 13.52 | 13520 | 0.0673 | | 0.0916 | 13.53 | 13530 | 0.0674 | | 0.1414 | 13.54 | 13540 | 0.0675 | | 0.1313 | 13.55 | 13550 | 0.0675 | | 0.0914 | 13.56 | 13560 | 0.0675 | | 0.1081 | 13.57 | 13570 | 0.0675 | | 0.1454 | 13.58 | 13580 | 0.0675 | | 0.1472 | 13.59 | 13590 | 0.0675 | | 0.099 | 13.6 | 13600 | 0.0676 | | 0.1465 | 13.61 | 13610 | 0.0676 | | 0.1276 | 13.62 | 13620 | 0.0675 | | 0.109 | 13.63 | 13630 | 0.0674 | | 0.1063 | 13.64 | 13640 | 0.0673 | | 0.1552 | 13.65 | 13650 | 0.0673 | | 0.105 | 13.66 | 13660 | 0.0673 | | 0.1142 | 13.67 | 13670 | 0.0675 | | 0.1519 | 13.68 | 13680 | 0.0676 | | 0.1658 | 13.69 | 13690 | 0.0677 | | 0.1445 | 13.7 | 13700 | 0.0677 | | 0.1349 | 13.71 | 13710 | 0.0678 | | 0.0989 | 13.72 | 13720 | 0.0678 | | 0.1476 | 13.73 | 13730 | 0.0678 | | 0.1408 | 13.74 | 13740 | 0.0678 | | 0.0973 | 13.75 | 13750 | 0.0678 | | 0.0911 | 13.76 | 13760 | 0.0678 | | 0.1329 | 13.77 | 13770 | 0.0679 | | 0.1466 | 13.78 | 13780 | 0.0678 | | 0.1559 | 13.79 | 13790 | 0.0678 | | 0.0985 | 13.8 | 13800 | 0.0677 | | 0.1517 | 13.81 | 13810 | 0.0677 | | 0.1126 | 13.82 | 13820 | 0.0677 | | 0.1354 | 13.83 | 13830 | 0.0677 | | 0.1257 | 13.84 | 13840 | 0.0677 | | 0.1092 | 13.85 | 13850 | 0.0677 | | 0.1596 | 13.86 | 13860 | 0.0676 | | 0.1083 | 13.87 | 13870 | 0.0676 | | 0.1004 | 13.88 | 13880 | 0.0678 | | 0.1178 | 13.89 | 13890 | 0.0679 | | 0.1418 | 13.9 | 13900 | 0.0680 | | 0.1353 | 13.91 | 13910 | 0.0681 | | 0.0727 | 13.92 | 13920 | 0.0681 | | 0.1375 | 13.93 | 13930 | 0.0682 | | 0.0958 | 13.94 | 13940 | 0.0681 | | 0.1113 | 13.95 | 13950 | 0.0681 | | 0.1047 | 13.96 | 13960 | 0.0680 | | 0.0958 | 13.97 | 13970 | 0.0679 | | 0.1237 | 13.98 | 13980 | 0.0678 | | 0.1115 | 13.99 | 13990 | 0.0676 | | 0.1609 | 14.0 | 14000 | 0.0674 | | 0.1468 | 14.01 | 14010 | 0.0674 | | 0.0906 | 14.02 | 14020 | 0.0674 | | 0.0827 | 14.03 | 14030 | 0.0675 | | 0.1283 | 14.04 | 14040 | 0.0674 | | 0.1501 | 14.05 | 14050 | 0.0674 | | 0.1385 | 14.06 | 14060 | 0.0674 | | 0.1529 | 14.07 | 14070 | 0.0674 | | 0.144 | 14.08 | 14080 | 0.0673 | | 0.1779 | 14.09 | 14090 | 0.0673 | | 0.1417 | 14.1 | 14100 | 0.0672 | | 0.1297 | 14.11 | 14110 | 0.0671 | | 0.1147 | 14.12 | 14120 | 0.0671 | | 0.0785 | 14.13 | 14130 | 0.0671 | | 0.1511 | 14.14 | 14140 | 0.0671 | | 0.1242 | 14.15 | 14150 | 0.0671 | | 0.1594 | 14.16 | 14160 | 0.0669 | | 0.0934 | 14.17 | 14170 | 0.0668 | | 0.1215 | 14.18 | 14180 | 0.0669 | | 0.1903 | 14.19 | 14190 | 0.0670 | | 0.1491 | 14.2 | 14200 | 0.0671 | | 0.1379 | 14.21 | 14210 | 0.0670 | | 0.1305 | 14.22 | 14220 | 0.0669 | | 0.1399 | 14.23 | 14230 | 0.0670 | | 0.1682 | 14.24 | 14240 | 0.0671 | | 0.1143 | 14.25 | 14250 | 0.0670 | | 0.0935 | 14.26 | 14260 | 0.0669 | | 0.1032 | 14.27 | 14270 | 0.0668 | | 0.1585 | 14.28 | 14280 | 0.0667 | | 0.1342 | 14.29 | 14290 | 0.0667 | | 0.1145 | 14.3 | 14300 | 0.0665 | | 0.1574 | 14.31 | 14310 | 0.0664 | | 0.1516 | 14.32 | 14320 | 0.0663 | | 0.1492 | 14.33 | 14330 | 0.0663 | | 0.1126 | 14.34 | 14340 | 0.0663 | | 0.1084 | 14.35 | 14350 | 0.0663 | | 0.1372 | 14.36 | 14360 | 0.0663 | | 0.1479 | 14.37 | 14370 | 0.0662 | | 0.111 | 14.38 | 14380 | 0.0663 | | 0.1098 | 14.39 | 14390 | 0.0664 | | 0.1803 | 14.4 | 14400 | 0.0665 | | 0.161 | 14.41 | 14410 | 0.0665 | | 0.1318 | 14.42 | 14420 | 0.0666 | | 0.1728 | 14.43 | 14430 | 0.0666 | | 0.1026 | 14.44 | 14440 | 0.0666 | | 0.1062 | 14.45 | 14450 | 0.0665 | | 0.1605 | 14.46 | 14460 | 0.0665 | | 0.1153 | 14.47 | 14470 | 0.0667 | | 0.1491 | 14.48 | 14480 | 0.0667 | | 0.1702 | 14.49 | 14490 | 0.0666 | | 0.1372 | 14.5 | 14500 | 0.0666 | | 0.1517 | 14.51 | 14510 | 0.0665 | | 0.1009 | 14.52 | 14520 | 0.0664 | | 0.1384 | 14.53 | 14530 | 0.0664 | | 0.0812 | 14.54 | 14540 | 0.0665 | | 0.1405 | 14.55 | 14550 | 0.0665 | | 0.1006 | 14.56 | 14560 | 0.0666 | | 0.1215 | 14.57 | 14570 | 0.0665 | | 0.1642 | 14.58 | 14580 | 0.0665 | | 0.1275 | 14.59 | 14590 | 0.0665 | | 0.1197 | 14.6 | 14600 | 0.0665 | | 0.148 | 14.61 | 14610 | 0.0665 | | 0.1127 | 14.62 | 14620 | 0.0666 | | 0.0934 | 14.63 | 14630 | 0.0666 | | 0.1453 | 14.64 | 14640 | 0.0667 | | 0.1269 | 14.65 | 14650 | 0.0667 | | 0.1501 | 14.66 | 14660 | 0.0667 | | 0.1121 | 14.67 | 14670 | 0.0667 | | 0.1112 | 14.68 | 14680 | 0.0666 | | 0.1089 | 14.69 | 14690 | 0.0665 | | 0.1169 | 14.7 | 14700 | 0.0665 | | 0.1216 | 14.71 | 14710 | 0.0665 | | 0.134 | 14.72 | 14720 | 0.0665 | | 0.0875 | 14.73 | 14730 | 0.0665 | | 0.1196 | 14.74 | 14740 | 0.0665 | | 0.118 | 14.75 | 14750 | 0.0664 | | 0.1044 | 14.76 | 14760 | 0.0664 | | 0.0878 | 14.77 | 14770 | 0.0664 | | 0.1422 | 14.78 | 14780 | 0.0663 | | 0.1519 | 14.79 | 14790 | 0.0662 | | 0.1263 | 14.8 | 14800 | 0.0662 | | 0.0858 | 14.81 | 14810 | 0.0663 | | 0.1215 | 14.82 | 14820 | 0.0664 | | 0.1877 | 14.83 | 14830 | 0.0665 | | 0.1656 | 14.84 | 14840 | 0.0666 | | 0.1022 | 14.85 | 14850 | 0.0666 | | 0.1478 | 14.86 | 14860 | 0.0667 | | 0.1651 | 14.87 | 14870 | 0.0668 | | 0.163 | 14.88 | 14880 | 0.0669 | | 0.0913 | 14.89 | 14890 | 0.0670 | | 0.1002 | 14.9 | 14900 | 0.0671 | | 0.1255 | 14.91 | 14910 | 0.0671 | | 0.1246 | 14.92 | 14920 | 0.0671 | | 0.118 | 14.93 | 14930 | 0.0671 | | 0.1068 | 14.94 | 14940 | 0.0671 | | 0.1325 | 14.95 | 14950 | 0.0671 | | 0.1164 | 14.96 | 14960 | 0.0671 | | 0.1202 | 14.97 | 14970 | 0.0670 | | 0.1139 | 14.98 | 14980 | 0.0671 | | 0.1234 | 14.99 | 14990 | 0.0671 | | 0.0874 | 15.0 | 15000 | 0.0672 | | 0.0881 | 15.01 | 15010 | 0.0672 | | 0.1028 | 15.02 | 15020 | 0.0672 | | 0.1213 | 15.03 | 15030 | 0.0673 | | 0.0887 | 15.04 | 15040 | 0.0672 | | 0.1143 | 15.05 | 15050 | 0.0672 | | 0.1755 | 15.06 | 15060 | 0.0672 | | 0.11 | 15.07 | 15070 | 0.0672 | | 0.1509 | 15.08 | 15080 | 0.0673 | | 0.1111 | 15.09 | 15090 | 0.0674 | | 0.129 | 15.1 | 15100 | 0.0674 | | 0.1429 | 15.11 | 15110 | 0.0674 | | 0.1164 | 15.12 | 15120 | 0.0674 | | 0.1095 | 15.13 | 15130 | 0.0674 | | 0.1083 | 15.14 | 15140 | 0.0674 | | 0.1697 | 15.15 | 15150 | 0.0675 | | 0.1123 | 15.16 | 15160 | 0.0676 | | 0.1351 | 15.17 | 15170 | 0.0676 | | 0.1246 | 15.18 | 15180 | 0.0676 | | 0.1339 | 15.19 | 15190 | 0.0677 | | 0.1753 | 15.2 | 15200 | 0.0679 | | 0.1071 | 15.21 | 15210 | 0.0680 | | 0.1145 | 15.22 | 15220 | 0.0682 | | 0.1707 | 15.23 | 15230 | 0.0683 | | 0.1358 | 15.24 | 15240 | 0.0683 | | 0.1306 | 15.25 | 15250 | 0.0682 | | 0.1291 | 15.26 | 15260 | 0.0681 | | 0.0895 | 15.27 | 15270 | 0.0680 | | 0.149 | 15.28 | 15280 | 0.0679 | | 0.0662 | 15.29 | 15290 | 0.0679 | | 0.162 | 15.3 | 15300 | 0.0679 | | 0.0953 | 15.31 | 15310 | 0.0679 | | 0.12 | 15.32 | 15320 | 0.0680 | | 0.0858 | 15.33 | 15330 | 0.0680 | | 0.1321 | 15.34 | 15340 | 0.0681 | | 0.1988 | 15.35 | 15350 | 0.0682 | | 0.1258 | 15.36 | 15360 | 0.0682 | | 0.1262 | 15.37 | 15370 | 0.0681 | | 0.134 | 15.38 | 15380 | 0.0679 | | 0.1873 | 15.39 | 15390 | 0.0678 | | 0.1302 | 15.4 | 15400 | 0.0677 | | 0.103 | 15.41 | 15410 | 0.0677 | | 0.1638 | 15.42 | 15420 | 0.0676 | | 0.1407 | 15.43 | 15430 | 0.0676 | | 0.1575 | 15.44 | 15440 | 0.0675 | | 0.1308 | 15.45 | 15450 | 0.0676 | | 0.0824 | 15.46 | 15460 | 0.0676 | | 0.0911 | 15.47 | 15470 | 0.0676 | | 0.1256 | 15.48 | 15480 | 0.0676 | | 0.1219 | 15.49 | 15490 | 0.0676 | | 0.1313 | 15.5 | 15500 | 0.0676 | | 0.1056 | 15.51 | 15510 | 0.0676 | | 0.1176 | 15.52 | 15520 | 0.0676 | | 0.0867 | 15.53 | 15530 | 0.0676 | | 0.1419 | 15.54 | 15540 | 0.0676 | | 0.1312 | 15.55 | 15550 | 0.0676 | | 0.1618 | 15.56 | 15560 | 0.0677 | | 0.1562 | 15.57 | 15570 | 0.0678 | | 0.1319 | 15.58 | 15580 | 0.0679 | | 0.1625 | 15.59 | 15590 | 0.0680 | | 0.1143 | 15.6 | 15600 | 0.0679 | | 0.142 | 15.61 | 15610 | 0.0679 | | 0.1458 | 15.62 | 15620 | 0.0679 | | 0.1702 | 15.63 | 15630 | 0.0679 | | 0.1282 | 15.64 | 15640 | 0.0679 | | 0.1031 | 15.65 | 15650 | 0.0679 | | 0.1013 | 15.66 | 15660 | 0.0679 | | 0.1093 | 15.67 | 15670 | 0.0679 | | 0.1258 | 15.68 | 15680 | 0.0679 | | 0.0772 | 15.69 | 15690 | 0.0678 | | 0.0824 | 15.7 | 15700 | 0.0677 | | 0.1307 | 15.71 | 15710 | 0.0677 | | 0.0898 | 15.72 | 15720 | 0.0677 | | 0.1253 | 15.73 | 15730 | 0.0678 | | 0.1511 | 15.74 | 15740 | 0.0678 | | 0.1442 | 15.75 | 15750 | 0.0677 | | 0.1784 | 15.76 | 15760 | 0.0677 | | 0.0995 | 15.77 | 15770 | 0.0676 | | 0.0988 | 15.78 | 15780 | 0.0675 | | 0.1645 | 15.79 | 15790 | 0.0674 | | 0.1588 | 15.8 | 15800 | 0.0674 | | 0.1677 | 15.81 | 15810 | 0.0673 | | 0.1472 | 15.82 | 15820 | 0.0673 | | 0.1514 | 15.83 | 15830 | 0.0674 | | 0.1079 | 15.84 | 15840 | 0.0676 | | 0.1244 | 15.85 | 15850 | 0.0676 | | 0.107 | 15.86 | 15860 | 0.0676 | | 0.0886 | 15.87 | 15870 | 0.0676 | | 0.1113 | 15.88 | 15880 | 0.0676 | | 0.1499 | 15.89 | 15890 | 0.0676 | | 0.128 | 15.9 | 15900 | 0.0677 | | 0.1704 | 15.91 | 15910 | 0.0677 | | 0.1385 | 15.92 | 15920 | 0.0676 | | 0.1044 | 15.93 | 15930 | 0.0676 | | 0.183 | 15.94 | 15940 | 0.0676 | | 0.133 | 15.95 | 15950 | 0.0676 | | 0.1186 | 15.96 | 15960 | 0.0676 | | 0.112 | 15.97 | 15970 | 0.0675 | | 0.1365 | 15.98 | 15980 | 0.0675 | | 0.1066 | 15.99 | 15990 | 0.0674 | | 0.1408 | 16.0 | 16000 | 0.0674 | | 0.169 | 16.01 | 16010 | 0.0674 | | 0.1469 | 16.02 | 16020 | 0.0674 | | 0.1847 | 16.03 | 16030 | 0.0675 | | 0.1483 | 16.04 | 16040 | 0.0675 | | 0.1059 | 16.05 | 16050 | 0.0675 | | 0.1334 | 16.06 | 16060 | 0.0675 | | 0.1191 | 16.07 | 16070 | 0.0675 | | 0.1206 | 16.08 | 16080 | 0.0675 | | 0.1371 | 16.09 | 16090 | 0.0675 | | 0.1313 | 16.1 | 16100 | 0.0676 | | 0.1131 | 16.11 | 16110 | 0.0676 | | 0.1578 | 16.12 | 16120 | 0.0676 | | 0.0963 | 16.13 | 16130 | 0.0676 | | 0.2233 | 16.14 | 16140 | 0.0675 | | 0.1579 | 16.15 | 16150 | 0.0675 | | 0.1269 | 16.16 | 16160 | 0.0675 | | 0.1296 | 16.17 | 16170 | 0.0675 | | 0.1473 | 16.18 | 16180 | 0.0676 | | 0.1081 | 16.19 | 16190 | 0.0676 | | 0.1054 | 16.2 | 16200 | 0.0677 | | 0.1052 | 16.21 | 16210 | 0.0677 | | 0.1317 | 16.22 | 16220 | 0.0677 | | 0.1284 | 16.23 | 16230 | 0.0677 | | 0.1332 | 16.24 | 16240 | 0.0677 | | 0.1071 | 16.25 | 16250 | 0.0677 | | 0.1343 | 16.26 | 16260 | 0.0677 | | 0.1501 | 16.27 | 16270 | 0.0677 | | 0.1277 | 16.28 | 16280 | 0.0677 | | 0.0923 | 16.29 | 16290 | 0.0678 | | 0.1248 | 16.3 | 16300 | 0.0678 | | 0.1534 | 16.31 | 16310 | 0.0678 | | 0.0914 | 16.32 | 16320 | 0.0678 | | 0.2013 | 16.33 | 16330 | 0.0679 | | 0.1221 | 16.34 | 16340 | 0.0679 | | 0.1002 | 16.35 | 16350 | 0.0679 | | 0.1697 | 16.36 | 16360 | 0.0678 | | 0.2087 | 16.37 | 16370 | 0.0677 | | 0.1306 | 16.38 | 16380 | 0.0677 | | 0.1411 | 16.39 | 16390 | 0.0677 | | 0.1174 | 16.4 | 16400 | 0.0677 | | 0.1129 | 16.41 | 16410 | 0.0677 | | 0.1288 | 16.42 | 16420 | 0.0677 | | 0.1741 | 16.43 | 16430 | 0.0677 | | 0.106 | 16.44 | 16440 | 0.0678 | | 0.1714 | 16.45 | 16450 | 0.0678 | | 0.1097 | 16.46 | 16460 | 0.0678 | | 0.1027 | 16.47 | 16470 | 0.0679 | | 0.146 | 16.48 | 16480 | 0.0679 | | 0.123 | 16.49 | 16490 | 0.0679 | | 0.1437 | 16.5 | 16500 | 0.0679 | | 0.1062 | 16.51 | 16510 | 0.0680 | | 0.1634 | 16.52 | 16520 | 0.0679 | | 0.0851 | 16.53 | 16530 | 0.0679 | | 0.0735 | 16.54 | 16540 | 0.0679 | | 0.1056 | 16.55 | 16550 | 0.0679 | | 0.1538 | 16.56 | 16560 | 0.0680 | | 0.1675 | 16.57 | 16570 | 0.0679 | | 0.123 | 16.58 | 16580 | 0.0680 | | 0.1112 | 16.59 | 16590 | 0.0680 | | 0.1596 | 16.6 | 16600 | 0.0680 | | 0.1279 | 16.61 | 16610 | 0.0680 | | 0.1091 | 16.62 | 16620 | 0.0680 | | 0.0994 | 16.63 | 16630 | 0.0680 | | 0.1385 | 16.64 | 16640 | 0.0680 | | 0.0764 | 16.65 | 16650 | 0.0680 | | 0.1032 | 16.66 | 16660 | 0.0680 | | 0.1808 | 16.67 | 16670 | 0.0679 | | 0.1235 | 16.68 | 16680 | 0.0680 | | 0.1034 | 16.69 | 16690 | 0.0679 | | 0.1096 | 16.7 | 16700 | 0.0680 | | 0.1252 | 16.71 | 16710 | 0.0680 | | 0.0921 | 16.72 | 16720 | 0.0680 | | 0.1656 | 16.73 | 16730 | 0.0679 | | 0.0974 | 16.74 | 16740 | 0.0679 | | 0.1252 | 16.75 | 16750 | 0.0679 | | 0.1263 | 16.76 | 16760 | 0.0679 | | 0.1502 | 16.77 | 16770 | 0.0679 | | 0.1424 | 16.78 | 16780 | 0.0679 | | 0.11 | 16.79 | 16790 | 0.0680 | | 0.1081 | 16.8 | 16800 | 0.0680 | | 0.1256 | 16.81 | 16810 | 0.0680 | | 0.0993 | 16.82 | 16820 | 0.0680 | | 0.1148 | 16.83 | 16830 | 0.0681 | | 0.1431 | 16.84 | 16840 | 0.0681 | | 0.1085 | 16.85 | 16850 | 0.0680 | | 0.1077 | 16.86 | 16860 | 0.0679 | | 0.1247 | 16.87 | 16870 | 0.0679 | | 0.087 | 16.88 | 16880 | 0.0678 | | 0.1145 | 16.89 | 16890 | 0.0678 | | 0.1615 | 16.9 | 16900 | 0.0678 | | 0.1338 | 16.91 | 16910 | 0.0677 | | 0.116 | 16.92 | 16920 | 0.0677 | | 0.125 | 16.93 | 16930 | 0.0677 | | 0.0954 | 16.94 | 16940 | 0.0677 | | 0.1586 | 16.95 | 16950 | 0.0677 | | 0.1027 | 16.96 | 16960 | 0.0677 | | 0.097 | 16.97 | 16970 | 0.0678 | | 0.1298 | 16.98 | 16980 | 0.0678 | | 0.1255 | 16.99 | 16990 | 0.0678 | | 0.0878 | 17.0 | 17000 | 0.0678 | | 0.1538 | 17.01 | 17010 | 0.0679 | | 0.1039 | 17.02 | 17020 | 0.0679 | | 0.1264 | 17.03 | 17030 | 0.0679 | | 0.1695 | 17.04 | 17040 | 0.0679 | | 0.0961 | 17.05 | 17050 | 0.0679 | | 0.1451 | 17.06 | 17060 | 0.0679 | | 0.1206 | 17.07 | 17070 | 0.0679 | | 0.1072 | 17.08 | 17080 | 0.0679 | | 0.0849 | 17.09 | 17090 | 0.0679 | | 0.1567 | 17.1 | 17100 | 0.0679 | | 0.1017 | 17.11 | 17110 | 0.0679 | | 0.1369 | 17.12 | 17120 | 0.0679 | | 0.1033 | 17.13 | 17130 | 0.0679 | | 0.1308 | 17.14 | 17140 | 0.0679 | | 0.1366 | 17.15 | 17150 | 0.0679 | | 0.1151 | 17.16 | 17160 | 0.0679 | | 0.1282 | 17.17 | 17170 | 0.0679 | | 0.1321 | 17.18 | 17180 | 0.0679 | | 0.1374 | 17.19 | 17190 | 0.0679 | | 0.1526 | 17.2 | 17200 | 0.0679 | | 0.1288 | 17.21 | 17210 | 0.0680 | | 0.1322 | 17.22 | 17220 | 0.0680 | | 0.0959 | 17.23 | 17230 | 0.0680 | | 0.1165 | 17.24 | 17240 | 0.0680 | | 0.1482 | 17.25 | 17250 | 0.0680 | | 0.1022 | 17.26 | 17260 | 0.0679 | | 0.119 | 17.27 | 17270 | 0.0679 | | 0.0637 | 17.28 | 17280 | 0.0679 | | 0.0862 | 17.29 | 17290 | 0.0679 | | 0.1719 | 17.3 | 17300 | 0.0679 | | 0.1184 | 17.31 | 17310 | 0.0679 | | 0.1071 | 17.32 | 17320 | 0.0679 | | 0.0975 | 17.33 | 17330 | 0.0679 | | 0.1764 | 17.34 | 17340 | 0.0680 | | 0.1265 | 17.35 | 17350 | 0.0680 | | 0.0872 | 17.36 | 17360 | 0.0681 | | 0.0905 | 17.37 | 17370 | 0.0681 | | 0.1804 | 17.38 | 17380 | 0.0682 | | 0.1631 | 17.39 | 17390 | 0.0682 | | 0.1525 | 17.4 | 17400 | 0.0683 | | 0.2399 | 17.41 | 17410 | 0.0683 | | 0.173 | 17.42 | 17420 | 0.0682 | | 0.1068 | 17.43 | 17430 | 0.0682 | | 0.1128 | 17.44 | 17440 | 0.0682 | | 0.1486 | 17.45 | 17450 | 0.0682 | | 0.1218 | 17.46 | 17460 | 0.0682 | | 0.1763 | 17.47 | 17470 | 0.0683 | | 0.2045 | 17.48 | 17480 | 0.0683 | | 0.0808 | 17.49 | 17490 | 0.0683 | | 0.0647 | 17.5 | 17500 | 0.0683 | | 0.1647 | 17.51 | 17510 | 0.0683 | | 0.1576 | 17.52 | 17520 | 0.0683 | | 0.1091 | 17.53 | 17530 | 0.0683 | | 0.1157 | 17.54 | 17540 | 0.0682 | | 0.1159 | 17.55 | 17550 | 0.0682 | | 0.1928 | 17.56 | 17560 | 0.0682 | | 0.132 | 17.57 | 17570 | 0.0682 | | 0.1011 | 17.58 | 17580 | 0.0682 | | 0.1373 | 17.59 | 17590 | 0.0682 | | 0.1239 | 17.6 | 17600 | 0.0682 | | 0.1264 | 17.61 | 17610 | 0.0681 | | 0.075 | 17.62 | 17620 | 0.0681 | | 0.127 | 17.63 | 17630 | 0.0680 | | 0.1296 | 17.64 | 17640 | 0.0680 | | 0.0834 | 17.65 | 17650 | 0.0680 | | 0.1357 | 17.66 | 17660 | 0.0681 | | 0.1118 | 17.67 | 17670 | 0.0681 | | 0.1179 | 17.68 | 17680 | 0.0681 | | 0.1222 | 17.69 | 17690 | 0.0681 | | 0.1234 | 17.7 | 17700 | 0.0682 | | 0.0625 | 17.71 | 17710 | 0.0681 | | 0.0986 | 17.72 | 17720 | 0.0681 | | 0.2145 | 17.73 | 17730 | 0.0681 | | 0.1318 | 17.74 | 17740 | 0.0680 | | 0.1073 | 17.75 | 17750 | 0.0680 | | 0.145 | 17.76 | 17760 | 0.0680 | | 0.1493 | 17.77 | 17770 | 0.0679 | | 0.0863 | 17.78 | 17780 | 0.0679 | | 0.1664 | 17.79 | 17790 | 0.0679 | | 0.1424 | 17.8 | 17800 | 0.0679 | | 0.1012 | 17.81 | 17810 | 0.0679 | | 0.1332 | 17.82 | 17820 | 0.0678 | | 0.1487 | 17.83 | 17830 | 0.0678 | | 0.1096 | 17.84 | 17840 | 0.0679 | | 0.1239 | 17.85 | 17850 | 0.0679 | | 0.1455 | 17.86 | 17860 | 0.0679 | | 0.0981 | 17.87 | 17870 | 0.0680 | | 0.1229 | 17.88 | 17880 | 0.0680 | | 0.1192 | 17.89 | 17890 | 0.0679 | | 0.1313 | 17.9 | 17900 | 0.0679 | | 0.104 | 17.91 | 17910 | 0.0679 | | 0.1355 | 17.92 | 17920 | 0.0678 | | 0.0957 | 17.93 | 17930 | 0.0678 | | 0.171 | 17.94 | 17940 | 0.0678 | | 0.1029 | 17.95 | 17950 | 0.0678 | | 0.1273 | 17.96 | 17960 | 0.0678 | | 0.1102 | 17.97 | 17970 | 0.0678 | | 0.1794 | 17.98 | 17980 | 0.0678 | | 0.1301 | 17.99 | 17990 | 0.0678 | | 0.1305 | 18.0 | 18000 | 0.0678 | | 0.1745 | 18.01 | 18010 | 0.0678 | | 0.1337 | 18.02 | 18020 | 0.0678 | | 0.1219 | 18.03 | 18030 | 0.0678 | | 0.1326 | 18.04 | 18040 | 0.0678 | | 0.0912 | 18.05 | 18050 | 0.0678 | | 0.115 | 18.06 | 18060 | 0.0679 | | 0.1402 | 18.07 | 18070 | 0.0679 | | 0.0903 | 18.08 | 18080 | 0.0679 | | 0.1204 | 18.09 | 18090 | 0.0679 | | 0.1296 | 18.1 | 18100 | 0.0679 | | 0.1176 | 18.11 | 18110 | 0.0678 | | 0.0926 | 18.12 | 18120 | 0.0678 | | 0.1387 | 18.13 | 18130 | 0.0678 | | 0.1098 | 18.14 | 18140 | 0.0677 | | 0.1264 | 18.15 | 18150 | 0.0677 | | 0.0864 | 18.16 | 18160 | 0.0677 | | 0.2153 | 18.17 | 18170 | 0.0677 | | 0.0984 | 18.18 | 18180 | 0.0678 | | 0.1249 | 18.19 | 18190 | 0.0678 | | 0.1411 | 18.2 | 18200 | 0.0678 | | 0.1237 | 18.21 | 18210 | 0.0678 | | 0.1076 | 18.22 | 18220 | 0.0678 | | 0.1547 | 18.23 | 18230 | 0.0678 | | 0.1031 | 18.24 | 18240 | 0.0679 | | 0.1305 | 18.25 | 18250 | 0.0678 | | 0.1385 | 18.26 | 18260 | 0.0678 | | 0.1488 | 18.27 | 18270 | 0.0678 | | 0.124 | 18.28 | 18280 | 0.0678 | | 0.1043 | 18.29 | 18290 | 0.0678 | | 0.1105 | 18.3 | 18300 | 0.0679 | | 0.1424 | 18.31 | 18310 | 0.0679 | | 0.109 | 18.32 | 18320 | 0.0679 | | 0.0968 | 18.33 | 18330 | 0.0679 | | 0.1356 | 18.34 | 18340 | 0.0679 | | 0.1464 | 18.35 | 18350 | 0.0678 | | 0.117 | 18.36 | 18360 | 0.0679 | | 0.0959 | 18.37 | 18370 | 0.0679 | | 0.162 | 18.38 | 18380 | 0.0678 | | 0.1577 | 18.39 | 18390 | 0.0679 | | 0.115 | 18.4 | 18400 | 0.0679 | | 0.0833 | 18.41 | 18410 | 0.0679 | | 0.1108 | 18.42 | 18420 | 0.0679 | | 0.1653 | 18.43 | 18430 | 0.0679 | | 0.1894 | 18.44 | 18440 | 0.0679 | | 0.1565 | 18.45 | 18450 | 0.0679 | | 0.1001 | 18.46 | 18460 | 0.0679 | | 0.1084 | 18.47 | 18470 | 0.0679 | | 0.164 | 18.48 | 18480 | 0.0679 | | 0.1232 | 18.49 | 18490 | 0.0679 | | 0.0927 | 18.5 | 18500 | 0.0679 | | 0.1665 | 18.51 | 18510 | 0.0679 | | 0.1389 | 18.52 | 18520 | 0.0679 | | 0.121 | 18.53 | 18530 | 0.0679 | | 0.1347 | 18.54 | 18540 | 0.0679 | | 0.1238 | 18.55 | 18550 | 0.0679 | | 0.0981 | 18.56 | 18560 | 0.0679 | | 0.1181 | 18.57 | 18570 | 0.0678 | | 0.1339 | 18.58 | 18580 | 0.0678 | | 0.13 | 18.59 | 18590 | 0.0678 | | 0.1145 | 18.6 | 18600 | 0.0678 | | 0.1181 | 18.61 | 18610 | 0.0678 | | 0.189 | 18.62 | 18620 | 0.0679 | | 0.1237 | 18.63 | 18630 | 0.0679 | | 0.1588 | 18.64 | 18640 | 0.0679 | | 0.1375 | 18.65 | 18650 | 0.0679 | | 0.1067 | 18.66 | 18660 | 0.0679 | | 0.1511 | 18.67 | 18670 | 0.0679 | | 0.1255 | 18.68 | 18680 | 0.0679 | | 0.1328 | 18.69 | 18690 | 0.0679 | | 0.1626 | 18.7 | 18700 | 0.0679 | | 0.0661 | 18.71 | 18710 | 0.0679 | | 0.0785 | 18.72 | 18720 | 0.0679 | | 0.1707 | 18.73 | 18730 | 0.0679 | | 0.1522 | 18.74 | 18740 | 0.0679 | | 0.1449 | 18.75 | 18750 | 0.0679 | | 0.083 | 18.76 | 18760 | 0.0679 | | 0.0882 | 18.77 | 18770 | 0.0679 | | 0.1285 | 18.78 | 18780 | 0.0679 | | 0.0934 | 18.79 | 18790 | 0.0678 | | 0.1788 | 18.8 | 18800 | 0.0678 | | 0.0998 | 18.81 | 18810 | 0.0678 | | 0.1165 | 18.82 | 18820 | 0.0678 | | 0.1367 | 18.83 | 18830 | 0.0678 | | 0.1192 | 18.84 | 18840 | 0.0679 | | 0.14 | 18.85 | 18850 | 0.0679 | | 0.1594 | 18.86 | 18860 | 0.0678 | | 0.1493 | 18.87 | 18870 | 0.0678 | | 0.1266 | 18.88 | 18880 | 0.0679 | | 0.0926 | 18.89 | 18890 | 0.0678 | | 0.1058 | 18.9 | 18900 | 0.0679 | | 0.0981 | 18.91 | 18910 | 0.0679 | | 0.0955 | 18.92 | 18920 | 0.0679 | | 0.1762 | 18.93 | 18930 | 0.0679 | | 0.1086 | 18.94 | 18940 | 0.0678 | | 0.1509 | 18.95 | 18950 | 0.0678 | | 0.1192 | 18.96 | 18960 | 0.0678 | | 0.1253 | 18.97 | 18970 | 0.0678 | | 0.1322 | 18.98 | 18980 | 0.0678 | | 0.112 | 18.99 | 18990 | 0.0679 | | 0.1392 | 19.0 | 19000 | 0.0679 | | 0.1317 | 19.01 | 19010 | 0.0679 | | 0.1201 | 19.02 | 19020 | 0.0679 | | 0.1217 | 19.03 | 19030 | 0.0679 | | 0.1015 | 19.04 | 19040 | 0.0679 | | 0.0911 | 19.05 | 19050 | 0.0679 | | 0.1095 | 19.06 | 19060 | 0.0679 | | 0.1423 | 19.07 | 19070 | 0.0679 | | 0.144 | 19.08 | 19080 | 0.0679 | | 0.1376 | 19.09 | 19090 | 0.0679 | | 0.0957 | 19.1 | 19100 | 0.0679 | | 0.1214 | 19.11 | 19110 | 0.0679 | | 0.1052 | 19.12 | 19120 | 0.0679 | | 0.1113 | 19.13 | 19130 | 0.0679 | | 0.1393 | 19.14 | 19140 | 0.0679 | | 0.1622 | 19.15 | 19150 | 0.0679 | | 0.1259 | 19.16 | 19160 | 0.0679 | | 0.0982 | 19.17 | 19170 | 0.0679 | | 0.1379 | 19.18 | 19180 | 0.0679 | | 0.1301 | 19.19 | 19190 | 0.0679 | | 0.1403 | 19.2 | 19200 | 0.0679 | | 0.163 | 19.21 | 19210 | 0.0679 | | 0.1091 | 19.22 | 19220 | 0.0679 | | 0.1102 | 19.23 | 19230 | 0.0679 | | 0.1017 | 19.24 | 19240 | 0.0679 | | 0.0861 | 19.25 | 19250 | 0.0679 | | 0.154 | 19.26 | 19260 | 0.0679 | | 0.1095 | 19.27 | 19270 | 0.0679 | | 0.1119 | 19.28 | 19280 | 0.0679 | | 0.1117 | 19.29 | 19290 | 0.0679 | | 0.1372 | 19.3 | 19300 | 0.0679 | | 0.0972 | 19.31 | 19310 | 0.0679 | | 0.1029 | 19.32 | 19320 | 0.0679 | | 0.1148 | 19.33 | 19330 | 0.0679 | | 0.1427 | 19.34 | 19340 | 0.0679 | | 0.0774 | 19.35 | 19350 | 0.0679 | | 0.0988 | 19.36 | 19360 | 0.0679 | | 0.1259 | 19.37 | 19370 | 0.0679 | | 0.1383 | 19.38 | 19380 | 0.0679 | | 0.1604 | 19.39 | 19390 | 0.0679 | | 0.1802 | 19.4 | 19400 | 0.0679 | | 0.0756 | 19.41 | 19410 | 0.0680 | | 0.1623 | 19.42 | 19420 | 0.0680 | | 0.2077 | 19.43 | 19430 | 0.0680 | | 0.1435 | 19.44 | 19440 | 0.0680 | | 0.1506 | 19.45 | 19450 | 0.0680 | | 0.101 | 19.46 | 19460 | 0.0680 | | 0.1593 | 19.47 | 19470 | 0.0680 | | 0.076 | 19.48 | 19480 | 0.0680 | | 0.1329 | 19.49 | 19490 | 0.0680 | | 0.1212 | 19.5 | 19500 | 0.0680 | | 0.1458 | 19.51 | 19510 | 0.0680 | | 0.1582 | 19.52 | 19520 | 0.0680 | | 0.1203 | 19.53 | 19530 | 0.0680 | | 0.1434 | 19.54 | 19540 | 0.0680 | | 0.1366 | 19.55 | 19550 | 0.0680 | | 0.1633 | 19.56 | 19560 | 0.0680 | | 0.1484 | 19.57 | 19570 | 0.0680 | | 0.1363 | 19.58 | 19580 | 0.0680 | | 0.1428 | 19.59 | 19590 | 0.0680 | | 0.1465 | 19.6 | 19600 | 0.0680 | | 0.0957 | 19.61 | 19610 | 0.0680 | | 0.1345 | 19.62 | 19620 | 0.0680 | | 0.1382 | 19.63 | 19630 | 0.0680 | | 0.1468 | 19.64 | 19640 | 0.0680 | | 0.1237 | 19.65 | 19650 | 0.0680 | | 0.1178 | 19.66 | 19660 | 0.0680 | | 0.0848 | 19.67 | 19670 | 0.0680 | | 0.1159 | 19.68 | 19680 | 0.0680 | | 0.1639 | 19.69 | 19690 | 0.0680 | | 0.1084 | 19.7 | 19700 | 0.0680 | | 0.0811 | 19.71 | 19710 | 0.0680 | | 0.0745 | 19.72 | 19720 | 0.0680 | | 0.1026 | 19.73 | 19730 | 0.0680 | | 0.0895 | 19.74 | 19740 | 0.0680 | | 0.1719 | 19.75 | 19750 | 0.0680 | | 0.1247 | 19.76 | 19760 | 0.0680 | | 0.1174 | 19.77 | 19770 | 0.0680 | | 0.1476 | 19.78 | 19780 | 0.0680 | | 0.1718 | 19.79 | 19790 | 0.0680 | | 0.104 | 19.8 | 19800 | 0.0680 | | 0.166 | 19.81 | 19810 | 0.0680 | | 0.1547 | 19.82 | 19820 | 0.0680 | | 0.18 | 19.83 | 19830 | 0.0680 | | 0.1405 | 19.84 | 19840 | 0.0680 | | 0.1047 | 19.85 | 19850 | 0.0680 | | 0.1585 | 19.86 | 19860 | 0.0680 | | 0.1295 | 19.87 | 19870 | 0.0680 | | 0.1498 | 19.88 | 19880 | 0.0680 | | 0.0994 | 19.89 | 19890 | 0.0680 | | 0.1122 | 19.9 | 19900 | 0.0680 | | 0.0903 | 19.91 | 19910 | 0.0680 | | 0.0906 | 19.92 | 19920 | 0.0680 | | 0.1394 | 19.93 | 19930 | 0.0680 | | 0.0912 | 19.94 | 19940 | 0.0680 | | 0.1074 | 19.95 | 19950 | 0.0680 | | 0.1219 | 19.96 | 19960 | 0.0680 | | 0.1278 | 19.97 | 19970 | 0.0680 | | 0.1234 | 19.98 | 19980 | 0.0680 | | 0.1645 | 19.99 | 19990 | 0.0680 | | 0.19 | 20.0 | 20000 | 0.0680 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

roa7n/gpt2-human_nontata_promoters-randomized_6_layers_3e-05_lr_8_e

roa7n

2023-09-28T21:01:12Z

0

peft

[ "peft", "region:us" ]

null

2023-09-28T21:01:10Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

sak-red/a2c-PandaReachDense-v3

sak-red

2023-09-28T20:37:26Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T20:30:58Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -11.78 +/- 3.46 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

wy2029/a2c-PandaReachDense-v3

wy2029

2023-09-28T20:35:54Z

0

stable-baselines3

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T20:29:58Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -11.51 +/- 8.88 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

KxSystems/mri_resnet_model

KxSystems

2023-09-28T20:33:41Z

0

keras

[ "keras", "tf-keras", "license:apache-2.0", "region:us" ]

null

2023-09-28T20:29:56Z

--- license: apache-2.0 library_name: keras ---

CyberHarem/tachibana_nina_citrus

CyberHarem

2023-09-28T20:19:28Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/tachibana_nina_citrus", "license:mit", "region:us" ]

text-to-image

2023-09-28T20:08:36Z

--- license: mit datasets: - CyberHarem/tachibana_nina_citrus pipeline_tag: text-to-image tags: - art --- # Lora of tachibana_nina_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/tachibana_nina_citrus.pt` as the embedding and `4760/tachibana_nina_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.970. The trigger words are: 1. `tachibana_nina_citrus` 2. `long_hair, grey_hair, hairband, blue_eyes, purple_eyes, bow, hair_between_eyes, hair_bow` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.952 | [Download](5100/tachibana_nina_citrus.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.970** | [**Download**](4760/tachibana_nina_citrus.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.969 | [Download](4420/tachibana_nina_citrus.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.945 | [Download](4080/tachibana_nina_citrus.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.949 | [Download](3740/tachibana_nina_citrus.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.947 | [Download](3400/tachibana_nina_citrus.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.950 | [Download](3060/tachibana_nina_citrus.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.956 | [Download](2720/tachibana_nina_citrus.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.919 | [Download](2380/tachibana_nina_citrus.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.940 | [Download](2040/tachibana_nina_citrus.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.933 | [Download](1700/tachibana_nina_citrus.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.941 | [Download](1360/tachibana_nina_citrus.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.839 | [Download](1020/tachibana_nina_citrus.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.911 | [Download](680/tachibana_nina_citrus.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.725 | [Download](340/tachibana_nina_citrus.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Gurusha/sd-hand-model-lora-sdxl_text_encoder

Gurusha

2023-09-28T19:55:49Z

2

diffusers

[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-28T13:43:40Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-xl-base-1.0 dataset: None tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - Gurusha/sd-hand-model-lora-sdxl_text_encoder These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were fine-tuned on the None dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

criceca/food_classifier

criceca

2023-09-28T19:54:44Z

63

0

transformers

[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-28T19:50:36Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: criceca/food_classifier results: [] ---  # criceca/food_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3562 - Validation Loss: 0.5212 - Train Accuracy: 1.0 - Epoch: 4 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 10, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.7778 | 0.6301 | 1.0 | 0 | | 0.5779 | 0.5862 | 1.0 | 1 | | 0.4803 | 0.5538 | 1.0 | 2 | | 0.4108 | 0.5343 | 1.0 | 3 | | 0.3562 | 0.5212 | 1.0 | 4 | ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

gokuls/HBERTv1_emb_compress_48_L10_H128_A2

gokuls

2023-09-28T19:38:05Z

45

0

transformers

[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-27T06:40:16Z

--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_emb_compress_48_L10_H128_A2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.14372973477498716 ---  # HBERTv1_emb_compress_48_L10_H128_A2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.2119 - Accuracy: 0.1437 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 80 - eval_batch_size: 80 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 7.7198 | 0.14 | 10000 | 7.6446 | 0.0636 | | 6.9424 | 0.27 | 20000 | 6.9379 | 0.0825 | | 6.7559 | 0.41 | 30000 | 6.7492 | 0.0974 | | 6.6513 | 0.55 | 40000 | 6.6455 | 0.1047 | | 6.5706 | 0.68 | 50000 | 6.5734 | 0.1113 | | 6.5236 | 0.82 | 60000 | 6.5176 | 0.1170 | | 6.479 | 0.96 | 70000 | 6.4744 | 0.1212 | | 6.4372 | 1.09 | 80000 | 6.4382 | 0.1255 | | 6.4069 | 1.23 | 90000 | 6.4071 | 0.1275 | | 6.3918 | 1.37 | 100000 | 6.3806 | 0.1302 | | 6.3625 | 1.5 | 110000 | 6.3581 | 0.1320 | | 6.339 | 1.64 | 120000 | 6.3400 | 0.1341 | | 6.3189 | 1.78 | 130000 | 6.3237 | 0.1350 | | 6.3115 | 1.91 | 140000 | 6.3103 | 0.1366 | | 6.298 | 2.05 | 150000 | 6.2982 | 0.1374 | | 6.2888 | 2.18 | 160000 | 6.2878 | 0.1382 | | 6.2786 | 2.32 | 170000 | 6.2778 | 0.1392 | | 6.2691 | 2.46 | 180000 | 6.2703 | 0.1398 | | 6.2675 | 2.59 | 190000 | 6.2622 | 0.1402 | | 6.255 | 2.73 | 200000 | 6.2571 | 0.1404 | | 6.2481 | 2.87 | 210000 | 6.2517 | 0.1407 | | 6.2465 | 3.0 | 220000 | 6.2447 | 0.1417 | | 6.2381 | 3.14 | 230000 | 6.2399 | 0.1419 | | 6.2332 | 3.28 | 240000 | 6.2371 | 0.1422 | | 6.2279 | 3.41 | 250000 | 6.2321 | 0.1424 | | 6.2275 | 3.55 | 260000 | 6.2274 | 0.1427 | | 6.2186 | 3.69 | 270000 | 6.2249 | 0.1429 | | 6.2192 | 3.82 | 280000 | 6.2231 | 0.1428 | | 6.2228 | 3.96 | 290000 | 6.2208 | 0.1433 | | 6.2154 | 4.1 | 300000 | 6.2192 | 0.1434 | | 6.2204 | 4.23 | 310000 | 6.2177 | 0.1433 | | 6.2123 | 4.37 | 320000 | 6.2162 | 0.1434 | | 6.2136 | 4.51 | 330000 | 6.2134 | 0.1437 | | 6.214 | 4.64 | 340000 | 6.2131 | 0.1439 | | 6.2139 | 4.78 | 350000 | 6.2129 | 0.1437 | | 6.211 | 4.92 | 360000 | 6.2130 | 0.1439 | ### Framework versions - Transformers 4.33.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/tachibana_sara_citrus

CyberHarem

2023-09-28T19:35:33Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/tachibana_sara_citrus", "license:mit", "region:us" ]

text-to-image

2023-09-28T19:24:37Z

--- license: mit datasets: - CyberHarem/tachibana_sara_citrus pipeline_tag: text-to-image tags: - art --- # Lora of tachibana_sara_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/tachibana_sara_citrus.pt` as the embedding and `4760/tachibana_sara_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.992. The trigger words are: 1. `tachibana_sara_citrus` 2. `long_hair, purple_eyes, serafuku, grey_hair, ahoge, blue_eyes, anime_coloring` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.953 | [Download](5100/tachibana_sara_citrus.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.992** | [**Download**](4760/tachibana_sara_citrus.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.983 | [Download](4420/tachibana_sara_citrus.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.946 | [Download](4080/tachibana_sara_citrus.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.896 | [Download](3740/tachibana_sara_citrus.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.899 | [Download](3400/tachibana_sara_citrus.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.899 | [Download](3060/tachibana_sara_citrus.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.942 | [Download](2720/tachibana_sara_citrus.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.929 | [Download](2380/tachibana_sara_citrus.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.863 | [Download](2040/tachibana_sara_citrus.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.792 | [Download](1700/tachibana_sara_citrus.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.858 | [Download](1360/tachibana_sara_citrus.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.844 | [Download](1020/tachibana_sara_citrus.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.493 | [Download](680/tachibana_sara_citrus.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.404 | [Download](340/tachibana_sara_citrus.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Schadom/dqn-SpaceInvadersNoFrameskip-v4-v6

Schadom

2023-09-28T19:29:52Z

4

0

stable-baselines3

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T19:29:08Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 796.00 +/- 319.83 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Schadom -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Schadom -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Schadom ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

vineetsharma/databricks-dolly-15k-distilgpt2-v1

vineetsharma

2023-09-28T19:23:57Z

141

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T18:13:40Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: databricks-dolly-15k-distilgpt2-v1 results: [] ---  # databricks-dolly-15k-distilgpt2-v1 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 3.3381 - eval_runtime: 25.854 - eval_samples_per_second: 58.057 - eval_steps_per_second: 7.272 - epoch: 5.01 - step: 7523 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

shikari2917/papapic

shikari2917

2023-09-28T19:23:04Z

5

0

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-28T19:22:57Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks atique tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.0003_lr_8_e

roa7n

2023-09-28T19:12:02Z

0

peft

[ "peft", "region:us" ]

null

2023-09-28T19:12:00Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

asmaa1/videomae-base-groub7-finetuned-SLT-subset

asmaa1

2023-09-28T18:53:22Z

64

0

transformers

[ "transformers", "pytorch", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-09-28T05:42:23Z

--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-groub7-finetuned-SLT-subset results: [] ---  # videomae-base-groub7-finetuned-SLT-subset This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.4002 - Accuracy: 0.1190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 80 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8439 | 0.14 | 11 | 3.6563 | 0.0714 | | 3.8227 | 1.14 | 22 | 3.6419 | 0.0476 | | 3.7396 | 2.14 | 33 | 3.5863 | 0.0952 | | 3.6939 | 3.14 | 44 | 3.5431 | 0.0952 | | 3.5364 | 4.14 | 55 | 3.4718 | 0.1190 | | 3.508 | 5.14 | 66 | 3.4199 | 0.1190 | | 3.5206 | 6.14 | 77 | 3.4016 | 0.1190 | | 3.425 | 7.04 | 80 | 3.4002 | 0.1190 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3

platzi/platzi-vit-model-Carlos-Moreno

platzi

2023-09-28T18:48:35Z

213

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:beans", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-28T18:44:17Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: platzi-vit-model-Carlos-Moreno results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9849624060150376 ---  # platzi-vit-model-Carlos-Moreno This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset. It achieves the following results on the evaluation set: - Loss: 0.0368 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.144 | 3.85 | 500 | 0.0368 | 0.9850 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

fp16-guy/CuriousMerge_2.5D_fp16_cleaned

fp16-guy

2023-09-28T18:32:22Z

0

null

[ "text-to-image", "region:us" ]

text-to-image

2023-09-28T17:46:17Z

--- pipeline_tag: text-to-image --- CuriousMerge 2.5D, but fp16/cleaned - smaller size, same result. ======== /// **[**original checkpoint link**](https://civitai.com/models/79070/curiousmerge-25d)** *(all rights to the model belong to Cur1ous)* --- *[*grid 01*](https://huggingface.co/datasets/fp16-guy/grids/blob/main/curiousmerge25D_v50V4V2%2001%2020230928205844-111-curiousmerge25D_v50V4V2-Euler%20a-6.png) *(1.99gb version)* *[*grid 02*](https://huggingface.co/datasets/fp16-guy/grids/blob/main/curiousmerge25D_v50V4V2%2002%2020230928205938-111-curiousmerge25D_v50V4V2-Euler%20a-6.png) *(1.83gb version - no vae)* *[*grid 03*](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/curiousmerge25D_v50V4V2%20inp%2001%2020230928184336-111-curiousmerge25D_v50V4V2-Euler%20a-8.png) *(1.99gb inpainting version)* *[*grid 04*](https://huggingface.co/datasets/fp16-guy/grids_inp/blob/main/curiousmerge25D_v50V4V2%20inp%2002%2020230928184150-111-curiousmerge25D_v50V4V2-Euler%20a-8.png) *(1.83gb inpainting version - no vae)*

Sooyung/ppo-LunarLander-v2

Sooyung

2023-09-28T18:26:38Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T18:26:22Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 297.29 +/- 8.93 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

deutsche-welle/t5_large_peft_wnc_debiaser

deutsche-welle

2023-09-28T18:18:39Z

115

1

peft

[ "peft", "media", "text2text-generation", "en", "base_model:google-t5/t5-large", "base_model:adapter:google-t5/t5-large", "region:us" ]

text2text-generation

2023-07-27T13:43:48Z

--- language: - en library_name: peft tags: - media metrics: - rouge pipeline_tag: text2text-generation base_model: t5-large --- The model is trained on [Wiki Neutrality Corpus (WNC)](https://ojs.aaai.org/index.php/AAAI/article/view/5385). Big kudos to the authors of WNC. We uploaded an example Python script (main.py), you can refer it if you want to host it via FastAPI.

deutsche-welle/bias_classifier_roberta_base_peft

deutsche-welle

2023-09-28T18:18:27Z

2

1

peft

[ "peft", "roberta", "text-classification", "en", "dataset:mediabiasgroup/BABE", "arxiv:1910.09700", "base_model:FacebookAI/roberta-base", "base_model:adapter:FacebookAI/roberta-base", "region:us" ]

text-classification

2023-07-31T13:02:47Z

--- language: - en library_name: peft tags: - roberta datasets: - mediabiasgroup/BABE metrics: - f1 pipeline_tag: text-classification base_model: roberta-base --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ArturGadek/LunarLander-v2

ArturGadek

2023-09-28T18:14:02Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T18:13:03Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 287.00 +/- 13.87 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

kadriu/shqip-mms-3

kadriu

2023-09-28T18:01:48Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-08-17T22:33:52Z

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - generated_from_trainer metrics: - wer model-index: - name: shqip-mms-3 results: [] ---  # shqip-mms-3 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4195 - Wer: 0.3447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.5717 | 0.31 | 300 | 0.9548 | 0.7373 | | 0.7823 | 0.63 | 600 | 0.7372 | 0.5886 | | 0.6863 | 0.94 | 900 | 0.6122 | 0.5261 | | 0.5908 | 1.25 | 1200 | 0.5215 | 0.4440 | | 0.5237 | 1.57 | 1500 | 0.4693 | 0.3987 | | 0.4662 | 1.88 | 1800 | 0.4195 | 0.3447 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.1.0.dev20230810 - Datasets 2.14.3 - Tokenizers 0.13.3

infCapital/viet-llama2-ft

infCapital

2023-09-28T17:57:21Z

1,447

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "vi", "dataset:tatsu-lab/alpaca", "dataset:ewof/alpaca-instruct-unfiltered", "dataset:databricks/databricks-dolly-15k", "dataset:teknium/GPTeacher-General-Instruct", "dataset:garage-bAInd/Open-Platypus", "dataset:Honkware/oasst1-alpaca-json", "dataset:GAIR/lima", "dataset:infCapital/viet-llama2-ft-tiny", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T16:45:15Z

--- datasets: - tatsu-lab/alpaca - ewof/alpaca-instruct-unfiltered - databricks/databricks-dolly-15k - teknium/GPTeacher-General-Instruct - garage-bAInd/Open-Platypus - Honkware/oasst1-alpaca-json - GAIR/lima - infCapital/viet-llama2-ft-tiny language: - vi --- + LLaMa2 - 7B Chat models, extend vocab size to 44800 for Vietnamese understanding. + Continual Pre-Train with 2B Vietnames Tokens aligned from VnNews Corpus, 10K vnthuquan books, wikipedia_vi + Fine-Tuning with infCapital/viet-llama2-ft-tiny dataset, the combination of vaious dataset then translated into Vietnamese using OpenAI GPT-3 + For more information: email me at duyhunghd6@gmail.com | http://fb.com/hungbui2013

Yntec/nuipenimix2

Yntec

2023-09-28T17:39:27Z

12,747

4

diffusers

[ "diffusers", "safetensors", "Anime", "Cute", "Animals", "McSionnaigh", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-28T16:58:20Z

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Anime - Cute - Animals - McSionnaigh - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # nuipenimix 2 fp16 no-ema version of this model. Original page: https://civitai.com/models/81937?modelVersionId=139841 Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/SoiwQEhZqvAW9JdXn5Abu.png) ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/MIbzralOKww2sWggu9lFW.png) icon of cute little red panda, round frame, blue glow, purple background

gokuls/HBERTv1_emb_compress_48_L12_H64_A2

gokuls

2023-09-28T17:39:14Z

46

0

transformers

[ "transformers", "pytorch", "hybridbert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-27T07:12:38Z

--- tags: - generated_from_trainer datasets: - gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - accuracy model-index: - name: HBERTv1_emb_compress_48_L12_H64_A2 results: - task: name: Masked Language Modeling type: fill-mask dataset: name: gokuls/wiki_book_corpus_complete_processed_bert_dataset type: gokuls/wiki_book_corpus_complete_processed_bert_dataset metrics: - name: Accuracy type: accuracy value: 0.12850906143802152 ---  # HBERTv1_emb_compress_48_L12_H64_A2 This model is a fine-tuned version of [](https://huggingface.co/) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 6.4079 - Accuracy: 0.1285 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 96 - eval_batch_size: 96 - seed: 10 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10000 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 8.6554 | 0.16 | 10000 | 8.5846 | 0.0483 | | 7.2331 | 0.33 | 20000 | 7.2280 | 0.0542 | | 7.0014 | 0.49 | 30000 | 6.9927 | 0.0677 | | 6.8699 | 0.66 | 40000 | 6.8637 | 0.0856 | | 6.7777 | 0.82 | 50000 | 6.7726 | 0.0922 | | 6.7091 | 0.98 | 60000 | 6.7101 | 0.0974 | | 6.6626 | 1.15 | 70000 | 6.6620 | 0.1015 | | 6.6279 | 1.31 | 80000 | 6.6255 | 0.1040 | | 6.5917 | 1.47 | 90000 | 6.5948 | 0.1068 | | 6.5691 | 1.64 | 100000 | 6.5695 | 0.1094 | | 6.5486 | 1.8 | 110000 | 6.5460 | 0.1122 | | 6.5246 | 1.97 | 120000 | 6.5275 | 0.1144 | | 6.5069 | 2.13 | 130000 | 6.5115 | 0.1162 | | 6.5001 | 2.29 | 140000 | 6.4962 | 0.1180 | | 6.4785 | 2.46 | 150000 | 6.4822 | 0.1197 | | 6.4706 | 2.62 | 160000 | 6.4714 | 0.1212 | | 6.4612 | 2.79 | 170000 | 6.4610 | 0.1225 | | 6.4485 | 2.95 | 180000 | 6.4530 | 0.1233 | | 6.4477 | 3.11 | 190000 | 6.4441 | 0.1243 | | 6.4373 | 3.28 | 200000 | 6.4395 | 0.1251 | | 6.4351 | 3.44 | 210000 | 6.4322 | 0.1259 | | 6.4273 | 3.6 | 220000 | 6.4264 | 0.1262 | | 6.4153 | 3.77 | 230000 | 6.4219 | 0.1269 | | 6.4188 | 3.93 | 240000 | 6.4182 | 0.1274 | | 6.4128 | 4.1 | 250000 | 6.4150 | 0.1278 | | 6.4189 | 4.26 | 260000 | 6.4121 | 0.1280 | | 6.4102 | 4.42 | 270000 | 6.4112 | 0.1282 | | 6.4105 | 4.59 | 280000 | 6.4087 | 0.1285 | | 6.4065 | 4.75 | 290000 | 6.4067 | 0.1287 | | 6.4082 | 4.92 | 300000 | 6.4070 | 0.1285 | ### Framework versions - Transformers 4.33.2 - Pytorch 1.14.0a0+410ce96 - Datasets 2.14.5 - Tokenizers 0.13.3

thewiz/kd-bertBase-bertTiny

thewiz

2023-09-28T17:13:49Z

120

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:google/bert_uncased_L-2_H-128_A-2", "base_model:finetune:google/bert_uncased_L-2_H-128_A-2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-28T16:36:52Z

--- license: apache-2.0 base_model: google/bert_uncased_L-2_H-128_A-2 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: kd-bertBase-bertTiny results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: sst2 split: validation args: sst2 metrics: - name: Accuracy type: accuracy value: 0.8268348623853211 ---  # kd-bertBase-bertTiny This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0591 - Accuracy: 0.8268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.4776 | 1.0 | 527 | 1.1240 | 0.8062 | | 0.8442 | 2.0 | 1054 | 1.0475 | 0.8165 | | 0.6568 | 3.0 | 1581 | 1.0529 | 0.8131 | | 0.5623 | 4.0 | 2108 | 1.0503 | 0.8188 | | 0.5066 | 5.0 | 2635 | 1.0471 | 0.8303 | | 0.4736 | 6.0 | 3162 | 1.0711 | 0.8280 | | 0.4603 | 7.0 | 3689 | 1.0591 | 0.8268 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

afaranda/my_awesome_billsum_model

afaranda

2023-09-28T16:58:13Z

103

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:billsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T14:03:18Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - billsum metrics: - rouge model-index: - name: my_awesome_billsum_model results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: billsum type: billsum config: default split: ca_test args: default metrics: - name: Rouge1 type: rouge value: 0.1398 ---  # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset. It achieves the following results on the evaluation set: - Loss: 2.4899 - Rouge1: 0.1398 - Rouge2: 0.0512 - Rougel: 0.117 - Rougelsum: 0.117 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.7815 | 0.1245 | 0.0363 | 0.1044 | 0.1044 | 19.0 | | No log | 2.0 | 124 | 2.5728 | 0.133 | 0.0452 | 0.1112 | 0.1114 | 19.0 | | No log | 3.0 | 186 | 2.5064 | 0.1382 | 0.0514 | 0.1163 | 0.1164 | 19.0 | | No log | 4.0 | 248 | 2.4899 | 0.1398 | 0.0512 | 0.117 | 0.117 | 19.0 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

vineetsharma/databricks-dolly-15k-pythia-70m-deduped-v1

vineetsharma

2023-09-28T16:55:16Z

156

0

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "base_model:EleutherAI/pythia-70m-deduped", "base_model:finetune:EleutherAI/pythia-70m-deduped", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-24T17:01:45Z

--- license: apache-2.0 base_model: EleutherAI/pythia-70m-deduped tags: - generated_from_trainer model-index: - name: databricks-dolly-15k-pythia-70m-deduped-v1 results: [] ---  # databricks-dolly-15k-pythia-70m-deduped-v1 This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.3063 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 5.8656 | 1.0 | 1501 | 4.6547 | | 4.609 | 2.0 | 3002 | 4.4454 | | 4.1706 | 3.0 | 4503 | 4.3141 | | 3.8124 | 4.0 | 6004 | 4.3063 | | 3.4925 | 5.0 | 7505 | 4.3213 | | 3.1927 | 6.0 | 9006 | 4.3806 | | 2.9257 | 7.0 | 10507 | 4.4425 | | 2.6838 | 8.0 | 12008 | 4.5654 | | 2.4619 | 9.0 | 13509 | 4.7032 | | 2.2826 | 10.0 | 15010 | 4.8537 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

maulairfani/autocomplete_model

maulairfani

2023-09-28T16:51:34Z

180

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:indolem/indobert-base-uncased", "base_model:finetune:indolem/indobert-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-28T16:11:58Z

--- license: mit base_model: indolem/indobert-base-uncased tags: - generated_from_trainer model-index: - name: autocomplete_model results: [] ---  # autocomplete_model This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2526 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 17 | 3.2168 | | No log | 2.0 | 34 | 3.1874 | | No log | 3.0 | 51 | 3.2537 | | No log | 4.0 | 68 | 3.2260 | | No log | 5.0 | 85 | 3.1759 | | 3.4421 | 6.0 | 102 | 3.1777 | | 3.4421 | 7.0 | 119 | 3.2093 | | 3.4421 | 8.0 | 136 | 3.2277 | | 3.4421 | 9.0 | 153 | 3.1694 | | 3.4421 | 10.0 | 170 | 3.1333 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

erkam/sg2im-256-bs-16x2

erkam

2023-09-28T16:50:24Z

2

0

diffusers

[ "diffusers", "sg-to-image", "scene-graph", "stable-diffusion", "stable-diffusion-diffusers", "lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "region:us" ]

null

2023-09-23T01:13:28Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2 tags: - sg-to-image - scene-graph - stable-diffusion - stable-diffusion-diffusers - diffusers - lora inference: true --- # LoRA text2image fine-tuning - erkam/sg2im-256-bs-16x2 These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.

AmirH98/Reinforce-CartPole-v1

AmirH98

2023-09-28T16:48:12Z

0

null

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T16:48:03Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

facebook/mbart-large-50-many-to-many-mmt

facebook

2023-09-28T16:42:59Z

295,669

336

transformers

[ "transformers", "pytorch", "tf", "jax", "rust", "safetensors", "mbart", "text2text-generation", "mbart-50", "translation", "multilingual", "ar", "cs", "de", "en", "es", "et", "fi", "fr", "gu", "hi", "it", "ja", "kk", "ko", "lt", "lv", "my", "ne", "nl", "ro", "ru", "si", "tr", "vi", "zh", "af", "az", "bn", "fa", "he", "hr", "id", "ka", "km", "mk", "ml", "mn", "mr", "pl", "ps", "pt", "sv", "sw", "ta", "te", "th", "tl", "uk", "ur", "xh", "gl", "sl", "arxiv:2008.00401", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-03-02T23:29:05Z

--- language: - multilingual - ar - cs - de - en - es - et - fi - fr - gu - hi - it - ja - kk - ko - lt - lv - my - ne - nl - ro - ru - si - tr - vi - zh - af - az - bn - fa - he - hr - id - ka - km - mk - ml - mn - mr - pl - ps - pt - sv - sw - ta - te - th - tl - uk - ur - xh - gl - sl tags: - mbart-50 pipeline_tag: translation --- # mBART-50 many to many multilingual machine translation This model is a fine-tuned checkpoint of [mBART-large-50](https://huggingface.co/facebook/mbart-large-50). `mbart-large-50-many-to-many-mmt` is fine-tuned for multilingual machine translation. It was introduced in [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) paper. The model can translate directly between any pair of 50 languages. To translate into a target language, the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the `forced_bos_token_id` parameter to the `generate` method. ```python from transformers import MBartForConditionalGeneration, MBart50TokenizerFast article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है" article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا." model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") # translate Hindi to French tokenizer.src_lang = "hi_IN" encoded_hi = tokenizer(article_hi, return_tensors="pt") generated_tokens = model.generate( **encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire dans la Syrie." # translate Arabic to English tokenizer.src_lang = "ar_AR" encoded_ar = tokenizer(article_ar, return_tensors="pt") generated_tokens = model.generate( **encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"] ) tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # => "The Secretary-General of the United Nations says there is no military solution in Syria." ``` See the [model hub](https://huggingface.co/models?filter=mbart-50) to look for more fine-tuned versions. ## Languages covered Arabic (ar_AR), Czech (cs_CZ), German (de_DE), English (en_XX), Spanish (es_XX), Estonian (et_EE), Finnish (fi_FI), French (fr_XX), Gujarati (gu_IN), Hindi (hi_IN), Italian (it_IT), Japanese (ja_XX), Kazakh (kk_KZ), Korean (ko_KR), Lithuanian (lt_LT), Latvian (lv_LV), Burmese (my_MM), Nepali (ne_NP), Dutch (nl_XX), Romanian (ro_RO), Russian (ru_RU), Sinhala (si_LK), Turkish (tr_TR), Vietnamese (vi_VN), Chinese (zh_CN), Afrikaans (af_ZA), Azerbaijani (az_AZ), Bengali (bn_IN), Persian (fa_IR), Hebrew (he_IL), Croatian (hr_HR), Indonesian (id_ID), Georgian (ka_GE), Khmer (km_KH), Macedonian (mk_MK), Malayalam (ml_IN), Mongolian (mn_MN), Marathi (mr_IN), Polish (pl_PL), Pashto (ps_AF), Portuguese (pt_XX), Swedish (sv_SE), Swahili (sw_KE), Tamil (ta_IN), Telugu (te_IN), Thai (th_TH), Tagalog (tl_XX), Ukrainian (uk_UA), Urdu (ur_PK), Xhosa (xh_ZA), Galician (gl_ES), Slovene (sl_SI) ## BibTeX entry and citation info ``` @article{tang2020multilingual, title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning}, author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan}, year={2020}, eprint={2008.00401}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

CyberHarem/kurumi_lycorisrecoil

CyberHarem

2023-09-28T16:38:36Z

0

1

null

[ "art", "text-to-image", "dataset:CyberHarem/kurumi_lycorisrecoil", "license:mit", "region:us" ]

text-to-image

2023-09-28T16:26:50Z

--- license: mit datasets: - CyberHarem/kurumi_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of kurumi_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 5100, you need to download `5100/kurumi_lycorisrecoil.pt` as the embedding and `5100/kurumi_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 5100**, with the score of 0.996. The trigger words are: 1. `kurumi_lycorisrecoil` 2. `blonde_hair, long_hair, hairband, black_hairband, forehead, hoodie, black_ribbon, blue_eyes, ribbon, closed_mouth, hair_ribbon, white_hoodie, indoors` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **5100** | **0.996** | [**Download**](5100/kurumi_lycorisrecoil.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.993 | [Download](4760/kurumi_lycorisrecoil.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.989 | [Download](4420/kurumi_lycorisrecoil.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.991 | [Download](4080/kurumi_lycorisrecoil.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.989 | [Download](3740/kurumi_lycorisrecoil.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.985 | [Download](3400/kurumi_lycorisrecoil.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.994 | [Download](3060/kurumi_lycorisrecoil.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.917 | [Download](2720/kurumi_lycorisrecoil.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.991 | [Download](2380/kurumi_lycorisrecoil.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.980 | [Download](2040/kurumi_lycorisrecoil.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.991 | [Download](1700/kurumi_lycorisrecoil.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.986 | [Download](1360/kurumi_lycorisrecoil.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.987 | [Download](1020/kurumi_lycorisrecoil.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.924 | [Download](680/kurumi_lycorisrecoil.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.989 | [Download](340/kurumi_lycorisrecoil.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Crataco/Pygmalion-6B-GGML

Crataco

2023-09-28T16:37:54Z

0

1

null

[ "text-generation", "conversational", "gpt-j", "ggml", "en", "license:creativeml-openrail-m", "region:us" ]

text-generation

2023-09-25T18:52:41Z

--- license: creativeml-openrail-m language: - en thumbnail: tags: - text-generation - conversational - gpt-j - ggml inference: false --- # Pygmalion 6B GGML ### This repository contains quantized conversions of the current Pygmalion 6B checkpoints. *For use with frontends that support GGML quantized GPT-J models, such as KoboldCpp and Oobabooga (with the CTransformers loader).* *Last updated on 2023-09-26.* **Description:** - The `pygmalion-6b-main` files are quantized from the main branch of Pygmalion 6B. Also known as "experiment 2", released on January 13th. - The `pygmalion-6b-dev` files are quantized from the dev branch of Pygmalion 6B. Also known as "part 4/10 of experiment 7", released on March 12th. - The motivation behind these quantizations was to have one repository for both the main and dev versions of Pygmalion, as well as all quantization formats available. Some users may prefer the prose and creativity of Pygmalion 6B (and its lack of synthetic GPT-4 data) over newer models, or find 6B's requirements more affordable than 7B. For a modern alternative, [Pygmalion 2 7B](https://huggingface.co/TheBloke/Pygmalion-2-7B-GGUF) is worth investigating. **RAM usage:** Model | Startup RAM usage (KoboldCpp) | Startup RAM usage (Oobabooga) :--:|:--:|:--: pygmalion-6b-dev.q4_0.bin | 3.7 GiB | 3.7 GiB pygmalion-6b-dev.q4_1.bin | 4.1 GiB | 4.1 GiB pygmalion-6b-dev.q5_0.bin | 4.4 GiB | 4.4 GiB pygmalion-6b-dev.q5_1.bin | 4.8 GiB | 4.8 GiB pygmalion-6b-dev.q8_0.bin | 6.5 GiB | 6.6 GiB **Notes:** - Tested with these SillyTavern settings: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6251b9851842c08ef3111c4f/6Zo7uCqPY3ku96PymqrKo.png) - ggerganov/ggml [[8ca2c19]](https://github.com/ggerganov/ggml/tree/8ca2c19a3bb8622954d858fbf6383522684eaf34)'s gpt-j conversion script was used for conversion and quantization. First they were converted to f16 ggml files, then quantized. The original model can be found [here](https://huggingface.co/PygmalionAI/pygmalion-6b), and the original model card can be found below. * * * # Pygmalion 6B ## Model description Pymalion 6B is a proof-of-concept dialogue model based on EleutherAI's [GPT-J-6B](https://huggingface.co/EleutherAI/gpt-j-6B). **Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances. ## Training data The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations. ## Training procedure Model weights were initialized from the `uft-6b` ConvoGPT model made available in [this commit](https://huggingface.co/hakurei/convogpt/tree/41b67bfddb6cd97070ffddf708e9720c9cb8d224/6b-uft). The model was then further fine-tuned on ~48.5 million tokens for ~5k steps on 4 NVIDIA A40s using DeepSpeed. ## Intended use ### The easy way We provide a notebook with a Gradio UI for playing around with the model without having to manually format inputs. This notebook can be found [here](https://github.com/PygmalionAI/gradio-ui/blob/master/notebooks/GPU.ipynb). ### The manual way The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format: ``` [CHARACTER]'s Persona: [A few sentences about the character you want the model to play] <START> [DIALOGUE HISTORY] You: [Your input message here] [CHARACTER]: ``` Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like: ``` [CHARACTER]: [some dialogue here] You: [your response to the dialogue above] ``` Apart from chat history, you can also just add example conversations in `[DIALOGUE HISTORY]` to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition. ## Known issues We haven't played around with the model enough to enumerate them. Feel free to give us some feedback!

TheBloke/leo-hessianai-7B-GGUF

TheBloke

2023-09-28T16:28:04Z

239

3

transformers

[ "transformers", "gguf", "llama", "text-generation", "en", "de", "dataset:oscar-corpus/OSCAR-2301", "dataset:wikipedia", "dataset:bjoernp/tagesschau-2018-2023", "base_model:LeoLM/leo-hessianai-7b", "base_model:quantized:LeoLM/leo-hessianai-7b", "license:llama2", "region:us" ]

text-generation

2023-09-28T16:23:50Z

--- base_model: LeoLM/leo-hessianai-7b datasets: - oscar-corpus/OSCAR-2301 - wikipedia - bjoernp/tagesschau-2018-2023 inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B model_type: llama pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Leo Hessianai 7B - GGUF - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b)  ## Description This repo contains GGUF format model files for [LAION LeoLM's Leo Hessianai 7B](https://huggingface.co/LeoLM/leo-hessianai-7b).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b)   ## Prompt template: None ``` {prompt} ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [leo-hessianai-7b.Q2_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes | | [leo-hessianai-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss | | [leo-hessianai-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss | | [leo-hessianai-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss | | [leo-hessianai-7b.Q4_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [leo-hessianai-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss | | [leo-hessianai-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended | | [leo-hessianai-7b.Q5_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [leo-hessianai-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended | | [leo-hessianai-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended | | [leo-hessianai-7b.Q6_K.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss | | [leo-hessianai-7b.Q8_0.gguf](https://huggingface.co/TheBloke/leo-hessianai-7B-GGUF/blob/main/leo-hessianai-7b.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/leo-hessianai-7B-GGUF and below it, a specific filename to download, such as: leo-hessianai-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/leo-hessianai-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-GGUF leo-hessianai-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m leo-hessianai-7b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/leo-hessianai-7B-GGUF", model_file="leo-hessianai-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: LAION LeoLM's Leo Hessianai 7B # LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! *A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.* ## Model Details - **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) - **Model type:** Causal decoder-only transformer language model - **Language:** English and German - **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( model="LeoLM/leo-hessianai-7b", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True # True for flash-attn2 else False ) ``` ## Training parameters ![training_parameters](imgs/training_params.png "Training Hyperparameters") ## Benchmarks ![benchmarks](imgs/benchmarks.png "Benchmark Scores")

LicensedViber/Lora

LicensedViber

2023-09-28T16:06:58Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2023-09-28T16:06:58Z

--- license: creativeml-openrail-m ---

CyberHarem/nakahara_mizuki_lycorisrecoil

CyberHarem

2023-09-28T15:55:34Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/nakahara_mizuki_lycorisrecoil", "license:mit", "region:us" ]

text-to-image

2023-09-28T15:44:35Z

--- license: mit datasets: - CyberHarem/nakahara_mizuki_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of nakahara_mizuki_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/nakahara_mizuki_lycorisrecoil.pt` as the embedding and `4420/nakahara_mizuki_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4420**, with the score of 0.997. The trigger words are: 1. `nakahara_mizuki_lycorisrecoil` 2. `brown_hair, long_hair, glasses, red-framed_eyewear, hair_between_eyes, brown_eyes, semi-rimless_eyewear` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.996 | [Download](5100/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.993 | [Download](4760/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | **4420** | **0.997** | [**Download**](4420/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.996 | [Download](4080/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.987 | [Download](3740/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.994 | [Download](3400/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.994 | [Download](3060/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.994 | [Download](2720/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.993 | [Download](2380/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.994 | [Download](2040/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.992 | [Download](1700/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.990 | [Download](1360/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.982 | [Download](1020/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.982 | [Download](680/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.975 | [Download](340/nakahara_mizuki_lycorisrecoil.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

gsl22/testando

gsl22

2023-09-28T15:52:59Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-28T14:21:31Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: testando results: [] ---  # testando This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.0 - Tokenizers 0.13.3

CyberHarem/aihara_yuzu_citrus

CyberHarem

2023-09-28T15:45:15Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/aihara_yuzu_citrus", "license:mit", "region:us" ]

text-to-image

2023-09-28T15:26:55Z

--- license: mit datasets: - CyberHarem/aihara_yuzu_citrus pipeline_tag: text-to-image tags: - art --- # Lora of aihara_yuzu_citrus This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 9000, you need to download `9000/aihara_yuzu_citrus.pt` as the embedding and `9000/aihara_yuzu_citrus.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 9000**, with the score of 0.603. The trigger words are: 1. `aihara_yuzu_citrus` 2. `blonde_hair, green_eyes, long_hair, jewelry, earrings, brown_hair` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **9000** | **0.603** | [**Download**](9000/aihara_yuzu_citrus.zip) | ![pattern_1-9000](9000/previews/pattern_1.png) | ![pattern_2-9000](9000/previews/pattern_2.png) | ![pattern_3-9000](9000/previews/pattern_3.png) | ![pattern_4-9000](9000/previews/pattern_4.png) | ![pattern_5-9000](9000/previews/pattern_5.png) | ![pattern_6-9000](9000/previews/pattern_6.png) | ![pattern_7-9000](9000/previews/pattern_7.png) | ![pattern_8-9000](9000/previews/pattern_8.png) | ![pattern_9-9000](9000/previews/pattern_9.png) | ![pattern_10-9000](9000/previews/pattern_10.png) | [<NSFW, click to see>](9000/previews/pattern_11.png) | ![pattern_12-9000](9000/previews/pattern_12.png) | ![pattern_13-9000](9000/previews/pattern_13.png) | ![pattern_14-9000](9000/previews/pattern_14.png) | ![bikini-9000](9000/previews/bikini.png) | [<NSFW, click to see>](9000/previews/bondage.png) | ![free-9000](9000/previews/free.png) | ![maid-9000](9000/previews/maid.png) | ![miko-9000](9000/previews/miko.png) | [<NSFW, click to see>](9000/previews/nude.png) | [<NSFW, click to see>](9000/previews/nude2.png) | ![suit-9000](9000/previews/suit.png) | ![yukata-9000](9000/previews/yukata.png) | | 8400 | 0.592 | [Download](8400/aihara_yuzu_citrus.zip) | ![pattern_1-8400](8400/previews/pattern_1.png) | ![pattern_2-8400](8400/previews/pattern_2.png) | ![pattern_3-8400](8400/previews/pattern_3.png) | ![pattern_4-8400](8400/previews/pattern_4.png) | ![pattern_5-8400](8400/previews/pattern_5.png) | ![pattern_6-8400](8400/previews/pattern_6.png) | ![pattern_7-8400](8400/previews/pattern_7.png) | ![pattern_8-8400](8400/previews/pattern_8.png) | ![pattern_9-8400](8400/previews/pattern_9.png) | ![pattern_10-8400](8400/previews/pattern_10.png) | [<NSFW, click to see>](8400/previews/pattern_11.png) | ![pattern_12-8400](8400/previews/pattern_12.png) | ![pattern_13-8400](8400/previews/pattern_13.png) | ![pattern_14-8400](8400/previews/pattern_14.png) | ![bikini-8400](8400/previews/bikini.png) | [<NSFW, click to see>](8400/previews/bondage.png) | ![free-8400](8400/previews/free.png) | ![maid-8400](8400/previews/maid.png) | ![miko-8400](8400/previews/miko.png) | [<NSFW, click to see>](8400/previews/nude.png) | [<NSFW, click to see>](8400/previews/nude2.png) | ![suit-8400](8400/previews/suit.png) | ![yukata-8400](8400/previews/yukata.png) | | 7800 | 0.539 | [Download](7800/aihara_yuzu_citrus.zip) | ![pattern_1-7800](7800/previews/pattern_1.png) | ![pattern_2-7800](7800/previews/pattern_2.png) | ![pattern_3-7800](7800/previews/pattern_3.png) | ![pattern_4-7800](7800/previews/pattern_4.png) | ![pattern_5-7800](7800/previews/pattern_5.png) | ![pattern_6-7800](7800/previews/pattern_6.png) | ![pattern_7-7800](7800/previews/pattern_7.png) | ![pattern_8-7800](7800/previews/pattern_8.png) | ![pattern_9-7800](7800/previews/pattern_9.png) | ![pattern_10-7800](7800/previews/pattern_10.png) | [<NSFW, click to see>](7800/previews/pattern_11.png) | ![pattern_12-7800](7800/previews/pattern_12.png) | ![pattern_13-7800](7800/previews/pattern_13.png) | ![pattern_14-7800](7800/previews/pattern_14.png) | ![bikini-7800](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) | ![free-7800](7800/previews/free.png) | ![maid-7800](7800/previews/maid.png) | ![miko-7800](7800/previews/miko.png) | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) | ![suit-7800](7800/previews/suit.png) | ![yukata-7800](7800/previews/yukata.png) | | 7200 | 0.560 | [Download](7200/aihara_yuzu_citrus.zip) | ![pattern_1-7200](7200/previews/pattern_1.png) | ![pattern_2-7200](7200/previews/pattern_2.png) | ![pattern_3-7200](7200/previews/pattern_3.png) | ![pattern_4-7200](7200/previews/pattern_4.png) | ![pattern_5-7200](7200/previews/pattern_5.png) | ![pattern_6-7200](7200/previews/pattern_6.png) | ![pattern_7-7200](7200/previews/pattern_7.png) | ![pattern_8-7200](7200/previews/pattern_8.png) | ![pattern_9-7200](7200/previews/pattern_9.png) | ![pattern_10-7200](7200/previews/pattern_10.png) | [<NSFW, click to see>](7200/previews/pattern_11.png) | ![pattern_12-7200](7200/previews/pattern_12.png) | ![pattern_13-7200](7200/previews/pattern_13.png) | ![pattern_14-7200](7200/previews/pattern_14.png) | ![bikini-7200](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) | ![free-7200](7200/previews/free.png) | ![maid-7200](7200/previews/maid.png) | ![miko-7200](7200/previews/miko.png) | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) | ![suit-7200](7200/previews/suit.png) | ![yukata-7200](7200/previews/yukata.png) | | 6600 | 0.538 | [Download](6600/aihara_yuzu_citrus.zip) | ![pattern_1-6600](6600/previews/pattern_1.png) | ![pattern_2-6600](6600/previews/pattern_2.png) | ![pattern_3-6600](6600/previews/pattern_3.png) | ![pattern_4-6600](6600/previews/pattern_4.png) | ![pattern_5-6600](6600/previews/pattern_5.png) | ![pattern_6-6600](6600/previews/pattern_6.png) | ![pattern_7-6600](6600/previews/pattern_7.png) | ![pattern_8-6600](6600/previews/pattern_8.png) | ![pattern_9-6600](6600/previews/pattern_9.png) | ![pattern_10-6600](6600/previews/pattern_10.png) | [<NSFW, click to see>](6600/previews/pattern_11.png) | ![pattern_12-6600](6600/previews/pattern_12.png) | ![pattern_13-6600](6600/previews/pattern_13.png) | ![pattern_14-6600](6600/previews/pattern_14.png) | ![bikini-6600](6600/previews/bikini.png) | [<NSFW, click to see>](6600/previews/bondage.png) | ![free-6600](6600/previews/free.png) | ![maid-6600](6600/previews/maid.png) | ![miko-6600](6600/previews/miko.png) | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) | ![suit-6600](6600/previews/suit.png) | ![yukata-6600](6600/previews/yukata.png) | | 6000 | 0.526 | [Download](6000/aihara_yuzu_citrus.zip) | ![pattern_1-6000](6000/previews/pattern_1.png) | ![pattern_2-6000](6000/previews/pattern_2.png) | ![pattern_3-6000](6000/previews/pattern_3.png) | ![pattern_4-6000](6000/previews/pattern_4.png) | ![pattern_5-6000](6000/previews/pattern_5.png) | ![pattern_6-6000](6000/previews/pattern_6.png) | ![pattern_7-6000](6000/previews/pattern_7.png) | ![pattern_8-6000](6000/previews/pattern_8.png) | ![pattern_9-6000](6000/previews/pattern_9.png) | ![pattern_10-6000](6000/previews/pattern_10.png) | [<NSFW, click to see>](6000/previews/pattern_11.png) | ![pattern_12-6000](6000/previews/pattern_12.png) | ![pattern_13-6000](6000/previews/pattern_13.png) | ![pattern_14-6000](6000/previews/pattern_14.png) | ![bikini-6000](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) | ![free-6000](6000/previews/free.png) | ![maid-6000](6000/previews/maid.png) | ![miko-6000](6000/previews/miko.png) | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) | ![suit-6000](6000/previews/suit.png) | ![yukata-6000](6000/previews/yukata.png) | | 5400 | 0.514 | [Download](5400/aihara_yuzu_citrus.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![pattern_6-5400](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![pattern_9-5400](5400/previews/pattern_9.png) | ![pattern_10-5400](5400/previews/pattern_10.png) | [<NSFW, click to see>](5400/previews/pattern_11.png) | ![pattern_12-5400](5400/previews/pattern_12.png) | ![pattern_13-5400](5400/previews/pattern_13.png) | ![pattern_14-5400](5400/previews/pattern_14.png) | ![bikini-5400](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4800 | 0.533 | [Download](4800/aihara_yuzu_citrus.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | ![pattern_3-4800](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | [<NSFW, click to see>](4800/previews/pattern_11.png) | ![pattern_12-4800](4800/previews/pattern_12.png) | ![pattern_13-4800](4800/previews/pattern_13.png) | ![pattern_14-4800](4800/previews/pattern_14.png) | ![bikini-4800](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4200 | 0.557 | [Download](4200/aihara_yuzu_citrus.zip) | ![pattern_1-4200](4200/previews/pattern_1.png) | ![pattern_2-4200](4200/previews/pattern_2.png) | ![pattern_3-4200](4200/previews/pattern_3.png) | ![pattern_4-4200](4200/previews/pattern_4.png) | ![pattern_5-4200](4200/previews/pattern_5.png) | ![pattern_6-4200](4200/previews/pattern_6.png) | ![pattern_7-4200](4200/previews/pattern_7.png) | ![pattern_8-4200](4200/previews/pattern_8.png) | ![pattern_9-4200](4200/previews/pattern_9.png) | ![pattern_10-4200](4200/previews/pattern_10.png) | [<NSFW, click to see>](4200/previews/pattern_11.png) | ![pattern_12-4200](4200/previews/pattern_12.png) | ![pattern_13-4200](4200/previews/pattern_13.png) | ![pattern_14-4200](4200/previews/pattern_14.png) | ![bikini-4200](4200/previews/bikini.png) | [<NSFW, click to see>](4200/previews/bondage.png) | ![free-4200](4200/previews/free.png) | ![maid-4200](4200/previews/maid.png) | ![miko-4200](4200/previews/miko.png) | [<NSFW, click to see>](4200/previews/nude.png) | [<NSFW, click to see>](4200/previews/nude2.png) | ![suit-4200](4200/previews/suit.png) | ![yukata-4200](4200/previews/yukata.png) | | 3600 | 0.460 | [Download](3600/aihara_yuzu_citrus.zip) | ![pattern_1-3600](3600/previews/pattern_1.png) | ![pattern_2-3600](3600/previews/pattern_2.png) | ![pattern_3-3600](3600/previews/pattern_3.png) | ![pattern_4-3600](3600/previews/pattern_4.png) | ![pattern_5-3600](3600/previews/pattern_5.png) | ![pattern_6-3600](3600/previews/pattern_6.png) | ![pattern_7-3600](3600/previews/pattern_7.png) | ![pattern_8-3600](3600/previews/pattern_8.png) | ![pattern_9-3600](3600/previews/pattern_9.png) | ![pattern_10-3600](3600/previews/pattern_10.png) | [<NSFW, click to see>](3600/previews/pattern_11.png) | ![pattern_12-3600](3600/previews/pattern_12.png) | ![pattern_13-3600](3600/previews/pattern_13.png) | ![pattern_14-3600](3600/previews/pattern_14.png) | ![bikini-3600](3600/previews/bikini.png) | [<NSFW, click to see>](3600/previews/bondage.png) | ![free-3600](3600/previews/free.png) | ![maid-3600](3600/previews/maid.png) | ![miko-3600](3600/previews/miko.png) | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) | ![suit-3600](3600/previews/suit.png) | ![yukata-3600](3600/previews/yukata.png) | | 3000 | 0.328 | [Download](3000/aihara_yuzu_citrus.zip) | ![pattern_1-3000](3000/previews/pattern_1.png) | ![pattern_2-3000](3000/previews/pattern_2.png) | ![pattern_3-3000](3000/previews/pattern_3.png) | ![pattern_4-3000](3000/previews/pattern_4.png) | ![pattern_5-3000](3000/previews/pattern_5.png) | ![pattern_6-3000](3000/previews/pattern_6.png) | ![pattern_7-3000](3000/previews/pattern_7.png) | ![pattern_8-3000](3000/previews/pattern_8.png) | ![pattern_9-3000](3000/previews/pattern_9.png) | ![pattern_10-3000](3000/previews/pattern_10.png) | [<NSFW, click to see>](3000/previews/pattern_11.png) | ![pattern_12-3000](3000/previews/pattern_12.png) | ![pattern_13-3000](3000/previews/pattern_13.png) | ![pattern_14-3000](3000/previews/pattern_14.png) | ![bikini-3000](3000/previews/bikini.png) | [<NSFW, click to see>](3000/previews/bondage.png) | ![free-3000](3000/previews/free.png) | ![maid-3000](3000/previews/maid.png) | ![miko-3000](3000/previews/miko.png) | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) | ![suit-3000](3000/previews/suit.png) | ![yukata-3000](3000/previews/yukata.png) | | 2400 | 0.427 | [Download](2400/aihara_yuzu_citrus.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | [<NSFW, click to see>](2400/previews/pattern_11.png) | ![pattern_12-2400](2400/previews/pattern_12.png) | ![pattern_13-2400](2400/previews/pattern_13.png) | ![pattern_14-2400](2400/previews/pattern_14.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 1800 | 0.395 | [Download](1800/aihara_yuzu_citrus.zip) | ![pattern_1-1800](1800/previews/pattern_1.png) | ![pattern_2-1800](1800/previews/pattern_2.png) | ![pattern_3-1800](1800/previews/pattern_3.png) | ![pattern_4-1800](1800/previews/pattern_4.png) | ![pattern_5-1800](1800/previews/pattern_5.png) | ![pattern_6-1800](1800/previews/pattern_6.png) | ![pattern_7-1800](1800/previews/pattern_7.png) | ![pattern_8-1800](1800/previews/pattern_8.png) | ![pattern_9-1800](1800/previews/pattern_9.png) | ![pattern_10-1800](1800/previews/pattern_10.png) | [<NSFW, click to see>](1800/previews/pattern_11.png) | ![pattern_12-1800](1800/previews/pattern_12.png) | ![pattern_13-1800](1800/previews/pattern_13.png) | ![pattern_14-1800](1800/previews/pattern_14.png) | ![bikini-1800](1800/previews/bikini.png) | [<NSFW, click to see>](1800/previews/bondage.png) | ![free-1800](1800/previews/free.png) | ![maid-1800](1800/previews/maid.png) | ![miko-1800](1800/previews/miko.png) | [<NSFW, click to see>](1800/previews/nude.png) | [<NSFW, click to see>](1800/previews/nude2.png) | ![suit-1800](1800/previews/suit.png) | ![yukata-1800](1800/previews/yukata.png) | | 1200 | 0.288 | [Download](1200/aihara_yuzu_citrus.zip) | ![pattern_1-1200](1200/previews/pattern_1.png) | ![pattern_2-1200](1200/previews/pattern_2.png) | ![pattern_3-1200](1200/previews/pattern_3.png) | ![pattern_4-1200](1200/previews/pattern_4.png) | ![pattern_5-1200](1200/previews/pattern_5.png) | ![pattern_6-1200](1200/previews/pattern_6.png) | ![pattern_7-1200](1200/previews/pattern_7.png) | ![pattern_8-1200](1200/previews/pattern_8.png) | ![pattern_9-1200](1200/previews/pattern_9.png) | ![pattern_10-1200](1200/previews/pattern_10.png) | [<NSFW, click to see>](1200/previews/pattern_11.png) | ![pattern_12-1200](1200/previews/pattern_12.png) | ![pattern_13-1200](1200/previews/pattern_13.png) | ![pattern_14-1200](1200/previews/pattern_14.png) | ![bikini-1200](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/bondage.png) | ![free-1200](1200/previews/free.png) | ![maid-1200](1200/previews/maid.png) | ![miko-1200](1200/previews/miko.png) | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) | ![suit-1200](1200/previews/suit.png) | ![yukata-1200](1200/previews/yukata.png) | | 600 | 0.221 | [Download](600/aihara_yuzu_citrus.zip) | ![pattern_1-600](600/previews/pattern_1.png) | ![pattern_2-600](600/previews/pattern_2.png) | ![pattern_3-600](600/previews/pattern_3.png) | ![pattern_4-600](600/previews/pattern_4.png) | ![pattern_5-600](600/previews/pattern_5.png) | ![pattern_6-600](600/previews/pattern_6.png) | ![pattern_7-600](600/previews/pattern_7.png) | ![pattern_8-600](600/previews/pattern_8.png) | ![pattern_9-600](600/previews/pattern_9.png) | ![pattern_10-600](600/previews/pattern_10.png) | [<NSFW, click to see>](600/previews/pattern_11.png) | ![pattern_12-600](600/previews/pattern_12.png) | ![pattern_13-600](600/previews/pattern_13.png) | ![pattern_14-600](600/previews/pattern_14.png) | ![bikini-600](600/previews/bikini.png) | [<NSFW, click to see>](600/previews/bondage.png) | ![free-600](600/previews/free.png) | ![maid-600](600/previews/maid.png) | ![miko-600](600/previews/miko.png) | [<NSFW, click to see>](600/previews/nude.png) | [<NSFW, click to see>](600/previews/nude2.png) | ![suit-600](600/previews/suit.png) | ![yukata-600](600/previews/yukata.png) |

vaibhav9/hangman-bert-large

vaibhav9

2023-09-28T15:39:27Z

196

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-28T09:19:00Z

--- tags: - generated_from_trainer model-index: - name: hangman-bert-large results: [] ---  # hangman-bert-large This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8323 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.8263 | 1.0 | 2609 | 1.8458 | | 1.8227 | 2.0 | 5218 | 1.8441 | | 1.8206 | 3.0 | 7827 | 1.8376 | | 1.8146 | 4.0 | 10436 | 1.8349 | | 1.8108 | 5.0 | 13045 | 1.8323 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

joachimsallstrom/aether-glitch-lora-for-sdxl

joachimsallstrom

2023-09-28T15:35:34Z

910

17

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "style", "vhs glitch", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:other", "region:us" ]

text-to-image

2023-09-28T15:35:20Z

--- license: other tags: - text-to-image - stable-diffusion - lora - diffusers - style - vhs glitch base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: vhs glitch widget: - text: " vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic" - text: " vhs glitch man on a mountain looking at the sunset, cinematic, black background" - text: " vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background" - text: " vhs glitch award-winning photo of kung fury battling a monster, cinematic horror" - text: " vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain" - text: " vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background" - text: " vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora" - text: " vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background" - text: " vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background" --- # Aether Glitch - LoRA for SDXL ![Image 0](2680627.jpeg) > vhs glitch a skeleton news reporter wearing a black wig and suit, they live 1988, movie still cinematic <p><strong><span style="color:rgb(230, 73, 128)">MAKE SURE TO CHECK THE IMAGES ON THIS PAGE FOR PROMPTS AND SETTINGS TO GET GOOD RESULTS.</span></strong><br /><br />This is <strong><span style="color:rgb(230, 73, 128)">A</span><span style="color:rgb(190, 75, 219)">e</span><span style="color:rgb(121, 80, 242)">t</span><span style="color:rgb(76, 110, 245)">h</span><span style="color:rgb(34, 139, 230)">e</span><span style="color:rgb(21, 170, 191)">r</span></strong> <strong><span style="color:rgb(21, 170, 191)">G</span><span style="color:rgb(64, 192, 87)">l</span><span style="color:rgb(130, 201, 30)">it</span><span style="color:rgb(250, 176, 5)">c</span><span style="color:rgb(253, 126, 20)">h</span></strong>, an SDXL LoRA that simulates vhs tape glitches over subjects. Make sure to trigger the LoRA with <strong><em>vhs glitch</em></strong>, and play with weights to get more or less balanced glitch or crt tv looks. Looks cool on both photoreal and animated stuff. Gaming, movies and horror works well, and of course synthwave and such. Overall a great mood setter!<br /><br />Thanks to <a target="_blank" rel="ugc" href="https://rundiffusion.com/">RunDiffusion</a> who sponsored the finetuning of this LoRA. <span style="color:rgb(219, 222, 225)">It was made on Lastben’s SDXL LoRA trainer through RunDiffusion which will soon be made public for everyone. Aether Glitch is of course available on their platform to try it out.</span></p><p></p><p><span style="color:rgb(219, 222, 225)">Also thanks to Masslevel, Warlock and Adam D for contributing with awesome images.</span></p> ## Image examples for the model: ![Image 1](2675921.jpeg) > vhs glitch man on a mountain looking at the sunset, cinematic, black background ![Image 2](2675899.jpeg) > vhs glitch cinematic 80s movie still of Margot Robbie, Barbie movie, intricate, atom bomb in the background ![Image 3](2680127.jpeg) > vhs glitch award-winning photo of kung fury battling a monster, cinematic horror ![Image 4](2665925.jpeg) > vhs glitch movie still of cyberpunk 2077 female character, cinematic, hires 8k, intricate, white background ![Image 5](2680711.jpeg) > vhs glitch movie still of by 50s Jennifer Connelly jazz singer at a dark club, Dark City 1998, cinematic, heavy grain ![Image 6](2665924.jpeg) > vhs glitch movie still of cyberpunk 2077 black character, cinematic, hires 8k, intricate, white background ![Image 7](2675806.jpeg) > vhs glitch cinematic movie still of Cillian Murphy wearing a 40s fedora ![Image 8](2675809.jpeg) > vhs glitch found footage of horror girl in the woods, cinematic, hires 8k, intricate, black background ![Image 9](2675807.jpeg) > vhs glitch movie still of cyberpunk 2077 character with cigar in mouth, cinematic, hires 8k, intricate, white background

TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ

TheBloke

2023-09-28T15:34:24Z

33

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "dataset:garage-bAInd/Open-Platypus", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:bjoernp/oasst25-08-23-filtered", "base_model:LeoLM/leo-hessianai-7b-chat-bilingual", "base_model:quantized:LeoLM/leo-hessianai-7b-chat-bilingual", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

2023-09-28T14:52:03Z

--- base_model: LeoLM/leo-hessianai-7b-chat-bilingual datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs - garage-bAInd/Open-Platypus - WizardLM/WizardLM_evol_instruct_70k - bjoernp/oasst25-08-23-filtered inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 7B Chat Bilingual model_type: llama pipeline_tag: text-generation prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Leo Hessianai 7B Chat Bilingual - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual)  ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 7B Chat Bilingual](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-7b-chat-bilingual)   ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ```   ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 3.90 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.01 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.16 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.62 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 4.03 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |   ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-7B-chat-bilingual-GPTQ`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-7B-chat-bilingual-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ --local-dir leo-hessianai-7B-chat-bilingual-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-7B-chat-bilingual-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!   ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-7B-chat-bilingual-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ```   ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: LAION LeoLM's Leo Hessianai 7B Chat Bilingual # LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! *A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.* ## LeoLM Chat `LeoLM/leo-hessianai-7b-chat-bilingual` is a bilingual English-German chat model built on our foundation model `LeoLM/leo-hessianai-7b` and finetuned on a selection of German translateed instruction datasets and their English counterparts. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench scores: ``` { "first_turn": 5.64375, "second_turn": 4.075, "categories": { "writing": 5.925, "roleplay": 5.25, "reasoning": 3.1, "math": 1.8, "coding": 3.4, "extraction": 5, "stem": 6.5, "humanities": 7.9 }, "average": 4.859375 } ``` ## Model Details - **Finetuned from:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) - **Model type:** Causal decoder-only transformer language model - **Language:** English and German - **Demo:** [Web Demo]() - **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<|im_start|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|> """ prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-7b-chat-bilingual", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "*Als KI kann ich keine persönlichen Beobachtungen teilen, aber ich kann einige allgemeine Informationen zur Fahrradwegesituation in Hamburg liefern. Im Vergleich zu vielen anderen großen Städten hat Hamburg eine hohe Anzahl von Fahrradfahrern und nimmt seine Verantwortung für nachhaltige Verkehrsmittel sehr ernst. Es gibt viele Fahrradwege und separate Fahrspuren, die Radfahrern ermöglichen, zusammen mit dem Autoverkehr zu fahren. Diese Fahrradspuren sind oft mit Markierungen gekennzeichnet und durch physische Trennungen von anderen Fahrspuren abgegrenzt. Darüber hinaus gibt es viele Fahrradstraßen, auf denen Radfahrer Vorfahrt haben und Autos langsamer fahren müssen.* *In einigen städtischen Gebieten können Fahrradwege jedoch eng oder überfüllt sein, besonders während der Stoßzeiten. Es gibt auch viele Kreuzungen, an denen Radfahrer anhalten und auf Grün warten müssen, ähnlich wie Autofahrer. Insgesamt ist die Fahrradinfrastruktur in Hamburg ziemlich gut, aber wie überall gibt es immer Raum für Verbesserungen.*" ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <|im_start|>user {prompt 1}<|im_end|> <|im_start|>assistant {reply 1}<|im_end|> <|im_start|>user {prompt 2}<|im_end|> <|im_start|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-7b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-7b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details | Hyperparameter | Value | |---|---| | Num epochs | 3 | | Examples per epoch | 233275 | | Global batch size | 256 | | Learning rate | 3e-5 | | Warmup steps | 100 | | LR scheduler | Cosine | | Adam betas | (0.9, 0.95) | | Weight decay | 0.001 | ## Dataset Details ``` ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of garage-bAInd/Open-Platypus' (24427 samples (100.0%)) ----------------- Accepted: 24427/24427 (100.0%) Accepted tokens: 9549043 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5054 Avg tokens per sample: 390.9216440823679 ----------------- ## Stats for 'Subset of WizardLM/WizardLM_evol_instruct_70k' (68600 samples (100.0%)) ----------------- Accepted: 68600/68600 (100.0%) Accepted tokens: 33045040 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 481.7061224489796 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'Subset of OpenAssistant/OASST_DE' (3646 samples (100.0%)) ----------------- Accepted: 3646/3646 (100.0%) Accepted tokens: 2338738 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 641.4530992868897 ----------------- ## Stats for 'Subset of bjoernp/oasst25-08-23-filtered' (8922 samples (100.0%)) ----------------- Accepted: 8922/8922 (100.0%) Accepted tokens: 4526427 Skipped: 0 (0.0%) Min tokens per sample: 23 Max tokens per sample: 5407 Avg tokens per sample: 507.3332212508406 ----------------- ## Stats for 'total' (235632 samples (100.0%)) ----------------- Accepted: 235632/235632 (100.0%) Accepted tokens: 115862397 Skipped: 0 (0.0%) Min tokens per sample: 18 Max tokens per sample: 11810 Avg tokens per sample: 491.70909299246284 ----------------- ```

roa7n/gpt2-human_nontata_promoters-randomized_6_layers_3e-05_lr_2_e

roa7n

2023-09-28T15:33:46Z

0

peft

[ "peft", "region:us" ]

null

2023-09-28T15:33:43Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

MerziaAdamjee/codellama2-finetuned-sqldata

MerziaAdamjee

2023-09-28T15:18:32Z

4

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:finetune:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T13:32:27Z

--- license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - generated_from_trainer model-index: - name: codellama2-finetuned-sqldata results: [] ---  # codellama2-finetuned-sqldata This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 100 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.14.0

Ori/lama-2-13b-peft-no-ret-seed-1

Ori

2023-09-28T15:17:14Z

3

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-09-28T15:16:03Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

CyberHarem/inoue_takina_lycorisrecoil

CyberHarem

2023-09-28T15:13:39Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/inoue_takina_lycorisrecoil", "license:mit", "region:us" ]

text-to-image

2023-09-28T15:00:07Z

--- license: mit datasets: - CyberHarem/inoue_takina_lycorisrecoil pipeline_tag: text-to-image tags: - art --- # Lora of inoue_takina_lycorisrecoil This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 7020, you need to download `7020/inoue_takina_lycorisrecoil.pt` as the embedding and `7020/inoue_takina_lycorisrecoil.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 7020**, with the score of 0.986. The trigger words are: 1. `inoue_takina_lycorisrecoil` 2. `black_hair, long_hair, bangs, purple_eyes, closed_mouth, ribbon, green_ribbon, neck_ribbon` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 8100 | 0.875 | [Download](8100/inoue_takina_lycorisrecoil.zip) | ![pattern_1-8100](8100/previews/pattern_1.png) | ![pattern_2-8100](8100/previews/pattern_2.png) | ![pattern_3-8100](8100/previews/pattern_3.png) | ![pattern_4-8100](8100/previews/pattern_4.png) | ![pattern_5-8100](8100/previews/pattern_5.png) | ![pattern_6-8100](8100/previews/pattern_6.png) | ![pattern_7-8100](8100/previews/pattern_7.png) | ![pattern_8-8100](8100/previews/pattern_8.png) | ![bikini-8100](8100/previews/bikini.png) | [<NSFW, click to see>](8100/previews/bondage.png) | ![free-8100](8100/previews/free.png) | ![maid-8100](8100/previews/maid.png) | ![miko-8100](8100/previews/miko.png) | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) | ![suit-8100](8100/previews/suit.png) | ![yukata-8100](8100/previews/yukata.png) | | 7560 | 0.974 | [Download](7560/inoue_takina_lycorisrecoil.zip) | ![pattern_1-7560](7560/previews/pattern_1.png) | ![pattern_2-7560](7560/previews/pattern_2.png) | ![pattern_3-7560](7560/previews/pattern_3.png) | ![pattern_4-7560](7560/previews/pattern_4.png) | ![pattern_5-7560](7560/previews/pattern_5.png) | ![pattern_6-7560](7560/previews/pattern_6.png) | ![pattern_7-7560](7560/previews/pattern_7.png) | ![pattern_8-7560](7560/previews/pattern_8.png) | ![bikini-7560](7560/previews/bikini.png) | [<NSFW, click to see>](7560/previews/bondage.png) | ![free-7560](7560/previews/free.png) | ![maid-7560](7560/previews/maid.png) | ![miko-7560](7560/previews/miko.png) | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) | ![suit-7560](7560/previews/suit.png) | ![yukata-7560](7560/previews/yukata.png) | | **7020** | **0.986** | [**Download**](7020/inoue_takina_lycorisrecoil.zip) | ![pattern_1-7020](7020/previews/pattern_1.png) | ![pattern_2-7020](7020/previews/pattern_2.png) | ![pattern_3-7020](7020/previews/pattern_3.png) | ![pattern_4-7020](7020/previews/pattern_4.png) | ![pattern_5-7020](7020/previews/pattern_5.png) | ![pattern_6-7020](7020/previews/pattern_6.png) | ![pattern_7-7020](7020/previews/pattern_7.png) | ![pattern_8-7020](7020/previews/pattern_8.png) | ![bikini-7020](7020/previews/bikini.png) | [<NSFW, click to see>](7020/previews/bondage.png) | ![free-7020](7020/previews/free.png) | ![maid-7020](7020/previews/maid.png) | ![miko-7020](7020/previews/miko.png) | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) | ![suit-7020](7020/previews/suit.png) | ![yukata-7020](7020/previews/yukata.png) | | 6480 | 0.979 | [Download](6480/inoue_takina_lycorisrecoil.zip) | ![pattern_1-6480](6480/previews/pattern_1.png) | ![pattern_2-6480](6480/previews/pattern_2.png) | ![pattern_3-6480](6480/previews/pattern_3.png) | ![pattern_4-6480](6480/previews/pattern_4.png) | ![pattern_5-6480](6480/previews/pattern_5.png) | ![pattern_6-6480](6480/previews/pattern_6.png) | ![pattern_7-6480](6480/previews/pattern_7.png) | ![pattern_8-6480](6480/previews/pattern_8.png) | ![bikini-6480](6480/previews/bikini.png) | [<NSFW, click to see>](6480/previews/bondage.png) | ![free-6480](6480/previews/free.png) | ![maid-6480](6480/previews/maid.png) | ![miko-6480](6480/previews/miko.png) | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) | ![suit-6480](6480/previews/suit.png) | ![yukata-6480](6480/previews/yukata.png) | | 5940 | 0.972 | [Download](5940/inoue_takina_lycorisrecoil.zip) | ![pattern_1-5940](5940/previews/pattern_1.png) | ![pattern_2-5940](5940/previews/pattern_2.png) | ![pattern_3-5940](5940/previews/pattern_3.png) | ![pattern_4-5940](5940/previews/pattern_4.png) | ![pattern_5-5940](5940/previews/pattern_5.png) | ![pattern_6-5940](5940/previews/pattern_6.png) | ![pattern_7-5940](5940/previews/pattern_7.png) | ![pattern_8-5940](5940/previews/pattern_8.png) | ![bikini-5940](5940/previews/bikini.png) | [<NSFW, click to see>](5940/previews/bondage.png) | ![free-5940](5940/previews/free.png) | ![maid-5940](5940/previews/maid.png) | ![miko-5940](5940/previews/miko.png) | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) | ![suit-5940](5940/previews/suit.png) | ![yukata-5940](5940/previews/yukata.png) | | 5400 | 0.921 | [Download](5400/inoue_takina_lycorisrecoil.zip) | ![pattern_1-5400](5400/previews/pattern_1.png) | ![pattern_2-5400](5400/previews/pattern_2.png) | ![pattern_3-5400](5400/previews/pattern_3.png) | ![pattern_4-5400](5400/previews/pattern_4.png) | ![pattern_5-5400](5400/previews/pattern_5.png) | ![pattern_6-5400](5400/previews/pattern_6.png) | ![pattern_7-5400](5400/previews/pattern_7.png) | ![pattern_8-5400](5400/previews/pattern_8.png) | ![bikini-5400](5400/previews/bikini.png) | [<NSFW, click to see>](5400/previews/bondage.png) | ![free-5400](5400/previews/free.png) | ![maid-5400](5400/previews/maid.png) | ![miko-5400](5400/previews/miko.png) | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) | ![suit-5400](5400/previews/suit.png) | ![yukata-5400](5400/previews/yukata.png) | | 4860 | 0.982 | [Download](4860/inoue_takina_lycorisrecoil.zip) | ![pattern_1-4860](4860/previews/pattern_1.png) | ![pattern_2-4860](4860/previews/pattern_2.png) | ![pattern_3-4860](4860/previews/pattern_3.png) | ![pattern_4-4860](4860/previews/pattern_4.png) | ![pattern_5-4860](4860/previews/pattern_5.png) | ![pattern_6-4860](4860/previews/pattern_6.png) | ![pattern_7-4860](4860/previews/pattern_7.png) | ![pattern_8-4860](4860/previews/pattern_8.png) | ![bikini-4860](4860/previews/bikini.png) | [<NSFW, click to see>](4860/previews/bondage.png) | ![free-4860](4860/previews/free.png) | ![maid-4860](4860/previews/maid.png) | ![miko-4860](4860/previews/miko.png) | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) | ![suit-4860](4860/previews/suit.png) | ![yukata-4860](4860/previews/yukata.png) | | 4320 | 0.982 | [Download](4320/inoue_takina_lycorisrecoil.zip) | ![pattern_1-4320](4320/previews/pattern_1.png) | ![pattern_2-4320](4320/previews/pattern_2.png) | ![pattern_3-4320](4320/previews/pattern_3.png) | ![pattern_4-4320](4320/previews/pattern_4.png) | ![pattern_5-4320](4320/previews/pattern_5.png) | ![pattern_6-4320](4320/previews/pattern_6.png) | ![pattern_7-4320](4320/previews/pattern_7.png) | ![pattern_8-4320](4320/previews/pattern_8.png) | ![bikini-4320](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) | ![free-4320](4320/previews/free.png) | ![maid-4320](4320/previews/maid.png) | ![miko-4320](4320/previews/miko.png) | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) | ![suit-4320](4320/previews/suit.png) | ![yukata-4320](4320/previews/yukata.png) | | 3780 | 0.982 | [Download](3780/inoue_takina_lycorisrecoil.zip) | ![pattern_1-3780](3780/previews/pattern_1.png) | ![pattern_2-3780](3780/previews/pattern_2.png) | ![pattern_3-3780](3780/previews/pattern_3.png) | ![pattern_4-3780](3780/previews/pattern_4.png) | ![pattern_5-3780](3780/previews/pattern_5.png) | ![pattern_6-3780](3780/previews/pattern_6.png) | ![pattern_7-3780](3780/previews/pattern_7.png) | ![pattern_8-3780](3780/previews/pattern_8.png) | ![bikini-3780](3780/previews/bikini.png) | [<NSFW, click to see>](3780/previews/bondage.png) | ![free-3780](3780/previews/free.png) | ![maid-3780](3780/previews/maid.png) | ![miko-3780](3780/previews/miko.png) | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) | ![suit-3780](3780/previews/suit.png) | ![yukata-3780](3780/previews/yukata.png) | | 3240 | 0.967 | [Download](3240/inoue_takina_lycorisrecoil.zip) | ![pattern_1-3240](3240/previews/pattern_1.png) | ![pattern_2-3240](3240/previews/pattern_2.png) | ![pattern_3-3240](3240/previews/pattern_3.png) | ![pattern_4-3240](3240/previews/pattern_4.png) | ![pattern_5-3240](3240/previews/pattern_5.png) | ![pattern_6-3240](3240/previews/pattern_6.png) | ![pattern_7-3240](3240/previews/pattern_7.png) | ![pattern_8-3240](3240/previews/pattern_8.png) | ![bikini-3240](3240/previews/bikini.png) | [<NSFW, click to see>](3240/previews/bondage.png) | ![free-3240](3240/previews/free.png) | ![maid-3240](3240/previews/maid.png) | ![miko-3240](3240/previews/miko.png) | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) | ![suit-3240](3240/previews/suit.png) | ![yukata-3240](3240/previews/yukata.png) | | 2700 | 0.908 | [Download](2700/inoue_takina_lycorisrecoil.zip) | ![pattern_1-2700](2700/previews/pattern_1.png) | ![pattern_2-2700](2700/previews/pattern_2.png) | ![pattern_3-2700](2700/previews/pattern_3.png) | ![pattern_4-2700](2700/previews/pattern_4.png) | ![pattern_5-2700](2700/previews/pattern_5.png) | ![pattern_6-2700](2700/previews/pattern_6.png) | ![pattern_7-2700](2700/previews/pattern_7.png) | ![pattern_8-2700](2700/previews/pattern_8.png) | ![bikini-2700](2700/previews/bikini.png) | [<NSFW, click to see>](2700/previews/bondage.png) | ![free-2700](2700/previews/free.png) | ![maid-2700](2700/previews/maid.png) | ![miko-2700](2700/previews/miko.png) | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) | ![suit-2700](2700/previews/suit.png) | ![yukata-2700](2700/previews/yukata.png) | | 2160 | 0.885 | [Download](2160/inoue_takina_lycorisrecoil.zip) | ![pattern_1-2160](2160/previews/pattern_1.png) | ![pattern_2-2160](2160/previews/pattern_2.png) | ![pattern_3-2160](2160/previews/pattern_3.png) | ![pattern_4-2160](2160/previews/pattern_4.png) | ![pattern_5-2160](2160/previews/pattern_5.png) | ![pattern_6-2160](2160/previews/pattern_6.png) | ![pattern_7-2160](2160/previews/pattern_7.png) | ![pattern_8-2160](2160/previews/pattern_8.png) | ![bikini-2160](2160/previews/bikini.png) | [<NSFW, click to see>](2160/previews/bondage.png) | ![free-2160](2160/previews/free.png) | ![maid-2160](2160/previews/maid.png) | ![miko-2160](2160/previews/miko.png) | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) | ![suit-2160](2160/previews/suit.png) | ![yukata-2160](2160/previews/yukata.png) | | 1620 | 0.941 | [Download](1620/inoue_takina_lycorisrecoil.zip) | ![pattern_1-1620](1620/previews/pattern_1.png) | ![pattern_2-1620](1620/previews/pattern_2.png) | ![pattern_3-1620](1620/previews/pattern_3.png) | ![pattern_4-1620](1620/previews/pattern_4.png) | ![pattern_5-1620](1620/previews/pattern_5.png) | ![pattern_6-1620](1620/previews/pattern_6.png) | ![pattern_7-1620](1620/previews/pattern_7.png) | ![pattern_8-1620](1620/previews/pattern_8.png) | ![bikini-1620](1620/previews/bikini.png) | [<NSFW, click to see>](1620/previews/bondage.png) | ![free-1620](1620/previews/free.png) | ![maid-1620](1620/previews/maid.png) | ![miko-1620](1620/previews/miko.png) | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) | ![suit-1620](1620/previews/suit.png) | ![yukata-1620](1620/previews/yukata.png) | | 1080 | 0.960 | [Download](1080/inoue_takina_lycorisrecoil.zip) | ![pattern_1-1080](1080/previews/pattern_1.png) | ![pattern_2-1080](1080/previews/pattern_2.png) | ![pattern_3-1080](1080/previews/pattern_3.png) | ![pattern_4-1080](1080/previews/pattern_4.png) | ![pattern_5-1080](1080/previews/pattern_5.png) | ![pattern_6-1080](1080/previews/pattern_6.png) | ![pattern_7-1080](1080/previews/pattern_7.png) | ![pattern_8-1080](1080/previews/pattern_8.png) | ![bikini-1080](1080/previews/bikini.png) | [<NSFW, click to see>](1080/previews/bondage.png) | ![free-1080](1080/previews/free.png) | ![maid-1080](1080/previews/maid.png) | ![miko-1080](1080/previews/miko.png) | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) | ![suit-1080](1080/previews/suit.png) | ![yukata-1080](1080/previews/yukata.png) | | 540 | 0.952 | [Download](540/inoue_takina_lycorisrecoil.zip) | ![pattern_1-540](540/previews/pattern_1.png) | ![pattern_2-540](540/previews/pattern_2.png) | ![pattern_3-540](540/previews/pattern_3.png) | ![pattern_4-540](540/previews/pattern_4.png) | ![pattern_5-540](540/previews/pattern_5.png) | ![pattern_6-540](540/previews/pattern_6.png) | ![pattern_7-540](540/previews/pattern_7.png) | ![pattern_8-540](540/previews/pattern_8.png) | ![bikini-540](540/previews/bikini.png) | [<NSFW, click to see>](540/previews/bondage.png) | ![free-540](540/previews/free.png) | ![maid-540](540/previews/maid.png) | ![miko-540](540/previews/miko.png) | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) | ![suit-540](540/previews/suit.png) | ![yukata-540](540/previews/yukata.png) |

Ori/lama-2-13b-peft-mh-at-1-v2-seed-1

Ori

2023-09-28T15:13:15Z

1

0

peft

[ "peft", "safetensors", "region:us" ]

null

2023-09-28T15:12:10Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0

panfengfreedom/ModelTv

panfengfreedom

2023-09-28T15:08:21Z

0

adapter-transformers

[ "adapter-transformers", "art", "aa", "dataset:fka/awesome-chatgpt-prompts", "license:apache-2.0", "region:us" ]

null

2023-09-28T15:07:13Z

--- license: apache-2.0 datasets: - fka/awesome-chatgpt-prompts language: - aa metrics: - accuracy library_name: adapter-transformers tags: - art ---

TinyLlama/tinyLlama-intermediate-checkpoints

TinyLlama

2023-09-28T14:53:27Z

51

12

transformers

[ "transformers", "llama", "text-generation", "en", "dataset:cerebras/SlimPajama-627B", "dataset:bigcode/starcoderdata", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-09-28T14:27:51Z

--- license: apache-2.0 datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata language: - en --- <div align="center"> # TinyLlama-1.1B </div> https://github.com/jzhang38/TinyLlama The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. <div align="center"> <img src="./TinyLlama_logo.png" width="300"/> </div> We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. #### This Model This is an intermediate checkpoint with 50K steps and 105B tokens. #### Releases Schedule We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison. | Date | HF Checkpoint | Tokens | Step | HellaSwag Acc_norm | |------------|-------------------------------------------------|--------|------|---------------------| | Baseline | [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)| 800B | -- | 38.31 | | Baseline | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) | 105B | 50k | 42.04 | | Baseline | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | 300B | 143k | 47.16 | | 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) | 105B | 50k | 43.50 | | 2023-09-16 | -- | 500B | -- | -- | | 2023-10-01 | -- | 1T | -- | -- | | 2023-10-16 | -- | 1.5T | -- | -- | | 2023-10-31 | -- | 2T | -- | -- | | 2023-11-15 | -- | 2.5T | -- | -- | | 2023-12-01 | -- | 3T | -- | -- | #### How to use You will need the transformers>=4.31 Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. ``` from transformers import AutoTokenizer import transformers import torch model = "PY007/TinyLlama-1.1B-step-50K-105b" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.', do_sample=True, top_k=10, num_return_sequences=1, repetition_penalty=1.5, eos_token_id=tokenizer.eos_token_id, max_length=500, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```

anders0204/pyramids

anders0204

2023-09-28T14:38:24Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2023-09-28T14:38:22Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: anders0204/pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

badokorach/flan-t5-small-qa-91

badokorach

2023-09-28T14:35:47Z

8

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-small", "base_model:finetune:google/flan-t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T12:33:04Z

--- license: apache-2.0 base_model: google/flan-t5-small tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-91 results: [] ---  # flan-t5-small-qa-91 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.1207 | 1.0 | 500 | 0.0685 | | 0.1176 | 2.0 | 1000 | 0.0693 | | 0.116 | 3.0 | 1500 | 0.0696 | | 0.1152 | 4.0 | 2000 | 0.0709 | | 0.1138 | 5.0 | 2500 | 0.0703 | | 0.1134 | 6.0 | 3000 | 0.0717 | | 0.1125 | 7.0 | 3500 | 0.0707 | | 0.1119 | 8.0 | 4000 | 0.0710 | | 0.1115 | 9.0 | 4500 | 0.0717 | | 0.1113 | 10.0 | 5000 | 0.0713 | | 0.111 | 11.0 | 5500 | 0.0721 | | 0.1107 | 12.0 | 6000 | 0.0726 | | 0.1106 | 13.0 | 6500 | 0.0721 | | 0.111 | 14.0 | 7000 | 0.0720 | | 0.1108 | 15.0 | 7500 | 0.0721 | | 0.1106 | 16.0 | 8000 | 0.0722 | | 0.1104 | 17.0 | 8500 | 0.0729 | | 0.1102 | 18.0 | 9000 | 0.0727 | | 0.1101 | 19.0 | 9500 | 0.0727 | | 0.11 | 20.0 | 10000 | 0.0727 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

albusdumbledore/llama-trial

albusdumbledore

2023-09-28T14:29:48Z

0

peft

[ "peft", "region:us" ]

null

2023-09-26T19:16:22Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0

yyjun/yyjun.KoAlpaca_mingu_ver

yyjun

2023-09-28T14:28:35Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-28T14:28:32Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0

roa7n/gpt2-human_nontata_promoters-randomized_6_layers_0.003_lr_2_e

roa7n

2023-09-28T14:28:15Z

0

peft

[ "peft", "region:us" ]

null

2023-09-28T14:28:13Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0

anuragrawal/my-awesome-setfit-model

anuragrawal

2023-09-28T14:27:30Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-28T14:26:41Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # anuragrawal/my-awesome-setfit-model This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("anuragrawal/my-awesome-setfit-model") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

santiviquez/amazon-reviews-sentiment-distilbert-base-uncased-6000-samples

santiviquez

2023-09-28T14:16:42Z

112

0

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-28T13:48:30Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: amazon-reviews-sentiment-distilbert-base-uncased-6000-samples results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: en split: validation args: en metrics: - name: Accuracy type: accuracy value: 0.7355 - name: F1 type: f1 value: 0.6586935295304587 ---  # amazon-reviews-sentiment-distilbert-base-uncased-6000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.6126 - Accuracy: 0.7355 - F1: 0.6587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 188 | 0.6172 | 0.7335 | 0.6516 | | No log | 2.0 | 376 | 0.6126 | 0.7355 | 0.6587 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3

skaliy/spine-segmentation

skaliy

2023-09-28T14:16:33Z

0

6

fastMONAI

[ "fastMONAI", "medical", "image-segmentation", "en", "region:us" ]

image-segmentation

2022-11-08T15:19:16Z

--- language: - en pipeline_tag: image-segmentation tags: - medical library_name: fastMONAI --- # Multi-Center CNN-based spine segmentation from T2w MRI using small amounts of data Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research ## Requirements Last checked and validated with fastMONAI version 0.3.2. Please ensure that you have the correct version of fastMONAI installed to guarantee the correct operation of the model. ![Suggested pipeline](pipeline.png) ## Data ### Haukeland University Hospital (HUH): 15 T2-weighted MRI scans - [Study](https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-2306-8) #### Scanner Specifications - Type: Siemens MAGNETOM Avanto 1.5 T - Slice Thickness: 4 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x269 - Interslice Gap: 0.4 mm - Repetition Time: 3700 ms - Echo Time: 87 ms - Number of Acquisitions: 2 ### Sahlgren-ska University Hospital (SUH): 10 T2-weighted MRI scans - [Study](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789539/) #### Scanner Specifications - Type: Siemens MAGNETOM Aera 1.5 T - Slice Thickness: 3.5 mm - Field of View (FOV): 300x300 mm^2 - Matrix: 384x384 - Interslice Gap: 0.7 mm - Repetition Time: 3500 ms - Echo Time: 95 ms - Number of Acquisitions: 1 ### Data Annotation - **Process**: Manual segmentation of vertebrae - **Experts**: Conducted by an MRI expert with consensus from a senior radiologist - **Software**: ITK-SNAP - **Scope**: Segmented vertebrae in all slices, including bone marrow but excluding vertebral cortex ### External Data Evaluation - **Source**: [Annotated T2-weighted MR images of the Lower Spine](https://zenodo.org/record/22304) - **Scan Specifications**: 23 scans, 1.5 T in sagittal orientation - **Segments**: Seven vertebrae are manually segmented on each image (Th11–L5) - **Modification**: Th11 manually removed for consistency with the annotation for SUH and HUH.

Gman/sd-class-butterflies-32

Gman

2023-09-28T14:12:59Z

48

0

diffusers

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

unconditional-image-generation

2023-09-28T14:11:38Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Gman/sd-class-butterflies-32') image = pipeline().images[0] image ```

herve76/bbhf2

herve76

2023-09-28T14:07:28Z

2

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "dataset:herve76/bbhf2", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-28T13:21:53Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: bbhf2 tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: false datasets: - herve76/bbhf2 --- # LoRA DreamBooth - herve76/bbhf2 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on the concept prompt: ``` bbhf2 ``` Use this keyword to trigger your custom model in your prompts. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Usage Make sure to upgrade diffusers to >= 0.19.0: ``` pip install diffusers --upgrade ``` In addition make sure to install transformers, safetensors, accelerate as well as the invisible watermark: ``` pip install invisible_watermark transformers accelerate safetensors ``` To just use the base model, you can run: ```python import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained('madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16) pipe = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) pipe.to("cuda") # This is where you load your trained weights pipe.load_lora_weights('herve76/bbhf2') prompt = "A majestic bbhf2 jumping from a big stone at night" image = pipe(prompt=prompt, num_inference_steps=50).images[0] ```

ArnaudHureaux/Llama-2-70b-chat-hf-miniguanaco

ArnaudHureaux

2023-09-28T14:03:35Z

13

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-09-26T10:42:23Z

--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**" extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - facebook - meta - pytorch - llama - llama-2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>| |Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

VuongQuoc/checkpoints_28_9_microsoft_deberta_V4

VuongQuoc

2023-09-28T13:50:03Z

69

0

transformers

[ "transformers", "pytorch", "deberta-v2", "multiple-choice", "generated_from_trainer", "base_model:microsoft/deberta-v3-large", "base_model:finetune:microsoft/deberta-v3-large", "license:mit", "endpoints_compatible", "region:us" ]

multiple-choice

2023-09-28T02:49:56Z

--- license: mit base_model: microsoft/deberta-v3-large tags: - generated_from_trainer metrics: - accuracy model-index: - name: checkpoints_28_9_microsoft_deberta_V4 results: [] ---  # checkpoints_28_9_microsoft_deberta_V4 This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2854 - Map@3: 0.5483 - Accuracy: 0.435 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Map@3 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| | 1.2365 | 0.11 | 100 | 1.0631 | 0.7583 | 0.64 | | 0.8608 | 0.21 | 200 | 0.7329 | 0.8383 | 0.75 | | 0.8527 | 0.32 | 300 | 0.6985 | 0.8575 | 0.78 | | 0.744 | 0.43 | 400 | 0.6498 | 0.8625 | 0.785 | | 0.7686 | 0.53 | 500 | 0.7450 | 0.8575 | 0.765 | | 1.4098 | 0.64 | 600 | 1.3030 | 0.5575 | 0.4 | | 1.4246 | 0.75 | 700 | 1.3018 | 0.5575 | 0.435 | | 1.3987 | 0.85 | 800 | 1.2906 | 0.5450 | 0.41 | | 1.4121 | 0.96 | 900 | 1.2854 | 0.5483 | 0.435 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.0 - Datasets 2.9.0 - Tokenizers 0.13.3

CyberHarem/kodama_miyako_yagatekimininaru

CyberHarem

2023-09-28T13:49:16Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/kodama_miyako_yagatekimininaru", "license:mit", "region:us" ]

text-to-image

2023-09-28T13:39:51Z

--- license: mit datasets: - CyberHarem/kodama_miyako_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of kodama_miyako_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2380, you need to download `2380/kodama_miyako_yagatekimininaru.pt` as the embedding and `2380/kodama_miyako_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 2380**, with the score of 1.000. The trigger words are: 1. `kodama_miyako_yagatekimininaru` 2. `brown_hair, short_hair, indoors, bangs, collarbone, blurry` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:--------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.900 | [Download](5100/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.996 | [Download](4760/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 1.000 | [Download](4420/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.800 | [Download](4080/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.990 | [Download](3740/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 1.000 | [Download](3400/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 1.000 | [Download](3060/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.964 | [Download](2720/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | **2380** | **1.000** | [**Download**](2380/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.960 | [Download](2040/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.867 | [Download](1700/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.788 | [Download](1360/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.749 | [Download](1020/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.497 | [Download](680/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.715 | [Download](340/kodama_miyako_yagatekimininaru.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

drhyrum/bert-tiny-torch-picklebomb

drhyrum

2023-09-28T13:46:25Z

122

0

transformers

[ "transformers", "pytorch", "bert", "BERT", "MNLI", "NLI", "transformer", "pre-training", "en", "arxiv:1908.08962", "arxiv:2110.01518", "license:mit", "endpoints_compatible", "region:us" ]

null

2023-09-28T13:27:51Z

--- language: - en license: - mit tags: - BERT - MNLI - NLI - transformer - pre-training --- *DISCLAIMER*: This repo demonstrates a picklebomb payload in pytorch that may go undetected by superficial scanning. The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert). This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task. If you use the model, please consider citing both the papers: ``` @misc{bhargava2021generalization, title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics}, author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers}, year={2021}, eprint={2110.01518}, archivePrefix={arXiv}, primaryClass={cs.CL} } @article{DBLP:journals/corr/abs-1908-08962, author = {Iulia Turc and Ming{-}Wei Chang and Kenton Lee and Kristina Toutanova}, title = {Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation}, journal = {CoRR}, volume = {abs/1908.08962}, year = {2019}, url = {http://arxiv.org/abs/1908.08962}, eprinttype = {arXiv}, eprint = {1908.08962}, timestamp = {Thu, 29 Aug 2019 16:32:34 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` Config of this model: - `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny) Other models to check out: - `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini) - `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small) - `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium) Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli). Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)

anders0204/snowball

anders0204

2023-09-28T13:46:01Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]

reinforcement-learning

2023-09-28T13:45:57Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: anders0204/snowball 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

pejho/bloom_prompt_tuning_1695907859.055798

pejho

2023-09-28T13:39:03Z

2

0

peft

[ "peft", "region:us" ]

null

2023-09-28T13:39:01Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0

joemaatouk/donut-base-sroie

joemaatouk

2023-09-28T13:36:07Z

9

0

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:ez_pass-dataset", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-09-11T17:56:59Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - ez_pass-dataset model-index: - name: donut-base-sroie results: [] ---  # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the ez_pass-dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

TheBloke/leo-hessianai-13B-chat-GPTQ

TheBloke

2023-09-28T13:34:46Z

41

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "en", "de", "dataset:LeoLM/OpenSchnabeltier", "dataset:OpenAssistant/OASST-DE", "dataset:FreedomIntelligence/alpaca-gpt4-deutsch", "dataset:FreedomIntelligence/evol-instruct-deutsch", "dataset:LeoLM/German_Poems", "dataset:LeoLM/German_Songs", "base_model:LeoLM/leo-hessianai-13b-chat", "base_model:quantized:LeoLM/leo-hessianai-13b-chat", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]

text-generation

2023-09-28T12:22:36Z

--- base_model: LeoLM/leo-hessianai-13b-chat datasets: - LeoLM/OpenSchnabeltier - OpenAssistant/OASST-DE - FreedomIntelligence/alpaca-gpt4-deutsch - FreedomIntelligence/evol-instruct-deutsch - LeoLM/German_Poems - LeoLM/German_Songs inference: false language: - en - de library_name: transformers license: llama2 model_creator: LAION LeoLM model_name: Leo Hessianai 13B Chat model_type: llama pipeline_tag: text-generation prompt_template: '<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ' quantized_by: TheBloke ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # Leo Hessianai 13B Chat - GPTQ - Model creator: [LAION LeoLM](https://huggingface.co/LeoLM) - Original model: [Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat)  ## Description This repo contains GPTQ model files for [LAION LeoLM's Leo Hessianai 13B Chat](https://huggingface.co/LeoLM/leo-hessianai-13b-chat). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GGUF) * [LAION LeoLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LeoLM/leo-hessianai-13b-chat)   ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ```   ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> | Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 8.01 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 13.66 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | | [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 14.55 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. | | [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad) | 8192 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |   ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/leo-hessianai-13B-chat-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `leo-hessianai-13B-chat-GPTQ`: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir leo-hessianai-13B-chat-GPTQ huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir leo-hessianai-13B-chat-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/leo-hessianai-13B-chat-GPTQ --local-dir leo-hessianai-13B-chat-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/leo-hessianai-13B-chat-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)   ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/leo-hessianai-13B-chat-GPTQ`. - To download from a specific branch, enter for example `TheBloke/leo-hessianai-13B-chat-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `leo-hessianai-13B-chat-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. * Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!   ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/leo-hessianai-13B-chat-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ```   ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.  # Original model card: LAION LeoLM's Leo Hessianai 13B Chat # LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2. Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text. Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length, [`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀). With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption. Read our [blog post]() or our paper (preprint coming soon) for more details! *A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.* ## LeoLM Chat `LeoLM/leo-hessianai-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German instruction datasets. The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores: ``` { "first_turn": 6.525, "second_turn": 5.15, "categories": { "writing": 6.925, "roleplay": 6.7, "reasoning": 4.55, "math": 3.25, "coding": 3.45, "extraction": 5.4, "stem": 7.55, "humanities": 8.875 }, "average": 5.8375 } ``` ## Model Details - **Finetuned from:** [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b) - **Model type:** Causal decoder-only transformer language model - **Language:** English and German - **Demo:** [Web Demo]() - **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) - **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de) ## Use in 🤗Transformers First install direct dependencies: ``` pip install transformers torch sentencepiece ``` If you want faster inference using flash-attention2, you need to install these dependencies: ```bash pip install packaging ninja pip install flash-attn==v2.1.1 --no-build-isolation pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary ``` Then load the model in transformers: ```python from transformers import pipeline import torch system_prompt = """<|im_start|>system Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|> """ prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist." generator = pipeline(model="LeoLM/leo-hessianai-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192)) ``` "*Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren.*" ## Prompting / Prompt Template Prompt dialogue template (ChatML format): ``` """ <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant """ ``` The model input can contain multiple conversation turns between user and assistant, e.g. ``` <|im_start|>user {prompt 1}<|im_end|> <|im_start|>assistant {reply 1}<|im_end|> <|im_start|>user {prompt 2}<|im_end|> <|im_start|>assistant (...) ``` ## Ethical Considerations and Limitations LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-13b-chat` cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-13b-chat`, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). ## Finetuning Details | Hyperparameter | Value | |---|---| | Num epochs | 3 | | Examples per epoch | 131214 | | Global batch size | 256 | | Learning rate | 3e-5 | | Warmup steps | 100 | | LR scheduler | Cosine | | Adam betas | (0.9, 0.95) | ## Dataset Details ``` ## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%)) ----------------- Accepted: 3534/3534 (100.0%) Accepted tokens: 2259302 Skipped: 0 (0.0%) Min tokens per sample: 29 Max tokens per sample: 2484 Avg tokens per sample: 639.3044708545557 ----------------- ## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%)) ----------------- Accepted: 57841/57841 (100.0%) Accepted tokens: 42958192 Skipped: 0 (0.0%) Min tokens per sample: 33 Max tokens per sample: 5507 Avg tokens per sample: 742.6944900675991 ----------------- ## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%)) ----------------- Accepted: 48969/48969 (100.0%) Accepted tokens: 13372005 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 1359 Avg tokens per sample: 273.07082031489307 ----------------- ## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%)) ----------------- Accepted: 21314/21314 (100.0%) Accepted tokens: 8134690 Skipped: 0 (0.0%) Min tokens per sample: 25 Max tokens per sample: 1202 Avg tokens per sample: 381.65947264708643 ----------------- ## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%)) ----------------- Accepted: 490/490 (100.0%) Accepted tokens: 618642 Skipped: 0 (0.0%) Min tokens per sample: 747 Max tokens per sample: 1678 Avg tokens per sample: 1262.534693877551 ----------------- ## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%)) ----------------- Accepted: 392/392 (100.0%) Accepted tokens: 187897 Skipped: 0 (0.0%) Min tokens per sample: 231 Max tokens per sample: 826 Avg tokens per sample: 479.3290816326531 ----------------- ## Stats for 'total' (132540 samples (100.0%)) ----------------- Accepted: 132540/132540 (100.0%) Accepted tokens: 67530728 Skipped: 0 (0.0%) Min tokens per sample: 19 Max tokens per sample: 5507 Avg tokens per sample: 509.51205673758864 ----------------- ```

Aliki/Llama-2-13b-chat-german-areto26-test1

Aliki

2023-09-28T13:32:14Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-28T13:32:05Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

EvinFrancis/my-pet-dog-xzr

EvinFrancis

2023-09-28T13:26:46Z

4

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-28T13:22:47Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog-xzr Dreambooth model trained by EvinFrancis following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: GoXJITD9 Sample pictures of this concept: ![0](https://huggingface.co/EvinFrancis/my-pet-dog-xzr/resolve/main/sample_images/download_(1).png) ![1](https://huggingface.co/EvinFrancis/my-pet-dog-xzr/resolve/main/sample_images/download.png)

joejoequte/test

joejoequte

2023-09-28T12:58:28Z

1

0

transformers

[ "transformers", "endpoints_compatible", "region:us" ]

null

2023-09-28T12:57:18Z

# v2ray_for_Huggingface 在Huggingface搭建v2ray节点配置，Caddy+V2ray实现需修改密码见config.json

shahzain/whisper-sm-urdu-finetuned

shahzain

2023-09-28T12:52:20Z

113

0

transformers

[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-19T04:07:53Z

--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-sm-urdu-finetuned results: [] ---  # whisper-sm-urdu-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [Common Voice 13.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0/viewer/ur/train) dataset. It achieves the following results on the evaluation set: - Loss: 0.1777 - Wer: 36.0220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4017 | 0.18 | 1000 | 0.2591 | 65.3388 | | 0.3963 | 0.36 | 2000 | 0.2135 | 43.6355 | | 0.3162 | 0.54 | 3000 | 0.1880 | 35.8573 | | 0.3479 | 0.72 | 4000 | 0.1777 | 36.0220 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1 - Datasets 2.14.5 - Tokenizers 0.13.3

sxandie/autotrain-air-sea-services-28092023-91915144626

sxandie

2023-09-28T12:40:34Z

108

0

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "token-classification", "autotrain", "unk", "dataset:sxandie/autotrain-data-air-sea-services-28092023", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-28T12:38:08Z

--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain" datasets: - sxandie/autotrain-data-air-sea-services-28092023 co2_eq_emissions: emissions: 0.8518554225909252 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 91915144626 - CO2 Emissions (in grams): 0.8519 ## Validation Metrics - Loss: 0.921 - Accuracy: 0.759 - Precision: 0.159 - Recall: 0.098 - F1: 0.122 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/sxandie/autotrain-air-sea-services-28092023-91915144626 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("sxandie/autotrain-air-sea-services-28092023-91915144626", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

shikari2917/mypic4

shikari2917

2023-09-28T12:40:17Z

5

1

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-28T12:40:15Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks rafeh tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

mHossain/bangla_idiom_generation_v3

mHossain

2023-09-28T12:36:51Z

103

1

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T12:20:38Z

--- tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla_idiom_generation_v3 results: [] ---  # bangla_idiom_generation_v3 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.5201 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 10.8938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 226 | 4.7622 | 0.0 | 0.0 | 0.0 | 0.0 | 10.5398 | | No log | 2.0 | 452 | 4.7514 | 0.0 | 0.0 | 0.0 | 0.0 | 10.6018 | | 5.1485 | 3.0 | 678 | 4.7347 | 0.0 | 0.0 | 0.0 | 0.0 | 11.2478 | | 5.1485 | 4.0 | 904 | 4.7101 | 0.0 | 0.0 | 0.0 | 0.0 | 11.9912 | | 5.0936 | 5.0 | 1130 | 4.6840 | 0.0 | 0.0 | 0.0 | 0.0 | 12.0841 | | 5.0936 | 6.0 | 1356 | 4.6527 | 0.0 | 0.0 | 0.0 | 0.0 | 12.1991 | | 4.9965 | 7.0 | 1582 | 4.6199 | 0.0 | 0.0 | 0.0 | 0.0 | 12.5044 | | 4.9965 | 8.0 | 1808 | 4.5845 | 0.0 | 0.0 | 0.0 | 0.0 | 11.8761 | | 4.8494 | 9.0 | 2034 | 4.5543 | 0.0 | 0.0 | 0.0 | 0.0 | 11.5398 | | 4.8494 | 10.0 | 2260 | 4.5201 | 0.0 | 0.0 | 0.0 | 0.0 | 10.8938 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

badokorach/flan-t5-small-qa-9-qa-91

badokorach

2023-09-28T12:28:24Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:badokorach/flan-t5-small-qa-9", "base_model:finetune:badokorach/flan-t5-small-qa-9", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T09:51:48Z

--- license: apache-2.0 base_model: badokorach/flan-t5-small-qa-9 tags: - generated_from_trainer model-index: - name: flan-t5-small-qa-9-qa-91 results: [] ---  # flan-t5-small-qa-9-qa-91 This model is a fine-tuned version of [badokorach/flan-t5-small-qa-9](https://huggingface.co/badokorach/flan-t5-small-qa-9) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 343 | 0.1041 | | 0.0207 | 2.0 | 686 | 0.1041 | | 0.0196 | 3.0 | 1029 | 0.1057 | | 0.0196 | 4.0 | 1372 | 0.1052 | | 0.0186 | 5.0 | 1715 | 0.1043 | | 0.0173 | 6.0 | 2058 | 0.1050 | | 0.0173 | 7.0 | 2401 | 0.1079 | | 0.0165 | 8.0 | 2744 | 0.1068 | | 0.0161 | 9.0 | 3087 | 0.1081 | | 0.0161 | 10.0 | 3430 | 0.1084 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Tokenizers 0.13.3

CyberHarem/kanou_koyomi_yagatekimininaru

CyberHarem

2023-09-28T12:21:10Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/kanou_koyomi_yagatekimininaru", "license:mit", "region:us" ]

text-to-image

2023-09-28T12:09:24Z

--- license: mit datasets: - CyberHarem/kanou_koyomi_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of kanou_koyomi_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/kanou_koyomi_yagatekimininaru.pt` as the embedding and `4420/kanou_koyomi_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4420**, with the score of 0.882. The trigger words are: 1. `kanou_koyomi_yagatekimininaru` 2. `short_hair, black_hair, closed_mouth, indoors, brown_hair, blue_eyes, black_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.815 | [Download](5100/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.719 | [Download](4760/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | **4420** | **0.882** | [**Download**](4420/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.746 | [Download](4080/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.794 | [Download](3740/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.755 | [Download](3400/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.700 | [Download](3060/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.664 | [Download](2720/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.799 | [Download](2380/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.666 | [Download](2040/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.819 | [Download](1700/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.724 | [Download](1360/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.675 | [Download](1020/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.607 | [Download](680/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.528 | [Download](340/kanou_koyomi_yagatekimininaru.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

CyberHarem/aoyama_midori_istheorderarabbit

CyberHarem

2023-09-28T12:17:17Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/aoyama_midori_istheorderarabbit", "license:mit", "region:us" ]

text-to-image

2023-09-28T11:48:41Z

--- license: mit datasets: - CyberHarem/aoyama_midori_istheorderarabbit pipeline_tag: text-to-image tags: - art --- # Lora of aoyama_midori_istheorderarabbit This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4800, you need to download `4800/aoyama_midori_istheorderarabbit.pt` as the embedding and `4800/aoyama_midori_istheorderarabbit.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4800**, with the score of 0.872. The trigger words are: 1. `aoyama_midori_istheorderarabbit` 2. `blush, blonde_hair, hair_between_eyes, smile, blue_eyes, bangs, long_hair, closed_mouth` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 6000 | 0.822 | [Download](6000/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-6000](6000/previews/pattern_1.png) | ![pattern_2-6000](6000/previews/pattern_2.png) | ![pattern_3-6000](6000/previews/pattern_3.png) | ![pattern_4-6000](6000/previews/pattern_4.png) | ![pattern_5-6000](6000/previews/pattern_5.png) | ![pattern_6-6000](6000/previews/pattern_6.png) | ![pattern_7-6000](6000/previews/pattern_7.png) | ![pattern_8-6000](6000/previews/pattern_8.png) | ![pattern_9-6000](6000/previews/pattern_9.png) | ![pattern_10-6000](6000/previews/pattern_10.png) | ![pattern_11-6000](6000/previews/pattern_11.png) | ![bikini-6000](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) | ![free-6000](6000/previews/free.png) | ![maid-6000](6000/previews/maid.png) | ![miko-6000](6000/previews/miko.png) | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) | ![suit-6000](6000/previews/suit.png) | ![yukata-6000](6000/previews/yukata.png) | | 5600 | 0.838 | [Download](5600/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-5600](5600/previews/pattern_1.png) | ![pattern_2-5600](5600/previews/pattern_2.png) | ![pattern_3-5600](5600/previews/pattern_3.png) | ![pattern_4-5600](5600/previews/pattern_4.png) | ![pattern_5-5600](5600/previews/pattern_5.png) | ![pattern_6-5600](5600/previews/pattern_6.png) | ![pattern_7-5600](5600/previews/pattern_7.png) | ![pattern_8-5600](5600/previews/pattern_8.png) | ![pattern_9-5600](5600/previews/pattern_9.png) | ![pattern_10-5600](5600/previews/pattern_10.png) | ![pattern_11-5600](5600/previews/pattern_11.png) | ![bikini-5600](5600/previews/bikini.png) | [<NSFW, click to see>](5600/previews/bondage.png) | ![free-5600](5600/previews/free.png) | ![maid-5600](5600/previews/maid.png) | ![miko-5600](5600/previews/miko.png) | [<NSFW, click to see>](5600/previews/nude.png) | [<NSFW, click to see>](5600/previews/nude2.png) | ![suit-5600](5600/previews/suit.png) | ![yukata-5600](5600/previews/yukata.png) | | 5200 | 0.833 | [Download](5200/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-5200](5200/previews/pattern_1.png) | ![pattern_2-5200](5200/previews/pattern_2.png) | ![pattern_3-5200](5200/previews/pattern_3.png) | ![pattern_4-5200](5200/previews/pattern_4.png) | ![pattern_5-5200](5200/previews/pattern_5.png) | ![pattern_6-5200](5200/previews/pattern_6.png) | ![pattern_7-5200](5200/previews/pattern_7.png) | ![pattern_8-5200](5200/previews/pattern_8.png) | ![pattern_9-5200](5200/previews/pattern_9.png) | ![pattern_10-5200](5200/previews/pattern_10.png) | ![pattern_11-5200](5200/previews/pattern_11.png) | ![bikini-5200](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) | ![free-5200](5200/previews/free.png) | ![maid-5200](5200/previews/maid.png) | ![miko-5200](5200/previews/miko.png) | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) | ![suit-5200](5200/previews/suit.png) | ![yukata-5200](5200/previews/yukata.png) | | **4800** | **0.872** | [**Download**](4800/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-4800](4800/previews/pattern_1.png) | ![pattern_2-4800](4800/previews/pattern_2.png) | ![pattern_3-4800](4800/previews/pattern_3.png) | ![pattern_4-4800](4800/previews/pattern_4.png) | ![pattern_5-4800](4800/previews/pattern_5.png) | ![pattern_6-4800](4800/previews/pattern_6.png) | ![pattern_7-4800](4800/previews/pattern_7.png) | ![pattern_8-4800](4800/previews/pattern_8.png) | ![pattern_9-4800](4800/previews/pattern_9.png) | ![pattern_10-4800](4800/previews/pattern_10.png) | ![pattern_11-4800](4800/previews/pattern_11.png) | ![bikini-4800](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) | ![free-4800](4800/previews/free.png) | ![maid-4800](4800/previews/maid.png) | ![miko-4800](4800/previews/miko.png) | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) | ![suit-4800](4800/previews/suit.png) | ![yukata-4800](4800/previews/yukata.png) | | 4400 | 0.786 | [Download](4400/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-4400](4400/previews/pattern_1.png) | ![pattern_2-4400](4400/previews/pattern_2.png) | ![pattern_3-4400](4400/previews/pattern_3.png) | ![pattern_4-4400](4400/previews/pattern_4.png) | ![pattern_5-4400](4400/previews/pattern_5.png) | ![pattern_6-4400](4400/previews/pattern_6.png) | ![pattern_7-4400](4400/previews/pattern_7.png) | ![pattern_8-4400](4400/previews/pattern_8.png) | ![pattern_9-4400](4400/previews/pattern_9.png) | ![pattern_10-4400](4400/previews/pattern_10.png) | ![pattern_11-4400](4400/previews/pattern_11.png) | ![bikini-4400](4400/previews/bikini.png) | [<NSFW, click to see>](4400/previews/bondage.png) | ![free-4400](4400/previews/free.png) | ![maid-4400](4400/previews/maid.png) | ![miko-4400](4400/previews/miko.png) | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) | ![suit-4400](4400/previews/suit.png) | ![yukata-4400](4400/previews/yukata.png) | | 4000 | 0.834 | [Download](4000/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-4000](4000/previews/pattern_1.png) | ![pattern_2-4000](4000/previews/pattern_2.png) | ![pattern_3-4000](4000/previews/pattern_3.png) | ![pattern_4-4000](4000/previews/pattern_4.png) | ![pattern_5-4000](4000/previews/pattern_5.png) | ![pattern_6-4000](4000/previews/pattern_6.png) | ![pattern_7-4000](4000/previews/pattern_7.png) | ![pattern_8-4000](4000/previews/pattern_8.png) | ![pattern_9-4000](4000/previews/pattern_9.png) | ![pattern_10-4000](4000/previews/pattern_10.png) | ![pattern_11-4000](4000/previews/pattern_11.png) | ![bikini-4000](4000/previews/bikini.png) | [<NSFW, click to see>](4000/previews/bondage.png) | ![free-4000](4000/previews/free.png) | ![maid-4000](4000/previews/maid.png) | ![miko-4000](4000/previews/miko.png) | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) | ![suit-4000](4000/previews/suit.png) | ![yukata-4000](4000/previews/yukata.png) | | 3600 | 0.787 | [Download](3600/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-3600](3600/previews/pattern_1.png) | ![pattern_2-3600](3600/previews/pattern_2.png) | ![pattern_3-3600](3600/previews/pattern_3.png) | ![pattern_4-3600](3600/previews/pattern_4.png) | ![pattern_5-3600](3600/previews/pattern_5.png) | ![pattern_6-3600](3600/previews/pattern_6.png) | ![pattern_7-3600](3600/previews/pattern_7.png) | ![pattern_8-3600](3600/previews/pattern_8.png) | ![pattern_9-3600](3600/previews/pattern_9.png) | ![pattern_10-3600](3600/previews/pattern_10.png) | ![pattern_11-3600](3600/previews/pattern_11.png) | ![bikini-3600](3600/previews/bikini.png) | [<NSFW, click to see>](3600/previews/bondage.png) | ![free-3600](3600/previews/free.png) | ![maid-3600](3600/previews/maid.png) | ![miko-3600](3600/previews/miko.png) | [<NSFW, click to see>](3600/previews/nude.png) | [<NSFW, click to see>](3600/previews/nude2.png) | ![suit-3600](3600/previews/suit.png) | ![yukata-3600](3600/previews/yukata.png) | | 3200 | 0.829 | [Download](3200/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-3200](3200/previews/pattern_1.png) | ![pattern_2-3200](3200/previews/pattern_2.png) | ![pattern_3-3200](3200/previews/pattern_3.png) | ![pattern_4-3200](3200/previews/pattern_4.png) | ![pattern_5-3200](3200/previews/pattern_5.png) | ![pattern_6-3200](3200/previews/pattern_6.png) | ![pattern_7-3200](3200/previews/pattern_7.png) | ![pattern_8-3200](3200/previews/pattern_8.png) | ![pattern_9-3200](3200/previews/pattern_9.png) | ![pattern_10-3200](3200/previews/pattern_10.png) | ![pattern_11-3200](3200/previews/pattern_11.png) | ![bikini-3200](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | ![free-3200](3200/previews/free.png) | ![maid-3200](3200/previews/maid.png) | ![miko-3200](3200/previews/miko.png) | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) | ![suit-3200](3200/previews/suit.png) | ![yukata-3200](3200/previews/yukata.png) | | 2800 | 0.778 | [Download](2800/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-2800](2800/previews/pattern_1.png) | ![pattern_2-2800](2800/previews/pattern_2.png) | ![pattern_3-2800](2800/previews/pattern_3.png) | ![pattern_4-2800](2800/previews/pattern_4.png) | ![pattern_5-2800](2800/previews/pattern_5.png) | ![pattern_6-2800](2800/previews/pattern_6.png) | ![pattern_7-2800](2800/previews/pattern_7.png) | ![pattern_8-2800](2800/previews/pattern_8.png) | ![pattern_9-2800](2800/previews/pattern_9.png) | ![pattern_10-2800](2800/previews/pattern_10.png) | ![pattern_11-2800](2800/previews/pattern_11.png) | ![bikini-2800](2800/previews/bikini.png) | [<NSFW, click to see>](2800/previews/bondage.png) | ![free-2800](2800/previews/free.png) | ![maid-2800](2800/previews/maid.png) | ![miko-2800](2800/previews/miko.png) | [<NSFW, click to see>](2800/previews/nude.png) | [<NSFW, click to see>](2800/previews/nude2.png) | ![suit-2800](2800/previews/suit.png) | ![yukata-2800](2800/previews/yukata.png) | | 2400 | 0.749 | [Download](2400/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-2400](2400/previews/pattern_1.png) | ![pattern_2-2400](2400/previews/pattern_2.png) | ![pattern_3-2400](2400/previews/pattern_3.png) | ![pattern_4-2400](2400/previews/pattern_4.png) | ![pattern_5-2400](2400/previews/pattern_5.png) | ![pattern_6-2400](2400/previews/pattern_6.png) | ![pattern_7-2400](2400/previews/pattern_7.png) | ![pattern_8-2400](2400/previews/pattern_8.png) | ![pattern_9-2400](2400/previews/pattern_9.png) | ![pattern_10-2400](2400/previews/pattern_10.png) | ![pattern_11-2400](2400/previews/pattern_11.png) | ![bikini-2400](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) | ![free-2400](2400/previews/free.png) | ![maid-2400](2400/previews/maid.png) | ![miko-2400](2400/previews/miko.png) | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) | ![suit-2400](2400/previews/suit.png) | ![yukata-2400](2400/previews/yukata.png) | | 2000 | 0.806 | [Download](2000/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-2000](2000/previews/pattern_1.png) | ![pattern_2-2000](2000/previews/pattern_2.png) | ![pattern_3-2000](2000/previews/pattern_3.png) | ![pattern_4-2000](2000/previews/pattern_4.png) | ![pattern_5-2000](2000/previews/pattern_5.png) | ![pattern_6-2000](2000/previews/pattern_6.png) | ![pattern_7-2000](2000/previews/pattern_7.png) | ![pattern_8-2000](2000/previews/pattern_8.png) | ![pattern_9-2000](2000/previews/pattern_9.png) | ![pattern_10-2000](2000/previews/pattern_10.png) | ![pattern_11-2000](2000/previews/pattern_11.png) | ![bikini-2000](2000/previews/bikini.png) | [<NSFW, click to see>](2000/previews/bondage.png) | ![free-2000](2000/previews/free.png) | ![maid-2000](2000/previews/maid.png) | ![miko-2000](2000/previews/miko.png) | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) | ![suit-2000](2000/previews/suit.png) | ![yukata-2000](2000/previews/yukata.png) | | 1600 | 0.687 | [Download](1600/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-1600](1600/previews/pattern_1.png) | ![pattern_2-1600](1600/previews/pattern_2.png) | ![pattern_3-1600](1600/previews/pattern_3.png) | ![pattern_4-1600](1600/previews/pattern_4.png) | ![pattern_5-1600](1600/previews/pattern_5.png) | ![pattern_6-1600](1600/previews/pattern_6.png) | ![pattern_7-1600](1600/previews/pattern_7.png) | ![pattern_8-1600](1600/previews/pattern_8.png) | ![pattern_9-1600](1600/previews/pattern_9.png) | ![pattern_10-1600](1600/previews/pattern_10.png) | ![pattern_11-1600](1600/previews/pattern_11.png) | ![bikini-1600](1600/previews/bikini.png) | [<NSFW, click to see>](1600/previews/bondage.png) | ![free-1600](1600/previews/free.png) | ![maid-1600](1600/previews/maid.png) | ![miko-1600](1600/previews/miko.png) | [<NSFW, click to see>](1600/previews/nude.png) | [<NSFW, click to see>](1600/previews/nude2.png) | ![suit-1600](1600/previews/suit.png) | ![yukata-1600](1600/previews/yukata.png) | | 1200 | 0.718 | [Download](1200/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-1200](1200/previews/pattern_1.png) | ![pattern_2-1200](1200/previews/pattern_2.png) | ![pattern_3-1200](1200/previews/pattern_3.png) | ![pattern_4-1200](1200/previews/pattern_4.png) | ![pattern_5-1200](1200/previews/pattern_5.png) | ![pattern_6-1200](1200/previews/pattern_6.png) | ![pattern_7-1200](1200/previews/pattern_7.png) | ![pattern_8-1200](1200/previews/pattern_8.png) | ![pattern_9-1200](1200/previews/pattern_9.png) | ![pattern_10-1200](1200/previews/pattern_10.png) | ![pattern_11-1200](1200/previews/pattern_11.png) | ![bikini-1200](1200/previews/bikini.png) | [<NSFW, click to see>](1200/previews/bondage.png) | ![free-1200](1200/previews/free.png) | ![maid-1200](1200/previews/maid.png) | ![miko-1200](1200/previews/miko.png) | [<NSFW, click to see>](1200/previews/nude.png) | [<NSFW, click to see>](1200/previews/nude2.png) | ![suit-1200](1200/previews/suit.png) | ![yukata-1200](1200/previews/yukata.png) | | 800 | 0.673 | [Download](800/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-800](800/previews/pattern_1.png) | ![pattern_2-800](800/previews/pattern_2.png) | ![pattern_3-800](800/previews/pattern_3.png) | ![pattern_4-800](800/previews/pattern_4.png) | ![pattern_5-800](800/previews/pattern_5.png) | ![pattern_6-800](800/previews/pattern_6.png) | ![pattern_7-800](800/previews/pattern_7.png) | ![pattern_8-800](800/previews/pattern_8.png) | ![pattern_9-800](800/previews/pattern_9.png) | ![pattern_10-800](800/previews/pattern_10.png) | ![pattern_11-800](800/previews/pattern_11.png) | ![bikini-800](800/previews/bikini.png) | [<NSFW, click to see>](800/previews/bondage.png) | ![free-800](800/previews/free.png) | ![maid-800](800/previews/maid.png) | ![miko-800](800/previews/miko.png) | [<NSFW, click to see>](800/previews/nude.png) | [<NSFW, click to see>](800/previews/nude2.png) | ![suit-800](800/previews/suit.png) | ![yukata-800](800/previews/yukata.png) | | 400 | 0.435 | [Download](400/aoyama_midori_istheorderarabbit.zip) | ![pattern_1-400](400/previews/pattern_1.png) | ![pattern_2-400](400/previews/pattern_2.png) | ![pattern_3-400](400/previews/pattern_3.png) | ![pattern_4-400](400/previews/pattern_4.png) | ![pattern_5-400](400/previews/pattern_5.png) | ![pattern_6-400](400/previews/pattern_6.png) | ![pattern_7-400](400/previews/pattern_7.png) | ![pattern_8-400](400/previews/pattern_8.png) | ![pattern_9-400](400/previews/pattern_9.png) | ![pattern_10-400](400/previews/pattern_10.png) | ![pattern_11-400](400/previews/pattern_11.png) | ![bikini-400](400/previews/bikini.png) | [<NSFW, click to see>](400/previews/bondage.png) | ![free-400](400/previews/free.png) | ![maid-400](400/previews/maid.png) | ![miko-400](400/previews/miko.png) | [<NSFW, click to see>](400/previews/nude.png) | [<NSFW, click to see>](400/previews/nude2.png) | ![suit-400](400/previews/suit.png) | ![yukata-400](400/previews/yukata.png) |

mHossain/bangla_idiom_generation_v2

mHossain

2023-09-28T12:11:45Z

103

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:mHossain/bangla-para-v3-500000", "base_model:finetune:mHossain/bangla-para-v3-500000", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-28T11:29:55Z

--- base_model: mHossain/bangla-para-v3-500000 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla_idiom_generation_v2 results: [] ---  # bangla_idiom_generation_v2 This model is a fine-tuned version of [mHossain/bangla-para-v3-500000](https://huggingface.co/mHossain/bangla-para-v3-500000) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.7655 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 10.4292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 226 | 7.4650 | 0.0 | 0.0 | 0.0 | 0.0 | 11.1195 | | No log | 2.0 | 452 | 7.1989 | 0.0 | 0.0 | 0.0 | 0.0 | 10.677 | | 7.7649 | 3.0 | 678 | 6.8146 | 0.0 | 0.0 | 0.0 | 0.0 | 9.8938 | | 7.7649 | 4.0 | 904 | 6.3650 | 0.0 | 0.0 | 0.0 | 0.0 | 9.3805 | | 7.0991 | 5.0 | 1130 | 5.8781 | 0.0 | 0.0 | 0.0 | 0.0 | 9.2478 | | 7.0991 | 6.0 | 1356 | 5.4460 | 0.0 | 0.0 | 0.0 | 0.0 | 9.0487 | | 6.1699 | 7.0 | 1582 | 5.1824 | 0.0 | 0.0 | 0.0 | 0.0 | 9.3363 | | 6.1699 | 8.0 | 1808 | 4.9930 | 0.0 | 0.0 | 0.0 | 0.0 | 10.2478 | | 5.5395 | 9.0 | 2034 | 4.8596 | 0.0 | 0.0 | 0.0 | 0.0 | 10.5575 | | 5.5395 | 10.0 | 2260 | 4.7655 | 0.0 | 0.0 | 0.0 | 0.0 | 10.4292 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

yudiwbs/absa_elektronik_aspek_v2

yudiwbs

2023-09-28T11:52:36Z

0

flair

[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]

token-classification

2023-09-24T13:59:16Z

--- tags: - flair - token-classification - sequence-tagger-model widget: - text: "Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus keyboard jp jg. Thanks gan." --- Klasifikasi aspek dalam review laptop ### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`) ```python from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("yudiwbs/absa_elektronik_aspek_v2") sentence = Sentence('Sesuai diskusi. Barang mulus walau ada hairline scratch di casing depan. Battery masih awet. Dapat bonus jg. Thanks gan.') # predict model.predict(sentence) print(sentence.to_tagged_string()) # iterate over entities and print for entity in sentence.get_spans('aspek'): print(entity) ```

facebook/regnet-y-640-seer

facebook

2023-09-28T11:51:06Z

162

0

transformers

[ "transformers", "pytorch", "regnet", "image-feature-extraction", "vision", "arxiv:2202.08360", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-feature-extraction

2022-03-18T15:56:16Z

--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNetModel RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetModel >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1088, 7, 7] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).

facebook/regnet-y-320-seer

facebook

2023-09-28T11:50:27Z

213

2

transformers

[ "transformers", "pytorch", "tf", "regnet", "image-feature-extraction", "vision", "arxiv:2202.08360", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-feature-extraction

2022-03-18T13:51:03Z

--- license: apache-2.0 tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # RegNetModel RegNetModel model was introduced in the paper [Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision](https://arxiv.org/abs/2202.08360) and first released in [this repository](https://github.com/facebookresearch/vissl/tree/main/projects/SEER). Disclaimer: The team releasing RegNetModel did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The authors trained [RegNets](https://huggingface.co/?models=regnet) models in a self-supervised fashion on bilion of random images from the internet ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/regnet_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=regnet) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import AutoFeatureExtractor, RegNetModel >>> import torch >>> from datasets import load_dataset >>> dataset = load_dataset("huggingface/cats-image") >>> image = dataset["test"]["image"][0] >>> feature_extractor = AutoFeatureExtractor.from_pretrained("zuppif/regnet-y-040") >>> model = RegNetModel.from_pretrained("zuppif/regnet-y-040") >>> inputs = feature_extractor(image, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 1088, 7, 7] ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/regnet).

hmxiong/ScanNet_Finetune_global_guidance_10epoch_bs64

hmxiong

2023-09-28T11:47:16Z

0

null

[ "region:us" ]

null

2023-09-28T02:29:07Z

每个场景使用的是全部的18个类别信息

hmxiong/ScanNet_Finetune_no_guidance_10epoch_bs64

hmxiong

2023-09-28T11:45:57Z

0

null

[ "region:us" ]

null

2023-09-28T02:30:25Z

训练阶段不加入任何的类别guidance

CyberHarem/saeki_sayaka_yagatekimininaru

CyberHarem

2023-09-28T11:36:35Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/saeki_sayaka_yagatekimininaru", "license:mit", "region:us" ]

text-to-image

2023-09-28T11:23:30Z

--- license: mit datasets: - CyberHarem/saeki_sayaka_yagatekimininaru pipeline_tag: text-to-image tags: - art --- # Lora of saeki_sayaka_yagatekimininaru This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/saeki_sayaka_yagatekimininaru.pt` as the embedding and `4760/saeki_sayaka_yagatekimininaru.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.932. The trigger words are: 1. `saeki_sayaka_yagatekimininaru` 2. `long_hair, brown_hair, braid, green_eyes, bow` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.889 | [Download](5100/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![pattern_8-5100](5100/previews/pattern_8.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.932** | [**Download**](4760/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.827 | [Download](4420/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![pattern_8-4420](4420/previews/pattern_8.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.920 | [Download](4080/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.851 | [Download](3740/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![pattern_8-3740](3740/previews/pattern_8.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.835 | [Download](3400/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.832 | [Download](3060/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![pattern_8-3060](3060/previews/pattern_8.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.857 | [Download](2720/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.805 | [Download](2380/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![pattern_8-2380](2380/previews/pattern_8.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.798 | [Download](2040/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.769 | [Download](1700/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![pattern_8-1700](1700/previews/pattern_8.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.829 | [Download](1360/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.711 | [Download](1020/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![pattern_8-1020](1020/previews/pattern_8.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.705 | [Download](680/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.580 | [Download](340/saeki_sayaka_yagatekimininaru.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![pattern_8-340](340/previews/pattern_8.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Udayy26/my-pet-cat-xzg

Udayy26

2023-09-28T11:29:15Z

1

0

diffusers

[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-28T11:22:58Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Cat-xzg Dreambooth model trained by Udayy26 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CBIT50 Sample pictures of this concept: ![0](https://huggingface.co/Udayy26/my-pet-cat-xzg/resolve/main/sample_images/xzg.jpg)

colab086/roberta-finetuned-subjqa-movies_2

colab086

2023-09-28T11:13:44Z

122

0

transformers

[ "transformers", "pytorch", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2023-09-28T10:57:30Z

--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-finetuned-subjqa-movies_2 results: [] ---  # roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

SamuelGalaxys/iTEMX-bloxerBETA

SamuelGalaxys

2023-09-28T11:12:03Z

0

null

[ "license:mit", "region:us" ]

null

2023-09-28T10:15:08Z

--- license: mit --- # iTEMX-bloxerBETA ### 재고관리 앱 bloxer5의 초기 버전 [![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger) Bloxer는 재고관리 앱 하이브리드 소프트웨어로, 당신이 어느 OS를 사용하던 손쉽게 접근할 수 있습니다. ## Features 기능들 - HTML, csv, xlsx등 정형 데이터 테이블 삽입 지원 - JSON(반정형 데이터)지원 - 정형 데이터로 Excel 추출 - AI를 통해 대시보드에서 테이블의 전체적인 프로세스 확인 - 환율 계산 및 결과 반영 Markdown is a lightweight markup language based on the formatting conventions that people naturally use in email. As [John Gruber] writes on the [Markdown site][df1] > Bloxer는 소규모-중규모 사업장이 사용하기 좋습니다. > 원활한 입출고 관리, 여러 유형의 데이터를 적용하고 > 그리고 우리의 강력한 인공지능이 제공해주는 대시보드에서 > 데이터 테이블의 현재 상황을 한눈에 보세요. > 대한민국에 거주하는 자영업자 및 소상공인(월 ₩5,000,000 이하) > 이라면 15개월간 50% 할인된 가격에 사용할 수 있습니다. 만약 마음에 들지 않는다면 언제든지 취소하세요. 우리는 부담되지 않은 가격에 모두에게 제공할 것입니다. ## Tech Bloxer uses a number of open source projects to work properly: - [electron] - Hybrid for Web or Windows, MAC, Linux! - [BUN.js] - awesome backgrounder - [Next.js] - awesome backender - [BootstrapUI] - great UI boilerplate for modern web apps - [node.js] - Database with AWS or MongoDB - [Firebase] - Database built by Google Cloud - [Python] - Analyze table - [jQuery] - Convert JS code tracks renderer process - - [Hahaha](http://samuelgalaxys.github.io) - SamuelGalaxys And of course Dillinger itself is open source with a [public repository][dill] on GitHub. ## Installation iTEMX-Bloxer requires [Node.js](https://nodejs.org/) v10+ to run. Install the dependencies and devDependencies and start the server. ```sh npm install electron npm i node app ``` 셀레니움 설치 ```sh npm install --selenium NODE_ENV=production node app ```

SophiaPu/RLunit2

SophiaPu

2023-09-28T11:07:00Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T11:06:56Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: RLunit2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="SophiaPu/RLunit2", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

santiviquez/amazon-reviews-finetuning-distilbert-base-uncased

santiviquez

2023-09-28T11:04:26Z

133

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:amazon_reviews_multi", "base_model:nlptown/bert-base-multilingual-uncased-sentiment", "base_model:finetune:nlptown/bert-base-multilingual-uncased-sentiment", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-27T12:41:22Z

--- license: mit base_model: nlptown/bert-base-multilingual-uncased-sentiment tags: - generated_from_trainer datasets: - amazon_reviews_multi metrics: - accuracy - f1 model-index: - name: amazon-reviews-finetuning-distilbert-base-uncased results: - task: name: Text Classification type: text-classification dataset: name: amazon_reviews_multi type: amazon_reviews_multi config: en split: validation args: en metrics: - name: Accuracy type: accuracy value: 0.7703180212014135 - name: F1 type: f1 value: 0.7271375381543915 ---  # amazon-reviews-finetuning-distilbert-base-uncased This model is a fine-tuned version of [nlptown/bert-base-multilingual-uncased-sentiment](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) on the amazon_reviews_multi dataset. It achieves the following results on the evaluation set: - Loss: 0.5859 - Accuracy: 0.7703 - F1: 0.7271 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 188 | 0.5587 | 0.7756 | 0.7297 | | No log | 2.0 | 376 | 0.5859 | 0.7703 | 0.7271 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.6.dev0 - Tokenizers 0.13.3

poisson-fish/Marcoroni-70B-v1-AWQ

poisson-fish

2023-09-28T10:59:20Z

14

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "lmdeploy", "awq", "marcoroni", "70b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-09-25T02:23:56Z

--- tags: - lmdeploy - awq - marcoroni - 70b --- This is [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) quantized to LMDeploy 4bit AWQ with the following config: ```bash python3 -m lmdeploy.lite.apis.auto_awq \ --model ./Marcoroni-70B-v1 \ --w_bits 4 \ --w_group_size 128 \ --work_dir ./quant ``` # Original Model Card: # Marcoroni-70B # Model Details * **Trained by**: trained by AIDC AI-Business. * **Model type:** **Marcoroni-70B** is an auto-regressive language model based on the Llama 2 transformer architecture. * **Language(s)**: English * **License for Marcoroni-70B base weights**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) # Prompting ## Prompt Template for alpaca style ``` ### Instruction: <prompt> (without the <>) ### Response: ```

SophiaPu/q-FrozenLake-v1-4x4-noSlippery

SophiaPu

2023-09-28T10:56:43Z

0

null

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2023-09-28T10:53:00Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="SophiaPu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

dss107/news5

dss107

2023-09-28T10:39:29Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]

text-classification

2023-09-28T10:38:23Z

--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # dss107/news5 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("dss107/news5") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```