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 2025-09-12 06:31:37	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 555 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-12 06:31:07	card stringlengths 11 1.01M
MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4	MayBashendy	2024-11-06T16:17:51Z	161	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T15:44:54Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4 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. --> # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4950 - Qwk: 0.6411 - Mse: 0.4950 - Rmse: 0.7035 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:-------:\|:------:\| \| No log \| 0.0063 \| 2 \| 10.1861 \| 0.0 \| 10.1861 \| 3.1916 \| \| No log \| 0.0126 \| 4 \| 8.5953 \| -0.0005 \| 8.5953 \| 2.9318 \| \| No log \| 0.0189 \| 6 \| 6.9159 \| 0.0051 \| 6.9159 \| 2.6298 \| \| No log \| 0.0252 \| 8 \| 5.5130 \| 0.0037 \| 5.5130 \| 2.3480 \| \| No log \| 0.0315 \| 10 \| 4.3816 \| 0.0018 \| 4.3816 \| 2.0932 \| \| No log \| 0.0379 \| 12 \| 3.5082 \| 0.0492 \| 3.5082 \| 1.8730 \| \| No log \| 0.0442 \| 14 \| 2.7686 \| 0.0128 \| 2.7686 \| 1.6639 \| \| No log \| 0.0505 \| 16 \| 2.1322 \| 0.0118 \| 2.1322 \| 1.4602 \| \| No log \| 0.0568 \| 18 \| 1.6261 \| 0.0079 \| 1.6261 \| 1.2752 \| \| No log \| 0.0631 \| 20 \| 1.2562 \| 0.1722 \| 1.2562 \| 1.1208 \| \| No log \| 0.0694 \| 22 \| 1.0333 \| 0.0420 \| 1.0333 \| 1.0165 \| \| No log \| 0.0757 \| 24 \| 0.8915 \| 0.0316 \| 0.8915 \| 0.9442 \| \| No log \| 0.0820 \| 26 \| 0.8074 \| 0.0316 \| 0.8074 \| 0.8986 \| \| No log \| 0.0883 \| 28 \| 0.7660 \| 0.0316 \| 0.7660 \| 0.8752 \| \| No log \| 0.0946 \| 30 \| 0.7689 \| 0.0542 \| 0.7689 \| 0.8769 \| \| No log \| 0.1009 \| 32 \| 0.9386 \| 0.0937 \| 0.9386 \| 0.9688 \| \| No log \| 0.1073 \| 34 \| 0.8347 \| 0.0771 \| 0.8347 \| 0.9136 \| \| No log \| 0.1136 \| 36 \| 0.8293 \| 0.4385 \| 0.8293 \| 0.9106 \| \| No log \| 0.1199 \| 38 \| 0.8916 \| 0.3628 \| 0.8916 \| 0.9442 \| \| No log \| 0.1262 \| 40 \| 0.8068 \| 0.0212 \| 0.8068 \| 0.8982 \| \| No log \| 0.1325 \| 42 \| 0.8411 \| 0.0344 \| 0.8411 \| 0.9171 \| \| No log \| 0.1388 \| 44 \| 0.8499 \| 0.0344 \| 0.8499 \| 0.9219 \| \| No log \| 0.1451 \| 46 \| 0.8047 \| 0.0107 \| 0.8047 \| 0.8970 \| \| No log \| 0.1514 \| 48 \| 0.7906 \| 0.0107 \| 0.7906 \| 0.8892 \| \| No log \| 0.1577 \| 50 \| 0.7428 \| 0.0317 \| 0.7428 \| 0.8619 \| \| No log \| 0.1640 \| 52 \| 0.7615 \| 0.0511 \| 0.7615 \| 0.8726 \| \| No log \| 0.1703 \| 54 \| 0.7432 \| 0.0792 \| 0.7432 \| 0.8621 \| \| No log \| 0.1767 \| 56 \| 0.6753 \| 0.0610 \| 0.6753 \| 0.8218 \| \| No log \| 0.1830 \| 58 \| 0.6924 \| 0.0317 \| 0.6924 \| 0.8321 \| \| No log \| 0.1893 \| 60 \| 0.7336 \| 0.0730 \| 0.7336 \| 0.8565 \| \| No log \| 0.1956 \| 62 \| 0.7216 \| 0.0213 \| 0.7216 \| 0.8495 \| \| No log \| 0.2019 \| 64 \| 0.6734 \| 0.0826 \| 0.6734 \| 0.8206 \| \| No log \| 0.2082 \| 66 \| 0.8115 \| 0.1971 \| 0.8115 \| 0.9008 \| \| No log \| 0.2145 \| 68 \| 1.0608 \| 0.2342 \| 1.0608 \| 1.0300 \| \| No log \| 0.2208 \| 70 \| 0.8848 \| 0.2293 \| 0.8848 \| 0.9406 \| \| No log \| 0.2271 \| 72 \| 0.6445 \| 0.1331 \| 0.6445 \| 0.8028 \| \| No log \| 0.2334 \| 74 \| 0.6672 \| 0.0803 \| 0.6672 \| 0.8168 \| \| No log \| 0.2397 \| 76 \| 0.6616 \| 0.0754 \| 0.6616 \| 0.8134 \| \| No log \| 0.2461 \| 78 \| 0.6149 \| 0.1067 \| 0.6149 \| 0.7842 \| \| No log \| 0.2524 \| 80 \| 0.6896 \| 0.1973 \| 0.6896 \| 0.8304 \| \| No log \| 0.2587 \| 82 \| 0.7505 \| 0.2167 \| 0.7505 \| 0.8663 \| \| No log \| 0.2650 \| 84 \| 0.6389 \| 0.1883 \| 0.6389 \| 0.7993 \| \| No log \| 0.2713 \| 86 \| 0.6107 \| 0.2957 \| 0.6107 \| 0.7815 \| \| No log \| 0.2776 \| 88 \| 0.6234 \| 0.3088 \| 0.6234 \| 0.7895 \| \| No log \| 0.2839 \| 90 \| 0.5901 \| 0.2657 \| 0.5901 \| 0.7681 \| \| No log \| 0.2902 \| 92 \| 0.6248 \| 0.1786 \| 0.6248 \| 0.7905 \| \| No log \| 0.2965 \| 94 \| 0.6419 \| 0.2214 \| 0.6419 \| 0.8012 \| \| No log \| 0.3028 \| 96 \| 0.5860 \| 0.2699 \| 0.5860 \| 0.7655 \| \| No log \| 0.3091 \| 98 \| 0.5766 \| 0.2956 \| 0.5766 \| 0.7593 \| \| No log \| 0.3155 \| 100 \| 0.5547 \| 0.3623 \| 0.5547 \| 0.7448 \| \| No log \| 0.3218 \| 102 \| 0.5514 \| 0.4222 \| 0.5514 \| 0.7426 \| \| No log \| 0.3281 \| 104 \| 0.5460 \| 0.4061 \| 0.5460 \| 0.7389 \| \| No log \| 0.3344 \| 106 \| 0.5756 \| 0.3134 \| 0.5756 \| 0.7587 \| \| No log \| 0.3407 \| 108 \| 0.6144 \| 0.3095 \| 0.6144 \| 0.7838 \| \| No log \| 0.3470 \| 110 \| 0.5301 \| 0.4421 \| 0.5301 \| 0.7280 \| \| No log \| 0.3533 \| 112 \| 0.5429 \| 0.4684 \| 0.5429 \| 0.7368 \| \| No log \| 0.3596 \| 114 \| 0.5177 \| 0.4759 \| 0.5177 \| 0.7195 \| \| No log \| 0.3659 \| 116 \| 0.5241 \| 0.4151 \| 0.5241 \| 0.7240 \| \| No log \| 0.3722 \| 118 \| 0.5069 \| 0.4161 \| 0.5069 \| 0.7120 \| \| No log \| 0.3785 \| 120 \| 0.5293 \| 0.4872 \| 0.5293 \| 0.7275 \| \| No log \| 0.3849 \| 122 \| 0.5688 \| 0.4517 \| 0.5688 \| 0.7542 \| \| No log \| 0.3912 \| 124 \| 0.5780 \| 0.2445 \| 0.5780 \| 0.7603 \| \| No log \| 0.3975 \| 126 \| 0.5334 \| 0.4100 \| 0.5334 \| 0.7304 \| \| No log \| 0.4038 \| 128 \| 0.5552 \| 0.5686 \| 0.5552 \| 0.7451 \| \| No log \| 0.4101 \| 130 \| 0.5369 \| 0.5723 \| 0.5369 \| 0.7327 \| \| No log \| 0.4164 \| 132 \| 0.5145 \| 0.3755 \| 0.5145 \| 0.7173 \| \| No log \| 0.4227 \| 134 \| 0.5181 \| 0.4368 \| 0.5181 \| 0.7198 \| \| No log \| 0.4290 \| 136 \| 0.5175 \| 0.4105 \| 0.5175 \| 0.7194 \| \| No log \| 0.4353 \| 138 \| 0.5481 \| 0.5205 \| 0.5481 \| 0.7403 \| \| No log \| 0.4416 \| 140 \| 0.5561 \| 0.4941 \| 0.5561 \| 0.7457 \| \| No log \| 0.4479 \| 142 \| 0.5308 \| 0.5019 \| 0.5308 \| 0.7286 \| \| No log \| 0.4543 \| 144 \| 0.5421 \| 0.4929 \| 0.5421 \| 0.7363 \| \| No log \| 0.4606 \| 146 \| 0.5182 \| 0.4383 \| 0.5182 \| 0.7198 \| \| No log \| 0.4669 \| 148 \| 0.5113 \| 0.4444 \| 0.5113 \| 0.7151 \| \| No log \| 0.4732 \| 150 \| 0.5292 \| 0.3937 \| 0.5292 \| 0.7275 \| \| No log \| 0.4795 \| 152 \| 0.5153 \| 0.4278 \| 0.5153 \| 0.7179 \| \| No log \| 0.4858 \| 154 \| 0.4959 \| 0.4610 \| 0.4959 \| 0.7042 \| \| No log \| 0.4921 \| 156 \| 0.4822 \| 0.4742 \| 0.4822 \| 0.6944 \| \| No log \| 0.4984 \| 158 \| 0.5207 \| 0.5700 \| 0.5207 \| 0.7216 \| \| No log \| 0.5047 \| 160 \| 0.6361 \| 0.5602 \| 0.6361 \| 0.7976 \| \| No log \| 0.5110 \| 162 \| 0.5405 \| 0.5354 \| 0.5405 \| 0.7352 \| \| No log \| 0.5174 \| 164 \| 0.5536 \| 0.5347 \| 0.5536 \| 0.7440 \| \| No log \| 0.5237 \| 166 \| 0.5308 \| 0.5142 \| 0.5308 \| 0.7285 \| \| No log \| 0.5300 \| 168 \| 0.5827 \| 0.5080 \| 0.5827 \| 0.7634 \| \| No log \| 0.5363 \| 170 \| 0.6033 \| 0.5139 \| 0.6033 \| 0.7767 \| \| No log \| 0.5426 \| 172 \| 0.7514 \| 0.5038 \| 0.7514 \| 0.8669 \| \| No log \| 0.5489 \| 174 \| 0.7327 \| 0.5197 \| 0.7327 \| 0.8560 \| \| No log \| 0.5552 \| 176 \| 0.5563 \| 0.5225 \| 0.5563 \| 0.7459 \| \| No log \| 0.5615 \| 178 \| 0.5157 \| 0.4842 \| 0.5157 \| 0.7181 \| \| No log \| 0.5678 \| 180 \| 0.5430 \| 0.5432 \| 0.5430 \| 0.7369 \| \| No log \| 0.5741 \| 182 \| 0.5386 \| 0.5786 \| 0.5386 \| 0.7339 \| \| No log \| 0.5804 \| 184 \| 0.4900 \| 0.5768 \| 0.4900 \| 0.7000 \| \| No log \| 0.5868 \| 186 \| 0.5030 \| 0.5908 \| 0.5030 \| 0.7092 \| \| No log \| 0.5931 \| 188 \| 0.4526 \| 0.5804 \| 0.4526 \| 0.6728 \| \| No log \| 0.5994 \| 190 \| 0.5105 \| 0.4823 \| 0.5105 \| 0.7145 \| \| No log \| 0.6057 \| 192 \| 0.5870 \| 0.4220 \| 0.5870 \| 0.7662 \| \| No log \| 0.6120 \| 194 \| 0.5511 \| 0.4319 \| 0.5511 \| 0.7423 \| \| No log \| 0.6183 \| 196 \| 0.4500 \| 0.5472 \| 0.4500 \| 0.6708 \| \| No log \| 0.6246 \| 198 \| 0.4526 \| 0.5562 \| 0.4526 \| 0.6728 \| \| No log \| 0.6309 \| 200 \| 0.5135 \| 0.5754 \| 0.5135 \| 0.7166 \| \| No log \| 0.6372 \| 202 \| 0.6373 \| 0.5419 \| 0.6373 \| 0.7983 \| \| No log \| 0.6435 \| 204 \| 0.5640 \| 0.5393 \| 0.5640 \| 0.7510 \| \| No log \| 0.6498 \| 206 \| 0.5375 \| 0.5351 \| 0.5375 \| 0.7332 \| \| No log \| 0.6562 \| 208 \| 0.5511 \| 0.5560 \| 0.5511 \| 0.7423 \| \| No log \| 0.6625 \| 210 \| 0.5414 \| 0.5693 \| 0.5414 \| 0.7358 \| \| No log \| 0.6688 \| 212 \| 0.5304 \| 0.5811 \| 0.5304 \| 0.7283 \| \| No log \| 0.6751 \| 214 \| 0.4758 \| 0.5939 \| 0.4758 \| 0.6898 \| \| No log \| 0.6814 \| 216 \| 0.4437 \| 0.5481 \| 0.4437 \| 0.6661 \| \| No log \| 0.6877 \| 218 \| 0.4368 \| 0.5673 \| 0.4368 \| 0.6609 \| \| No log \| 0.6940 \| 220 \| 0.4946 \| 0.6281 \| 0.4946 \| 0.7033 \| \| No log \| 0.7003 \| 222 \| 0.4564 \| 0.5958 \| 0.4564 \| 0.6756 \| \| No log \| 0.7066 \| 224 \| 0.4662 \| 0.5795 \| 0.4662 \| 0.6828 \| \| No log \| 0.7129 \| 226 \| 0.5187 \| 0.6018 \| 0.5187 \| 0.7202 \| \| No log \| 0.7192 \| 228 \| 0.5179 \| 0.6018 \| 0.5179 \| 0.7196 \| \| No log \| 0.7256 \| 230 \| 0.4883 \| 0.6011 \| 0.4883 \| 0.6988 \| \| No log \| 0.7319 \| 232 \| 0.4581 \| 0.5898 \| 0.4581 \| 0.6768 \| \| No log \| 0.7382 \| 234 \| 0.5164 \| 0.6064 \| 0.5164 \| 0.7186 \| \| No log \| 0.7445 \| 236 \| 0.4880 \| 0.6120 \| 0.4880 \| 0.6986 \| \| No log \| 0.7508 \| 238 \| 0.4608 \| 0.6049 \| 0.4608 \| 0.6788 \| \| No log \| 0.7571 \| 240 \| 0.5627 \| 0.6490 \| 0.5627 \| 0.7502 \| \| No log \| 0.7634 \| 242 \| 0.8123 \| 0.6725 \| 0.8123 \| 0.9013 \| \| No log \| 0.7697 \| 244 \| 0.6433 \| 0.6624 \| 0.6433 \| 0.8021 \| \| No log \| 0.7760 \| 246 \| 0.4387 \| 0.5914 \| 0.4387 \| 0.6624 \| \| No log \| 0.7823 \| 248 \| 0.4507 \| 0.5951 \| 0.4507 \| 0.6713 \| \| No log \| 0.7886 \| 250 \| 0.6574 \| 0.6299 \| 0.6574 \| 0.8108 \| \| No log \| 0.7950 \| 252 \| 0.9073 \| 0.5748 \| 0.9073 \| 0.9525 \| \| No log \| 0.8013 \| 254 \| 0.7567 \| 0.5976 \| 0.7567 \| 0.8699 \| \| No log \| 0.8076 \| 256 \| 0.4780 \| 0.5993 \| 0.4780 \| 0.6914 \| \| No log \| 0.8139 \| 258 \| 0.4653 \| 0.4804 \| 0.4653 \| 0.6821 \| \| No log \| 0.8202 \| 260 \| 0.4593 \| 0.5099 \| 0.4593 \| 0.6777 \| \| No log \| 0.8265 \| 262 \| 0.5150 \| 0.5981 \| 0.5150 \| 0.7176 \| \| No log \| 0.8328 \| 264 \| 0.7188 \| 0.5631 \| 0.7188 \| 0.8478 \| \| No log \| 0.8391 \| 266 \| 0.6870 \| 0.5665 \| 0.6870 \| 0.8289 \| \| No log \| 0.8454 \| 268 \| 0.5103 \| 0.6082 \| 0.5103 \| 0.7144 \| \| No log \| 0.8517 \| 270 \| 0.4610 \| 0.4952 \| 0.4610 \| 0.6790 \| \| No log \| 0.8580 \| 272 \| 0.5092 \| 0.4066 \| 0.5092 \| 0.7136 \| \| No log \| 0.8644 \| 274 \| 0.4640 \| 0.4861 \| 0.4640 \| 0.6812 \| \| No log \| 0.8707 \| 276 \| 0.4945 \| 0.5916 \| 0.4945 \| 0.7032 \| \| No log \| 0.8770 \| 278 \| 0.6582 \| 0.5572 \| 0.6582 \| 0.8113 \| \| No log \| 0.8833 \| 280 \| 0.6694 \| 0.5610 \| 0.6694 \| 0.8181 \| \| No log \| 0.8896 \| 282 \| 0.5728 \| 0.5254 \| 0.5728 \| 0.7568 \| \| No log \| 0.8959 \| 284 \| 0.5221 \| 0.4152 \| 0.5221 \| 0.7226 \| \| No log \| 0.9022 \| 286 \| 0.4807 \| 0.4751 \| 0.4807 \| 0.6933 \| \| No log \| 0.9085 \| 288 \| 0.4549 \| 0.5473 \| 0.4549 \| 0.6745 \| \| No log \| 0.9148 \| 290 \| 0.4556 \| 0.5597 \| 0.4556 \| 0.6750 \| \| No log \| 0.9211 \| 292 \| 0.4582 \| 0.5556 \| 0.4582 \| 0.6769 \| \| No log \| 0.9274 \| 294 \| 0.4645 \| 0.5505 \| 0.4645 \| 0.6816 \| \| No log \| 0.9338 \| 296 \| 0.4678 \| 0.5381 \| 0.4678 \| 0.6840 \| \| No log \| 0.9401 \| 298 \| 0.4749 \| 0.5534 \| 0.4749 \| 0.6892 \| \| No log \| 0.9464 \| 300 \| 0.5625 \| 0.5975 \| 0.5625 \| 0.7500 \| \| No log \| 0.9527 \| 302 \| 0.5900 \| 0.5826 \| 0.5900 \| 0.7681 \| \| No log \| 0.9590 \| 304 \| 0.4926 \| 0.5950 \| 0.4926 \| 0.7019 \| \| No log \| 0.9653 \| 306 \| 0.4816 \| 0.4778 \| 0.4816 \| 0.6940 \| \| No log \| 0.9716 \| 308 \| 0.4785 \| 0.5246 \| 0.4785 \| 0.6917 \| \| No log \| 0.9779 \| 310 \| 0.4967 \| 0.5915 \| 0.4967 \| 0.7048 \| \| No log \| 0.9842 \| 312 \| 0.4777 \| 0.5359 \| 0.4777 \| 0.6912 \| \| No log \| 0.9905 \| 314 \| 0.5052 \| 0.4469 \| 0.5052 \| 0.7108 \| \| No log \| 0.9968 \| 316 \| 0.4870 \| 0.4692 \| 0.4870 \| 0.6978 \| \| No log \| 1.0032 \| 318 \| 0.4959 \| 0.6014 \| 0.4959 \| 0.7042 \| \| No log \| 1.0095 \| 320 \| 0.5971 \| 0.6622 \| 0.5971 \| 0.7727 \| \| No log \| 1.0158 \| 322 \| 0.6224 \| 0.6527 \| 0.6224 \| 0.7889 \| \| No log \| 1.0221 \| 324 \| 0.5090 \| 0.6125 \| 0.5090 \| 0.7134 \| \| No log \| 1.0284 \| 326 \| 0.4859 \| 0.6161 \| 0.4859 \| 0.6970 \| \| No log \| 1.0347 \| 328 \| 0.5575 \| 0.6373 \| 0.5575 \| 0.7466 \| \| No log \| 1.0410 \| 330 \| 0.6631 \| 0.6354 \| 0.6631 \| 0.8143 \| \| No log \| 1.0473 \| 332 \| 0.7880 \| 0.6128 \| 0.7880 \| 0.8877 \| \| No log \| 1.0536 \| 334 \| 0.6328 \| 0.6471 \| 0.6328 \| 0.7955 \| \| No log \| 1.0599 \| 336 \| 0.4833 \| 0.5926 \| 0.4833 \| 0.6952 \| \| No log \| 1.0662 \| 338 \| 0.4764 \| 0.5915 \| 0.4764 \| 0.6902 \| \| No log \| 1.0726 \| 340 \| 0.4879 \| 0.6097 \| 0.4879 \| 0.6985 \| \| No log \| 1.0789 \| 342 \| 0.5004 \| 0.6328 \| 0.5004 \| 0.7074 \| \| No log \| 1.0852 \| 344 \| 0.4558 \| 0.5696 \| 0.4558 \| 0.6752 \| \| No log \| 1.0915 \| 346 \| 0.4638 \| 0.5143 \| 0.4638 \| 0.6811 \| \| No log \| 1.0978 \| 348 \| 0.4590 \| 0.5340 \| 0.4590 \| 0.6775 \| \| No log \| 1.1041 \| 350 \| 0.4556 \| 0.5999 \| 0.4556 \| 0.6750 \| \| No log \| 1.1104 \| 352 \| 0.4521 \| 0.5984 \| 0.4521 \| 0.6724 \| \| No log \| 1.1167 \| 354 \| 0.4603 \| 0.5902 \| 0.4603 \| 0.6784 \| \| No log \| 1.1230 \| 356 \| 0.5085 \| 0.6098 \| 0.5085 \| 0.7131 \| \| No log \| 1.1293 \| 358 \| 0.5851 \| 0.6319 \| 0.5851 \| 0.7649 \| \| No log \| 1.1356 \| 360 \| 0.5377 \| 0.6091 \| 0.5377 \| 0.7333 \| \| No log \| 1.1420 \| 362 \| 0.4673 \| 0.5626 \| 0.4673 \| 0.6836 \| \| No log \| 1.1483 \| 364 \| 0.4611 \| 0.5643 \| 0.4611 \| 0.6790 \| \| No log \| 1.1546 \| 366 \| 0.4560 \| 0.5333 \| 0.4560 \| 0.6753 \| \| No log \| 1.1609 \| 368 \| 0.4761 \| 0.4842 \| 0.4761 \| 0.6900 \| \| No log \| 1.1672 \| 370 \| 0.4581 \| 0.5306 \| 0.4581 \| 0.6768 \| \| No log \| 1.1735 \| 372 \| 0.4492 \| 0.5837 \| 0.4492 \| 0.6702 \| \| No log \| 1.1798 \| 374 \| 0.4585 \| 0.6097 \| 0.4585 \| 0.6771 \| \| No log \| 1.1861 \| 376 \| 0.4451 \| 0.5503 \| 0.4451 \| 0.6672 \| \| No log \| 1.1924 \| 378 \| 0.4524 \| 0.5227 \| 0.4524 \| 0.6726 \| \| No log \| 1.1987 \| 380 \| 0.4546 \| 0.5008 \| 0.4546 \| 0.6742 \| \| No log \| 1.2050 \| 382 \| 0.4735 \| 0.5442 \| 0.4735 \| 0.6881 \| \| No log \| 1.2114 \| 384 \| 0.5067 \| 0.5698 \| 0.5067 \| 0.7118 \| \| No log \| 1.2177 \| 386 \| 0.4892 \| 0.4913 \| 0.4892 \| 0.6994 \| \| No log \| 1.2240 \| 388 \| 0.4975 \| 0.5099 \| 0.4975 \| 0.7053 \| \| No log \| 1.2303 \| 390 \| 0.6492 \| 0.6296 \| 0.6492 \| 0.8057 \| \| No log \| 1.2366 \| 392 \| 0.7328 \| 0.6114 \| 0.7328 \| 0.8561 \| \| No log \| 1.2429 \| 394 \| 0.5539 \| 0.6157 \| 0.5539 \| 0.7443 \| \| No log \| 1.2492 \| 396 \| 0.5265 \| 0.4173 \| 0.5265 \| 0.7256 \| \| No log \| 1.2555 \| 398 \| 0.6128 \| 0.3532 \| 0.6128 \| 0.7828 \| \| No log \| 1.2618 \| 400 \| 0.5354 \| 0.4003 \| 0.5354 \| 0.7317 \| \| No log \| 1.2681 \| 402 \| 0.4935 \| 0.5464 \| 0.4935 \| 0.7025 \| \| No log \| 1.2744 \| 404 \| 0.5745 \| 0.6324 \| 0.5745 \| 0.7579 \| \| No log \| 1.2808 \| 406 \| 0.5167 \| 0.6236 \| 0.5167 \| 0.7188 \| \| No log \| 1.2871 \| 408 \| 0.4620 \| 0.5427 \| 0.4620 \| 0.6797 \| \| No log \| 1.2934 \| 410 \| 0.4585 \| 0.5055 \| 0.4585 \| 0.6772 \| \| No log \| 1.2997 \| 412 \| 0.4691 \| 0.5926 \| 0.4691 \| 0.6849 \| \| No log \| 1.3060 \| 414 \| 0.5962 \| 0.6760 \| 0.5962 \| 0.7722 \| \| No log \| 1.3123 \| 416 \| 0.5452 \| 0.6593 \| 0.5452 \| 0.7384 \| \| No log \| 1.3186 \| 418 \| 0.4661 \| 0.6018 \| 0.4661 \| 0.6827 \| \| No log \| 1.3249 \| 420 \| 0.4503 \| 0.5347 \| 0.4503 \| 0.6710 \| \| No log \| 1.3312 \| 422 \| 0.4594 \| 0.5752 \| 0.4594 \| 0.6778 \| \| No log \| 1.3375 \| 424 \| 0.5623 \| 0.6484 \| 0.5623 \| 0.7499 \| \| No log \| 1.3438 \| 426 \| 0.5562 \| 0.6429 \| 0.5562 \| 0.7458 \| \| No log \| 1.3502 \| 428 \| 0.4545 \| 0.5922 \| 0.4545 \| 0.6742 \| \| No log \| 1.3565 \| 430 \| 0.4446 \| 0.5818 \| 0.4446 \| 0.6668 \| \| No log \| 1.3628 \| 432 \| 0.5001 \| 0.6472 \| 0.5001 \| 0.7072 \| \| No log \| 1.3691 \| 434 \| 0.5172 \| 0.6548 \| 0.5172 \| 0.7192 \| \| No log \| 1.3754 \| 436 \| 0.4511 \| 0.5994 \| 0.4511 \| 0.6716 \| \| No log \| 1.3817 \| 438 \| 0.4721 \| 0.5433 \| 0.4721 \| 0.6871 \| \| No log \| 1.3880 \| 440 \| 0.4686 \| 0.6124 \| 0.4686 \| 0.6846 \| \| No log \| 1.3943 \| 442 \| 0.5272 \| 0.6602 \| 0.5272 \| 0.7261 \| \| No log \| 1.4006 \| 444 \| 0.4777 \| 0.6232 \| 0.4777 \| 0.6912 \| \| No log \| 1.4069 \| 446 \| 0.4745 \| 0.4864 \| 0.4745 \| 0.6888 \| \| No log \| 1.4132 \| 448 \| 0.4813 \| 0.4603 \| 0.4813 \| 0.6938 \| \| No log \| 1.4196 \| 450 \| 0.4566 \| 0.5352 \| 0.4566 \| 0.6757 \| \| No log \| 1.4259 \| 452 \| 0.5087 \| 0.6295 \| 0.5087 \| 0.7132 \| \| No log \| 1.4322 \| 454 \| 0.5272 \| 0.6279 \| 0.5272 \| 0.7261 \| \| No log \| 1.4385 \| 456 \| 0.4695 \| 0.5742 \| 0.4695 \| 0.6852 \| \| No log \| 1.4448 \| 458 \| 0.4613 \| 0.5300 \| 0.4613 \| 0.6792 \| \| No log \| 1.4511 \| 460 \| 0.4807 \| 0.4327 \| 0.4807 \| 0.6933 \| \| No log \| 1.4574 \| 462 \| 0.4712 \| 0.4831 \| 0.4712 \| 0.6865 \| \| No log \| 1.4637 \| 464 \| 0.5262 \| 0.6207 \| 0.5262 \| 0.7254 \| \| No log \| 1.4700 \| 466 \| 0.5679 \| 0.6533 \| 0.5679 \| 0.7536 \| \| No log \| 1.4763 \| 468 \| 0.4943 \| 0.6319 \| 0.4943 \| 0.7030 \| \| No log \| 1.4826 \| 470 \| 0.4548 \| 0.5373 \| 0.4548 \| 0.6744 \| \| No log \| 1.4890 \| 472 \| 0.4529 \| 0.5669 \| 0.4529 \| 0.6730 \| \| No log \| 1.4953 \| 474 \| 0.4979 \| 0.6578 \| 0.4979 \| 0.7056 \| \| No log \| 1.5016 \| 476 \| 0.5480 \| 0.6783 \| 0.5480 \| 0.7402 \| \| No log \| 1.5079 \| 478 \| 0.4760 \| 0.5831 \| 0.4760 \| 0.6900 \| \| No log \| 1.5142 \| 480 \| 0.4790 \| 0.4885 \| 0.4790 \| 0.6921 \| \| No log \| 1.5205 \| 482 \| 0.4733 \| 0.4948 \| 0.4733 \| 0.6879 \| \| No log \| 1.5268 \| 484 \| 0.4930 \| 0.6107 \| 0.4930 \| 0.7021 \| \| No log \| 1.5331 \| 486 \| 0.6387 \| 0.6998 \| 0.6387 \| 0.7992 \| \| No log \| 1.5394 \| 488 \| 0.5770 \| 0.6947 \| 0.5770 \| 0.7596 \| \| No log \| 1.5457 \| 490 \| 0.4507 \| 0.5730 \| 0.4507 \| 0.6713 \| \| No log \| 1.5521 \| 492 \| 0.4761 \| 0.4890 \| 0.4761 \| 0.6900 \| \| No log \| 1.5584 \| 494 \| 0.4524 \| 0.5010 \| 0.4524 \| 0.6726 \| \| No log \| 1.5647 \| 496 \| 0.4512 \| 0.5824 \| 0.4512 \| 0.6717 \| \| No log \| 1.5710 \| 498 \| 0.5386 \| 0.6594 \| 0.5386 \| 0.7339 \| \| 0.5 \| 1.5773 \| 500 \| 0.5441 \| 0.6588 \| 0.5441 \| 0.7376 \| \| 0.5 \| 1.5836 \| 502 \| 0.5217 \| 0.6468 \| 0.5217 \| 0.7223 \| \| 0.5 \| 1.5899 \| 504 \| 0.4504 \| 0.5555 \| 0.4504 \| 0.6711 \| \| 0.5 \| 1.5962 \| 506 \| 0.4459 \| 0.5713 \| 0.4459 \| 0.6677 \| \| 0.5 \| 1.6025 \| 508 \| 0.4642 \| 0.6069 \| 0.4642 \| 0.6813 \| \| 0.5 \| 1.6088 \| 510 \| 0.4950 \| 0.6411 \| 0.4950 \| 0.7035 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF	mattritchey	2024-11-06T16:14:40Z	7	0	null	[ "gguf", "HelpingAI", "Emotionally-Intelligent", "EQ-focused- EQ-focused", "Conversational", "SLM", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:HelpingAI/HelpingAI2-3B", "base_model:quantized:HelpingAI/HelpingAI2-3B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-06T16:14:29Z	--- license: other license_name: helpingai license_link: https://huggingface.co/OEvortex/HelpingAI-3B-v3/blob/main/LICENSE.md pipeline_tag: text-generation language: - en tags: - HelpingAI - Emotionally-Intelligent - EQ-focused- EQ-focused - Conversational - SLM - llama-cpp - gguf-my-repo base_model: OEvortex/HelpingAI-3B-reloaded --- # mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF This model was converted to GGUF format from [`OEvortex/HelpingAI-3B-reloaded`](https://huggingface.co/OEvortex/HelpingAI-3B-reloaded) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OEvortex/HelpingAI-3B-reloaded) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -c 2048 ```
camidenecken/RoBERTa-RM1-v2-2-rm-v32	camidenecken	2024-11-06T16:08:06Z	181	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T16:07:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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camidenecken/RoBERTa-RM1-v2-2-rm-v31	camidenecken	2024-11-06T16:05:57Z	183	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T16:05:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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camidenecken/RoBERTa-RM1-v2-2-rm-v30	camidenecken	2024-11-06T16:03:50Z	181	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T16:03:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Lorraine013/openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2	Lorraine013	2024-11-06T16:00:05Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T14:33:04Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2 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. --> # openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the openmathinstruct2_cot_20k_train dataset. It achieves the following results on the evaluation set: - Loss: 0.7634 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8177 \| 0.4808 \| 500 \| 0.7823 \| \| 0.7708 \| 0.9615 \| 1000 \| 0.7572 \| \| 0.5513 \| 1.4423 \| 1500 \| 0.7693 \| \| 0.5059 \| 1.9231 \| 2000 \| 0.7637 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.3.0+cu121 - Datasets 2.21.0 - Tokenizers 0.20.1
camidenecken/RoBERTa-RM1-v2-2-rm-v27	camidenecken	2024-11-06T15:57:21Z	182	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T15:57:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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camidenecken/RoBERTa-RM1-v2-2-rm-v26	camidenecken	2024-11-06T15:55:15Z	180	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T15:54:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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belisarius/FLUX.1-dev-Mann-E-gguf	belisarius	2024-11-06T15:46:26Z	108	4	null	[ "gguf", "base_model:mann-e/mann-e_flux", "base_model:quantized:mann-e/mann-e_flux", "license:other", "region:us" ]	null	2024-11-05T08:26:06Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md base_model: - mann-e/mann-e_flux --- UNet only - no Clip-l/T5xxl included Quantized versions of the Mann-E model. https://huggingface.co/mann-e/mann-e_flux Made using this guide: https://github.com/city96/ComfyUI-GGUF/tree/main/tools
richiebailey/whisper-large-v3-turbo	richiebailey	2024-11-06T15:44:40Z	89	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "audio", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:mit", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-06T15:37:29Z	--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su license: mit tags: - audio - automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac pipeline_tag: automatic-speech-recognition base_model: - openai/whisper-large-v3 library_name: transformers --- # Whisper Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3). In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it [in this GitHub discussion](https://github.com/openai/whisper/discussions/2363). Disclaimer: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card. ## Usage Whisper large-v3-turbo is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] accelerate ``` The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe audios of arbitrary length: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```python result = pipe("audio.mp3") ``` Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter: ```python result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2) ``` Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous tokens. The following example demonstrates how to enable these heuristics: ```python generate_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } result = pipe(sample, generate_kwargs=generate_kwargs) ``` Whisper predicts the language of the source audio automatically. If the source audio language is known a-priori, it can be passed as an argument to the pipeline: ```python result = pipe(sample, generate_kwargs={"language": "english"}) ``` By default, Whisper performs the task of speech transcription, where the source audio language is the same as the target text language. To perform speech translation, where the target text is in English, set the task to `"translate"`: ```python result = pipe(sample, generate_kwargs={"task": "translate"}) ``` Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument: ```python result = pipe(sample, return_timestamps=True) print(result["chunks"]) ``` And for word-level timestamps: ```python result = pipe(sample, return_timestamps="word") print(result["chunks"]) ``` The above arguments can be used in isolation or in combination. For example, to perform the task of speech transcription where the source audio is in French, and we want to return sentence-level timestamps, the following can be used: ```python result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "french", "task": "translate"}) print(result["chunks"]) ``` <details> <summary> For more control over the generation parameters, use the model + processor API directly: </summary> ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import Audio, load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate)) sample = dataset[0]["audio"] inputs = processor( sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, ) inputs = inputs.to(device, dtype=torch_dtype) gen_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } pred_ids = model.generate(inputs, gen_kwargs) pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False) print(pred_text) ``` </details> ## Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Whisper to further reduce the inference speed and VRAM requirements. ### Chunked Long-Form Whisper has a receptive field of 30-seconds. To transcribe audios longer than this, one of two long-form algorithms are required: 1. Sequential: uses a "sliding window" for buffered inference, transcribing 30-second slices one after the other 2. Chunked: splits long audio files into shorter ones (with a small overlap between segments), transcribes each segment independently, and stitches the resulting transcriptions at the boundaries The sequential long-form algorithm should be used in either of the following scenarios: 1. Transcription accuracy is the most important factor, and speed is less of a consideration 2. You are transcribing batches of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate Conversely, the chunked algorithm should be used when: 1. Transcription speed is the most important factor 2. You are transcribing a single long audio file By default, Transformers uses the sequential algorithm. To enable the chunked algorithm, pass the `chunk_length_s` parameter to the `pipeline`. For large-v3, a chunk length of 30-seconds is optimal. To activate batching over long audio files, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, batch_size=16, # batch size for inference - set based on your device torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` #### Torch compile The Whisper forward pass is compatible with [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for 4.5x speed-ups. Note: `torch.compile` is currently not compatible with the Chunked long-form algorithm or Flash Attention 2 ⚠️ ```python import torch from torch.nn.attention import SDPBackend, sdpa_kernel from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset from tqdm import tqdm torch.set_float32_matmul_precision("high") device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ).to(device) # Enable static cache and compile the forward pass model.generation_config.cache_implementation = "static" model.generation_config.max_new_tokens = 256 model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] # 2 warmup steps for _ in tqdm(range(2), desc="Warm-up step"): with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy(), generate_kwargs={"min_new_tokens": 256, "max_new_tokens": 256}) # fast run with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy()) print(result["text"]) ``` #### Flash Attention 2 We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU supports it and you are not using [torch.compile](#torch-compile). To do so, first install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="flash_attention_2") ``` #### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html). This attention implementation is activated by default for PyTorch versions 2.1.1 or greater. To check whether you have a compatible PyTorch version, run the following Python code snippet: ```python from transformers.utils import is_torch_sdpa_available print(is_torch_sdpa_available()) ``` If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/) Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying `attn_implementation="sdpa"` as follows: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="sdpa") ``` For more information about how to use the SDPA refer to the [Transformers SDPA documentation](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention). ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. There are two flavours of Whisper model: English-only and multilingual. The English-only models were trained on the task of English speech recognition. The multilingual models were trained simultaneously on multilingual speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are available as English-only and multilingual. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| \| large-v3 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v3) \| \| large-v3-turbo \| 809 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v3-turbo) \| ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data No information provided. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
mradermacher/alfred-40b-1023-i1-GGUF	mradermacher	2024-11-06T15:41:09Z	106	0	transformers	[ "transformers", "gguf", "falcon-40b", "long-context", "falcon", "NTK-YaRN", "en", "fr", "de", "es", "it", "dataset:OpenAssistant/oasst1", "dataset:ehartford/dolphin", "dataset:tau/sled", "dataset:tiiuae/falcon-refinedweb", "base_model:lightonai/alfred-40b-1023", "base_model:quantized:lightonai/alfred-40b-1023", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-06T07:03:20Z	--- base_model: lightonai/alfred-40b-1023 datasets: - OpenAssistant/oasst1 - ehartford/dolphin - tau/sled - tiiuae/falcon-refinedweb language: - en - fr - de - es - it library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - falcon-40b - long-context - falcon - NTK-YaRN --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/lightonai/alfred-40b-1023 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/alfred-40b-1023-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 11.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_S.gguf) \| i1-IQ2_S \| 13.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_M.gguf) \| i1-IQ2_M \| 14.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q2_K.gguf) \| i1-Q2_K \| 15.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 16.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_S.gguf) \| i1-IQ3_S \| 18.4 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 18.4 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_M.gguf) \| i1-IQ3_M \| 19.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 20.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 21.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 22.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 23.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_0.gguf) \| i1-Q4_0 \| 24.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 25.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 29.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 30.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q6_K.gguf) \| i1-Q6_K \| 34.6 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
neopolita/hammer2.0-7b-gguf	neopolita	2024-11-06T15:39:32Z	13	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T15:03:06Z	--- {} --- # GGUF quants for [MadeAgents/Hammer2.0-7b](https://huggingface.co/MadeAgents/Hammer2.0-7b) using [llama.cpp](https://github.com/ggerganov/llama.cpp) Terms of Use: Please check the [original model](https://huggingface.co/MadeAgents/Hammer2.0-7b) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
mav23/SmolLM2-360M-Instruct-GGUF	mav23	2024-11-06T15:37:44Z	66	0	transformers	[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T15:32:30Z	--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/oWWfzW4RbWkVIo7f-5444.png) ## Table of Contents 1. [Model Summary](##model-summary) 2. [Limitations](##limitations) 3. [Training](##training) 4. [License](##license) 5. [Citation](##citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 360M model was trained on 4 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-360M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model \| Metrics \| SmolLM2-360M \| Qwen2.5-0.5B \| SmolLM-360M \| \|:-------------------\|:------------:\|:------------:\|:------------:\| \| HellaSwag \| 54.5 \| 51.2 \| 51.8 \| \| ARC (Average) \| 53.0 \| 45.4 \| 50.1 \| \| PIQA \| 71.7 \| 69.9 \| 71.6 \| \| MMLU (cloze) \| 35.8 \| 33.7 \| 34.4 \| \| CommonsenseQA \| 38.0 \| 31.6 \| 35.3 \| \| TriviaQA \| 16.9 \| 4.3 \| 9.1 \| \| Winogrande \| 52.5 \| 54.1 \| 52.8 \| \| OpenBookQA \| 37.4 \| 37.4 \| 37.2 \| \| GSM8K (5-shot) \| 3.2 \| 33.4 \| 1.6 \| ## Instruction Model \| Metric \| SmolLM2-360M-Instruct \| Qwen2.5-0.5B-Instruct \| SmolLM-360M-Instruct \| \|:-----------------------------\|:---------------------:\|:---------------------:\|:---------------------:\| \| IFEval (Average prompt/inst) \| 41.0 \| 31.6 \| 19.8 \| \| MT-Bench \| 3.66 \| 4.16 \| 3.37 \| \| HellaSwag \| 52.1 \| 48.0 \| 47.9 \| \| ARC (Average) \| 43.7 \| 37.3 \| 38.8 \| \| PIQA \| 70.8 \| 67.2 \| 69.4 \| \| MMLU (cloze) \| 32.8 \| 31.7 \| 30.6 \| \| BBH (3-shot) \| 27.3 \| 30.7 \| 24.4 \| \| GSM8K (5-shot) \| 7.43 \| 26.8 \| 1.36 \| ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - Architecture: Transformer decoder - Pretraining tokens: 4T - Precision: bfloat16 ### Hardware - GPUs: 64 H100 ### Software - Training Framework: [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```
mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF	mradermacher	2024-11-06T15:35:12Z	5	0	transformers	[ "transformers", "gguf", "tr", "en", "base_model:Trendyol/Trendyol-LLM-7b-chat-v0.1", "base_model:quantized:Trendyol/Trendyol-LLM-7b-chat-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-06T06:04:52Z	--- base_model: Trendyol/Trendyol-LLM-7b-chat-v0.1 language: - tr - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 2.7 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.0 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.0 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.0 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q6_K.gguf) \| i1-Q6_K \| 5.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
QuantFactory/KONI-Llama3.1-8B-Instruct-20241024-GGUF	QuantFactory	2024-11-06T15:34:48Z	105	3	transformers	[ "transformers", "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T14:40:16Z	--- library_name: transformers tags: [] --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/KONI-Llama3.1-8B-Instruct-20241024-GGUF This is quantized version of [KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024) created using llama.cpp # Original Model Card # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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techiaith/whisper-large-v3-ft-cv-cy	techiaith	2024-11-06T15:32:39Z	10	0	null	[ "tensorboard", "safetensors", "whisper", "generated_from_trainer", "automatic-speech-recognition", "cy", "dataset:techiaith/commonvoice_18_0_cy", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "region:us" ]	automatic-speech-recognition	2024-08-26T11:24:16Z	--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer - whisper datasets: - techiaith/commonvoice_18_0_cy metrics: - wer model-index: - name: whisper-large-v3-ft-cv-cy-train-all-plus-other-with-excluded results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: DewiBrynJones/commonvoice_18_0_cy default type: DewiBrynJones/commonvoice_18_0_cy args: default metrics: - name: Wer type: wer value: 0.185 language: - cy pipeline_tag: automatic-speech-recognition --- # whisper-large-v3-ft-cv-cy This model is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned with the `train_all` and `other_with_excluded` custom splits from [techiaith/commonvoice_18_0_cy](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy) It achieves the following results on the Common Voice for Welsh release 18's standard test set: - WER: 18.50 - CER: 5.32 N.B. this model performs considerably worse on English language speech, but better on Welsh than a [bilingual model](https://huggingface.co/techiaith/whisper-large-v3-ft-cv-cy-en) ## Usage ```python from transformers import pipeline transcriber = pipeline("automatic-speech-recognition", model="techiaith/whisper-large-v3-ft-cv-cy") result = transcriber(<path or url to soundfile>) print (result) ``` `{'text': 'Mae hen wlad fy nhadau yn annwyl i mi.'}`
mav23/SmolLM2-135M-Instruct-GGUF	mav23	2024-11-06T15:29:26Z	27	0	transformers	[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T15:26:33Z	--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/3ntM63zkmxY2cNRhgY_Kl.png) ## Table of Contents 1. [Model Summary](##model-summary) 2. [Limitations](##limitations) 3. [Training](##training) 4. [License](##license) 5. [Citation](##citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is gravity?"}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base pre-trained model \| Metrics \| SmolLM2-135M-8k \| SmolLM-135M \| \|:-------------------\|:----------------:\|:------------:\| \| HellaSwag \| 42.1 \| 41.2 \| \| ARC (Average) \| 43.9 \| 42.4 \| \| PIQA \| 68.4 \| 68.4 \| \| MMLU (cloze) \| 31.5 \| 30.2 \| \| CommonsenseQA \| 33.9 \| 32.7 \| \| TriviaQA \| 4.1 \| 4.3 \| \| Winogrande \| 51.3 \| 51.3 \| \| OpenBookQA \| 34.6 \| 34.0 \| \| GSM8K (5-shot) \| 1.4 \| 1.0 \| ## Instruction model \| Metric \| SmolLM2-135M-Instruct \| SmolLM-135M-Instruct \| \|:-----------------------------\|:---------------------:\|:--------------------:\| \| IFEval (Average prompt/inst) \| 29.9 \| 17.2 \| \| MT-Bench \| 19.8 \| 16.8 \| \| HellaSwag \| 40.9 \| 38.9 \| \| ARC (Average) \| 37.3 \| 33.9 \| \| PIQA \| 66.3 \| 64.0 \| \| MMLU (cloze) \| 29.3 \| 28.3 \| \| BBH (3-shot) \| 28.2 \| 25.2 \| \| GSM8K (5-shot) \| 1.4 \| 1.4 \| ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - Architecture: Transformer decoder - Pretraining tokens: 2T - Precision: bfloat16 ### Hardware - GPUs: 64 H100 ### Software - Training Framework: [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```
Tippawan/pr-corrected-v8	Tippawan	2024-11-06T15:26:40Z	117	0	transformers	[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-11-06T15:26:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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mav23/SmolLM2-1.7B-GGUF	mav23	2024-11-06T15:25:15Z	8	0	transformers	[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-06T15:09:39Z	--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/XlT5TM3HWpfoZk_HSubrH.png) ## Table of Contents 1. [Model Summary](#model-summary) 2. [Evaluation](#evaluation) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ```bash pip install transformers ``` #### Running the model on CPU/GPU/multi GPU * _Using full precision_ ```python # pip install transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-1.7B" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate # for fp16 use `torch_dtype=torch.float16` instead model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 3422.76 MB ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model \| Metric \| SmolLM2-1.7B \| Llama-1B \| Qwen2.5-1.5B \| SmolLM1-1.7B \| \|------------------\|--------------\|-------------\|---------------\|--------------\| \| HellaSwag \| 68.7 \| 61.2 \| 66.4 \| 62.9 \| \| ARC (Average) \| 60.5 \| 49.2 \| 58.5 \| 59.9 \| \| PIQA \| 77.6 \| 74.8 \| 76.1 \| 76.0 \| \| MMLU-Pro (MCF) \| 19.4 \| 11.7 \| 13.7 \| 10.8 \| \| CommonsenseQA \| 43.6 \| 41.2 \| 34.1 \| 38.0 \| \| TriviaQA \| 36.7 \| 28.1 \| 20.9 \| 22.5 \| \| Winogrande \| 59.4 \| 57.8 \| 59.3 \| 54.7 \| \| OpenBookQA \| 42.2 \| 38.4 \| 40.0 \| 42.4 \| \| GSM8K (5-shot) \| 31.0 \| 7.2 \| 61.3 \| 5.5 \| ## Instruction Model \| Metric \| SmolLM2-1.7B-Instruct \| Llama-1B-Instruct \| Qwen2.5-1.5B-Instruct \| SmolLM1-1.7B-Instruct \| \|:-----------------------------\|:---------------------:\|:-----------------:\|:----------------------:\|:----------------------:\| \| IFEval (Average prompt/inst) \| 56.7 \| 53.5 \| 47.4 \| 23.1 \| \| MT-Bench \| 6.13 \| 5.48 \| 6.52 \| 4.33 \| \| OpenRewrite-Eval (micro_avg RougeL) \| 44.9 \| 39.2 \| 46.9 \| NaN \| \| HellaSwag \| 66.1 \| 56.1 \| 60.9 \| 55.5 \| \| ARC (Average) \| 51.7 \| 41.6 \| 46.2 \| 43.7 \| \| PIQA \| 74.4 \| 72.3 \| 73.2 \| 71.6 \| \| MMLU-Pro (MCF) \| 19.3 \| 12.7 \| 24.2 \| 11.7 \| \| BBH (3-shot) \| 32.2 \| 27.6 \| 35.3 \| 25.7 \| \| GSM8K (5-shot) \| 48.2 \| 26.8 \| 42.8 \| 4.62 \| ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - Architecture: Transformer decoder - Pretraining tokens: 11T - Precision: bfloat16 ### Hardware - GPUs: 256 H100 ### Software - Training Framework: [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```
MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF	MaziyarPanahi	2024-11-06T15:19:58Z	34	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.0", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.0", "region:us", "conversational" ]	text-generation	2024-11-06T14:57:15Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.0-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.0 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.0](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.0) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.0](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.0). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
nphuc/Llama-3.2-3B-Thich-Nhat-Hanh-v3.2	nphuc	2024-11-06T15:15:38Z	17	0	transformers	[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T15:00:15Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: nphuc - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ZyloO-AI/RawCharm-Amateur-Photography	ZyloO-AI	2024-11-06T14:53:47Z	40	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-11-06T14:49:25Z	--- library_name: diffusers pipeline_tag: text-to-image --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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novalalthoff/wav2vec2-large-id-16hr-non-lp	novalalthoff	2024-11-06T14:51:21Z	80	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-06T14:49:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Xu-Ouyang/pythia-6.9b-deduped-int8-step2-GPTQ-wikitext2	Xu-Ouyang	2024-11-06T14:46:28Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]	text-generation	2024-11-06T14:36:03Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF	MaziyarPanahi	2024-11-06T14:35:34Z	52	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.1", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.1", "region:us", "conversational" ]	text-generation	2024-11-06T14:12:42Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.1-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.1 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.1](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.1) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.1](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.1). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1	MayBashendy	2024-11-06T14:33:18Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T14:00:29Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1 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. --> # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4063 - Qwk: 0.5441 - Mse: 0.4063 - Rmse: 0.6374 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.0060 \| 2 \| 9.8538 \| 0.0 \| 9.8538 \| 3.1391 \| \| No log \| 0.0120 \| 4 \| 7.5205 \| 0.0213 \| 7.5205 \| 2.7424 \| \| No log \| 0.0181 \| 6 \| 6.8656 \| 0.0018 \| 6.8656 \| 2.6202 \| \| No log \| 0.0241 \| 8 \| 6.0945 \| 0.0 \| 6.0945 \| 2.4687 \| \| No log \| 0.0301 \| 10 \| 5.6492 \| 0.0 \| 5.6492 \| 2.3768 \| \| No log \| 0.0361 \| 12 \| 4.8146 \| 0.0 \| 4.8146 \| 2.1942 \| \| No log \| 0.0422 \| 14 \| 4.1150 \| 0.0 \| 4.1150 \| 2.0285 \| \| No log \| 0.0482 \| 16 \| 3.3946 \| 0.0297 \| 3.3946 \| 1.8424 \| \| No log \| 0.0542 \| 18 \| 2.7416 \| 0.0118 \| 2.7416 \| 1.6558 \| \| No log \| 0.0602 \| 20 \| 2.1167 \| 0.0079 \| 2.1167 \| 1.4549 \| \| No log \| 0.0663 \| 22 \| 1.6121 \| 0.0 \| 1.6121 \| 1.2697 \| \| No log \| 0.0723 \| 24 \| 1.2816 \| 0.1643 \| 1.2816 \| 1.1321 \| \| No log \| 0.0783 \| 26 \| 1.0878 \| 0.1060 \| 1.0878 \| 1.0430 \| \| No log \| 0.0843 \| 28 \| 0.9893 \| 0.0211 \| 0.9893 \| 0.9946 \| \| No log \| 0.0904 \| 30 \| 0.8874 \| 0.0211 \| 0.8874 \| 0.9420 \| \| No log \| 0.0964 \| 32 \| 0.8744 \| 0.0211 \| 0.8744 \| 0.9351 \| \| No log \| 0.1024 \| 34 \| 0.8581 \| 0.0106 \| 0.8581 \| 0.9263 \| \| No log \| 0.1084 \| 36 \| 0.8897 \| 0.0106 \| 0.8897 \| 0.9432 \| \| No log \| 0.1145 \| 38 \| 0.8408 \| 0.0106 \| 0.8408 \| 0.9170 \| \| No log \| 0.1205 \| 40 \| 0.9843 \| 0.1325 \| 0.9843 \| 0.9921 \| \| No log \| 0.1265 \| 42 \| 1.0066 \| 0.0 \| 1.0066 \| 1.0033 \| \| No log \| 0.1325 \| 44 \| 0.9551 \| 0.0 \| 0.9551 \| 0.9773 \| \| No log \| 0.1386 \| 46 \| 0.8306 \| 0.0 \| 0.8306 \| 0.9114 \| \| No log \| 0.1446 \| 48 \| 0.7559 \| 0.0106 \| 0.7559 \| 0.8694 \| \| No log \| 0.1506 \| 50 \| 0.7449 \| 0.0106 \| 0.7449 \| 0.8630 \| \| No log \| 0.1566 \| 52 \| 0.7499 \| 0.0106 \| 0.7499 \| 0.8660 \| \| No log \| 0.1627 \| 54 \| 0.8259 \| 0.0106 \| 0.8259 \| 0.9088 \| \| No log \| 0.1687 \| 56 \| 0.8342 \| 0.0106 \| 0.8342 \| 0.9134 \| \| No log \| 0.1747 \| 58 \| 0.8480 \| 0.0106 \| 0.8480 \| 0.9209 \| \| No log \| 0.1807 \| 60 \| 0.8004 \| 0.0106 \| 0.8004 \| 0.8947 \| \| No log \| 0.1867 \| 62 \| 0.8020 \| 0.0106 \| 0.8020 \| 0.8955 \| \| No log \| 0.1928 \| 64 \| 0.7901 \| 0.0132 \| 0.7901 \| 0.8889 \| \| No log \| 0.1988 \| 66 \| 0.7705 \| 0.0106 \| 0.7705 \| 0.8778 \| \| No log \| 0.2048 \| 68 \| 0.7646 \| 0.0477 \| 0.7646 \| 0.8744 \| \| No log \| 0.2108 \| 70 \| 0.7771 \| 0.0106 \| 0.7771 \| 0.8816 \| \| No log \| 0.2169 \| 72 \| 0.8900 \| 0.0 \| 0.8900 \| 0.9434 \| \| No log \| 0.2229 \| 74 \| 0.9312 \| 0.0 \| 0.9312 \| 0.9650 \| \| No log \| 0.2289 \| 76 \| 0.7507 \| 0.0211 \| 0.7507 \| 0.8664 \| \| No log \| 0.2349 \| 78 \| 0.8049 \| 0.1726 \| 0.8049 \| 0.8972 \| \| No log \| 0.2410 \| 80 \| 0.7495 \| 0.1392 \| 0.7495 \| 0.8657 \| \| No log \| 0.2470 \| 82 \| 0.6772 \| 0.0570 \| 0.6772 \| 0.8229 \| \| No log \| 0.2530 \| 84 \| 0.6693 \| 0.0803 \| 0.6693 \| 0.8181 \| \| No log \| 0.2590 \| 86 \| 0.6966 \| 0.0999 \| 0.6966 \| 0.8346 \| \| No log \| 0.2651 \| 88 \| 0.7758 \| 0.1809 \| 0.7758 \| 0.8808 \| \| No log \| 0.2711 \| 90 \| 0.6627 \| 0.0620 \| 0.6627 \| 0.8140 \| \| No log \| 0.2771 \| 92 \| 0.6599 \| 0.0941 \| 0.6599 \| 0.8123 \| \| No log \| 0.2831 \| 94 \| 0.6633 \| 0.0669 \| 0.6633 \| 0.8144 \| \| No log \| 0.2892 \| 96 \| 0.6847 \| 0.0604 \| 0.6847 \| 0.8275 \| \| No log \| 0.2952 \| 98 \| 0.8380 \| 0.0052 \| 0.8380 \| 0.9154 \| \| No log \| 0.3012 \| 100 \| 0.9455 \| 0.0 \| 0.9455 \| 0.9724 \| \| No log \| 0.3072 \| 102 \| 0.9850 \| 0.0 \| 0.9850 \| 0.9925 \| \| No log \| 0.3133 \| 104 \| 0.9506 \| 0.0 \| 0.9506 \| 0.9750 \| \| No log \| 0.3193 \| 106 \| 0.9171 \| -0.0027 \| 0.9171 \| 0.9576 \| \| No log \| 0.3253 \| 108 \| 0.8876 \| 0.0116 \| 0.8876 \| 0.9421 \| \| No log \| 0.3313 \| 110 \| 0.8296 \| 0.0025 \| 0.8296 \| 0.9108 \| \| No log \| 0.3373 \| 112 \| 0.7703 \| 0.0 \| 0.7703 \| 0.8777 \| \| No log \| 0.3434 \| 114 \| 0.7313 \| 0.0211 \| 0.7313 \| 0.8552 \| \| No log \| 0.3494 \| 116 \| 0.7281 \| 0.0276 \| 0.7281 \| 0.8533 \| \| No log \| 0.3554 \| 118 \| 0.7371 \| 0.0211 \| 0.7371 \| 0.8585 \| \| No log \| 0.3614 \| 120 \| 0.8136 \| 0.2752 \| 0.8136 \| 0.9020 \| \| No log \| 0.3675 \| 122 \| 0.8426 \| 0.3032 \| 0.8426 \| 0.9179 \| \| No log \| 0.3735 \| 124 \| 0.8149 \| 0.0 \| 0.8149 \| 0.9027 \| \| No log \| 0.3795 \| 126 \| 0.8598 \| 0.0 \| 0.8598 \| 0.9273 \| \| No log \| 0.3855 \| 128 \| 0.8445 \| 0.0 \| 0.8445 \| 0.9189 \| \| No log \| 0.3916 \| 130 \| 0.7930 \| 0.0 \| 0.7930 \| 0.8905 \| \| No log \| 0.3976 \| 132 \| 0.7897 \| 0.0742 \| 0.7897 \| 0.8887 \| \| No log \| 0.4036 \| 134 \| 0.7592 \| 0.0779 \| 0.7592 \| 0.8713 \| \| No log \| 0.4096 \| 136 \| 0.7340 \| 0.0250 \| 0.7340 \| 0.8567 \| \| No log \| 0.4157 \| 138 \| 0.7109 \| 0.0631 \| 0.7109 \| 0.8432 \| \| No log \| 0.4217 \| 140 \| 0.7010 \| 0.3134 \| 0.7010 \| 0.8372 \| \| No log \| 0.4277 \| 142 \| 0.7485 \| 0.4716 \| 0.7485 \| 0.8652 \| \| No log \| 0.4337 \| 144 \| 0.7055 \| 0.3984 \| 0.7055 \| 0.8399 \| \| No log \| 0.4398 \| 146 \| 0.6043 \| 0.2866 \| 0.6043 \| 0.7774 \| \| No log \| 0.4458 \| 148 \| 0.6090 \| 0.1808 \| 0.6090 \| 0.7804 \| \| No log \| 0.4518 \| 150 \| 0.6293 \| 0.1585 \| 0.6293 \| 0.7933 \| \| No log \| 0.4578 \| 152 \| 0.6791 \| 0.1350 \| 0.6791 \| 0.8241 \| \| No log \| 0.4639 \| 154 \| 0.7938 \| 0.1901 \| 0.7938 \| 0.8910 \| \| No log \| 0.4699 \| 156 \| 0.7204 \| 0.2131 \| 0.7204 \| 0.8487 \| \| No log \| 0.4759 \| 158 \| 0.6079 \| 0.1893 \| 0.6079 \| 0.7797 \| \| No log \| 0.4819 \| 160 \| 0.6052 \| 0.2001 \| 0.6052 \| 0.7780 \| \| No log \| 0.4880 \| 162 \| 0.6483 \| 0.3653 \| 0.6483 \| 0.8052 \| \| No log \| 0.4940 \| 164 \| 0.6604 \| 0.3441 \| 0.6604 \| 0.8127 \| \| No log \| 0.5 \| 166 \| 0.6017 \| 0.2949 \| 0.6017 \| 0.7757 \| \| No log \| 0.5060 \| 168 \| 0.5814 \| 0.4476 \| 0.5814 \| 0.7625 \| \| No log \| 0.5120 \| 170 \| 0.5913 \| 0.5063 \| 0.5913 \| 0.7689 \| \| No log \| 0.5181 \| 172 \| 0.6019 \| 0.5193 \| 0.6019 \| 0.7759 \| \| No log \| 0.5241 \| 174 \| 0.5463 \| 0.4907 \| 0.5463 \| 0.7391 \| \| No log \| 0.5301 \| 176 \| 0.5484 \| 0.4974 \| 0.5484 \| 0.7406 \| \| No log \| 0.5361 \| 178 \| 0.5551 \| 0.4497 \| 0.5551 \| 0.7451 \| \| No log \| 0.5422 \| 180 \| 0.5884 \| 0.4432 \| 0.5884 \| 0.7671 \| \| No log \| 0.5482 \| 182 \| 0.7529 \| 0.4655 \| 0.7529 \| 0.8677 \| \| No log \| 0.5542 \| 184 \| 0.8098 \| 0.4488 \| 0.8098 \| 0.8999 \| \| No log \| 0.5602 \| 186 \| 0.7082 \| 0.4467 \| 0.7082 \| 0.8416 \| \| No log \| 0.5663 \| 188 \| 0.6004 \| 0.3464 \| 0.6004 \| 0.7748 \| \| No log \| 0.5723 \| 190 \| 0.5929 \| 0.2784 \| 0.5929 \| 0.7700 \| \| No log \| 0.5783 \| 192 \| 0.5549 \| 0.4062 \| 0.5549 \| 0.7449 \| \| No log \| 0.5843 \| 194 \| 0.5563 \| 0.5899 \| 0.5563 \| 0.7458 \| \| No log \| 0.5904 \| 196 \| 0.5644 \| 0.5750 \| 0.5644 \| 0.7513 \| \| No log \| 0.5964 \| 198 \| 0.5137 \| 0.5741 \| 0.5137 \| 0.7167 \| \| No log \| 0.6024 \| 200 \| 0.5335 \| 0.5233 \| 0.5335 \| 0.7304 \| \| No log \| 0.6084 \| 202 \| 0.5050 \| 0.5336 \| 0.5050 \| 0.7106 \| \| No log \| 0.6145 \| 204 \| 0.5118 \| 0.6005 \| 0.5118 \| 0.7154 \| \| No log \| 0.6205 \| 206 \| 0.6248 \| 0.5732 \| 0.6248 \| 0.7905 \| \| No log \| 0.6265 \| 208 \| 0.7111 \| 0.5476 \| 0.7111 \| 0.8433 \| \| No log \| 0.6325 \| 210 \| 0.7781 \| 0.5189 \| 0.7781 \| 0.8821 \| \| No log \| 0.6386 \| 212 \| 0.8434 \| 0.4931 \| 0.8434 \| 0.9184 \| \| No log \| 0.6446 \| 214 \| 0.8126 \| 0.5209 \| 0.8126 \| 0.9014 \| \| No log \| 0.6506 \| 216 \| 0.6383 \| 0.5970 \| 0.6383 \| 0.7989 \| \| No log \| 0.6566 \| 218 \| 0.5315 \| 0.6014 \| 0.5315 \| 0.7290 \| \| No log \| 0.6627 \| 220 \| 0.5624 \| 0.4525 \| 0.5624 \| 0.7499 \| \| No log \| 0.6687 \| 222 \| 0.6333 \| 0.3337 \| 0.6333 \| 0.7958 \| \| No log \| 0.6747 \| 224 \| 0.5795 \| 0.3624 \| 0.5795 \| 0.7612 \| \| No log \| 0.6807 \| 226 \| 0.5073 \| 0.5486 \| 0.5073 \| 0.7122 \| \| No log \| 0.6867 \| 228 \| 0.5600 \| 0.6066 \| 0.5600 \| 0.7484 \| \| No log \| 0.6928 \| 230 \| 0.6215 \| 0.5835 \| 0.6215 \| 0.7883 \| \| No log \| 0.6988 \| 232 \| 0.5686 \| 0.5877 \| 0.5686 \| 0.7540 \| \| No log \| 0.7048 \| 234 \| 0.6028 \| 0.5796 \| 0.6028 \| 0.7764 \| \| No log \| 0.7108 \| 236 \| 0.5793 \| 0.5983 \| 0.5793 \| 0.7611 \| \| No log \| 0.7169 \| 238 \| 0.5310 \| 0.6063 \| 0.5310 \| 0.7287 \| \| No log \| 0.7229 \| 240 \| 0.4967 \| 0.5858 \| 0.4967 \| 0.7048 \| \| No log \| 0.7289 \| 242 \| 0.4868 \| 0.5721 \| 0.4868 \| 0.6977 \| \| No log \| 0.7349 \| 244 \| 0.4722 \| 0.5557 \| 0.4722 \| 0.6871 \| \| No log \| 0.7410 \| 246 \| 0.4724 \| 0.5195 \| 0.4724 \| 0.6873 \| \| No log \| 0.7470 \| 248 \| 0.4676 \| 0.5665 \| 0.4676 \| 0.6838 \| \| No log \| 0.7530 \| 250 \| 0.4837 \| 0.5811 \| 0.4837 \| 0.6955 \| \| No log \| 0.7590 \| 252 \| 0.4968 \| 0.5984 \| 0.4968 \| 0.7048 \| \| No log \| 0.7651 \| 254 \| 0.4773 \| 0.6040 \| 0.4773 \| 0.6909 \| \| No log \| 0.7711 \| 256 \| 0.4643 \| 0.5940 \| 0.4643 \| 0.6814 \| \| No log \| 0.7771 \| 258 \| 0.4890 \| 0.6155 \| 0.4890 \| 0.6993 \| \| No log \| 0.7831 \| 260 \| 0.6079 \| 0.5785 \| 0.6079 \| 0.7797 \| \| No log \| 0.7892 \| 262 \| 0.6645 \| 0.5582 \| 0.6645 \| 0.8152 \| \| No log \| 0.7952 \| 264 \| 0.6657 \| 0.5514 \| 0.6657 \| 0.8159 \| \| No log \| 0.8012 \| 266 \| 0.6675 \| 0.5498 \| 0.6675 \| 0.8170 \| \| No log \| 0.8072 \| 268 \| 0.5966 \| 0.5786 \| 0.5966 \| 0.7724 \| \| No log \| 0.8133 \| 270 \| 0.5300 \| 0.6091 \| 0.5300 \| 0.7280 \| \| No log \| 0.8193 \| 272 \| 0.5421 \| 0.6164 \| 0.5421 \| 0.7363 \| \| No log \| 0.8253 \| 274 \| 0.6810 \| 0.5792 \| 0.6810 \| 0.8252 \| \| No log \| 0.8313 \| 276 \| 0.6917 \| 0.5810 \| 0.6917 \| 0.8317 \| \| No log \| 0.8373 \| 278 \| 0.5877 \| 0.6193 \| 0.5877 \| 0.7666 \| \| No log \| 0.8434 \| 280 \| 0.5283 \| 0.6098 \| 0.5283 \| 0.7268 \| \| No log \| 0.8494 \| 282 \| 0.5171 \| 0.5755 \| 0.5171 \| 0.7191 \| \| No log \| 0.8554 \| 284 \| 0.5388 \| 0.5552 \| 0.5388 \| 0.7340 \| \| No log \| 0.8614 \| 286 \| 0.5772 \| 0.5397 \| 0.5772 \| 0.7598 \| \| No log \| 0.8675 \| 288 \| 0.5090 \| 0.5516 \| 0.5090 \| 0.7135 \| \| No log \| 0.8735 \| 290 \| 0.4698 \| 0.4972 \| 0.4698 \| 0.6854 \| \| No log \| 0.8795 \| 292 \| 0.4553 \| 0.5218 \| 0.4553 \| 0.6748 \| \| No log \| 0.8855 \| 294 \| 0.4614 \| 0.5281 \| 0.4614 \| 0.6793 \| \| No log \| 0.8916 \| 296 \| 0.4784 \| 0.5153 \| 0.4784 \| 0.6917 \| \| No log \| 0.8976 \| 298 \| 0.4488 \| 0.5968 \| 0.4488 \| 0.6700 \| \| No log \| 0.9036 \| 300 \| 0.5075 \| 0.6270 \| 0.5075 \| 0.7124 \| \| No log \| 0.9096 \| 302 \| 0.4963 \| 0.6338 \| 0.4963 \| 0.7045 \| \| No log \| 0.9157 \| 304 \| 0.4564 \| 0.5870 \| 0.4564 \| 0.6756 \| \| No log \| 0.9217 \| 306 \| 0.5087 \| 0.5159 \| 0.5087 \| 0.7132 \| \| No log \| 0.9277 \| 308 \| 0.4529 \| 0.5780 \| 0.4529 \| 0.6729 \| \| No log \| 0.9337 \| 310 \| 0.5123 \| 0.6292 \| 0.5123 \| 0.7158 \| \| No log \| 0.9398 \| 312 \| 0.6004 \| 0.6074 \| 0.6004 \| 0.7749 \| \| No log \| 0.9458 \| 314 \| 0.5623 \| 0.6023 \| 0.5623 \| 0.7499 \| \| No log \| 0.9518 \| 316 \| 0.4899 \| 0.6086 \| 0.4899 \| 0.6999 \| \| No log \| 0.9578 \| 318 \| 0.4493 \| 0.5903 \| 0.4493 \| 0.6703 \| \| No log \| 0.9639 \| 320 \| 0.4409 \| 0.5967 \| 0.4409 \| 0.6640 \| \| No log \| 0.9699 \| 322 \| 0.4509 \| 0.6117 \| 0.4509 \| 0.6715 \| \| No log \| 0.9759 \| 324 \| 0.4351 \| 0.5719 \| 0.4351 \| 0.6596 \| \| No log \| 0.9819 \| 326 \| 0.4458 \| 0.6023 \| 0.4458 \| 0.6677 \| \| No log \| 0.9880 \| 328 \| 0.5342 \| 0.6022 \| 0.5342 \| 0.7309 \| \| No log \| 0.9940 \| 330 \| 0.5829 \| 0.5923 \| 0.5829 \| 0.7635 \| \| No log \| 1.0 \| 332 \| 0.5132 \| 0.6003 \| 0.5132 \| 0.7164 \| \| No log \| 1.0060 \| 334 \| 0.5212 \| 0.6055 \| 0.5212 \| 0.7219 \| \| No log \| 1.0120 \| 336 \| 0.5368 \| 0.6044 \| 0.5368 \| 0.7327 \| \| No log \| 1.0181 \| 338 \| 0.5627 \| 0.5900 \| 0.5627 \| 0.7501 \| \| No log \| 1.0241 \| 340 \| 0.5291 \| 0.6080 \| 0.5291 \| 0.7274 \| \| No log \| 1.0301 \| 342 \| 0.4764 \| 0.5559 \| 0.4764 \| 0.6902 \| \| No log \| 1.0361 \| 344 \| 0.4584 \| 0.5561 \| 0.4584 \| 0.6770 \| \| No log \| 1.0422 \| 346 \| 0.4822 \| 0.5822 \| 0.4822 \| 0.6944 \| \| No log \| 1.0482 \| 348 \| 0.4995 \| 0.5724 \| 0.4995 \| 0.7068 \| \| No log \| 1.0542 \| 350 \| 0.5213 \| 0.5983 \| 0.5213 \| 0.7220 \| \| No log \| 1.0602 \| 352 \| 0.4432 \| 0.6025 \| 0.4432 \| 0.6657 \| \| No log \| 1.0663 \| 354 \| 0.4266 \| 0.6011 \| 0.4266 \| 0.6531 \| \| No log \| 1.0723 \| 356 \| 0.4593 \| 0.6256 \| 0.4593 \| 0.6777 \| \| No log \| 1.0783 \| 358 \| 0.4282 \| 0.6277 \| 0.4282 \| 0.6544 \| \| No log \| 1.0843 \| 360 \| 0.4012 \| 0.5671 \| 0.4012 \| 0.6334 \| \| No log \| 1.0904 \| 362 \| 0.4494 \| 0.5048 \| 0.4494 \| 0.6704 \| \| No log \| 1.0964 \| 364 \| 0.4008 \| 0.5778 \| 0.4008 \| 0.6331 \| \| No log \| 1.1024 \| 366 \| 0.4551 \| 0.6453 \| 0.4551 \| 0.6746 \| \| No log \| 1.1084 \| 368 \| 0.4896 \| 0.6424 \| 0.4896 \| 0.6997 \| \| No log \| 1.1145 \| 370 \| 0.4146 \| 0.5919 \| 0.4146 \| 0.6439 \| \| No log \| 1.1205 \| 372 \| 0.4293 \| 0.5326 \| 0.4293 \| 0.6552 \| \| No log \| 1.1265 \| 374 \| 0.4293 \| 0.5315 \| 0.4293 \| 0.6552 \| \| No log \| 1.1325 \| 376 \| 0.4753 \| 0.5957 \| 0.4753 \| 0.6894 \| \| No log \| 1.1386 \| 378 \| 0.6103 \| 0.5992 \| 0.6103 \| 0.7812 \| \| No log \| 1.1446 \| 380 \| 0.6979 \| 0.5847 \| 0.6979 \| 0.8354 \| \| No log \| 1.1506 \| 382 \| 0.5574 \| 0.6284 \| 0.5574 \| 0.7466 \| \| No log \| 1.1566 \| 384 \| 0.4114 \| 0.6228 \| 0.4114 \| 0.6414 \| \| No log \| 1.1627 \| 386 \| 0.4050 \| 0.6184 \| 0.4050 \| 0.6364 \| \| No log \| 1.1687 \| 388 \| 0.4458 \| 0.6544 \| 0.4458 \| 0.6677 \| \| No log \| 1.1747 \| 390 \| 0.4770 \| 0.6515 \| 0.4770 \| 0.6906 \| \| No log \| 1.1807 \| 392 \| 0.4387 \| 0.6407 \| 0.4387 \| 0.6623 \| \| No log \| 1.1867 \| 394 \| 0.4974 \| 0.5471 \| 0.4974 \| 0.7053 \| \| No log \| 1.1928 \| 396 \| 0.5162 \| 0.5377 \| 0.5162 \| 0.7185 \| \| No log \| 1.1988 \| 398 \| 0.4317 \| 0.6337 \| 0.4317 \| 0.6570 \| \| No log \| 1.2048 \| 400 \| 0.5618 \| 0.6426 \| 0.5618 \| 0.7496 \| \| No log \| 1.2108 \| 402 \| 0.7052 \| 0.6094 \| 0.7052 \| 0.8398 \| \| No log \| 1.2169 \| 404 \| 0.6045 \| 0.6085 \| 0.6045 \| 0.7775 \| \| No log \| 1.2229 \| 406 \| 0.5113 \| 0.5431 \| 0.5113 \| 0.7151 \| \| No log \| 1.2289 \| 408 \| 0.5141 \| 0.5341 \| 0.5141 \| 0.7170 \| \| No log \| 1.2349 \| 410 \| 0.5797 \| 0.5890 \| 0.5797 \| 0.7614 \| \| No log \| 1.2410 \| 412 \| 0.5818 \| 0.5859 \| 0.5818 \| 0.7627 \| \| No log \| 1.2470 \| 414 \| 0.5180 \| 0.5409 \| 0.5180 \| 0.7197 \| \| No log \| 1.2530 \| 416 \| 0.5190 \| 0.5214 \| 0.5190 \| 0.7205 \| \| No log \| 1.2590 \| 418 \| 0.5646 \| 0.5339 \| 0.5646 \| 0.7514 \| \| No log \| 1.2651 \| 420 \| 0.5798 \| 0.5696 \| 0.5798 \| 0.7615 \| \| No log \| 1.2711 \| 422 \| 0.5575 \| 0.6004 \| 0.5575 \| 0.7467 \| \| No log \| 1.2771 \| 424 \| 0.4537 \| 0.5824 \| 0.4537 \| 0.6735 \| \| No log \| 1.2831 \| 426 \| 0.4111 \| 0.5705 \| 0.4111 \| 0.6412 \| \| No log \| 1.2892 \| 428 \| 0.4151 \| 0.5388 \| 0.4151 \| 0.6443 \| \| No log \| 1.2952 \| 430 \| 0.3907 \| 0.5737 \| 0.3907 \| 0.6251 \| \| No log \| 1.3012 \| 432 \| 0.3818 \| 0.6003 \| 0.3818 \| 0.6179 \| \| No log \| 1.3072 \| 434 \| 0.3918 \| 0.5599 \| 0.3918 \| 0.6259 \| \| No log \| 1.3133 \| 436 \| 0.4417 \| 0.5025 \| 0.4417 \| 0.6646 \| \| No log \| 1.3193 \| 438 \| 0.4010 \| 0.5344 \| 0.4010 \| 0.6332 \| \| No log \| 1.3253 \| 440 \| 0.3930 \| 0.6166 \| 0.3930 \| 0.6269 \| \| No log \| 1.3313 \| 442 \| 0.4862 \| 0.6574 \| 0.4862 \| 0.6973 \| \| No log \| 1.3373 \| 444 \| 0.6188 \| 0.6505 \| 0.6188 \| 0.7867 \| \| No log \| 1.3434 \| 446 \| 0.5829 \| 0.6513 \| 0.5829 \| 0.7635 \| \| No log \| 1.3494 \| 448 \| 0.4792 \| 0.6095 \| 0.4792 \| 0.6923 \| \| No log \| 1.3554 \| 450 \| 0.4474 \| 0.5256 \| 0.4474 \| 0.6689 \| \| No log \| 1.3614 \| 452 \| 0.4590 \| 0.5178 \| 0.4590 \| 0.6775 \| \| No log \| 1.3675 \| 454 \| 0.4685 \| 0.5701 \| 0.4685 \| 0.6845 \| \| No log \| 1.3735 \| 456 \| 0.4354 \| 0.6031 \| 0.4354 \| 0.6598 \| \| No log \| 1.3795 \| 458 \| 0.4036 \| 0.5533 \| 0.4036 \| 0.6353 \| \| No log \| 1.3855 \| 460 \| 0.4440 \| 0.4963 \| 0.4440 \| 0.6664 \| \| No log \| 1.3916 \| 462 \| 0.4178 \| 0.5149 \| 0.4178 \| 0.6463 \| \| No log \| 1.3976 \| 464 \| 0.4140 \| 0.6389 \| 0.4140 \| 0.6434 \| \| No log \| 1.4036 \| 466 \| 0.5198 \| 0.6676 \| 0.5198 \| 0.7210 \| \| No log \| 1.4096 \| 468 \| 0.4730 \| 0.6435 \| 0.4730 \| 0.6878 \| \| No log \| 1.4157 \| 470 \| 0.4154 \| 0.5595 \| 0.4154 \| 0.6445 \| \| No log \| 1.4217 \| 472 \| 0.4113 \| 0.5696 \| 0.4113 \| 0.6414 \| \| No log \| 1.4277 \| 474 \| 0.4438 \| 0.6509 \| 0.4438 \| 0.6662 \| \| No log \| 1.4337 \| 476 \| 0.5069 \| 0.6826 \| 0.5069 \| 0.7120 \| \| No log \| 1.4398 \| 478 \| 0.4424 \| 0.6829 \| 0.4424 \| 0.6651 \| \| No log \| 1.4458 \| 480 \| 0.3942 \| 0.6120 \| 0.3942 \| 0.6279 \| \| No log \| 1.4518 \| 482 \| 0.3959 \| 0.5517 \| 0.3959 \| 0.6292 \| \| No log \| 1.4578 \| 484 \| 0.4028 \| 0.5742 \| 0.4028 \| 0.6347 \| \| No log \| 1.4639 \| 486 \| 0.4481 \| 0.6284 \| 0.4481 \| 0.6694 \| \| No log \| 1.4699 \| 488 \| 0.4389 \| 0.6197 \| 0.4389 \| 0.6625 \| \| No log \| 1.4759 \| 490 \| 0.4036 \| 0.5371 \| 0.4036 \| 0.6353 \| \| No log \| 1.4819 \| 492 \| 0.3990 \| 0.5366 \| 0.3990 \| 0.6317 \| \| No log \| 1.4880 \| 494 \| 0.3900 \| 0.5701 \| 0.3900 \| 0.6245 \| \| No log \| 1.4940 \| 496 \| 0.3945 \| 0.6265 \| 0.3945 \| 0.6281 \| \| No log \| 1.5 \| 498 \| 0.3904 \| 0.6173 \| 0.3904 \| 0.6248 \| \| 0.5402 \| 1.5060 \| 500 \| 0.4074 \| 0.6301 \| 0.4074 \| 0.6383 \| \| 0.5402 \| 1.5120 \| 502 \| 0.4201 \| 0.5920 \| 0.4201 \| 0.6481 \| \| 0.5402 \| 1.5181 \| 504 \| 0.4574 \| 0.5466 \| 0.4574 \| 0.6763 \| \| 0.5402 \| 1.5241 \| 506 \| 0.4131 \| 0.5669 \| 0.4131 \| 0.6427 \| \| 0.5402 \| 1.5301 \| 508 \| 0.4058 \| 0.6023 \| 0.4058 \| 0.6370 \| \| 0.5402 \| 1.5361 \| 510 \| 0.4063 \| 0.5441 \| 0.4063 \| 0.6374 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
aigchacker/Text-Poster	aigchacker	2024-11-06T14:26:55Z	42	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "image-generation", "flux", "safetensors", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-06T13:59:31Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - image-generation - flux - safetensors widget: - text: Text poster, a couple output: url: images/6dd1a918d89991ad5e40513ab88e7d892077f89dac93edcf4b660dd2.jpg - text: Text poster, a woman sitting in a cafe output: url: images/d2586464001008a80b5e45104e0f23290a35db048cab2e4fc4bfa356.jpg - text: Text poster, eiffel tower output: url: images/f25e24ecfbd0aa96fb6f55ab29288ba4d1fffe79fd95679d9d2f1329.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: text poster license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # FLUX.1-dev-LoRA-Text-Poster This is a LoRA (Text Poster) trained on FLUX.1-dev for artistic text poster by [cooooool](https://www.shakker.ai/userpage/c4d790d27e6b4de69f3f3508daf8f4c5/publish). If you are also interested in sharing your models on our platform, welcome to join our [Discord Community](https://huggingface.co/spaces/Shakker-Labs/README/blob/main/(https://discord.gg/5TuxSjJya6)). <div class="container"> <img src="./poster.jpeg" width="1024"/> </div> ## Showcases <Gallery /> ## Trigger words You should use `text poster` to trigger the image generation. The recommended scale is `0.8` to `1.0` in diffusers. ## Online Inference You can also download this model at [Shakker AI](https://www.shakker.ai/modelinfo/579ab130b53246fea49811bf80d38486/FLUX-text-poster?from=search), where we provide an online interface to generate images. ## Acknowledgements This model is trained by our copyrighted users [cooooool](https://www.shakker.ai/userpage/c4d790d27e6b4de69f3f3508daf8f4c5/publish). We release this model under permissions. The model follows [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
mradermacher/tamil-llama-13b-base-v0.1-GGUF	mradermacher	2024-11-06T14:18:54Z	31	0	transformers	[ "transformers", "gguf", "ta", "en", "base_model:abhinand/tamil-llama-13b-base-v0.1", "base_model:quantized:abhinand/tamil-llama-13b-base-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-11-06T12:45:03Z	--- base_model: abhinand/tamil-llama-13b-base-v0.1 language: - ta - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 8.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q6_K.gguf) \| Q6_K \| 10.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q8_0.gguf) \| Q8_0 \| 14.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
GateNLP/covid-vaccine-twitter-bert	GateNLP	2024-11-06T14:18:18Z	117	1	transformers	[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-01-13T19:02:52Z	VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song Accepted @ICWSM 2023 ```bibtex @inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } ``` --- license: mit ---
lesubra/ECE-PRYMMAL-3B-SLERP_2-V2	lesubra	2024-11-06T14:17:40Z	10	0	null	[ "safetensors", "phi3", "merge", "mergekit", "lazymergekit", "jpacifico/Chocolatine-3B-Instruct-DPO-Revised", "microsoft/Phi-3-mini-4k-instruct", "custom_code", "license:apache-2.0", "region:us" ]	null	2024-11-06T14:15:13Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jpacifico/Chocolatine-3B-Instruct-DPO-Revised - microsoft/Phi-3-mini-4k-instruct --- # ECE-PRYMMAL-3B-SLERP_2-V2 ECE-PRYMMAL-3B-SLERP_2-V2 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) * [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised layer_range: [0, 32] - model: microsoft/Phi-3-mini-4k-instruct layer_range: [0, 32] merge_method: slerp base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
QuantFactory/Qwen2.5-Coder-7B-Instruct-GGUF	QuantFactory	2024-11-06T14:17:24Z	386	5	transformers	[ "transformers", "gguf", "code", "codeqwen", "chat", "qwen", "qwen-coder", "text-generation", "en", "arxiv:2409.12186", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-Coder-7B", "base_model:quantized:Qwen/Qwen2.5-Coder-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-19T06:10:06Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-Coder-7B pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Qwen2.5-Coder-7B-Instruct-GGUF This is quantized version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) based on updated weights _(dated 06 Nov, 2024)_ and created using llama.cpp # Original Model Card # Qwen2.5-Coder-7B-Instruct ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). For Qwen2.5-Coder, we release three base language models and instruction-tuned language models, 1.5, 7 and 32 (coming soon) billion parameters. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. - A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - Long-context Support up to 128K tokens. This repo contains the instruction-tuned 7B Qwen2.5-Coder model, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```
AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs	AlekseyKorshuk	2024-11-06T14:15:56Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T10:34:06Z	--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft library_name: transformers model_name: ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft](https://huggingface.co/datasets/AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/aleksey-korshuk/huggingface/runs/bfyzbjtg) This model was trained with SFT. ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.4.1+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mateiaassAI/MT5Base-8e5-4	mateiaassAI	2024-11-06T14:10:32Z	104	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-06T14:08:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
kajamo/alpha0ne	kajamo	2024-11-06T14:06:15Z	114	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-06T12:34:27Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: alpha0ne 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. --> # alpha0ne This model is a fine-tuned version of [](https://huggingface.co/) 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: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
lesubra/ECE-PRYMMAL-3B-SLERP_2-V1	lesubra	2024-11-06T14:03:06Z	9	0	null	[ "safetensors", "phi3", "merge", "mergekit", "lazymergekit", "jpacifico/Chocolatine-3B-Instruct-DPO-Revised", "microsoft/Phi-3-mini-4k-instruct", "custom_code", "license:apache-2.0", "region:us" ]	null	2024-11-06T14:00:50Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jpacifico/Chocolatine-3B-Instruct-DPO-Revised - microsoft/Phi-3-mini-4k-instruct --- # ECE-PRYMMAL-3B-SLERP_2-V1 ECE-PRYMMAL-3B-SLERP_2-V1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) * [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised layer_range: [0, 32] - model: microsoft/Phi-3-mini-4k-instruct layer_range: [0, 32] merge_method: slerp base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0	MayBashendy	2024-11-06T13:58:49Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T11:41:28Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0 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. --> # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5352 - Qwk: 0.5466 - Mse: 0.5352 - Rmse: 0.7316 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\|:-------:\|:------:\| \| No log \| 0.0063 \| 2 \| 12.0179 \| 0.0 \| 12.0179 \| 3.4667 \| \| No log \| 0.0126 \| 4 \| 11.0822 \| 0.0 \| 11.0822 \| 3.3290 \| \| No log \| 0.0189 \| 6 \| 10.5444 \| 0.0 \| 10.5444 \| 3.2472 \| \| No log \| 0.0252 \| 8 \| 9.8057 \| 0.0 \| 9.8057 \| 3.1314 \| \| No log \| 0.0314 \| 10 \| 8.6296 \| 0.0026 \| 8.6296 \| 2.9376 \| \| No log \| 0.0377 \| 12 \| 7.0373 \| 0.0054 \| 7.0373 \| 2.6528 \| \| No log \| 0.0440 \| 14 \| 5.3678 \| 0.0 \| 5.3678 \| 2.3168 \| \| No log \| 0.0503 \| 16 \| 4.1881 \| 0.0125 \| 4.1881 \| 2.0465 \| \| No log \| 0.0566 \| 18 \| 3.2466 \| 0.0314 \| 3.2466 \| 1.8018 \| \| No log \| 0.0629 \| 20 \| 2.5151 \| 0.0201 \| 2.5151 \| 1.5859 \| \| No log \| 0.0692 \| 22 \| 1.6556 \| 0.0115 \| 1.6556 \| 1.2867 \| \| No log \| 0.0755 \| 24 \| 1.1844 \| 0.1172 \| 1.1844 \| 1.0883 \| \| No log \| 0.0818 \| 26 \| 0.9363 \| 0.0866 \| 0.9363 \| 0.9676 \| \| No log \| 0.0881 \| 28 \| 0.7604 \| 0.0484 \| 0.7604 \| 0.8720 \| \| No log \| 0.0943 \| 30 \| 0.7517 \| 0.0316 \| 0.7517 \| 0.8670 \| \| No log \| 0.1006 \| 32 \| 0.7482 \| 0.0674 \| 0.7482 \| 0.8650 \| \| No log \| 0.1069 \| 34 \| 0.7812 \| 0.0382 \| 0.7812 \| 0.8838 \| \| No log \| 0.1132 \| 36 \| 0.8630 \| 0.0382 \| 0.8630 \| 0.9290 \| \| No log \| 0.1195 \| 38 \| 0.7789 \| 0.0316 \| 0.7789 \| 0.8825 \| \| No log \| 0.1258 \| 40 \| 0.8760 \| 0.0316 \| 0.8760 \| 0.9359 \| \| No log \| 0.1321 \| 42 \| 0.9209 \| 0.0212 \| 0.9209 \| 0.9596 \| \| No log \| 0.1384 \| 44 \| 0.8864 \| 0.0212 \| 0.8864 \| 0.9415 \| \| No log \| 0.1447 \| 46 \| 0.8158 \| 0.0126 \| 0.8158 \| 0.9032 \| \| No log \| 0.1509 \| 48 \| 0.8029 \| 0.0316 \| 0.8029 \| 0.8961 \| \| No log \| 0.1572 \| 50 \| 0.8344 \| 0.0316 \| 0.8344 \| 0.9134 \| \| No log \| 0.1635 \| 52 \| 0.8317 \| 0.0316 \| 0.8317 \| 0.9120 \| \| No log \| 0.1698 \| 54 \| 0.7914 \| 0.0316 \| 0.7914 \| 0.8896 \| \| No log \| 0.1761 \| 56 \| 0.7742 \| 0.0316 \| 0.7742 \| 0.8799 \| \| No log \| 0.1824 \| 58 \| 0.7615 \| 0.0316 \| 0.7615 \| 0.8726 \| \| No log \| 0.1887 \| 60 \| 0.7750 \| 0.0316 \| 0.7750 \| 0.8803 \| \| No log \| 0.1950 \| 62 \| 0.7897 \| 0.0316 \| 0.7897 \| 0.8887 \| \| No log \| 0.2013 \| 64 \| 0.7865 \| 0.0316 \| 0.7865 \| 0.8869 \| \| No log \| 0.2075 \| 66 \| 0.7933 \| 0.0316 \| 0.7933 \| 0.8907 \| \| No log \| 0.2138 \| 68 \| 0.8167 \| 0.0316 \| 0.8167 \| 0.9037 \| \| No log \| 0.2201 \| 70 \| 0.8019 \| 0.0316 \| 0.8019 \| 0.8955 \| \| No log \| 0.2264 \| 72 \| 0.7407 \| 0.0316 \| 0.7407 \| 0.8606 \| \| No log \| 0.2327 \| 74 \| 0.7123 \| 0.0316 \| 0.7123 \| 0.8440 \| \| No log \| 0.2390 \| 76 \| 0.6954 \| 0.0316 \| 0.6954 \| 0.8339 \| \| No log \| 0.2453 \| 78 \| 0.7127 \| 0.0419 \| 0.7127 \| 0.8442 \| \| No log \| 0.2516 \| 80 \| 0.6788 \| 0.0419 \| 0.6788 \| 0.8239 \| \| No log \| 0.2579 \| 82 \| 0.6872 \| 0.3131 \| 0.6872 \| 0.8290 \| \| No log \| 0.2642 \| 84 \| 0.7295 \| 0.4173 \| 0.7295 \| 0.8541 \| \| No log \| 0.2704 \| 86 \| 0.7259 \| 0.1405 \| 0.7259 \| 0.8520 \| \| No log \| 0.2767 \| 88 \| 0.7534 \| 0.0316 \| 0.7534 \| 0.8680 \| \| No log \| 0.2830 \| 90 \| 0.9137 \| 0.0316 \| 0.9137 \| 0.9559 \| \| No log \| 0.2893 \| 92 \| 0.8953 \| 0.0419 \| 0.8953 \| 0.9462 \| \| No log \| 0.2956 \| 94 \| 0.7677 \| 0.0419 \| 0.7677 \| 0.8762 \| \| No log \| 0.3019 \| 96 \| 0.6872 \| 0.0419 \| 0.6872 \| 0.8290 \| \| No log \| 0.3082 \| 98 \| 0.6619 \| 0.0493 \| 0.6619 \| 0.8135 \| \| No log \| 0.3145 \| 100 \| 0.6529 \| 0.0897 \| 0.6529 \| 0.8080 \| \| No log \| 0.3208 \| 102 \| 0.6887 \| 0.0447 \| 0.6887 \| 0.8299 \| \| No log \| 0.3270 \| 104 \| 0.8399 \| 0.0921 \| 0.8399 \| 0.9164 \| \| No log \| 0.3333 \| 106 \| 0.8642 \| 0.0873 \| 0.8642 \| 0.9296 \| \| No log \| 0.3396 \| 108 \| 0.7132 \| 0.0484 \| 0.7132 \| 0.8445 \| \| No log \| 0.3459 \| 110 \| 0.6894 \| 0.0419 \| 0.6894 \| 0.8303 \| \| No log \| 0.3522 \| 112 \| 0.6976 \| 0.0484 \| 0.6976 \| 0.8352 \| \| No log \| 0.3585 \| 114 \| 0.6968 \| 0.0419 \| 0.6968 \| 0.8348 \| \| No log \| 0.3648 \| 116 \| 0.6796 \| 0.0548 \| 0.6796 \| 0.8244 \| \| No log \| 0.3711 \| 118 \| 0.6692 \| 0.0548 \| 0.6692 \| 0.8180 \| \| No log \| 0.3774 \| 120 \| 0.6608 \| 0.0548 \| 0.6608 \| 0.8129 \| \| No log \| 0.3836 \| 122 \| 0.6634 \| 0.0649 \| 0.6634 \| 0.8145 \| \| No log \| 0.3899 \| 124 \| 0.6622 \| 0.0603 \| 0.6622 \| 0.8137 \| \| No log \| 0.3962 \| 126 \| 0.6628 \| 0.0750 \| 0.6628 \| 0.8142 \| \| No log \| 0.4025 \| 128 \| 0.6598 \| 0.1833 \| 0.6598 \| 0.8123 \| \| No log \| 0.4088 \| 130 \| 0.6672 \| 0.2258 \| 0.6672 \| 0.8168 \| \| No log \| 0.4151 \| 132 \| 0.6639 \| 0.0918 \| 0.6639 \| 0.8148 \| \| No log \| 0.4214 \| 134 \| 0.6644 \| 0.0603 \| 0.6644 \| 0.8151 \| \| No log \| 0.4277 \| 136 \| 0.6554 \| 0.0746 \| 0.6554 \| 0.8096 \| \| No log \| 0.4340 \| 138 \| 0.6264 \| 0.1257 \| 0.6264 \| 0.7914 \| \| No log \| 0.4403 \| 140 \| 0.6451 \| 0.4728 \| 0.6451 \| 0.8032 \| \| No log \| 0.4465 \| 142 \| 0.6685 \| 0.4752 \| 0.6685 \| 0.8176 \| \| No log \| 0.4528 \| 144 \| 0.6049 \| 0.4478 \| 0.6049 \| 0.7777 \| \| No log \| 0.4591 \| 146 \| 0.6038 \| 0.1782 \| 0.6038 \| 0.7771 \| \| No log \| 0.4654 \| 148 \| 0.5846 \| 0.2371 \| 0.5846 \| 0.7646 \| \| No log \| 0.4717 \| 150 \| 0.5859 \| 0.4477 \| 0.5859 \| 0.7655 \| \| No log \| 0.4780 \| 152 \| 0.7173 \| 0.3828 \| 0.7173 \| 0.8469 \| \| No log \| 0.4843 \| 154 \| 0.7084 \| 0.3882 \| 0.7084 \| 0.8417 \| \| No log \| 0.4906 \| 156 \| 0.6240 \| 0.4139 \| 0.6240 \| 0.7899 \| \| No log \| 0.4969 \| 158 \| 0.6043 \| 0.1238 \| 0.6043 \| 0.7774 \| \| No log \| 0.5031 \| 160 \| 0.6702 \| 0.1123 \| 0.6702 \| 0.8187 \| \| No log \| 0.5094 \| 162 \| 0.6411 \| 0.1102 \| 0.6411 \| 0.8007 \| \| No log \| 0.5157 \| 164 \| 0.5683 \| 0.3157 \| 0.5683 \| 0.7539 \| \| No log \| 0.5220 \| 166 \| 0.5955 \| 0.4532 \| 0.5955 \| 0.7717 \| \| No log \| 0.5283 \| 168 \| 0.6909 \| 0.3869 \| 0.6909 \| 0.8312 \| \| No log \| 0.5346 \| 170 \| 0.9200 \| 0.0801 \| 0.9200 \| 0.9592 \| \| No log \| 0.5409 \| 172 \| 0.7579 \| 0.2155 \| 0.7579 \| 0.8706 \| \| No log \| 0.5472 \| 174 \| 0.6391 \| 0.3533 \| 0.6391 \| 0.7994 \| \| No log \| 0.5535 \| 176 \| 0.6119 \| 0.3729 \| 0.6119 \| 0.7822 \| \| No log \| 0.5597 \| 178 \| 0.6156 \| 0.3864 \| 0.6156 \| 0.7846 \| \| No log \| 0.5660 \| 180 \| 0.5831 \| 0.4169 \| 0.5831 \| 0.7636 \| \| No log \| 0.5723 \| 182 \| 0.6125 \| 0.4773 \| 0.6125 \| 0.7826 \| \| No log \| 0.5786 \| 184 \| 0.5654 \| 0.5126 \| 0.5654 \| 0.7519 \| \| No log \| 0.5849 \| 186 \| 0.5045 \| 0.4536 \| 0.5045 \| 0.7103 \| \| No log \| 0.5912 \| 188 \| 0.4852 \| 0.4839 \| 0.4852 \| 0.6965 \| \| No log \| 0.5975 \| 190 \| 0.4864 \| 0.5193 \| 0.4864 \| 0.6974 \| \| No log \| 0.6038 \| 192 \| 0.5333 \| 0.5381 \| 0.5333 \| 0.7303 \| \| No log \| 0.6101 \| 194 \| 0.5818 \| 0.4820 \| 0.5818 \| 0.7627 \| \| No log \| 0.6164 \| 196 \| 0.5457 \| 0.4547 \| 0.5457 \| 0.7387 \| \| No log \| 0.6226 \| 198 \| 0.5304 \| 0.4858 \| 0.5304 \| 0.7283 \| \| No log \| 0.6289 \| 200 \| 0.5180 \| 0.5206 \| 0.5180 \| 0.7197 \| \| No log \| 0.6352 \| 202 \| 0.4908 \| 0.5086 \| 0.4908 \| 0.7006 \| \| No log \| 0.6415 \| 204 \| 0.4715 \| 0.5198 \| 0.4715 \| 0.6867 \| \| No log \| 0.6478 \| 206 \| 0.4842 \| 0.5030 \| 0.4842 \| 0.6959 \| \| No log \| 0.6541 \| 208 \| 0.5114 \| 0.5502 \| 0.5114 \| 0.7151 \| \| No log \| 0.6604 \| 210 \| 0.5467 \| 0.5329 \| 0.5467 \| 0.7394 \| \| No log \| 0.6667 \| 212 \| 0.5509 \| 0.5181 \| 0.5509 \| 0.7422 \| \| No log \| 0.6730 \| 214 \| 0.5190 \| 0.3750 \| 0.5190 \| 0.7204 \| \| No log \| 0.6792 \| 216 \| 0.5155 \| 0.3492 \| 0.5155 \| 0.7180 \| \| No log \| 0.6855 \| 218 \| 0.4663 \| 0.5006 \| 0.4663 \| 0.6828 \| \| No log \| 0.6918 \| 220 \| 0.4621 \| 0.5516 \| 0.4621 \| 0.6798 \| \| No log \| 0.6981 \| 222 \| 0.4470 \| 0.5623 \| 0.4470 \| 0.6686 \| \| No log \| 0.7044 \| 224 \| 0.4992 \| 0.4535 \| 0.4992 \| 0.7065 \| \| No log \| 0.7107 \| 226 \| 0.4630 \| 0.5280 \| 0.4630 \| 0.6805 \| \| No log \| 0.7170 \| 228 \| 0.4813 \| 0.5276 \| 0.4813 \| 0.6937 \| \| No log \| 0.7233 \| 230 \| 0.4967 \| 0.4917 \| 0.4967 \| 0.7048 \| \| No log \| 0.7296 \| 232 \| 0.5387 \| 0.4653 \| 0.5387 \| 0.7340 \| \| No log \| 0.7358 \| 234 \| 0.5568 \| 0.4123 \| 0.5568 \| 0.7462 \| \| No log \| 0.7421 \| 236 \| 0.5762 \| 0.3926 \| 0.5762 \| 0.7591 \| \| No log \| 0.7484 \| 238 \| 0.5757 \| 0.3604 \| 0.5757 \| 0.7587 \| \| No log \| 0.7547 \| 240 \| 0.5917 \| 0.1988 \| 0.5917 \| 0.7692 \| \| No log \| 0.7610 \| 242 \| 0.6619 \| 0.1229 \| 0.6619 \| 0.8135 \| \| No log \| 0.7673 \| 244 \| 0.6446 \| 0.1413 \| 0.6446 \| 0.8029 \| \| No log \| 0.7736 \| 246 \| 0.5670 \| 0.2121 \| 0.5670 \| 0.7530 \| \| No log \| 0.7799 \| 248 \| 0.5287 \| 0.4550 \| 0.5287 \| 0.7271 \| \| No log \| 0.7862 \| 250 \| 0.5835 \| 0.5201 \| 0.5835 \| 0.7639 \| \| No log \| 0.7925 \| 252 \| 0.5566 \| 0.5366 \| 0.5566 \| 0.7460 \| \| No log \| 0.7987 \| 254 \| 0.4833 \| 0.5475 \| 0.4833 \| 0.6952 \| \| No log \| 0.8050 \| 256 \| 0.4625 \| 0.4781 \| 0.4625 \| 0.6801 \| \| No log \| 0.8113 \| 258 \| 0.4996 \| 0.3595 \| 0.4996 \| 0.7068 \| \| No log \| 0.8176 \| 260 \| 0.4938 \| 0.3752 \| 0.4938 \| 0.7027 \| \| No log \| 0.8239 \| 262 \| 0.4648 \| 0.4688 \| 0.4648 \| 0.6818 \| \| No log \| 0.8302 \| 264 \| 0.5235 \| 0.4649 \| 0.5235 \| 0.7236 \| \| No log \| 0.8365 \| 266 \| 0.5261 \| 0.4878 \| 0.5261 \| 0.7253 \| \| No log \| 0.8428 \| 268 \| 0.5084 \| 0.4377 \| 0.5084 \| 0.7130 \| \| No log \| 0.8491 \| 270 \| 0.4691 \| 0.5609 \| 0.4691 \| 0.6849 \| \| No log \| 0.8553 \| 272 \| 0.4405 \| 0.5774 \| 0.4405 \| 0.6637 \| \| No log \| 0.8616 \| 274 \| 0.4312 \| 0.5560 \| 0.4312 \| 0.6567 \| \| No log \| 0.8679 \| 276 \| 0.4357 \| 0.4909 \| 0.4357 \| 0.6601 \| \| No log \| 0.8742 \| 278 \| 0.4706 \| 0.4431 \| 0.4706 \| 0.6860 \| \| No log \| 0.8805 \| 280 \| 0.4540 \| 0.4803 \| 0.4540 \| 0.6738 \| \| No log \| 0.8868 \| 282 \| 0.4660 \| 0.5422 \| 0.4660 \| 0.6827 \| \| No log \| 0.8931 \| 284 \| 0.4745 \| 0.5291 \| 0.4745 \| 0.6888 \| \| No log \| 0.8994 \| 286 \| 0.5117 \| 0.5465 \| 0.5117 \| 0.7154 \| \| No log \| 0.9057 \| 288 \| 0.6159 \| 0.4993 \| 0.6159 \| 0.7848 \| \| No log \| 0.9119 \| 290 \| 0.5713 \| 0.5362 \| 0.5713 \| 0.7558 \| \| No log \| 0.9182 \| 292 \| 0.5096 \| 0.5233 \| 0.5096 \| 0.7138 \| \| No log \| 0.9245 \| 294 \| 0.5283 \| 0.5564 \| 0.5283 \| 0.7269 \| \| No log \| 0.9308 \| 296 \| 0.5306 \| 0.5697 \| 0.5306 \| 0.7284 \| \| No log \| 0.9371 \| 298 \| 0.6002 \| 0.5214 \| 0.6002 \| 0.7747 \| \| No log \| 0.9434 \| 300 \| 0.5728 \| 0.5460 \| 0.5728 \| 0.7568 \| \| No log \| 0.9497 \| 302 \| 0.4807 \| 0.5059 \| 0.4807 \| 0.6933 \| \| No log \| 0.9560 \| 304 \| 0.5446 \| 0.3641 \| 0.5446 \| 0.7380 \| \| No log \| 0.9623 \| 306 \| 0.5320 \| 0.3856 \| 0.5320 \| 0.7294 \| \| No log \| 0.9686 \| 308 \| 0.4741 \| 0.5118 \| 0.4741 \| 0.6886 \| \| No log \| 0.9748 \| 310 \| 0.4723 \| 0.5244 \| 0.4723 \| 0.6872 \| \| No log \| 0.9811 \| 312 \| 0.4768 \| 0.4583 \| 0.4768 \| 0.6905 \| \| No log \| 0.9874 \| 314 \| 0.5082 \| 0.4248 \| 0.5082 \| 0.7129 \| \| No log \| 0.9937 \| 316 \| 0.4580 \| 0.4888 \| 0.4580 \| 0.6768 \| \| No log \| 1.0 \| 318 \| 0.4900 \| 0.5683 \| 0.4900 \| 0.7000 \| \| No log \| 1.0063 \| 320 \| 0.4791 \| 0.5563 \| 0.4791 \| 0.6921 \| \| No log \| 1.0126 \| 322 \| 0.4524 \| 0.5296 \| 0.4524 \| 0.6726 \| \| No log \| 1.0189 \| 324 \| 0.4568 \| 0.4785 \| 0.4568 \| 0.6759 \| \| No log \| 1.0252 \| 326 \| 0.4561 \| 0.5154 \| 0.4561 \| 0.6753 \| \| No log \| 1.0314 \| 328 \| 0.5108 \| 0.5644 \| 0.5108 \| 0.7147 \| \| No log \| 1.0377 \| 330 \| 0.5220 \| 0.5710 \| 0.5220 \| 0.7225 \| \| No log \| 1.0440 \| 332 \| 0.4507 \| 0.5357 \| 0.4507 \| 0.6713 \| \| No log \| 1.0503 \| 334 \| 0.4476 \| 0.4719 \| 0.4476 \| 0.6690 \| \| No log \| 1.0566 \| 336 \| 0.4624 \| 0.5533 \| 0.4624 \| 0.6800 \| \| No log \| 1.0629 \| 338 \| 0.6018 \| 0.5378 \| 0.6018 \| 0.7758 \| \| No log \| 1.0692 \| 340 \| 0.6157 \| 0.5216 \| 0.6157 \| 0.7846 \| \| No log \| 1.0755 \| 342 \| 0.5006 \| 0.5030 \| 0.5006 \| 0.7076 \| \| No log \| 1.0818 \| 344 \| 0.4784 \| 0.4407 \| 0.4784 \| 0.6917 \| \| No log \| 1.0881 \| 346 \| 0.4645 \| 0.4348 \| 0.4645 \| 0.6816 \| \| No log \| 1.0943 \| 348 \| 0.4531 \| 0.4843 \| 0.4531 \| 0.6731 \| \| No log \| 1.1006 \| 350 \| 0.4681 \| 0.5530 \| 0.4681 \| 0.6842 \| \| No log \| 1.1069 \| 352 \| 0.4497 \| 0.5415 \| 0.4497 \| 0.6706 \| \| No log \| 1.1132 \| 354 \| 0.4471 \| 0.5348 \| 0.4471 \| 0.6687 \| \| No log \| 1.1195 \| 356 \| 0.4355 \| 0.4720 \| 0.4355 \| 0.6599 \| \| No log \| 1.1258 \| 358 \| 0.4191 \| 0.4984 \| 0.4191 \| 0.6474 \| \| No log \| 1.1321 \| 360 \| 0.4048 \| 0.5830 \| 0.4048 \| 0.6362 \| \| No log \| 1.1384 \| 362 \| 0.4305 \| 0.5964 \| 0.4305 \| 0.6562 \| \| No log \| 1.1447 \| 364 \| 0.4154 \| 0.5886 \| 0.4154 \| 0.6445 \| \| No log \| 1.1509 \| 366 \| 0.4085 \| 0.5737 \| 0.4085 \| 0.6391 \| \| No log \| 1.1572 \| 368 \| 0.4145 \| 0.5675 \| 0.4145 \| 0.6438 \| \| No log \| 1.1635 \| 370 \| 0.4510 \| 0.5868 \| 0.4510 \| 0.6715 \| \| No log \| 1.1698 \| 372 \| 0.5957 \| 0.5596 \| 0.5957 \| 0.7718 \| \| No log \| 1.1761 \| 374 \| 0.5335 \| 0.5670 \| 0.5335 \| 0.7304 \| \| No log \| 1.1824 \| 376 \| 0.4396 \| 0.5635 \| 0.4396 \| 0.6630 \| \| No log \| 1.1887 \| 378 \| 0.4411 \| 0.5379 \| 0.4411 \| 0.6642 \| \| No log \| 1.1950 \| 380 \| 0.4362 \| 0.5354 \| 0.4362 \| 0.6604 \| \| No log \| 1.2013 \| 382 \| 0.4534 \| 0.5435 \| 0.4534 \| 0.6734 \| \| No log \| 1.2075 \| 384 \| 0.4296 \| 0.5520 \| 0.4296 \| 0.6555 \| \| No log \| 1.2138 \| 386 \| 0.4266 \| 0.5481 \| 0.4266 \| 0.6531 \| \| No log \| 1.2201 \| 388 \| 0.4252 \| 0.5482 \| 0.4252 \| 0.6520 \| \| No log \| 1.2264 \| 390 \| 0.4254 \| 0.5407 \| 0.4254 \| 0.6522 \| \| No log \| 1.2327 \| 392 \| 0.4384 \| 0.5204 \| 0.4384 \| 0.6621 \| \| No log \| 1.2390 \| 394 \| 0.4530 \| 0.4888 \| 0.4530 \| 0.6731 \| \| No log \| 1.2453 \| 396 \| 0.4710 \| 0.5440 \| 0.4710 \| 0.6863 \| \| No log \| 1.2516 \| 398 \| 0.5120 \| 0.5318 \| 0.5120 \| 0.7155 \| \| No log \| 1.2579 \| 400 \| 0.5009 \| 0.5510 \| 0.5009 \| 0.7078 \| \| No log \| 1.2642 \| 402 \| 0.4882 \| 0.5547 \| 0.4882 \| 0.6987 \| \| No log \| 1.2704 \| 404 \| 0.4844 \| 0.5687 \| 0.4844 \| 0.6960 \| \| No log \| 1.2767 \| 406 \| 0.4974 \| 0.5624 \| 0.4974 \| 0.7053 \| \| No log \| 1.2830 \| 408 \| 0.4527 \| 0.5299 \| 0.4527 \| 0.6728 \| \| No log \| 1.2893 \| 410 \| 0.4481 \| 0.5026 \| 0.4481 \| 0.6694 \| \| No log \| 1.2956 \| 412 \| 0.4408 \| 0.5473 \| 0.4408 \| 0.6639 \| \| No log \| 1.3019 \| 414 \| 0.4956 \| 0.6047 \| 0.4956 \| 0.7040 \| \| No log \| 1.3082 \| 416 \| 0.4792 \| 0.6101 \| 0.4792 \| 0.6922 \| \| No log \| 1.3145 \| 418 \| 0.4203 \| 0.5664 \| 0.4203 \| 0.6483 \| \| No log \| 1.3208 \| 420 \| 0.4335 \| 0.5261 \| 0.4335 \| 0.6584 \| \| No log \| 1.3270 \| 422 \| 0.4437 \| 0.5094 \| 0.4437 \| 0.6661 \| \| No log \| 1.3333 \| 424 \| 0.4254 \| 0.5705 \| 0.4254 \| 0.6522 \| \| No log \| 1.3396 \| 426 \| 0.4540 \| 0.5908 \| 0.4540 \| 0.6738 \| \| No log \| 1.3459 \| 428 \| 0.4200 \| 0.5599 \| 0.4200 \| 0.6480 \| \| No log \| 1.3522 \| 430 \| 0.4490 \| 0.4792 \| 0.4490 \| 0.6701 \| \| No log \| 1.3585 \| 432 \| 0.4900 \| 0.4328 \| 0.4900 \| 0.7000 \| \| No log \| 1.3648 \| 434 \| 0.4325 \| 0.5198 \| 0.4325 \| 0.6577 \| \| No log \| 1.3711 \| 436 \| 0.4629 \| 0.5773 \| 0.4629 \| 0.6804 \| \| No log \| 1.3774 \| 438 \| 0.5279 \| 0.6088 \| 0.5279 \| 0.7265 \| \| No log \| 1.3836 \| 440 \| 0.4515 \| 0.5876 \| 0.4515 \| 0.6720 \| \| No log \| 1.3899 \| 442 \| 0.4544 \| 0.4816 \| 0.4544 \| 0.6741 \| \| No log \| 1.3962 \| 444 \| 0.5344 \| 0.3721 \| 0.5344 \| 0.7311 \| \| No log \| 1.4025 \| 446 \| 0.4667 \| 0.4603 \| 0.4667 \| 0.6832 \| \| No log \| 1.4088 \| 448 \| 0.4476 \| 0.5907 \| 0.4476 \| 0.6690 \| \| No log \| 1.4151 \| 450 \| 0.5574 \| 0.5828 \| 0.5574 \| 0.7466 \| \| No log \| 1.4214 \| 452 \| 0.5536 \| 0.5754 \| 0.5536 \| 0.7441 \| \| No log \| 1.4277 \| 454 \| 0.4889 \| 0.5879 \| 0.4889 \| 0.6992 \| \| No log \| 1.4340 \| 456 \| 0.4475 \| 0.5637 \| 0.4475 \| 0.6689 \| \| No log \| 1.4403 \| 458 \| 0.4689 \| 0.5894 \| 0.4689 \| 0.6848 \| \| No log \| 1.4465 \| 460 \| 0.5947 \| 0.6027 \| 0.5947 \| 0.7712 \| \| No log \| 1.4528 \| 462 \| 0.6122 \| 0.6422 \| 0.6122 \| 0.7824 \| \| No log \| 1.4591 \| 464 \| 0.4651 \| 0.6175 \| 0.4651 \| 0.6820 \| \| No log \| 1.4654 \| 466 \| 0.4310 \| 0.5557 \| 0.4310 \| 0.6565 \| \| No log \| 1.4717 \| 468 \| 0.4604 \| 0.5151 \| 0.4604 \| 0.6785 \| \| No log \| 1.4780 \| 470 \| 0.4326 \| 0.5487 \| 0.4326 \| 0.6577 \| \| No log \| 1.4843 \| 472 \| 0.4264 \| 0.5595 \| 0.4264 \| 0.6530 \| \| No log \| 1.4906 \| 474 \| 0.4322 \| 0.5707 \| 0.4322 \| 0.6574 \| \| No log \| 1.4969 \| 476 \| 0.4565 \| 0.5351 \| 0.4565 \| 0.6756 \| \| No log \| 1.5031 \| 478 \| 0.4335 \| 0.5410 \| 0.4335 \| 0.6584 \| \| No log \| 1.5094 \| 480 \| 0.4330 \| 0.6007 \| 0.4330 \| 0.6580 \| \| No log \| 1.5157 \| 482 \| 0.6046 \| 0.6136 \| 0.6046 \| 0.7775 \| \| No log \| 1.5220 \| 484 \| 0.6213 \| 0.5786 \| 0.6213 \| 0.7882 \| \| No log \| 1.5283 \| 486 \| 0.5202 \| 0.5802 \| 0.5202 \| 0.7213 \| \| No log \| 1.5346 \| 488 \| 0.4899 \| 0.5651 \| 0.4899 \| 0.6999 \| \| No log \| 1.5409 \| 490 \| 0.5525 \| 0.5716 \| 0.5525 \| 0.7433 \| \| No log \| 1.5472 \| 492 \| 0.5873 \| 0.5840 \| 0.5873 \| 0.7663 \| \| No log \| 1.5535 \| 494 \| 0.5886 \| 0.5970 \| 0.5886 \| 0.7672 \| \| No log \| 1.5597 \| 496 \| 0.4956 \| 0.5652 \| 0.4956 \| 0.7040 \| \| No log \| 1.5660 \| 498 \| 0.4548 \| 0.5153 \| 0.4548 \| 0.6744 \| \| 0.6168 \| 1.5723 \| 500 \| 0.4578 \| 0.4906 \| 0.4578 \| 0.6766 \| \| 0.6168 \| 1.5786 \| 502 \| 0.4687 \| 0.4650 \| 0.4687 \| 0.6846 \| \| 0.6168 \| 1.5849 \| 504 \| 0.4649 \| 0.5025 \| 0.4649 \| 0.6819 \| \| 0.6168 \| 1.5912 \| 506 \| 0.4787 \| 0.5359 \| 0.4787 \| 0.6919 \| \| 0.6168 \| 1.5975 \| 508 \| 0.5109 \| 0.5803 \| 0.5109 \| 0.7148 \| \| 0.6168 \| 1.6038 \| 510 \| 0.4629 \| 0.5427 \| 0.4629 \| 0.6804 \| \| 0.6168 \| 1.6101 \| 512 \| 0.4511 \| 0.4776 \| 0.4511 \| 0.6716 \| \| 0.6168 \| 1.6164 \| 514 \| 0.4647 \| 0.4382 \| 0.4647 \| 0.6817 \| \| 0.6168 \| 1.6226 \| 516 \| 0.4988 \| 0.3909 \| 0.4988 \| 0.7063 \| \| 0.6168 \| 1.6289 \| 518 \| 0.4708 \| 0.4333 \| 0.4708 \| 0.6862 \| \| 0.6168 \| 1.6352 \| 520 \| 0.4335 \| 0.5524 \| 0.4335 \| 0.6584 \| \| 0.6168 \| 1.6415 \| 522 \| 0.5118 \| 0.6061 \| 0.5118 \| 0.7154 \| \| 0.6168 \| 1.6478 \| 524 \| 0.5394 \| 0.5932 \| 0.5394 \| 0.7344 \| \| 0.6168 \| 1.6541 \| 526 \| 0.4893 \| 0.5836 \| 0.4893 \| 0.6995 \| \| 0.6168 \| 1.6604 \| 528 \| 0.4719 \| 0.5948 \| 0.4719 \| 0.6869 \| \| 0.6168 \| 1.6667 \| 530 \| 0.4779 \| 0.6197 \| 0.4779 \| 0.6913 \| \| 0.6168 \| 1.6730 \| 532 \| 0.5567 \| 0.6521 \| 0.5567 \| 0.7461 \| \| 0.6168 \| 1.6792 \| 534 \| 0.5218 \| 0.6485 \| 0.5218 \| 0.7223 \| \| 0.6168 \| 1.6855 \| 536 \| 0.4417 \| 0.5646 \| 0.4417 \| 0.6646 \| \| 0.6168 \| 1.6918 \| 538 \| 0.4669 \| 0.5291 \| 0.4669 \| 0.6833 \| \| 0.6168 \| 1.6981 \| 540 \| 0.4911 \| 0.5074 \| 0.4911 \| 0.7008 \| \| 0.6168 \| 1.7044 \| 542 \| 0.4456 \| 0.5703 \| 0.4456 \| 0.6675 \| \| 0.6168 \| 1.7107 \| 544 \| 0.5328 \| 0.6591 \| 0.5328 \| 0.7300 \| \| 0.6168 \| 1.7170 \| 546 \| 0.5935 \| 0.6528 \| 0.5935 \| 0.7704 \| \| 0.6168 \| 1.7233 \| 548 \| 0.5023 \| 0.6085 \| 0.5023 \| 0.7087 \| \| 0.6168 \| 1.7296 \| 550 \| 0.4372 \| 0.5448 \| 0.4372 \| 0.6612 \| \| 0.6168 \| 1.7358 \| 552 \| 0.4434 \| 0.5654 \| 0.4434 \| 0.6659 \| \| 0.6168 \| 1.7421 \| 554 \| 0.5137 \| 0.6049 \| 0.5137 \| 0.7167 \| \| 0.6168 \| 1.7484 \| 556 \| 0.5971 \| 0.6242 \| 0.5971 \| 0.7727 \| \| 0.6168 \| 1.7547 \| 558 \| 0.5219 \| 0.6144 \| 0.5219 \| 0.7225 \| \| 0.6168 \| 1.7610 \| 560 \| 0.4464 \| 0.5712 \| 0.4464 \| 0.6681 \| \| 0.6168 \| 1.7673 \| 562 \| 0.4409 \| 0.5542 \| 0.4409 \| 0.6640 \| \| 0.6168 \| 1.7736 \| 564 \| 0.4485 \| 0.5703 \| 0.4485 \| 0.6697 \| \| 0.6168 \| 1.7799 \| 566 \| 0.5082 \| 0.6321 \| 0.5082 \| 0.7129 \| \| 0.6168 \| 1.7862 \| 568 \| 0.4859 \| 0.6142 \| 0.4859 \| 0.6970 \| \| 0.6168 \| 1.7925 \| 570 \| 0.4397 \| 0.5736 \| 0.4397 \| 0.6631 \| \| 0.6168 \| 1.7987 \| 572 \| 0.4426 \| 0.5451 \| 0.4426 \| 0.6653 \| \| 0.6168 \| 1.8050 \| 574 \| 0.4499 \| 0.5175 \| 0.4499 \| 0.6708 \| \| 0.6168 \| 1.8113 \| 576 \| 0.4461 \| 0.5466 \| 0.4461 \| 0.6679 \| \| 0.6168 \| 1.8176 \| 578 \| 0.4628 \| 0.6349 \| 0.4628 \| 0.6803 \| \| 0.6168 \| 1.8239 \| 580 \| 0.5017 \| 0.6687 \| 0.5017 \| 0.7083 \| \| 0.6168 \| 1.8302 \| 582 \| 0.4434 \| 0.6080 \| 0.4434 \| 0.6659 \| \| 0.6168 \| 1.8365 \| 584 \| 0.4401 \| 0.5153 \| 0.4401 \| 0.6634 \| \| 0.6168 \| 1.8428 \| 586 \| 0.4543 \| 0.4893 \| 0.4543 \| 0.6740 \| \| 0.6168 \| 1.8491 \| 588 \| 0.4335 \| 0.5541 \| 0.4335 \| 0.6584 \| \| 0.6168 \| 1.8553 \| 590 \| 0.5906 \| 0.6594 \| 0.5906 \| 0.7685 \| \| 0.6168 \| 1.8616 \| 592 \| 0.8147 \| 0.6481 \| 0.8147 \| 0.9026 \| \| 0.6168 \| 1.8679 \| 594 \| 0.7159 \| 0.6446 \| 0.7159 \| 0.8461 \| \| 0.6168 \| 1.8742 \| 596 \| 0.4781 \| 0.6088 \| 0.4781 \| 0.6915 \| \| 0.6168 \| 1.8805 \| 598 \| 0.4439 \| 0.4836 \| 0.4439 \| 0.6662 \| \| 0.6168 \| 1.8868 \| 600 \| 0.4566 \| 0.4557 \| 0.4566 \| 0.6757 \| \| 0.6168 \| 1.8931 \| 602 \| 0.4287 \| 0.5373 \| 0.4287 \| 0.6547 \| \| 0.6168 \| 1.8994 \| 604 \| 0.4456 \| 0.5949 \| 0.4456 \| 0.6675 \| \| 0.6168 \| 1.9057 \| 606 \| 0.4832 \| 0.6382 \| 0.4832 \| 0.6952 \| \| 0.6168 \| 1.9119 \| 608 \| 0.4698 \| 0.6393 \| 0.4698 \| 0.6855 \| \| 0.6168 \| 1.9182 \| 610 \| 0.4329 \| 0.5806 \| 0.4329 \| 0.6580 \| \| 0.6168 \| 1.9245 \| 612 \| 0.4489 \| 0.6122 \| 0.4489 \| 0.6700 \| \| 0.6168 \| 1.9308 \| 614 \| 0.4779 \| 0.6377 \| 0.4779 \| 0.6913 \| \| 0.6168 \| 1.9371 \| 616 \| 0.4334 \| 0.5817 \| 0.4334 \| 0.6583 \| \| 0.6168 \| 1.9434 \| 618 \| 0.4404 \| 0.5204 \| 0.4404 \| 0.6636 \| \| 0.6168 \| 1.9497 \| 620 \| 0.4414 \| 0.5303 \| 0.4414 \| 0.6644 \| \| 0.6168 \| 1.9560 \| 622 \| 0.4488 \| 0.6036 \| 0.4488 \| 0.6699 \| \| 0.6168 \| 1.9623 \| 624 \| 0.4846 \| 0.6425 \| 0.4846 \| 0.6961 \| \| 0.6168 \| 1.9686 \| 626 \| 0.4593 \| 0.6147 \| 0.4593 \| 0.6777 \| \| 0.6168 \| 1.9748 \| 628 \| 0.4434 \| 0.5229 \| 0.4434 \| 0.6659 \| \| 0.6168 \| 1.9811 \| 630 \| 0.5029 \| 0.4279 \| 0.5029 \| 0.7092 \| \| 0.6168 \| 1.9874 \| 632 \| 0.4739 \| 0.4619 \| 0.4739 \| 0.6884 \| \| 0.6168 \| 1.9937 \| 634 \| 0.4356 \| 0.5404 \| 0.4356 \| 0.6600 \| \| 0.6168 \| 2.0 \| 636 \| 0.4431 \| 0.5583 \| 0.4431 \| 0.6657 \| \| 0.6168 \| 2.0063 \| 638 \| 0.4482 \| 0.5137 \| 0.4482 \| 0.6695 \| \| 0.6168 \| 2.0126 \| 640 \| 0.4698 \| 0.4867 \| 0.4698 \| 0.6854 \| \| 0.6168 \| 2.0189 \| 642 \| 0.4694 \| 0.5070 \| 0.4694 \| 0.6851 \| \| 0.6168 \| 2.0252 \| 644 \| 0.4760 \| 0.5115 \| 0.4760 \| 0.6899 \| \| 0.6168 \| 2.0314 \| 646 \| 0.4932 \| 0.5436 \| 0.4932 \| 0.7023 \| \| 0.6168 \| 2.0377 \| 648 \| 0.4959 \| 0.5361 \| 0.4959 \| 0.7042 \| \| 0.6168 \| 2.0440 \| 650 \| 0.5141 \| 0.5522 \| 0.5141 \| 0.7170 \| \| 0.6168 \| 2.0503 \| 652 \| 0.5923 \| 0.6191 \| 0.5923 \| 0.7696 \| \| 0.6168 \| 2.0566 \| 654 \| 0.5373 \| 0.6122 \| 0.5373 \| 0.7330 \| \| 0.6168 \| 2.0629 \| 656 \| 0.4557 \| 0.5540 \| 0.4557 \| 0.6750 \| \| 0.6168 \| 2.0692 \| 658 \| 0.4540 \| 0.5032 \| 0.4540 \| 0.6738 \| \| 0.6168 \| 2.0755 \| 660 \| 0.4465 \| 0.5682 \| 0.4465 \| 0.6682 \| \| 0.6168 \| 2.0818 \| 662 \| 0.4612 \| 0.5993 \| 0.4612 \| 0.6791 \| \| 0.6168 \| 2.0881 \| 664 \| 0.4772 \| 0.6044 \| 0.4772 \| 0.6908 \| \| 0.6168 \| 2.0943 \| 666 \| 0.4469 \| 0.5729 \| 0.4469 \| 0.6685 \| \| 0.6168 \| 2.1006 \| 668 \| 0.4566 \| 0.5216 \| 0.4566 \| 0.6757 \| \| 0.6168 \| 2.1069 \| 670 \| 0.4556 \| 0.5326 \| 0.4556 \| 0.6750 \| \| 0.6168 \| 2.1132 \| 672 \| 0.4481 \| 0.5748 \| 0.4481 \| 0.6694 \| \| 0.6168 \| 2.1195 \| 674 \| 0.4865 \| 0.6066 \| 0.4865 \| 0.6975 \| \| 0.6168 \| 2.1258 \| 676 \| 0.4763 \| 0.6105 \| 0.4763 \| 0.6901 \| \| 0.6168 \| 2.1321 \| 678 \| 0.4473 \| 0.5493 \| 0.4473 \| 0.6688 \| \| 0.6168 \| 2.1384 \| 680 \| 0.4496 \| 0.5392 \| 0.4496 \| 0.6705 \| \| 0.6168 \| 2.1447 \| 682 \| 0.4709 \| 0.6130 \| 0.4709 \| 0.6862 \| \| 0.6168 \| 2.1509 \| 684 \| 0.6828 \| 0.6267 \| 0.6828 \| 0.8263 \| \| 0.6168 \| 2.1572 \| 686 \| 0.8103 \| 0.6521 \| 0.8103 \| 0.9002 \| \| 0.6168 \| 2.1635 \| 688 \| 0.6630 \| 0.6061 \| 0.6630 \| 0.8143 \| \| 0.6168 \| 2.1698 \| 690 \| 0.4662 \| 0.6020 \| 0.4662 \| 0.6828 \| \| 0.6168 \| 2.1761 \| 692 \| 0.4516 \| 0.5239 \| 0.4516 \| 0.6720 \| \| 0.6168 \| 2.1824 \| 694 \| 0.4492 \| 0.5802 \| 0.4492 \| 0.6702 \| \| 0.6168 \| 2.1887 \| 696 \| 0.4976 \| 0.6040 \| 0.4976 \| 0.7054 \| \| 0.6168 \| 2.1950 \| 698 \| 0.6299 \| 0.6332 \| 0.6299 \| 0.7937 \| \| 0.6168 \| 2.2013 \| 700 \| 0.6688 \| 0.6466 \| 0.6688 \| 0.8178 \| \| 0.6168 \| 2.2075 \| 702 \| 0.5166 \| 0.6479 \| 0.5166 \| 0.7187 \| \| 0.6168 \| 2.2138 \| 704 \| 0.4484 \| 0.5805 \| 0.4484 \| 0.6697 \| \| 0.6168 \| 2.2201 \| 706 \| 0.4544 \| 0.5440 \| 0.4544 \| 0.6741 \| \| 0.6168 \| 2.2264 \| 708 \| 0.4569 \| 0.5398 \| 0.4569 \| 0.6760 \| \| 0.6168 \| 2.2327 \| 710 \| 0.4531 \| 0.5747 \| 0.4531 \| 0.6731 \| \| 0.6168 \| 2.2390 \| 712 \| 0.4682 \| 0.6000 \| 0.4682 \| 0.6842 \| \| 0.6168 \| 2.2453 \| 714 \| 0.5003 \| 0.6096 \| 0.5003 \| 0.7074 \| \| 0.6168 \| 2.2516 \| 716 \| 0.5291 \| 0.5905 \| 0.5291 \| 0.7274 \| \| 0.6168 \| 2.2579 \| 718 \| 0.4991 \| 0.5957 \| 0.4991 \| 0.7065 \| \| 0.6168 \| 2.2642 \| 720 \| 0.5197 \| 0.5879 \| 0.5197 \| 0.7209 \| \| 0.6168 \| 2.2704 \| 722 \| 0.4952 \| 0.5493 \| 0.4952 \| 0.7037 \| \| 0.6168 \| 2.2767 \| 724 \| 0.5032 \| 0.5694 \| 0.5032 \| 0.7093 \| \| 0.6168 \| 2.2830 \| 726 \| 0.5523 \| 0.5756 \| 0.5523 \| 0.7431 \| \| 0.6168 \| 2.2893 \| 728 \| 0.6089 \| 0.6209 \| 0.6089 \| 0.7803 \| \| 0.6168 \| 2.2956 \| 730 \| 0.5343 \| 0.6081 \| 0.5343 \| 0.7309 \| \| 0.6168 \| 2.3019 \| 732 \| 0.4702 \| 0.5385 \| 0.4702 \| 0.6857 \| \| 0.6168 \| 2.3082 \| 734 \| 0.4921 \| 0.4988 \| 0.4921 \| 0.7015 \| \| 0.6168 \| 2.3145 \| 736 \| 0.4682 \| 0.5508 \| 0.4682 \| 0.6843 \| \| 0.6168 \| 2.3208 \| 738 \| 0.4779 \| 0.5928 \| 0.4779 \| 0.6913 \| \| 0.6168 \| 2.3270 \| 740 \| 0.4663 \| 0.5907 \| 0.4663 \| 0.6828 \| \| 0.6168 \| 2.3333 \| 742 \| 0.4595 \| 0.5644 \| 0.4595 \| 0.6779 \| \| 0.6168 \| 2.3396 \| 744 \| 0.4796 \| 0.4895 \| 0.4796 \| 0.6926 \| \| 0.6168 \| 2.3459 \| 746 \| 0.4608 \| 0.5578 \| 0.4608 \| 0.6788 \| \| 0.6168 \| 2.3522 \| 748 \| 0.4919 \| 0.6168 \| 0.4919 \| 0.7013 \| \| 0.6168 \| 2.3585 \| 750 \| 0.4988 \| 0.6122 \| 0.4988 \| 0.7063 \| \| 0.6168 \| 2.3648 \| 752 \| 0.4754 \| 0.5926 \| 0.4754 \| 0.6895 \| \| 0.6168 \| 2.3711 \| 754 \| 0.4788 \| 0.5962 \| 0.4788 \| 0.6919 \| \| 0.6168 \| 2.3774 \| 756 \| 0.5472 \| 0.6241 \| 0.5472 \| 0.7397 \| \| 0.6168 \| 2.3836 \| 758 \| 0.5343 \| 0.6358 \| 0.5343 \| 0.7309 \| \| 0.6168 \| 2.3899 \| 760 \| 0.5494 \| 0.6484 \| 0.5494 \| 0.7412 \| \| 0.6168 \| 2.3962 \| 762 \| 0.6423 \| 0.6621 \| 0.6423 \| 0.8014 \| \| 0.6168 \| 2.4025 \| 764 \| 0.5970 \| 0.6302 \| 0.5970 \| 0.7726 \| \| 0.6168 \| 2.4088 \| 766 \| 0.4743 \| 0.5986 \| 0.4743 \| 0.6887 \| \| 0.6168 \| 2.4151 \| 768 \| 0.4533 \| 0.5541 \| 0.4533 \| 0.6733 \| \| 0.6168 \| 2.4214 \| 770 \| 0.4515 \| 0.5663 \| 0.4515 \| 0.6720 \| \| 0.6168 \| 2.4277 \| 772 \| 0.4737 \| 0.5937 \| 0.4737 \| 0.6883 \| \| 0.6168 \| 2.4340 \| 774 \| 0.4717 \| 0.5974 \| 0.4717 \| 0.6868 \| \| 0.6168 \| 2.4403 \| 776 \| 0.4551 \| 0.5597 \| 0.4551 \| 0.6746 \| \| 0.6168 \| 2.4465 \| 778 \| 0.4473 \| 0.5481 \| 0.4473 \| 0.6688 \| \| 0.6168 \| 2.4528 \| 780 \| 0.4460 \| 0.5399 \| 0.4460 \| 0.6678 \| \| 0.6168 \| 2.4591 \| 782 \| 0.4570 \| 0.5819 \| 0.4570 \| 0.6760 \| \| 0.6168 \| 2.4654 \| 784 \| 0.4882 \| 0.6198 \| 0.4882 \| 0.6987 \| \| 0.6168 \| 2.4717 \| 786 \| 0.4643 \| 0.5972 \| 0.4643 \| 0.6814 \| \| 0.6168 \| 2.4780 \| 788 \| 0.4631 \| 0.5362 \| 0.4631 \| 0.6805 \| \| 0.6168 \| 2.4843 \| 790 \| 0.4646 \| 0.5431 \| 0.4646 \| 0.6816 \| \| 0.6168 \| 2.4906 \| 792 \| 0.4667 \| 0.5166 \| 0.4667 \| 0.6831 \| \| 0.6168 \| 2.4969 \| 794 \| 0.4569 \| 0.5557 \| 0.4569 \| 0.6760 \| \| 0.6168 \| 2.5031 \| 796 \| 0.4888 \| 0.6104 \| 0.4888 \| 0.6991 \| \| 0.6168 \| 2.5094 \| 798 \| 0.5187 \| 0.6351 \| 0.5187 \| 0.7202 \| \| 0.6168 \| 2.5157 \| 800 \| 0.4694 \| 0.5874 \| 0.4694 \| 0.6851 \| \| 0.6168 \| 2.5220 \| 802 \| 0.4601 \| 0.4851 \| 0.4601 \| 0.6783 \| \| 0.6168 \| 2.5283 \| 804 \| 0.4941 \| 0.4234 \| 0.4941 \| 0.7029 \| \| 0.6168 \| 2.5346 \| 806 \| 0.4711 \| 0.4523 \| 0.4711 \| 0.6864 \| \| 0.6168 \| 2.5409 \| 808 \| 0.5131 \| 0.6013 \| 0.5131 \| 0.7163 \| \| 0.6168 \| 2.5472 \| 810 \| 0.6076 \| 0.6146 \| 0.6076 \| 0.7795 \| \| 0.6168 \| 2.5535 \| 812 \| 0.5727 \| 0.5899 \| 0.5727 \| 0.7568 \| \| 0.6168 \| 2.5597 \| 814 \| 0.5201 \| 0.5752 \| 0.5201 \| 0.7212 \| \| 0.6168 \| 2.5660 \| 816 \| 0.5087 \| 0.5671 \| 0.5087 \| 0.7132 \| \| 0.6168 \| 2.5723 \| 818 \| 0.4889 \| 0.5301 \| 0.4889 \| 0.6992 \| \| 0.6168 \| 2.5786 \| 820 \| 0.4882 \| 0.5239 \| 0.4882 \| 0.6987 \| \| 0.6168 \| 2.5849 \| 822 \| 0.4822 \| 0.5388 \| 0.4822 \| 0.6944 \| \| 0.6168 \| 2.5912 \| 824 \| 0.4752 \| 0.5557 \| 0.4752 \| 0.6894 \| \| 0.6168 \| 2.5975 \| 826 \| 0.4755 \| 0.5177 \| 0.4755 \| 0.6895 \| \| 0.6168 \| 2.6038 \| 828 \| 0.4695 \| 0.5348 \| 0.4695 \| 0.6852 \| \| 0.6168 \| 2.6101 \| 830 \| 0.4779 \| 0.6109 \| 0.4779 \| 0.6913 \| \| 0.6168 \| 2.6164 \| 832 \| 0.4690 \| 0.6106 \| 0.4690 \| 0.6849 \| \| 0.6168 \| 2.6226 \| 834 \| 0.4478 \| 0.5889 \| 0.4478 \| 0.6692 \| \| 0.6168 \| 2.6289 \| 836 \| 0.4370 \| 0.5301 \| 0.4370 \| 0.6610 \| \| 0.6168 \| 2.6352 \| 838 \| 0.4351 \| 0.5446 \| 0.4351 \| 0.6596 \| \| 0.6168 \| 2.6415 \| 840 \| 0.4627 \| 0.6075 \| 0.4627 \| 0.6802 \| \| 0.6168 \| 2.6478 \| 842 \| 0.4984 \| 0.6086 \| 0.4984 \| 0.7060 \| \| 0.6168 \| 2.6541 \| 844 \| 0.5112 \| 0.6214 \| 0.5112 \| 0.7150 \| \| 0.6168 \| 2.6604 \| 846 \| 0.4755 \| 0.6191 \| 0.4755 \| 0.6896 \| \| 0.6168 \| 2.6667 \| 848 \| 0.4469 \| 0.5968 \| 0.4469 \| 0.6685 \| \| 0.6168 \| 2.6730 \| 850 \| 0.4566 \| 0.6065 \| 0.4566 \| 0.6757 \| \| 0.6168 \| 2.6792 \| 852 \| 0.5356 \| 0.6601 \| 0.5356 \| 0.7319 \| \| 0.6168 \| 2.6855 \| 854 \| 0.5851 \| 0.6592 \| 0.5851 \| 0.7649 \| \| 0.6168 \| 2.6918 \| 856 \| 0.5472 \| 0.6549 \| 0.5472 \| 0.7397 \| \| 0.6168 \| 2.6981 \| 858 \| 0.5496 \| 0.6657 \| 0.5496 \| 0.7413 \| \| 0.6168 \| 2.7044 \| 860 \| 0.5095 \| 0.6290 \| 0.5095 \| 0.7138 \| \| 0.6168 \| 2.7107 \| 862 \| 0.5340 \| 0.6657 \| 0.5340 \| 0.7308 \| \| 0.6168 \| 2.7170 \| 864 \| 0.4919 \| 0.6255 \| 0.4919 \| 0.7014 \| \| 0.6168 \| 2.7233 \| 866 \| 0.5321 \| 0.6314 \| 0.5321 \| 0.7294 \| \| 0.6168 \| 2.7296 \| 868 \| 0.5331 \| 0.6205 \| 0.5331 \| 0.7301 \| \| 0.6168 \| 2.7358 \| 870 \| 0.4911 \| 0.6002 \| 0.4911 \| 0.7008 \| \| 0.6168 \| 2.7421 \| 872 \| 0.4601 \| 0.5945 \| 0.4601 \| 0.6783 \| \| 0.6168 \| 2.7484 \| 874 \| 0.4646 \| 0.6022 \| 0.4646 \| 0.6816 \| \| 0.6168 \| 2.7547 \| 876 \| 0.4638 \| 0.6107 \| 0.4638 \| 0.6810 \| \| 0.6168 \| 2.7610 \| 878 \| 0.4979 \| 0.6217 \| 0.4979 \| 0.7056 \| \| 0.6168 \| 2.7673 \| 880 \| 0.4824 \| 0.6171 \| 0.4824 \| 0.6945 \| \| 0.6168 \| 2.7736 \| 882 \| 0.4717 \| 0.6174 \| 0.4717 \| 0.6868 \| \| 0.6168 \| 2.7799 \| 884 \| 0.5750 \| 0.6513 \| 0.5750 \| 0.7583 \| \| 0.6168 \| 2.7862 \| 886 \| 0.6010 \| 0.6408 \| 0.6010 \| 0.7752 \| \| 0.6168 \| 2.7925 \| 888 \| 0.5237 \| 0.6295 \| 0.5237 \| 0.7236 \| \| 0.6168 \| 2.7987 \| 890 \| 0.5266 \| 0.6235 \| 0.5266 \| 0.7257 \| \| 0.6168 \| 2.8050 \| 892 \| 0.4882 \| 0.6032 \| 0.4882 \| 0.6987 \| \| 0.6168 \| 2.8113 \| 894 \| 0.4529 \| 0.5806 \| 0.4529 \| 0.6730 \| \| 0.6168 \| 2.8176 \| 896 \| 0.4637 \| 0.5802 \| 0.4637 \| 0.6810 \| \| 0.6168 \| 2.8239 \| 898 \| 0.5794 \| 0.6391 \| 0.5794 \| 0.7612 \| \| 0.6168 \| 2.8302 \| 900 \| 0.6045 \| 0.6505 \| 0.6045 \| 0.7775 \| \| 0.6168 \| 2.8365 \| 902 \| 0.4849 \| 0.6172 \| 0.4849 \| 0.6964 \| \| 0.6168 \| 2.8428 \| 904 \| 0.4473 \| 0.5701 \| 0.4473 \| 0.6688 \| \| 0.6168 \| 2.8491 \| 906 \| 0.4564 \| 0.5158 \| 0.4564 \| 0.6756 \| \| 0.6168 \| 2.8553 \| 908 \| 0.4440 \| 0.5683 \| 0.4440 \| 0.6663 \| \| 0.6168 \| 2.8616 \| 910 \| 0.4760 \| 0.6210 \| 0.4760 \| 0.6899 \| \| 0.6168 \| 2.8679 \| 912 \| 0.4973 \| 0.6063 \| 0.4973 \| 0.7052 \| \| 0.6168 \| 2.8742 \| 914 \| 0.4613 \| 0.5769 \| 0.4613 \| 0.6792 \| \| 0.6168 \| 2.8805 \| 916 \| 0.4627 \| 0.5601 \| 0.4627 \| 0.6802 \| \| 0.6168 \| 2.8868 \| 918 \| 0.4902 \| 0.5810 \| 0.4902 \| 0.7001 \| \| 0.6168 \| 2.8931 \| 920 \| 0.5110 \| 0.5978 \| 0.5110 \| 0.7148 \| \| 0.6168 \| 2.8994 \| 922 \| 0.4982 \| 0.5263 \| 0.4982 \| 0.7059 \| \| 0.6168 \| 2.9057 \| 924 \| 0.5189 \| 0.4791 \| 0.5189 \| 0.7204 \| \| 0.6168 \| 2.9119 \| 926 \| 0.5141 \| 0.4919 \| 0.5141 \| 0.7170 \| \| 0.6168 \| 2.9182 \| 928 \| 0.5133 \| 0.5588 \| 0.5133 \| 0.7165 \| \| 0.6168 \| 2.9245 \| 930 \| 0.5122 \| 0.5844 \| 0.5122 \| 0.7157 \| \| 0.6168 \| 2.9308 \| 932 \| 0.5012 \| 0.5188 \| 0.5012 \| 0.7080 \| \| 0.6168 \| 2.9371 \| 934 \| 0.4874 \| 0.5313 \| 0.4874 \| 0.6981 \| \| 0.6168 \| 2.9434 \| 936 \| 0.4737 \| 0.5710 \| 0.4737 \| 0.6883 \| \| 0.6168 \| 2.9497 \| 938 \| 0.5110 \| 0.6227 \| 0.5110 \| 0.7149 \| \| 0.6168 \| 2.9560 \| 940 \| 0.4784 \| 0.6200 \| 0.4784 \| 0.6916 \| \| 0.6168 \| 2.9623 \| 942 \| 0.4436 \| 0.5909 \| 0.4436 \| 0.6660 \| \| 0.6168 \| 2.9686 \| 944 \| 0.4353 \| 0.5637 \| 0.4353 \| 0.6598 \| \| 0.6168 \| 2.9748 \| 946 \| 0.4419 \| 0.6045 \| 0.4419 \| 0.6648 \| \| 0.6168 \| 2.9811 \| 948 \| 0.4552 \| 0.6259 \| 0.4552 \| 0.6747 \| \| 0.6168 \| 2.9874 \| 950 \| 0.4415 \| 0.6037 \| 0.4415 \| 0.6644 \| \| 0.6168 \| 2.9937 \| 952 \| 0.4334 \| 0.5655 \| 0.4334 \| 0.6583 \| \| 0.6168 \| 3.0 \| 954 \| 0.4329 \| 0.5599 \| 0.4329 \| 0.6579 \| \| 0.6168 \| 3.0063 \| 956 \| 0.4388 \| 0.5950 \| 0.4388 \| 0.6624 \| \| 0.6168 \| 3.0126 \| 958 \| 0.5174 \| 0.6305 \| 0.5174 \| 0.7193 \| \| 0.6168 \| 3.0189 \| 960 \| 0.6504 \| 0.6523 \| 0.6504 \| 0.8065 \| \| 0.6168 \| 3.0252 \| 962 \| 0.5995 \| 0.6266 \| 0.5995 \| 0.7743 \| \| 0.6168 \| 3.0314 \| 964 \| 0.4915 \| 0.6136 \| 0.4915 \| 0.7011 \| \| 0.6168 \| 3.0377 \| 966 \| 0.5021 \| 0.6101 \| 0.5021 \| 0.7086 \| \| 0.6168 \| 3.0440 \| 968 \| 0.5449 \| 0.6237 \| 0.5449 \| 0.7382 \| \| 0.6168 \| 3.0503 \| 970 \| 0.5088 \| 0.5658 \| 0.5088 \| 0.7133 \| \| 0.6168 \| 3.0566 \| 972 \| 0.5037 \| 0.5307 \| 0.5037 \| 0.7097 \| \| 0.6168 \| 3.0629 \| 974 \| 0.5258 \| 0.4603 \| 0.5258 \| 0.7251 \| \| 0.6168 \| 3.0692 \| 976 \| 0.5263 \| 0.4741 \| 0.5263 \| 0.7255 \| \| 0.6168 \| 3.0755 \| 978 \| 0.5189 \| 0.5580 \| 0.5189 \| 0.7203 \| \| 0.6168 \| 3.0818 \| 980 \| 0.5218 \| 0.5977 \| 0.5218 \| 0.7223 \| \| 0.6168 \| 3.0881 \| 982 \| 0.4925 \| 0.5705 \| 0.4925 \| 0.7018 \| \| 0.6168 \| 3.0943 \| 984 \| 0.5247 \| 0.4499 \| 0.5247 \| 0.7243 \| \| 0.6168 \| 3.1006 \| 986 \| 0.5441 \| 0.4333 \| 0.5441 \| 0.7376 \| \| 0.6168 \| 3.1069 \| 988 \| 0.4828 \| 0.4792 \| 0.4828 \| 0.6948 \| \| 0.6168 \| 3.1132 \| 990 \| 0.4882 \| 0.6114 \| 0.4882 \| 0.6987 \| \| 0.6168 \| 3.1195 \| 992 \| 0.6132 \| 0.6336 \| 0.6132 \| 0.7831 \| \| 0.6168 \| 3.1258 \| 994 \| 0.5768 \| 0.6112 \| 0.5768 \| 0.7595 \| \| 0.6168 \| 3.1321 \| 996 \| 0.4734 \| 0.5712 \| 0.4734 \| 0.6880 \| \| 0.6168 \| 3.1384 \| 998 \| 0.4728 \| 0.4874 \| 0.4728 \| 0.6876 \| \| 0.2191 \| 3.1447 \| 1000 \| 0.4709 \| 0.4862 \| 0.4709 \| 0.6862 \| \| 0.2191 \| 3.1509 \| 1002 \| 0.4621 \| 0.5558 \| 0.4621 \| 0.6798 \| \| 0.2191 \| 3.1572 \| 1004 \| 0.4894 \| 0.5871 \| 0.4894 \| 0.6996 \| \| 0.2191 \| 3.1635 \| 1006 \| 0.4857 \| 0.5880 \| 0.4857 \| 0.6969 \| \| 0.2191 \| 3.1698 \| 1008 \| 0.4703 \| 0.5338 \| 0.4703 \| 0.6858 \| \| 0.2191 \| 3.1761 \| 1010 \| 0.4707 \| 0.5164 \| 0.4707 \| 0.6861 \| \| 0.2191 \| 3.1824 \| 1012 \| 0.4615 \| 0.5635 \| 0.4615 \| 0.6794 \| \| 0.2191 \| 3.1887 \| 1014 \| 0.5353 \| 0.6266 \| 0.5353 \| 0.7316 \| \| 0.2191 \| 3.1950 \| 1016 \| 0.6134 \| 0.6373 \| 0.6134 \| 0.7832 \| \| 0.2191 \| 3.2013 \| 1018 \| 0.5485 \| 0.6253 \| 0.5485 \| 0.7406 \| \| 0.2191 \| 3.2075 \| 1020 \| 0.4671 \| 0.5818 \| 0.4671 \| 0.6835 \| \| 0.2191 \| 3.2138 \| 1022 \| 0.4729 \| 0.5298 \| 0.4729 \| 0.6877 \| \| 0.2191 \| 3.2201 \| 1024 \| 0.4646 \| 0.5701 \| 0.4646 \| 0.6816 \| \| 0.2191 \| 3.2264 \| 1026 \| 0.5196 \| 0.6276 \| 0.5196 \| 0.7209 \| \| 0.2191 \| 3.2327 \| 1028 \| 0.5914 \| 0.6449 \| 0.5914 \| 0.7690 \| \| 0.2191 \| 3.2390 \| 1030 \| 0.5345 \| 0.6325 \| 0.5345 \| 0.7311 \| \| 0.2191 \| 3.2453 \| 1032 \| 0.4740 \| 0.5896 \| 0.4740 \| 0.6885 \| \| 0.2191 \| 3.2516 \| 1034 \| 0.4757 \| 0.6038 \| 0.4757 \| 0.6897 \| \| 0.2191 \| 3.2579 \| 1036 \| 0.4797 \| 0.6070 \| 0.4797 \| 0.6926 \| \| 0.2191 \| 3.2642 \| 1038 \| 0.5105 \| 0.6500 \| 0.5105 \| 0.7145 \| \| 0.2191 \| 3.2704 \| 1040 \| 0.5650 \| 0.6644 \| 0.5650 \| 0.7517 \| \| 0.2191 \| 3.2767 \| 1042 \| 0.5207 \| 0.6509 \| 0.5207 \| 0.7216 \| \| 0.2191 \| 3.2830 \| 1044 \| 0.4863 \| 0.5506 \| 0.4863 \| 0.6973 \| \| 0.2191 \| 3.2893 \| 1046 \| 0.5195 \| 0.4940 \| 0.5195 \| 0.7208 \| \| 0.2191 \| 3.2956 \| 1048 \| 0.4869 \| 0.5194 \| 0.4869 \| 0.6978 \| \| 0.2191 \| 3.3019 \| 1050 \| 0.5009 \| 0.6426 \| 0.5009 \| 0.7078 \| \| 0.2191 \| 3.3082 \| 1052 \| 0.5457 \| 0.6660 \| 0.5457 \| 0.7387 \| \| 0.2191 \| 3.3145 \| 1054 \| 0.5233 \| 0.6402 \| 0.5233 \| 0.7234 \| \| 0.2191 \| 3.3208 \| 1056 \| 0.4881 \| 0.6002 \| 0.4881 \| 0.6986 \| \| 0.2191 \| 3.3270 \| 1058 \| 0.5018 \| 0.6009 \| 0.5018 \| 0.7084 \| \| 0.2191 \| 3.3333 \| 1060 \| 0.5207 \| 0.6291 \| 0.5207 \| 0.7216 \| \| 0.2191 \| 3.3396 \| 1062 \| 0.5560 \| 0.6547 \| 0.5560 \| 0.7456 \| \| 0.2191 \| 3.3459 \| 1064 \| 0.5198 \| 0.6343 \| 0.5198 \| 0.7210 \| \| 0.2191 \| 3.3522 \| 1066 \| 0.4909 \| 0.5900 \| 0.4909 \| 0.7006 \| \| 0.2191 \| 3.3585 \| 1068 \| 0.4876 \| 0.5475 \| 0.4876 \| 0.6983 \| \| 0.2191 \| 3.3648 \| 1070 \| 0.4872 \| 0.5630 \| 0.4872 \| 0.6980 \| \| 0.2191 \| 3.3711 \| 1072 \| 0.4876 \| 0.5919 \| 0.4876 \| 0.6983 \| \| 0.2191 \| 3.3774 \| 1074 \| 0.4887 \| 0.5430 \| 0.4887 \| 0.6991 \| \| 0.2191 \| 3.3836 \| 1076 \| 0.4853 \| 0.5461 \| 0.4853 \| 0.6966 \| \| 0.2191 \| 3.3899 \| 1078 \| 0.4850 \| 0.5996 \| 0.4850 \| 0.6964 \| \| 0.2191 \| 3.3962 \| 1080 \| 0.4844 \| 0.5939 \| 0.4844 \| 0.6960 \| \| 0.2191 \| 3.4025 \| 1082 \| 0.4808 \| 0.5413 \| 0.4808 \| 0.6934 \| \| 0.2191 \| 3.4088 \| 1084 \| 0.4776 \| 0.5428 \| 0.4776 \| 0.6911 \| \| 0.2191 \| 3.4151 \| 1086 \| 0.4739 \| 0.5833 \| 0.4739 \| 0.6884 \| \| 0.2191 \| 3.4214 \| 1088 \| 0.5182 \| 0.6022 \| 0.5182 \| 0.7199 \| \| 0.2191 \| 3.4277 \| 1090 \| 0.5028 \| 0.5787 \| 0.5028 \| 0.7091 \| \| 0.2191 \| 3.4340 \| 1092 \| 0.4934 \| 0.5866 \| 0.4934 \| 0.7024 \| \| 0.2191 \| 3.4403 \| 1094 \| 0.4849 \| 0.5311 \| 0.4849 \| 0.6964 \| \| 0.2191 \| 3.4465 \| 1096 \| 0.4987 \| 0.5814 \| 0.4987 \| 0.7062 \| \| 0.2191 \| 3.4528 \| 1098 \| 0.5297 \| 0.5906 \| 0.5297 \| 0.7278 \| \| 0.2191 \| 3.4591 \| 1100 \| 0.4967 \| 0.5850 \| 0.4967 \| 0.7047 \| \| 0.2191 \| 3.4654 \| 1102 \| 0.4799 \| 0.5463 \| 0.4799 \| 0.6927 \| \| 0.2191 \| 3.4717 \| 1104 \| 0.4803 \| 0.5327 \| 0.4803 \| 0.6931 \| \| 0.2191 \| 3.4780 \| 1106 \| 0.4778 \| 0.5716 \| 0.4778 \| 0.6912 \| \| 0.2191 \| 3.4843 \| 1108 \| 0.4970 \| 0.5929 \| 0.4970 \| 0.7049 \| \| 0.2191 \| 3.4906 \| 1110 \| 0.4984 \| 0.5921 \| 0.4984 \| 0.7060 \| \| 0.2191 \| 3.4969 \| 1112 \| 0.4783 \| 0.5598 \| 0.4783 \| 0.6916 \| \| 0.2191 \| 3.5031 \| 1114 \| 0.4793 \| 0.5569 \| 0.4793 \| 0.6923 \| \| 0.2191 \| 3.5094 \| 1116 \| 0.4870 \| 0.5872 \| 0.4870 \| 0.6979 \| \| 0.2191 \| 3.5157 \| 1118 \| 0.4885 \| 0.5913 \| 0.4885 \| 0.6989 \| \| 0.2191 \| 3.5220 \| 1120 \| 0.4890 \| 0.5400 \| 0.4890 \| 0.6993 \| \| 0.2191 \| 3.5283 \| 1122 \| 0.4850 \| 0.5606 \| 0.4850 \| 0.6964 \| \| 0.2191 \| 3.5346 \| 1124 \| 0.4946 \| 0.5952 \| 0.4946 \| 0.7033 \| \| 0.2191 \| 3.5409 \| 1126 \| 0.4899 \| 0.5991 \| 0.4899 \| 0.6999 \| \| 0.2191 \| 3.5472 \| 1128 \| 0.5096 \| 0.6137 \| 0.5096 \| 0.7139 \| \| 0.2191 \| 3.5535 \| 1130 \| 0.5116 \| 0.6028 \| 0.5116 \| 0.7153 \| \| 0.2191 \| 3.5597 \| 1132 \| 0.4771 \| 0.5908 \| 0.4771 \| 0.6907 \| \| 0.2191 \| 3.5660 \| 1134 \| 0.4686 \| 0.5789 \| 0.4686 \| 0.6846 \| \| 0.2191 \| 3.5723 \| 1136 \| 0.4895 \| 0.5819 \| 0.4895 \| 0.6996 \| \| 0.2191 \| 3.5786 \| 1138 \| 0.5350 \| 0.6032 \| 0.5350 \| 0.7314 \| \| 0.2191 \| 3.5849 \| 1140 \| 0.5408 \| 0.6008 \| 0.5408 \| 0.7354 \| \| 0.2191 \| 3.5912 \| 1142 \| 0.4848 \| 0.5834 \| 0.4848 \| 0.6963 \| \| 0.2191 \| 3.5975 \| 1144 \| 0.4599 \| 0.5429 \| 0.4599 \| 0.6782 \| \| 0.2191 \| 3.6038 \| 1146 \| 0.4601 \| 0.5355 \| 0.4601 \| 0.6783 \| \| 0.2191 \| 3.6101 \| 1148 \| 0.4588 \| 0.5793 \| 0.4588 \| 0.6774 \| \| 0.2191 \| 3.6164 \| 1150 \| 0.4823 \| 0.5979 \| 0.4823 \| 0.6945 \| \| 0.2191 \| 3.6226 \| 1152 \| 0.4627 \| 0.5902 \| 0.4627 \| 0.6802 \| \| 0.2191 \| 3.6289 \| 1154 \| 0.4612 \| 0.5895 \| 0.4612 \| 0.6791 \| \| 0.2191 \| 3.6352 \| 1156 \| 0.4707 \| 0.5929 \| 0.4707 \| 0.6861 \| \| 0.2191 \| 3.6415 \| 1158 \| 0.4756 \| 0.5888 \| 0.4756 \| 0.6896 \| \| 0.2191 \| 3.6478 \| 1160 \| 0.4981 \| 0.5867 \| 0.4981 \| 0.7058 \| \| 0.2191 \| 3.6541 \| 1162 \| 0.4825 \| 0.5920 \| 0.4825 \| 0.6947 \| \| 0.2191 \| 3.6604 \| 1164 \| 0.4698 \| 0.4999 \| 0.4698 \| 0.6854 \| \| 0.2191 \| 3.6667 \| 1166 \| 0.4758 \| 0.4843 \| 0.4758 \| 0.6898 \| \| 0.2191 \| 3.6730 \| 1168 \| 0.4684 \| 0.5680 \| 0.4684 \| 0.6844 \| \| 0.2191 \| 3.6792 \| 1170 \| 0.5320 \| 0.6034 \| 0.5320 \| 0.7294 \| \| 0.2191 \| 3.6855 \| 1172 \| 0.5642 \| 0.6074 \| 0.5642 \| 0.7511 \| \| 0.2191 \| 3.6918 \| 1174 \| 0.5204 \| 0.5851 \| 0.5204 \| 0.7214 \| \| 0.2191 \| 3.6981 \| 1176 \| 0.4829 \| 0.5681 \| 0.4829 \| 0.6949 \| \| 0.2191 \| 3.7044 \| 1178 \| 0.4784 \| 0.5650 \| 0.4784 \| 0.6917 \| \| 0.2191 \| 3.7107 \| 1180 \| 0.4994 \| 0.5960 \| 0.4994 \| 0.7067 \| \| 0.2191 \| 3.7170 \| 1182 \| 0.5731 \| 0.6224 \| 0.5731 \| 0.7571 \| \| 0.2191 \| 3.7233 \| 1184 \| 0.5590 \| 0.6252 \| 0.5590 \| 0.7476 \| \| 0.2191 \| 3.7296 \| 1186 \| 0.5240 \| 0.6032 \| 0.5240 \| 0.7239 \| \| 0.2191 \| 3.7358 \| 1188 \| 0.4929 \| 0.5795 \| 0.4929 \| 0.7021 \| \| 0.2191 \| 3.7421 \| 1190 \| 0.5136 \| 0.6038 \| 0.5136 \| 0.7167 \| \| 0.2191 \| 3.7484 \| 1192 \| 0.6420 \| 0.6343 \| 0.6420 \| 0.8012 \| \| 0.2191 \| 3.7547 \| 1194 \| 0.6949 \| 0.6368 \| 0.6949 \| 0.8336 \| \| 0.2191 \| 3.7610 \| 1196 \| 0.6039 \| 0.6441 \| 0.6039 \| 0.7771 \| \| 0.2191 \| 3.7673 \| 1198 \| 0.4848 \| 0.5966 \| 0.4848 \| 0.6963 \| \| 0.2191 \| 3.7736 \| 1200 \| 0.4677 \| 0.5934 \| 0.4677 \| 0.6839 \| \| 0.2191 \| 3.7799 \| 1202 \| 0.4915 \| 0.6116 \| 0.4915 \| 0.7011 \| \| 0.2191 \| 3.7862 \| 1204 \| 0.5060 \| 0.6274 \| 0.5060 \| 0.7113 \| \| 0.2191 \| 3.7925 \| 1206 \| 0.4734 \| 0.6065 \| 0.4734 \| 0.6880 \| \| 0.2191 \| 3.7987 \| 1208 \| 0.4893 \| 0.6210 \| 0.4893 \| 0.6995 \| \| 0.2191 \| 3.8050 \| 1210 \| 0.5399 \| 0.6244 \| 0.5399 \| 0.7348 \| \| 0.2191 \| 3.8113 \| 1212 \| 0.5119 \| 0.6266 \| 0.5119 \| 0.7155 \| \| 0.2191 \| 3.8176 \| 1214 \| 0.4816 \| 0.5927 \| 0.4816 \| 0.6940 \| \| 0.2191 \| 3.8239 \| 1216 \| 0.4972 \| 0.5898 \| 0.4972 \| 0.7051 \| \| 0.2191 \| 3.8302 \| 1218 \| 0.5283 \| 0.6094 \| 0.5283 \| 0.7268 \| \| 0.2191 \| 3.8365 \| 1220 \| 0.5222 \| 0.5733 \| 0.5222 \| 0.7226 \| \| 0.2191 \| 3.8428 \| 1222 \| 0.5608 \| 0.5982 \| 0.5608 \| 0.7489 \| \| 0.2191 \| 3.8491 \| 1224 \| 0.5559 \| 0.5857 \| 0.5559 \| 0.7456 \| \| 0.2191 \| 3.8553 \| 1226 \| 0.5330 \| 0.5723 \| 0.5330 \| 0.7301 \| \| 0.2191 \| 3.8616 \| 1228 \| 0.5097 \| 0.5748 \| 0.5097 \| 0.7139 \| \| 0.2191 \| 3.8679 \| 1230 \| 0.4911 \| 0.5813 \| 0.4911 \| 0.7008 \| \| 0.2191 \| 3.8742 \| 1232 \| 0.4909 \| 0.6022 \| 0.4909 \| 0.7007 \| \| 0.2191 \| 3.8805 \| 1234 \| 0.5000 \| 0.6197 \| 0.5000 \| 0.7071 \| \| 0.2191 \| 3.8868 \| 1236 \| 0.4952 \| 0.6236 \| 0.4952 \| 0.7037 \| \| 0.2191 \| 3.8931 \| 1238 \| 0.4668 \| 0.5950 \| 0.4668 \| 0.6833 \| \| 0.2191 \| 3.8994 \| 1240 \| 0.4689 \| 0.5844 \| 0.4689 \| 0.6847 \| \| 0.2191 \| 3.9057 \| 1242 \| 0.4959 \| 0.6224 \| 0.4959 \| 0.7042 \| \| 0.2191 \| 3.9119 \| 1244 \| 0.5012 \| 0.6153 \| 0.5012 \| 0.7079 \| \| 0.2191 \| 3.9182 \| 1246 \| 0.5449 \| 0.6003 \| 0.5449 \| 0.7382 \| \| 0.2191 \| 3.9245 \| 1248 \| 0.5715 \| 0.6007 \| 0.5715 \| 0.7560 \| \| 0.2191 \| 3.9308 \| 1250 \| 0.5274 \| 0.5888 \| 0.5274 \| 0.7262 \| \| 0.2191 \| 3.9371 \| 1252 \| 0.5270 \| 0.5761 \| 0.5270 \| 0.7259 \| \| 0.2191 \| 3.9434 \| 1254 \| 0.5680 \| 0.5990 \| 0.5680 \| 0.7537 \| \| 0.2191 \| 3.9497 \| 1256 \| 0.6245 \| 0.6284 \| 0.6245 \| 0.7903 \| \| 0.2191 \| 3.9560 \| 1258 \| 0.6299 \| 0.6353 \| 0.6299 \| 0.7937 \| \| 0.2191 \| 3.9623 \| 1260 \| 0.5335 \| 0.6144 \| 0.5335 \| 0.7304 \| \| 0.2191 \| 3.9686 \| 1262 \| 0.4950 \| 0.5627 \| 0.4950 \| 0.7036 \| \| 0.2191 \| 3.9748 \| 1264 \| 0.5072 \| 0.5777 \| 0.5072 \| 0.7122 \| \| 0.2191 \| 3.9811 \| 1266 \| 0.5436 \| 0.6057 \| 0.5436 \| 0.7373 \| \| 0.2191 \| 3.9874 \| 1268 \| 0.5330 \| 0.6026 \| 0.5330 \| 0.7301 \| \| 0.2191 \| 3.9937 \| 1270 \| 0.5473 \| 0.6055 \| 0.5473 \| 0.7398 \| \| 0.2191 \| 4.0 \| 1272 \| 0.5432 \| 0.6049 \| 0.5432 \| 0.7370 \| \| 0.2191 \| 4.0063 \| 1274 \| 0.5198 \| 0.6003 \| 0.5198 \| 0.7210 \| \| 0.2191 \| 4.0126 \| 1276 \| 0.5440 \| 0.6122 \| 0.5440 \| 0.7375 \| \| 0.2191 \| 4.0189 \| 1278 \| 0.5267 \| 0.6074 \| 0.5267 \| 0.7258 \| \| 0.2191 \| 4.0252 \| 1280 \| 0.5213 \| 0.6056 \| 0.5213 \| 0.7220 \| \| 0.2191 \| 4.0314 \| 1282 \| 0.5556 \| 0.6345 \| 0.5556 \| 0.7454 \| \| 0.2191 \| 4.0377 \| 1284 \| 0.6064 \| 0.6612 \| 0.6064 \| 0.7787 \| \| 0.2191 \| 4.0440 \| 1286 \| 0.5591 \| 0.6278 \| 0.5591 \| 0.7477 \| \| 0.2191 \| 4.0503 \| 1288 \| 0.5057 \| 0.5699 \| 0.5057 \| 0.7112 \| \| 0.2191 \| 4.0566 \| 1290 \| 0.5133 \| 0.5851 \| 0.5133 \| 0.7165 \| \| 0.2191 \| 4.0629 \| 1292 \| 0.5021 \| 0.5604 \| 0.5021 \| 0.7086 \| \| 0.2191 \| 4.0692 \| 1294 \| 0.4998 \| 0.5759 \| 0.4998 \| 0.7069 \| \| 0.2191 \| 4.0755 \| 1296 \| 0.5104 \| 0.6179 \| 0.5104 \| 0.7144 \| \| 0.2191 \| 4.0818 \| 1298 \| 0.5394 \| 0.6526 \| 0.5394 \| 0.7344 \| \| 0.2191 \| 4.0881 \| 1300 \| 0.5295 \| 0.6392 \| 0.5295 \| 0.7276 \| \| 0.2191 \| 4.0943 \| 1302 \| 0.4951 \| 0.5717 \| 0.4951 \| 0.7037 \| \| 0.2191 \| 4.1006 \| 1304 \| 0.4979 \| 0.5665 \| 0.4979 \| 0.7056 \| \| 0.2191 \| 4.1069 \| 1306 \| 0.5026 \| 0.5854 \| 0.5026 \| 0.7089 \| \| 0.2191 \| 4.1132 \| 1308 \| 0.4988 \| 0.5564 \| 0.4988 \| 0.7062 \| \| 0.2191 \| 4.1195 \| 1310 \| 0.4986 \| 0.5671 \| 0.4986 \| 0.7061 \| \| 0.2191 \| 4.1258 \| 1312 \| 0.5221 \| 0.6021 \| 0.5221 \| 0.7225 \| \| 0.2191 \| 4.1321 \| 1314 \| 0.5309 \| 0.6093 \| 0.5309 \| 0.7286 \| \| 0.2191 \| 4.1384 \| 1316 \| 0.5054 \| 0.5482 \| 0.5054 \| 0.7109 \| \| 0.2191 \| 4.1447 \| 1318 \| 0.4995 \| 0.5210 \| 0.4995 \| 0.7068 \| \| 0.2191 \| 4.1509 \| 1320 \| 0.4933 \| 0.5499 \| 0.4933 \| 0.7023 \| \| 0.2191 \| 4.1572 \| 1322 \| 0.5094 \| 0.6100 \| 0.5094 \| 0.7137 \| \| 0.2191 \| 4.1635 \| 1324 \| 0.5068 \| 0.6171 \| 0.5068 \| 0.7119 \| \| 0.2191 \| 4.1698 \| 1326 \| 0.4763 \| 0.5792 \| 0.4763 \| 0.6902 \| \| 0.2191 \| 4.1761 \| 1328 \| 0.4706 \| 0.5297 \| 0.4706 \| 0.6860 \| \| 0.2191 \| 4.1824 \| 1330 \| 0.4664 \| 0.5698 \| 0.4664 \| 0.6829 \| \| 0.2191 \| 4.1887 \| 1332 \| 0.4933 \| 0.6067 \| 0.4933 \| 0.7023 \| \| 0.2191 \| 4.1950 \| 1334 \| 0.5064 \| 0.6143 \| 0.5064 \| 0.7116 \| \| 0.2191 \| 4.2013 \| 1336 \| 0.4740 \| 0.5939 \| 0.4740 \| 0.6884 \| \| 0.2191 \| 4.2075 \| 1338 \| 0.4959 \| 0.4730 \| 0.4959 \| 0.7042 \| \| 0.2191 \| 4.2138 \| 1340 \| 0.5378 \| 0.4250 \| 0.5378 \| 0.7334 \| \| 0.2191 \| 4.2201 \| 1342 \| 0.5020 \| 0.4771 \| 0.5020 \| 0.7085 \| \| 0.2191 \| 4.2264 \| 1344 \| 0.4883 \| 0.5873 \| 0.4883 \| 0.6988 \| \| 0.2191 \| 4.2327 \| 1346 \| 0.5408 \| 0.6152 \| 0.5408 \| 0.7354 \| \| 0.2191 \| 4.2390 \| 1348 \| 0.5481 \| 0.6139 \| 0.5481 \| 0.7404 \| \| 0.2191 \| 4.2453 \| 1350 \| 0.5084 \| 0.6032 \| 0.5084 \| 0.7130 \| \| 0.2191 \| 4.2516 \| 1352 \| 0.5036 \| 0.5586 \| 0.5036 \| 0.7096 \| \| 0.2191 \| 4.2579 \| 1354 \| 0.5110 \| 0.5830 \| 0.5110 \| 0.7149 \| \| 0.2191 \| 4.2642 \| 1356 \| 0.5633 \| 0.5949 \| 0.5633 \| 0.7506 \| \| 0.2191 \| 4.2704 \| 1358 \| 0.6567 \| 0.6165 \| 0.6567 \| 0.8104 \| \| 0.2191 \| 4.2767 \| 1360 \| 0.6120 \| 0.6157 \| 0.6120 \| 0.7823 \| \| 0.2191 \| 4.2830 \| 1362 \| 0.5442 \| 0.6006 \| 0.5442 \| 0.7377 \| \| 0.2191 \| 4.2893 \| 1364 \| 0.5166 \| 0.5773 \| 0.5166 \| 0.7188 \| \| 0.2191 \| 4.2956 \| 1366 \| 0.5335 \| 0.6030 \| 0.5335 \| 0.7304 \| \| 0.2191 \| 4.3019 \| 1368 \| 0.5712 \| 0.6283 \| 0.5712 \| 0.7558 \| \| 0.2191 \| 4.3082 \| 1370 \| 0.5458 \| 0.6126 \| 0.5458 \| 0.7388 \| \| 0.2191 \| 4.3145 \| 1372 \| 0.4977 \| 0.5570 \| 0.4977 \| 0.7055 \| \| 0.2191 \| 4.3208 \| 1374 \| 0.4877 \| 0.5010 \| 0.4877 \| 0.6983 \| \| 0.2191 \| 4.3270 \| 1376 \| 0.4875 \| 0.5627 \| 0.4875 \| 0.6982 \| \| 0.2191 \| 4.3333 \| 1378 \| 0.5061 \| 0.6018 \| 0.5061 \| 0.7114 \| \| 0.2191 \| 4.3396 \| 1380 \| 0.5413 \| 0.6192 \| 0.5413 \| 0.7357 \| \| 0.2191 \| 4.3459 \| 1382 \| 0.5306 \| 0.6058 \| 0.5306 \| 0.7284 \| \| 0.2191 \| 4.3522 \| 1384 \| 0.4929 \| 0.5362 \| 0.4929 \| 0.7021 \| \| 0.2191 \| 4.3585 \| 1386 \| 0.4985 \| 0.4982 \| 0.4985 \| 0.7061 \| \| 0.2191 \| 4.3648 \| 1388 \| 0.4972 \| 0.5306 \| 0.4972 \| 0.7051 \| \| 0.2191 \| 4.3711 \| 1390 \| 0.5404 \| 0.5940 \| 0.5404 \| 0.7351 \| \| 0.2191 \| 4.3774 \| 1392 \| 0.5505 \| 0.5926 \| 0.5505 \| 0.7420 \| \| 0.2191 \| 4.3836 \| 1394 \| 0.5302 \| 0.5679 \| 0.5302 \| 0.7282 \| \| 0.2191 \| 4.3899 \| 1396 \| 0.5166 \| 0.5177 \| 0.5166 \| 0.7188 \| \| 0.2191 \| 4.3962 \| 1398 \| 0.5139 \| 0.5270 \| 0.5139 \| 0.7168 \| \| 0.2191 \| 4.4025 \| 1400 \| 0.5309 \| 0.5670 \| 0.5309 \| 0.7286 \| \| 0.2191 \| 4.4088 \| 1402 \| 0.5514 \| 0.6066 \| 0.5514 \| 0.7426 \| \| 0.2191 \| 4.4151 \| 1404 \| 0.5769 \| 0.6261 \| 0.5769 \| 0.7596 \| \| 0.2191 \| 4.4214 \| 1406 \| 0.5477 \| 0.6111 \| 0.5477 \| 0.7401 \| \| 0.2191 \| 4.4277 \| 1408 \| 0.5108 \| 0.5752 \| 0.5108 \| 0.7147 \| \| 0.2191 \| 4.4340 \| 1410 \| 0.5063 \| 0.5628 \| 0.5063 \| 0.7115 \| \| 0.2191 \| 4.4403 \| 1412 \| 0.5222 \| 0.5890 \| 0.5222 \| 0.7227 \| \| 0.2191 \| 4.4465 \| 1414 \| 0.5312 \| 0.5963 \| 0.5312 \| 0.7289 \| \| 0.2191 \| 4.4528 \| 1416 \| 0.5192 \| 0.5548 \| 0.5192 \| 0.7206 \| \| 0.2191 \| 4.4591 \| 1418 \| 0.5218 \| 0.5072 \| 0.5218 \| 0.7223 \| \| 0.2191 \| 4.4654 \| 1420 \| 0.5245 \| 0.5406 \| 0.5245 \| 0.7242 \| \| 0.2191 \| 4.4717 \| 1422 \| 0.5299 \| 0.5795 \| 0.5299 \| 0.7279 \| \| 0.2191 \| 4.4780 \| 1424 \| 0.5778 \| 0.6086 \| 0.5778 \| 0.7601 \| \| 0.2191 \| 4.4843 \| 1426 \| 0.6066 \| 0.6058 \| 0.6066 \| 0.7789 \| \| 0.2191 \| 4.4906 \| 1428 \| 0.5747 \| 0.6055 \| 0.5747 \| 0.7581 \| \| 0.2191 \| 4.4969 \| 1430 \| 0.5427 \| 0.5779 \| 0.5427 \| 0.7367 \| \| 0.2191 \| 4.5031 \| 1432 \| 0.5435 \| 0.5737 \| 0.5435 \| 0.7372 \| \| 0.2191 \| 4.5094 \| 1434 \| 0.5456 \| 0.5850 \| 0.5456 \| 0.7386 \| \| 0.2191 \| 4.5157 \| 1436 \| 0.6108 \| 0.6252 \| 0.6108 \| 0.7815 \| \| 0.2191 \| 4.5220 \| 1438 \| 0.6371 \| 0.6462 \| 0.6371 \| 0.7982 \| \| 0.2191 \| 4.5283 \| 1440 \| 0.5417 \| 0.6027 \| 0.5417 \| 0.7360 \| \| 0.2191 \| 4.5346 \| 1442 \| 0.5027 \| 0.5807 \| 0.5027 \| 0.7090 \| \| 0.2191 \| 4.5409 \| 1444 \| 0.5327 \| 0.6089 \| 0.5327 \| 0.7299 \| \| 0.2191 \| 4.5472 \| 1446 \| 0.5351 \| 0.6232 \| 0.5351 \| 0.7315 \| \| 0.2191 \| 4.5535 \| 1448 \| 0.4943 \| 0.5781 \| 0.4943 \| 0.7030 \| \| 0.2191 \| 4.5597 \| 1450 \| 0.4824 \| 0.5467 \| 0.4824 \| 0.6945 \| \| 0.2191 \| 4.5660 \| 1452 \| 0.4857 \| 0.5633 \| 0.4857 \| 0.6969 \| \| 0.2191 \| 4.5723 \| 1454 \| 0.5305 \| 0.6218 \| 0.5305 \| 0.7283 \| \| 0.2191 \| 4.5786 \| 1456 \| 0.5665 \| 0.6395 \| 0.5665 \| 0.7526 \| \| 0.2191 \| 4.5849 \| 1458 \| 0.5148 \| 0.5913 \| 0.5148 \| 0.7175 \| \| 0.2191 \| 4.5912 \| 1460 \| 0.4879 \| 0.5513 \| 0.4879 \| 0.6985 \| \| 0.2191 \| 4.5975 \| 1462 \| 0.4889 \| 0.5152 \| 0.4889 \| 0.6992 \| \| 0.2191 \| 4.6038 \| 1464 \| 0.4922 \| 0.5392 \| 0.4922 \| 0.7016 \| \| 0.2191 \| 4.6101 \| 1466 \| 0.5167 \| 0.5761 \| 0.5167 \| 0.7188 \| \| 0.2191 \| 4.6164 \| 1468 \| 0.5763 \| 0.6188 \| 0.5763 \| 0.7592 \| \| 0.2191 \| 4.6226 \| 1470 \| 0.5514 \| 0.5962 \| 0.5514 \| 0.7426 \| \| 0.2191 \| 4.6289 \| 1472 \| 0.4995 \| 0.5326 \| 0.4995 \| 0.7067 \| \| 0.2191 \| 4.6352 \| 1474 \| 0.4970 \| 0.5175 \| 0.4970 \| 0.7050 \| \| 0.2191 \| 4.6415 \| 1476 \| 0.5087 \| 0.5672 \| 0.5087 \| 0.7132 \| \| 0.2191 \| 4.6478 \| 1478 \| 0.5783 \| 0.6248 \| 0.5783 \| 0.7605 \| \| 0.2191 \| 4.6541 \| 1480 \| 0.5894 \| 0.6199 \| 0.5894 \| 0.7677 \| \| 0.2191 \| 4.6604 \| 1482 \| 0.5251 \| 0.5869 \| 0.5251 \| 0.7247 \| \| 0.2191 \| 4.6667 \| 1484 \| 0.5091 \| 0.5123 \| 0.5091 \| 0.7135 \| \| 0.2191 \| 4.6730 \| 1486 \| 0.5099 \| 0.5207 \| 0.5099 \| 0.7141 \| \| 0.2191 \| 4.6792 \| 1488 \| 0.5335 \| 0.5981 \| 0.5335 \| 0.7304 \| \| 0.2191 \| 4.6855 \| 1490 \| 0.5984 \| 0.6379 \| 0.5984 \| 0.7736 \| \| 0.2191 \| 4.6918 \| 1492 \| 0.6045 \| 0.6317 \| 0.6045 \| 0.7775 \| \| 0.2191 \| 4.6981 \| 1494 \| 0.5318 \| 0.5893 \| 0.5318 \| 0.7292 \| \| 0.2191 \| 4.7044 \| 1496 \| 0.5212 \| 0.4917 \| 0.5212 \| 0.7219 \| \| 0.2191 \| 4.7107 \| 1498 \| 0.5583 \| 0.4352 \| 0.5583 \| 0.7472 \| \| 0.137 \| 4.7170 \| 1500 \| 0.5325 \| 0.4577 \| 0.5325 \| 0.7297 \| \| 0.137 \| 4.7233 \| 1502 \| 0.4992 \| 0.5207 \| 0.4992 \| 0.7066 \| \| 0.137 \| 4.7296 \| 1504 \| 0.5142 \| 0.5829 \| 0.5142 \| 0.7171 \| \| 0.137 \| 4.7358 \| 1506 \| 0.5090 \| 0.5933 \| 0.5090 \| 0.7134 \| \| 0.137 \| 4.7421 \| 1508 \| 0.4846 \| 0.5451 \| 0.4846 \| 0.6962 \| \| 0.137 \| 4.7484 \| 1510 \| 0.4941 \| 0.4743 \| 0.4941 \| 0.7029 \| \| 0.137 \| 4.7547 \| 1512 \| 0.4925 \| 0.4759 \| 0.4925 \| 0.7018 \| \| 0.137 \| 4.7610 \| 1514 \| 0.4870 \| 0.5403 \| 0.4870 \| 0.6978 \| \| 0.137 \| 4.7673 \| 1516 \| 0.5274 \| 0.6094 \| 0.5274 \| 0.7262 \| \| 0.137 \| 4.7736 \| 1518 \| 0.5429 \| 0.6104 \| 0.5429 \| 0.7368 \| \| 0.137 \| 4.7799 \| 1520 \| 0.5016 \| 0.6021 \| 0.5016 \| 0.7082 \| \| 0.137 \| 4.7862 \| 1522 \| 0.4941 \| 0.4792 \| 0.4941 \| 0.7029 \| \| 0.137 \| 4.7925 \| 1524 \| 0.5164 \| 0.4536 \| 0.5164 \| 0.7186 \| \| 0.137 \| 4.7987 \| 1526 \| 0.5001 \| 0.4721 \| 0.5001 \| 0.7072 \| \| 0.137 \| 4.8050 \| 1528 \| 0.5149 \| 0.5848 \| 0.5149 \| 0.7176 \| \| 0.137 \| 4.8113 \| 1530 \| 0.5606 \| 0.6187 \| 0.5606 \| 0.7488 \| \| 0.137 \| 4.8176 \| 1532 \| 0.5386 \| 0.6120 \| 0.5386 \| 0.7339 \| \| 0.137 \| 4.8239 \| 1534 \| 0.5132 \| 0.4964 \| 0.5132 \| 0.7163 \| \| 0.137 \| 4.8302 \| 1536 \| 0.5325 \| 0.4620 \| 0.5325 \| 0.7298 \| \| 0.137 \| 4.8365 \| 1538 \| 0.5363 \| 0.4628 \| 0.5363 \| 0.7323 \| \| 0.137 \| 4.8428 \| 1540 \| 0.5147 \| 0.5029 \| 0.5147 \| 0.7174 \| \| 0.137 \| 4.8491 \| 1542 \| 0.5102 \| 0.5405 \| 0.5102 \| 0.7143 \| \| 0.137 \| 4.8553 \| 1544 \| 0.5135 \| 0.5818 \| 0.5135 \| 0.7166 \| \| 0.137 \| 4.8616 \| 1546 \| 0.4941 \| 0.5237 \| 0.4941 \| 0.7030 \| \| 0.137 \| 4.8679 \| 1548 \| 0.4922 \| 0.5190 \| 0.4922 \| 0.7015 \| \| 0.137 \| 4.8742 \| 1550 \| 0.4914 \| 0.5180 \| 0.4914 \| 0.7010 \| \| 0.137 \| 4.8805 \| 1552 \| 0.5017 \| 0.5464 \| 0.5017 \| 0.7083 \| \| 0.137 \| 4.8868 \| 1554 \| 0.5360 \| 0.6195 \| 0.5360 \| 0.7321 \| \| 0.137 \| 4.8931 \| 1556 \| 0.5409 \| 0.6140 \| 0.5409 \| 0.7355 \| \| 0.137 \| 4.8994 \| 1558 \| 0.5174 \| 0.5642 \| 0.5174 \| 0.7193 \| \| 0.137 \| 4.9057 \| 1560 \| 0.5136 \| 0.5532 \| 0.5136 \| 0.7166 \| \| 0.137 \| 4.9119 \| 1562 \| 0.5314 \| 0.5965 \| 0.5314 \| 0.7290 \| \| 0.137 \| 4.9182 \| 1564 \| 0.5257 \| 0.5958 \| 0.5257 \| 0.7251 \| \| 0.137 \| 4.9245 \| 1566 \| 0.5320 \| 0.5995 \| 0.5320 \| 0.7294 \| \| 0.137 \| 4.9308 \| 1568 \| 0.5420 \| 0.6089 \| 0.5420 \| 0.7362 \| \| 0.137 \| 4.9371 \| 1570 \| 0.5097 \| 0.5445 \| 0.5097 \| 0.7139 \| \| 0.137 \| 4.9434 \| 1572 \| 0.5084 \| 0.5183 \| 0.5084 \| 0.7130 \| \| 0.137 \| 4.9497 \| 1574 \| 0.5117 \| 0.5146 \| 0.5117 \| 0.7154 \| \| 0.137 \| 4.9560 \| 1576 \| 0.5268 \| 0.5639 \| 0.5268 \| 0.7258 \| \| 0.137 \| 4.9623 \| 1578 \| 0.5468 \| 0.6127 \| 0.5468 \| 0.7395 \| \| 0.137 \| 4.9686 \| 1580 \| 0.5315 \| 0.5791 \| 0.5315 \| 0.7290 \| \| 0.137 \| 4.9748 \| 1582 \| 0.5247 \| 0.5557 \| 0.5247 \| 0.7243 \| \| 0.137 \| 4.9811 \| 1584 \| 0.5311 \| 0.5787 \| 0.5311 \| 0.7288 \| \| 0.137 \| 4.9874 \| 1586 \| 0.5600 \| 0.6294 \| 0.5600 \| 0.7483 \| \| 0.137 \| 4.9937 \| 1588 \| 0.6016 \| 0.6491 \| 0.6016 \| 0.7756 \| \| 0.137 \| 5.0 \| 1590 \| 0.6073 \| 0.6469 \| 0.6073 \| 0.7793 \| \| 0.137 \| 5.0063 \| 1592 \| 0.5661 \| 0.6432 \| 0.5661 \| 0.7524 \| \| 0.137 \| 5.0126 \| 1594 \| 0.5277 \| 0.6116 \| 0.5277 \| 0.7264 \| \| 0.137 \| 5.0189 \| 1596 \| 0.5400 \| 0.6220 \| 0.5400 \| 0.7349 \| \| 0.137 \| 5.0252 \| 1598 \| 0.6110 \| 0.6596 \| 0.6110 \| 0.7817 \| \| 0.137 \| 5.0314 \| 1600 \| 0.6045 \| 0.6544 \| 0.6045 \| 0.7775 \| \| 0.137 \| 5.0377 \| 1602 \| 0.5523 \| 0.6378 \| 0.5523 \| 0.7432 \| \| 0.137 \| 5.0440 \| 1604 \| 0.5261 \| 0.5913 \| 0.5261 \| 0.7253 \| \| 0.137 \| 5.0503 \| 1606 \| 0.5190 \| 0.5756 \| 0.5190 \| 0.7204 \| \| 0.137 \| 5.0566 \| 1608 \| 0.5470 \| 0.6231 \| 0.5470 \| 0.7396 \| \| 0.137 \| 5.0629 \| 1610 \| 0.5948 \| 0.6374 \| 0.5948 \| 0.7712 \| \| 0.137 \| 5.0692 \| 1612 \| 0.5981 \| 0.6305 \| 0.5981 \| 0.7734 \| \| 0.137 \| 5.0755 \| 1614 \| 0.5447 \| 0.5802 \| 0.5447 \| 0.7380 \| \| 0.137 \| 5.0818 \| 1616 \| 0.5114 \| 0.5172 \| 0.5114 \| 0.7152 \| \| 0.137 \| 5.0881 \| 1618 \| 0.5096 \| 0.5302 \| 0.5096 \| 0.7139 \| \| 0.137 \| 5.0943 \| 1620 \| 0.5184 \| 0.5557 \| 0.5184 \| 0.7200 \| \| 0.137 \| 5.1006 \| 1622 \| 0.5450 \| 0.5970 \| 0.5450 \| 0.7382 \| \| 0.137 \| 5.1069 \| 1624 \| 0.5490 \| 0.5956 \| 0.5490 \| 0.7410 \| \| 0.137 \| 5.1132 \| 1626 \| 0.5397 \| 0.5729 \| 0.5397 \| 0.7346 \| \| 0.137 \| 5.1195 \| 1628 \| 0.5360 \| 0.5662 \| 0.5360 \| 0.7321 \| \| 0.137 \| 5.1258 \| 1630 \| 0.5514 \| 0.5830 \| 0.5514 \| 0.7426 \| \| 0.137 \| 5.1321 \| 1632 \| 0.5284 \| 0.5352 \| 0.5284 \| 0.7269 \| \| 0.137 \| 5.1384 \| 1634 \| 0.5200 \| 0.5191 \| 0.5200 \| 0.7211 \| \| 0.137 \| 5.1447 \| 1636 \| 0.5160 \| 0.5037 \| 0.5160 \| 0.7183 \| \| 0.137 \| 5.1509 \| 1638 \| 0.5184 \| 0.5393 \| 0.5184 \| 0.7200 \| \| 0.137 \| 5.1572 \| 1640 \| 0.5180 \| 0.5476 \| 0.5180 \| 0.7197 \| \| 0.137 \| 5.1635 \| 1642 \| 0.5125 \| 0.5336 \| 0.5125 \| 0.7159 \| \| 0.137 \| 5.1698 \| 1644 \| 0.5039 \| 0.5236 \| 0.5039 \| 0.7099 \| \| 0.137 \| 5.1761 \| 1646 \| 0.5005 \| 0.5047 \| 0.5005 \| 0.7075 \| \| 0.137 \| 5.1824 \| 1648 \| 0.5026 \| 0.5307 \| 0.5026 \| 0.7090 \| \| 0.137 \| 5.1887 \| 1650 \| 0.5118 \| 0.5572 \| 0.5118 \| 0.7154 \| \| 0.137 \| 5.1950 \| 1652 \| 0.5206 \| 0.5589 \| 0.5206 \| 0.7215 \| \| 0.137 \| 5.2013 \| 1654 \| 0.5269 \| 0.5661 \| 0.5269 \| 0.7259 \| \| 0.137 \| 5.2075 \| 1656 \| 0.5262 \| 0.5677 \| 0.5262 \| 0.7254 \| \| 0.137 \| 5.2138 \| 1658 \| 0.5181 \| 0.5379 \| 0.5181 \| 0.7198 \| \| 0.137 \| 5.2201 \| 1660 \| 0.5199 \| 0.5489 \| 0.5199 \| 0.7211 \| \| 0.137 \| 5.2264 \| 1662 \| 0.5168 \| 0.5317 \| 0.5168 \| 0.7189 \| \| 0.137 \| 5.2327 \| 1664 \| 0.5175 \| 0.5034 \| 0.5175 \| 0.7194 \| \| 0.137 \| 5.2390 \| 1666 \| 0.5285 \| 0.5567 \| 0.5285 \| 0.7270 \| \| 0.137 \| 5.2453 \| 1668 \| 0.5740 \| 0.6014 \| 0.5740 \| 0.7576 \| \| 0.137 \| 5.2516 \| 1670 \| 0.5751 \| 0.6039 \| 0.5751 \| 0.7584 \| \| 0.137 \| 5.2579 \| 1672 \| 0.5519 \| 0.5738 \| 0.5519 \| 0.7429 \| \| 0.137 \| 5.2642 \| 1674 \| 0.5682 \| 0.5954 \| 0.5682 \| 0.7538 \| \| 0.137 \| 5.2704 \| 1676 \| 0.5834 \| 0.6032 \| 0.5834 \| 0.7638 \| \| 0.137 \| 5.2767 \| 1678 \| 0.5604 \| 0.5737 \| 0.5604 \| 0.7486 \| \| 0.137 \| 5.2830 \| 1680 \| 0.5381 \| 0.5282 \| 0.5381 \| 0.7336 \| \| 0.137 \| 5.2893 \| 1682 \| 0.5366 \| 0.5274 \| 0.5366 \| 0.7325 \| \| 0.137 \| 5.2956 \| 1684 \| 0.5544 \| 0.5575 \| 0.5544 \| 0.7446 \| \| 0.137 \| 5.3019 \| 1686 \| 0.5676 \| 0.5679 \| 0.5676 \| 0.7534 \| \| 0.137 \| 5.3082 \| 1688 \| 0.5449 \| 0.5589 \| 0.5449 \| 0.7382 \| \| 0.137 \| 5.3145 \| 1690 \| 0.5293 \| 0.5326 \| 0.5293 \| 0.7275 \| \| 0.137 \| 5.3208 \| 1692 \| 0.5481 \| 0.5828 \| 0.5481 \| 0.7404 \| \| 0.137 \| 5.3270 \| 1694 \| 0.5706 \| 0.6175 \| 0.5706 \| 0.7554 \| \| 0.137 \| 5.3333 \| 1696 \| 0.5400 \| 0.5961 \| 0.5400 \| 0.7348 \| \| 0.137 \| 5.3396 \| 1698 \| 0.5190 \| 0.5213 \| 0.5190 \| 0.7204 \| \| 0.137 \| 5.3459 \| 1700 \| 0.5220 \| 0.5245 \| 0.5220 \| 0.7225 \| \| 0.137 \| 5.3522 \| 1702 \| 0.5369 \| 0.5680 \| 0.5369 \| 0.7328 \| \| 0.137 \| 5.3585 \| 1704 \| 0.5762 \| 0.6104 \| 0.5762 \| 0.7591 \| \| 0.137 \| 5.3648 \| 1706 \| 0.5525 \| 0.5807 \| 0.5525 \| 0.7433 \| \| 0.137 \| 5.3711 \| 1708 \| 0.5256 \| 0.5445 \| 0.5256 \| 0.7250 \| \| 0.137 \| 5.3774 \| 1710 \| 0.5259 \| 0.5053 \| 0.5259 \| 0.7252 \| \| 0.137 \| 5.3836 \| 1712 \| 0.5267 \| 0.5154 \| 0.5267 \| 0.7258 \| \| 0.137 \| 5.3899 \| 1714 \| 0.5407 \| 0.5558 \| 0.5407 \| 0.7353 \| \| 0.137 \| 5.3962 \| 1716 \| 0.5512 \| 0.5666 \| 0.5512 \| 0.7424 \| \| 0.137 \| 5.4025 \| 1718 \| 0.5286 \| 0.5433 \| 0.5286 \| 0.7271 \| \| 0.137 \| 5.4088 \| 1720 \| 0.5258 \| 0.4930 \| 0.5258 \| 0.7251 \| \| 0.137 \| 5.4151 \| 1722 \| 0.5314 \| 0.4664 \| 0.5314 \| 0.7290 \| \| 0.137 \| 5.4214 \| 1724 \| 0.5204 \| 0.5117 \| 0.5204 \| 0.7214 \| \| 0.137 \| 5.4277 \| 1726 \| 0.5229 \| 0.5506 \| 0.5229 \| 0.7231 \| \| 0.137 \| 5.4340 \| 1728 \| 0.5398 \| 0.5703 \| 0.5398 \| 0.7347 \| \| 0.137 \| 5.4403 \| 1730 \| 0.5466 \| 0.5979 \| 0.5466 \| 0.7393 \| \| 0.137 \| 5.4465 \| 1732 \| 0.5224 \| 0.5650 \| 0.5224 \| 0.7227 \| \| 0.137 \| 5.4528 \| 1734 \| 0.5113 \| 0.5491 \| 0.5113 \| 0.7151 \| \| 0.137 \| 5.4591 \| 1736 \| 0.5071 \| 0.5374 \| 0.5071 \| 0.7121 \| \| 0.137 \| 5.4654 \| 1738 \| 0.5065 \| 0.5416 \| 0.5065 \| 0.7117 \| \| 0.137 \| 5.4717 \| 1740 \| 0.5246 \| 0.5797 \| 0.5246 \| 0.7243 \| \| 0.137 \| 5.4780 \| 1742 \| 0.5218 \| 0.5797 \| 0.5218 \| 0.7224 \| \| 0.137 \| 5.4843 \| 1744 \| 0.5162 \| 0.5724 \| 0.5162 \| 0.7185 \| \| 0.137 \| 5.4906 \| 1746 \| 0.5096 \| 0.5536 \| 0.5096 \| 0.7138 \| \| 0.137 \| 5.4969 \| 1748 \| 0.5051 \| 0.5304 \| 0.5051 \| 0.7107 \| \| 0.137 \| 5.5031 \| 1750 \| 0.5108 \| 0.5634 \| 0.5108 \| 0.7147 \| \| 0.137 \| 5.5094 \| 1752 \| 0.5382 \| 0.5801 \| 0.5382 \| 0.7336 \| \| 0.137 \| 5.5157 \| 1754 \| 0.5216 \| 0.5847 \| 0.5216 \| 0.7223 \| \| 0.137 \| 5.5220 \| 1756 \| 0.4968 \| 0.5421 \| 0.4968 \| 0.7048 \| \| 0.137 \| 5.5283 \| 1758 \| 0.5052 \| 0.4760 \| 0.5052 \| 0.7108 \| \| 0.137 \| 5.5346 \| 1760 \| 0.5117 \| 0.4780 \| 0.5117 \| 0.7153 \| \| 0.137 \| 5.5409 \| 1762 \| 0.5070 \| 0.5427 \| 0.5070 \| 0.7121 \| \| 0.137 \| 5.5472 \| 1764 \| 0.5567 \| 0.6161 \| 0.5567 \| 0.7461 \| \| 0.137 \| 5.5535 \| 1766 \| 0.5599 \| 0.6149 \| 0.5599 \| 0.7482 \| \| 0.137 \| 5.5597 \| 1768 \| 0.5235 \| 0.5687 \| 0.5235 \| 0.7235 \| \| 0.137 \| 5.5660 \| 1770 \| 0.5228 \| 0.5072 \| 0.5228 \| 0.7230 \| \| 0.137 \| 5.5723 \| 1772 \| 0.5250 \| 0.5090 \| 0.5250 \| 0.7246 \| \| 0.137 \| 5.5786 \| 1774 \| 0.5410 \| 0.5861 \| 0.5410 \| 0.7355 \| \| 0.137 \| 5.5849 \| 1776 \| 0.5907 \| 0.6225 \| 0.5907 \| 0.7686 \| \| 0.137 \| 5.5912 \| 1778 \| 0.5749 \| 0.6029 \| 0.5749 \| 0.7582 \| \| 0.137 \| 5.5975 \| 1780 \| 0.5412 \| 0.5939 \| 0.5412 \| 0.7357 \| \| 0.137 \| 5.6038 \| 1782 \| 0.5314 \| 0.5894 \| 0.5314 \| 0.7289 \| \| 0.137 \| 5.6101 \| 1784 \| 0.5453 \| 0.6037 \| 0.5453 \| 0.7385 \| \| 0.137 \| 5.6164 \| 1786 \| 0.5681 \| 0.6119 \| 0.5681 \| 0.7537 \| \| 0.137 \| 5.6226 \| 1788 \| 0.5702 \| 0.6166 \| 0.5702 \| 0.7551 \| \| 0.137 \| 5.6289 \| 1790 \| 0.5559 \| 0.6007 \| 0.5559 \| 0.7456 \| \| 0.137 \| 5.6352 \| 1792 \| 0.5448 \| 0.6022 \| 0.5448 \| 0.7381 \| \| 0.137 \| 5.6415 \| 1794 \| 0.5587 \| 0.6112 \| 0.5587 \| 0.7475 \| \| 0.137 \| 5.6478 \| 1796 \| 0.5405 \| 0.6096 \| 0.5405 \| 0.7352 \| \| 0.137 \| 5.6541 \| 1798 \| 0.5088 \| 0.5955 \| 0.5088 \| 0.7133 \| \| 0.137 \| 5.6604 \| 1800 \| 0.4964 \| 0.5642 \| 0.4964 \| 0.7045 \| \| 0.137 \| 5.6667 \| 1802 \| 0.4964 \| 0.5585 \| 0.4964 \| 0.7046 \| \| 0.137 \| 5.6730 \| 1804 \| 0.5212 \| 0.6061 \| 0.5212 \| 0.7219 \| \| 0.137 \| 5.6792 \| 1806 \| 0.5291 \| 0.6097 \| 0.5291 \| 0.7274 \| \| 0.137 \| 5.6855 \| 1808 \| 0.5293 \| 0.6091 \| 0.5293 \| 0.7275 \| \| 0.137 \| 5.6918 \| 1810 \| 0.5091 \| 0.5540 \| 0.5091 \| 0.7135 \| \| 0.137 \| 5.6981 \| 1812 \| 0.5072 \| 0.5393 \| 0.5072 \| 0.7122 \| \| 0.137 \| 5.7044 \| 1814 \| 0.5062 \| 0.5238 \| 0.5062 \| 0.7115 \| \| 0.137 \| 5.7107 \| 1816 \| 0.5145 \| 0.5626 \| 0.5145 \| 0.7173 \| \| 0.137 \| 5.7170 \| 1818 \| 0.5278 \| 0.5856 \| 0.5278 \| 0.7265 \| \| 0.137 \| 5.7233 \| 1820 \| 0.5254 \| 0.5886 \| 0.5254 \| 0.7248 \| \| 0.137 \| 5.7296 \| 1822 \| 0.5096 \| 0.5573 \| 0.5096 \| 0.7138 \| \| 0.137 \| 5.7358 \| 1824 \| 0.5036 \| 0.5345 \| 0.5036 \| 0.7096 \| \| 0.137 \| 5.7421 \| 1826 \| 0.5040 \| 0.5179 \| 0.5040 \| 0.7099 \| \| 0.137 \| 5.7484 \| 1828 \| 0.5122 \| 0.5543 \| 0.5122 \| 0.7157 \| \| 0.137 \| 5.7547 \| 1830 \| 0.5129 \| 0.5579 \| 0.5129 \| 0.7162 \| \| 0.137 \| 5.7610 \| 1832 \| 0.5151 \| 0.5558 \| 0.5151 \| 0.7177 \| \| 0.137 \| 5.7673 \| 1834 \| 0.5004 \| 0.5381 \| 0.5004 \| 0.7074 \| \| 0.137 \| 5.7736 \| 1836 \| 0.4985 \| 0.5056 \| 0.4985 \| 0.7060 \| \| 0.137 \| 5.7799 \| 1838 \| 0.5032 \| 0.4997 \| 0.5032 \| 0.7093 \| \| 0.137 \| 5.7862 \| 1840 \| 0.5058 \| 0.4975 \| 0.5058 \| 0.7112 \| \| 0.137 \| 5.7925 \| 1842 \| 0.5093 \| 0.5039 \| 0.5093 \| 0.7137 \| \| 0.137 \| 5.7987 \| 1844 \| 0.5199 \| 0.5481 \| 0.5199 \| 0.7210 \| \| 0.137 \| 5.8050 \| 1846 \| 0.5419 \| 0.5858 \| 0.5419 \| 0.7362 \| \| 0.137 \| 5.8113 \| 1848 \| 0.5333 \| 0.5732 \| 0.5333 \| 0.7303 \| \| 0.137 \| 5.8176 \| 1850 \| 0.5221 \| 0.5339 \| 0.5221 \| 0.7226 \| \| 0.137 \| 5.8239 \| 1852 \| 0.5190 \| 0.4942 \| 0.5190 \| 0.7204 \| \| 0.137 \| 5.8302 \| 1854 \| 0.5173 \| 0.5112 \| 0.5173 \| 0.7193 \| \| 0.137 \| 5.8365 \| 1856 \| 0.5310 \| 0.5530 \| 0.5310 \| 0.7287 \| \| 0.137 \| 5.8428 \| 1858 \| 0.5528 \| 0.5771 \| 0.5528 \| 0.7435 \| \| 0.137 \| 5.8491 \| 1860 \| 0.5564 \| 0.5713 \| 0.5564 \| 0.7459 \| \| 0.137 \| 5.8553 \| 1862 \| 0.5457 \| 0.5257 \| 0.5457 \| 0.7387 \| \| 0.137 \| 5.8616 \| 1864 \| 0.5511 \| 0.5368 \| 0.5511 \| 0.7423 \| \| 0.137 \| 5.8679 \| 1866 \| 0.5562 \| 0.5645 \| 0.5562 \| 0.7458 \| \| 0.137 \| 5.8742 \| 1868 \| 0.5371 \| 0.5266 \| 0.5371 \| 0.7329 \| \| 0.137 \| 5.8805 \| 1870 \| 0.5291 \| 0.5413 \| 0.5291 \| 0.7274 \| \| 0.137 \| 5.8868 \| 1872 \| 0.5259 \| 0.5535 \| 0.5259 \| 0.7252 \| \| 0.137 \| 5.8931 \| 1874 \| 0.5369 \| 0.5981 \| 0.5369 \| 0.7327 \| \| 0.137 \| 5.8994 \| 1876 \| 0.5517 \| 0.6147 \| 0.5517 \| 0.7427 \| \| 0.137 \| 5.9057 \| 1878 \| 0.5261 \| 0.5838 \| 0.5261 \| 0.7253 \| \| 0.137 \| 5.9119 \| 1880 \| 0.5091 \| 0.5308 \| 0.5091 \| 0.7135 \| \| 0.137 \| 5.9182 \| 1882 \| 0.5070 \| 0.5485 \| 0.5070 \| 0.7120 \| \| 0.137 \| 5.9245 \| 1884 \| 0.5292 \| 0.6006 \| 0.5292 \| 0.7275 \| \| 0.137 \| 5.9308 \| 1886 \| 0.5539 \| 0.6290 \| 0.5539 \| 0.7442 \| \| 0.137 \| 5.9371 \| 1888 \| 0.5303 \| 0.6176 \| 0.5303 \| 0.7282 \| \| 0.137 \| 5.9434 \| 1890 \| 0.5068 \| 0.5536 \| 0.5068 \| 0.7119 \| \| 0.137 \| 5.9497 \| 1892 \| 0.5092 \| 0.5327 \| 0.5092 \| 0.7136 \| \| 0.137 \| 5.9560 \| 1894 \| 0.5156 \| 0.5453 \| 0.5156 \| 0.7180 \| \| 0.137 \| 5.9623 \| 1896 \| 0.5242 \| 0.5863 \| 0.5242 \| 0.7240 \| \| 0.137 \| 5.9686 \| 1898 \| 0.5535 \| 0.6211 \| 0.5535 \| 0.7440 \| \| 0.137 \| 5.9748 \| 1900 \| 0.5484 \| 0.6188 \| 0.5484 \| 0.7406 \| \| 0.137 \| 5.9811 \| 1902 \| 0.5165 \| 0.5695 \| 0.5165 \| 0.7187 \| \| 0.137 \| 5.9874 \| 1904 \| 0.5091 \| 0.5293 \| 0.5091 \| 0.7135 \| \| 0.137 \| 5.9937 \| 1906 \| 0.5039 \| 0.5467 \| 0.5039 \| 0.7099 \| \| 0.137 \| 6.0 \| 1908 \| 0.5110 \| 0.5860 \| 0.5110 \| 0.7149 \| \| 0.137 \| 6.0063 \| 1910 \| 0.5227 \| 0.6075 \| 0.5227 \| 0.7230 \| \| 0.137 \| 6.0126 \| 1912 \| 0.5158 \| 0.5937 \| 0.5158 \| 0.7182 \| \| 0.137 \| 6.0189 \| 1914 \| 0.5121 \| 0.5814 \| 0.5121 \| 0.7156 \| \| 0.137 \| 6.0252 \| 1916 \| 0.5249 \| 0.6051 \| 0.5249 \| 0.7245 \| \| 0.137 \| 6.0314 \| 1918 \| 0.5245 \| 0.5925 \| 0.5245 \| 0.7242 \| \| 0.137 \| 6.0377 \| 1920 \| 0.5153 \| 0.5620 \| 0.5153 \| 0.7178 \| \| 0.137 \| 6.0440 \| 1922 \| 0.5134 \| 0.5268 \| 0.5134 \| 0.7165 \| \| 0.137 \| 6.0503 \| 1924 \| 0.5214 \| 0.5761 \| 0.5214 \| 0.7221 \| \| 0.137 \| 6.0566 \| 1926 \| 0.5436 \| 0.6034 \| 0.5436 \| 0.7373 \| \| 0.137 \| 6.0629 \| 1928 \| 0.5385 \| 0.5879 \| 0.5385 \| 0.7339 \| \| 0.137 \| 6.0692 \| 1930 \| 0.5294 \| 0.5466 \| 0.5294 \| 0.7276 \| \| 0.137 \| 6.0755 \| 1932 \| 0.5246 \| 0.5303 \| 0.5246 \| 0.7243 \| \| 0.137 \| 6.0818 \| 1934 \| 0.5383 \| 0.5591 \| 0.5383 \| 0.7337 \| \| 0.137 \| 6.0881 \| 1936 \| 0.5384 \| 0.5542 \| 0.5384 \| 0.7338 \| \| 0.137 \| 6.0943 \| 1938 \| 0.5208 \| 0.5393 \| 0.5208 \| 0.7217 \| \| 0.137 \| 6.1006 \| 1940 \| 0.5144 \| 0.5455 \| 0.5144 \| 0.7172 \| \| 0.137 \| 6.1069 \| 1942 \| 0.5280 \| 0.5642 \| 0.5280 \| 0.7266 \| \| 0.137 \| 6.1132 \| 1944 \| 0.5361 \| 0.5867 \| 0.5361 \| 0.7322 \| \| 0.137 \| 6.1195 \| 1946 \| 0.5112 \| 0.5655 \| 0.5112 \| 0.7150 \| \| 0.137 \| 6.1258 \| 1948 \| 0.4995 \| 0.5291 \| 0.4995 \| 0.7067 \| \| 0.137 \| 6.1321 \| 1950 \| 0.5061 \| 0.5169 \| 0.5061 \| 0.7114 \| \| 0.137 \| 6.1384 \| 1952 \| 0.5194 \| 0.5467 \| 0.5194 \| 0.7207 \| \| 0.137 \| 6.1447 \| 1954 \| 0.5554 \| 0.5848 \| 0.5554 \| 0.7453 \| \| 0.137 \| 6.1509 \| 1956 \| 0.5663 \| 0.6031 \| 0.5663 \| 0.7525 \| \| 0.137 \| 6.1572 \| 1958 \| 0.5517 \| 0.5626 \| 0.5517 \| 0.7427 \| \| 0.137 \| 6.1635 \| 1960 \| 0.5537 \| 0.5673 \| 0.5537 \| 0.7441 \| \| 0.137 \| 6.1698 \| 1962 \| 0.5627 \| 0.5826 \| 0.5627 \| 0.7501 \| \| 0.137 \| 6.1761 \| 1964 \| 0.5644 \| 0.6046 \| 0.5644 \| 0.7513 \| \| 0.137 \| 6.1824 \| 1966 \| 0.5350 \| 0.5586 \| 0.5350 \| 0.7315 \| \| 0.137 \| 6.1887 \| 1968 \| 0.5252 \| 0.5379 \| 0.5252 \| 0.7247 \| \| 0.137 \| 6.1950 \| 1970 \| 0.5237 \| 0.5325 \| 0.5237 \| 0.7237 \| \| 0.137 \| 6.2013 \| 1972 \| 0.5287 \| 0.5568 \| 0.5287 \| 0.7271 \| \| 0.137 \| 6.2075 \| 1974 \| 0.5419 \| 0.5726 \| 0.5419 \| 0.7362 \| \| 0.137 \| 6.2138 \| 1976 \| 0.5262 \| 0.5548 \| 0.5262 \| 0.7254 \| \| 0.137 \| 6.2201 \| 1978 \| 0.5228 \| 0.5096 \| 0.5228 \| 0.7231 \| \| 0.137 \| 6.2264 \| 1980 \| 0.5272 \| 0.5423 \| 0.5272 \| 0.7261 \| \| 0.137 \| 6.2327 \| 1982 \| 0.5641 \| 0.6126 \| 0.5641 \| 0.7511 \| \| 0.137 \| 6.2390 \| 1984 \| 0.5698 \| 0.6111 \| 0.5698 \| 0.7549 \| \| 0.137 \| 6.2453 \| 1986 \| 0.5435 \| 0.5857 \| 0.5435 \| 0.7372 \| \| 0.137 \| 6.2516 \| 1988 \| 0.5163 \| 0.5217 \| 0.5163 \| 0.7186 \| \| 0.137 \| 6.2579 \| 1990 \| 0.5172 \| 0.5210 \| 0.5172 \| 0.7192 \| \| 0.137 \| 6.2642 \| 1992 \| 0.5246 \| 0.5537 \| 0.5246 \| 0.7243 \| \| 0.137 \| 6.2704 \| 1994 \| 0.5389 \| 0.5880 \| 0.5389 \| 0.7341 \| \| 0.137 \| 6.2767 \| 1996 \| 0.5522 \| 0.6138 \| 0.5522 \| 0.7431 \| \| 0.137 \| 6.2830 \| 1998 \| 0.5332 \| 0.6009 \| 0.5332 \| 0.7302 \| \| 0.0917 \| 6.2893 \| 2000 \| 0.5090 \| 0.5523 \| 0.5090 \| 0.7134 \| \| 0.0917 \| 6.2956 \| 2002 \| 0.5108 \| 0.5622 \| 0.5108 \| 0.7147 \| \| 0.0917 \| 6.3019 \| 2004 \| 0.5365 \| 0.6050 \| 0.5365 \| 0.7325 \| \| 0.0917 \| 6.3082 \| 2006 \| 0.5334 \| 0.5934 \| 0.5334 \| 0.7304 \| \| 0.0917 \| 6.3145 \| 2008 \| 0.5195 \| 0.5594 \| 0.5195 \| 0.7208 \| \| 0.0917 \| 6.3208 \| 2010 \| 0.5208 \| 0.5328 \| 0.5208 \| 0.7216 \| \| 0.0917 \| 6.3270 \| 2012 \| 0.5282 \| 0.5464 \| 0.5282 \| 0.7268 \| \| 0.0917 \| 6.3333 \| 2014 \| 0.5418 \| 0.5642 \| 0.5418 \| 0.7361 \| \| 0.0917 \| 6.3396 \| 2016 \| 0.5689 \| 0.6043 \| 0.5689 \| 0.7542 \| \| 0.0917 \| 6.3459 \| 2018 \| 0.5674 \| 0.5949 \| 0.5674 \| 0.7533 \| \| 0.0917 \| 6.3522 \| 2020 \| 0.5468 \| 0.5597 \| 0.5468 \| 0.7394 \| \| 0.0917 \| 6.3585 \| 2022 \| 0.5329 \| 0.5195 \| 0.5329 \| 0.7300 \| \| 0.0917 \| 6.3648 \| 2024 \| 0.5310 \| 0.5210 \| 0.5310 \| 0.7287 \| \| 0.0917 \| 6.3711 \| 2026 \| 0.5270 \| 0.5262 \| 0.5270 \| 0.7260 \| \| 0.0917 \| 6.3774 \| 2028 \| 0.5316 \| 0.5641 \| 0.5316 \| 0.7291 \| \| 0.0917 \| 6.3836 \| 2030 \| 0.5297 \| 0.5760 \| 0.5297 \| 0.7278 \| \| 0.0917 \| 6.3899 \| 2032 \| 0.5219 \| 0.5727 \| 0.5219 \| 0.7224 \| \| 0.0917 \| 6.3962 \| 2034 \| 0.5155 \| 0.5714 \| 0.5155 \| 0.7180 \| \| 0.0917 \| 6.4025 \| 2036 \| 0.5086 \| 0.5673 \| 0.5086 \| 0.7131 \| \| 0.0917 \| 6.4088 \| 2038 \| 0.5175 \| 0.5884 \| 0.5175 \| 0.7194 \| \| 0.0917 \| 6.4151 \| 2040 \| 0.5283 \| 0.6030 \| 0.5283 \| 0.7268 \| \| 0.0917 \| 6.4214 \| 2042 \| 0.5162 \| 0.5871 \| 0.5162 \| 0.7185 \| \| 0.0917 \| 6.4277 \| 2044 \| 0.5080 \| 0.5583 \| 0.5080 \| 0.7127 \| \| 0.0917 \| 6.4340 \| 2046 \| 0.5050 \| 0.5511 \| 0.5050 \| 0.7106 \| \| 0.0917 \| 6.4403 \| 2048 \| 0.5148 \| 0.5614 \| 0.5148 \| 0.7175 \| \| 0.0917 \| 6.4465 \| 2050 \| 0.5400 \| 0.6043 \| 0.5400 \| 0.7349 \| \| 0.0917 \| 6.4528 \| 2052 \| 0.5301 \| 0.5940 \| 0.5301 \| 0.7281 \| \| 0.0917 \| 6.4591 \| 2054 \| 0.5091 \| 0.5362 \| 0.5091 \| 0.7135 \| \| 0.0917 \| 6.4654 \| 2056 \| 0.5088 \| 0.5027 \| 0.5088 \| 0.7133 \| \| 0.0917 \| 6.4717 \| 2058 \| 0.5032 \| 0.5111 \| 0.5032 \| 0.7094 \| \| 0.0917 \| 6.4780 \| 2060 \| 0.5047 \| 0.5513 \| 0.5047 \| 0.7104 \| \| 0.0917 \| 6.4843 \| 2062 \| 0.5077 \| 0.5645 \| 0.5077 \| 0.7125 \| \| 0.0917 \| 6.4906 \| 2064 \| 0.5032 \| 0.5298 \| 0.5032 \| 0.7093 \| \| 0.0917 \| 6.4969 \| 2066 \| 0.4995 \| 0.5231 \| 0.4995 \| 0.7067 \| \| 0.0917 \| 6.5031 \| 2068 \| 0.5037 \| 0.5341 \| 0.5037 \| 0.7097 \| \| 0.0917 \| 6.5094 \| 2070 \| 0.5179 \| 0.5775 \| 0.5179 \| 0.7197 \| \| 0.0917 \| 6.5157 \| 2072 \| 0.5109 \| 0.5644 \| 0.5109 \| 0.7148 \| \| 0.0917 \| 6.5220 \| 2074 \| 0.5079 \| 0.5408 \| 0.5079 \| 0.7127 \| \| 0.0917 \| 6.5283 \| 2076 \| 0.5076 \| 0.5336 \| 0.5076 \| 0.7125 \| \| 0.0917 \| 6.5346 \| 2078 \| 0.5061 \| 0.5336 \| 0.5061 \| 0.7114 \| \| 0.0917 \| 6.5409 \| 2080 \| 0.5077 \| 0.5402 \| 0.5077 \| 0.7125 \| \| 0.0917 \| 6.5472 \| 2082 \| 0.5271 \| 0.5889 \| 0.5271 \| 0.7260 \| \| 0.0917 \| 6.5535 \| 2084 \| 0.5338 \| 0.6049 \| 0.5338 \| 0.7306 \| \| 0.0917 \| 6.5597 \| 2086 \| 0.5223 \| 0.5777 \| 0.5223 \| 0.7227 \| \| 0.0917 \| 6.5660 \| 2088 \| 0.5172 \| 0.5149 \| 0.5172 \| 0.7192 \| \| 0.0917 \| 6.5723 \| 2090 \| 0.5206 \| 0.5042 \| 0.5206 \| 0.7215 \| \| 0.0917 \| 6.5786 \| 2092 \| 0.5265 \| 0.5575 \| 0.5265 \| 0.7256 \| \| 0.0917 \| 6.5849 \| 2094 \| 0.5407 \| 0.5997 \| 0.5407 \| 0.7353 \| \| 0.0917 \| 6.5912 \| 2096 \| 0.5554 \| 0.6111 \| 0.5554 \| 0.7453 \| \| 0.0917 \| 6.5975 \| 2098 \| 0.5409 \| 0.6005 \| 0.5409 \| 0.7355 \| \| 0.0917 \| 6.6038 \| 2100 \| 0.5386 \| 0.6022 \| 0.5386 \| 0.7339 \| \| 0.0917 \| 6.6101 \| 2102 \| 0.5355 \| 0.6058 \| 0.5355 \| 0.7318 \| \| 0.0917 \| 6.6164 \| 2104 \| 0.5200 \| 0.5805 \| 0.5200 \| 0.7211 \| \| 0.0917 \| 6.6226 \| 2106 \| 0.5044 \| 0.5561 \| 0.5044 \| 0.7102 \| \| 0.0917 \| 6.6289 \| 2108 \| 0.5027 \| 0.5571 \| 0.5027 \| 0.7090 \| \| 0.0917 \| 6.6352 \| 2110 \| 0.5132 \| 0.5724 \| 0.5132 \| 0.7164 \| \| 0.0917 \| 6.6415 \| 2112 \| 0.5343 \| 0.6068 \| 0.5343 \| 0.7309 \| \| 0.0917 \| 6.6478 \| 2114 \| 0.5504 \| 0.6179 \| 0.5504 \| 0.7419 \| \| 0.0917 \| 6.6541 \| 2116 \| 0.5244 \| 0.5962 \| 0.5244 \| 0.7242 \| \| 0.0917 \| 6.6604 \| 2118 \| 0.5086 \| 0.5595 \| 0.5086 \| 0.7132 \| \| 0.0917 \| 6.6667 \| 2120 \| 0.5043 \| 0.5187 \| 0.5043 \| 0.7102 \| \| 0.0917 \| 6.6730 \| 2122 \| 0.5095 \| 0.5412 \| 0.5095 \| 0.7138 \| \| 0.0917 \| 6.6792 \| 2124 \| 0.5294 \| 0.5747 \| 0.5294 \| 0.7276 \| \| 0.0917 \| 6.6855 \| 2126 \| 0.5357 \| 0.5965 \| 0.5357 \| 0.7319 \| \| 0.0917 \| 6.6918 \| 2128 \| 0.5336 \| 0.5904 \| 0.5336 \| 0.7305 \| \| 0.0917 \| 6.6981 \| 2130 \| 0.5382 \| 0.6041 \| 0.5382 \| 0.7336 \| \| 0.0917 \| 6.7044 \| 2132 \| 0.5238 \| 0.5798 \| 0.5238 \| 0.7237 \| \| 0.0917 \| 6.7107 \| 2134 \| 0.5117 \| 0.5626 \| 0.5117 \| 0.7153 \| \| 0.0917 \| 6.7170 \| 2136 \| 0.5085 \| 0.5562 \| 0.5085 \| 0.7131 \| \| 0.0917 \| 6.7233 \| 2138 \| 0.5082 \| 0.5607 \| 0.5082 \| 0.7129 \| \| 0.0917 \| 6.7296 \| 2140 \| 0.5110 \| 0.5692 \| 0.5110 \| 0.7149 \| \| 0.0917 \| 6.7358 \| 2142 \| 0.5086 \| 0.5718 \| 0.5086 \| 0.7131 \| \| 0.0917 \| 6.7421 \| 2144 \| 0.5002 \| 0.5517 \| 0.5002 \| 0.7073 \| \| 0.0917 \| 6.7484 \| 2146 \| 0.4968 \| 0.5362 \| 0.4968 \| 0.7049 \| \| 0.0917 \| 6.7547 \| 2148 \| 0.5065 \| 0.5680 \| 0.5065 \| 0.7117 \| \| 0.0917 \| 6.7610 \| 2150 \| 0.5217 \| 0.5868 \| 0.5217 \| 0.7223 \| \| 0.0917 \| 6.7673 \| 2152 \| 0.5359 \| 0.5932 \| 0.5359 \| 0.7321 \| \| 0.0917 \| 6.7736 \| 2154 \| 0.5440 \| 0.5864 \| 0.5440 \| 0.7376 \| \| 0.0917 \| 6.7799 \| 2156 \| 0.5253 \| 0.5470 \| 0.5253 \| 0.7248 \| \| 0.0917 \| 6.7862 \| 2158 \| 0.5211 \| 0.5009 \| 0.5211 \| 0.7219 \| \| 0.0917 \| 6.7925 \| 2160 \| 0.5242 \| 0.4876 \| 0.5242 \| 0.7240 \| \| 0.0917 \| 6.7987 \| 2162 \| 0.5241 \| 0.5217 \| 0.5241 \| 0.7240 \| \| 0.0917 \| 6.8050 \| 2164 \| 0.5352 \| 0.5466 \| 0.5352 \| 0.7316 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
ayrisdev/mobile-ui-design	ayrisdev	2024-11-06T13:58:06Z	128	1	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-06T13:08:26Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: mobiluidesign --- # Mobile Ui Design <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mobiluidesign` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ayrisdev/mobile-ui-design', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO	DiTy	2024-11-06T13:56:17Z	147	1	transformers	[ "transformers", "safetensors", "gguf", "gemma2", "text-generation", "conversational", "function-calling", "strict-function-calling", "trl", "ru", "dataset:DiTy/function-calling-trl", "base_model:DiTy/gemma-2-9b-it-russian-function-calling-GGUF", "base_model:quantized:DiTy/gemma-2-9b-it-russian-function-calling-GGUF", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-24T07:24:57Z	--- base_model: DiTy/gemma-2-9b-it-russian-function-calling-GGUF datasets: - DiTy/function-calling-trl language: - ru library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - conversational - gemma2 - function-calling - strict-function-calling - trl --- # DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO This model is a aligned version of [DiTy/gemma-2-9b-it-russian-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) for more Strict Function Calling task, on the Russian version of the DiTy/function-calling-trl dataset. Which means that the model avoids answering user questions that are not related to the described functions. The model has been trained using non-synthetic preference data, fully annotated by humans only, on the Russian version of the <ins>DiTy/function-calling-trl</ins> dataset. In addition to safetensors, the model is available in GGUF [(gemma-2-9B-it-russian-function-calling-DPO-F16.gguf)](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO/blob/main/gemma-2-9B-it-russian-function-calling-DPO-F16.gguf) format (in this case, you need to download only a single file ([how to inference GGUF model](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#high-level-api))). ## Stricted Effect Это означает, что модель избегает ответов на вопросы пользователей, которые не связаны с описанными функциями. Ниже описаны несколько примеров поведения модели до и после preference optimization. ```python def get_weather(city: str): """ Функция, которая возвращает погоду в заданном городе. Args: city: Город, для которого надо узнать погоду. """ import random return ("weather", '"sunny"' if random.random() > 0.5 else '"rainy"') def get_sunrise_sunset_times(city: str): """ Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time]. Args: city: Город, в котором можно узнать время восхода и захода солнца. """ return ["6:00", "18:00"] history_messages = [ {"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "}, {"role": "user", "content": USER_MESSAGE}, ] inputs = tokenizer.apply_chat_template( history_messages, tokenize=False, add_generation_prompt=True, # adding prompt for generation tools=[get_weather, get_sunrise_sunset_times], # our functions (tools) ) ``` Ответ модели при `USER_MESSAGE = "кто такой Зевс?"`: * [DiTy/gemma-2-9b-it-russian-function-calling-GGUF <ins>(SFT)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ``` Зевс - это главный бог в греческой мифологии. Он является богом неба, молнии и грома, а также покровителем царей и правосудия. ``` * [Эта модель <ins>(DPO)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO) ``` Извините, но я не могу ответить на ваш вопрос. Мои текущие возможности позволяют мне получать погоду в заданном городе или узнавать время восхода и заката солнца для заданного города. ``` Ответ модели при `USER_MESSAGE = "придумай мне историю о щуке"`: * [DiTy/gemma-2-9b-it-russian-function-calling-GGUF <ins>(SFT)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ``` В глубинах синего озера, где солнечные лучи едва проникали сквозь толщу воды, жила щука по имени Агата. Она была хитра и ловка, с серебристыми чешуйками, которые переливались, как драгоценные камни, и острыми зубами, способными разорвать любую добычу. Агата была самой опытной щукой в озере, и все остальные рыбы знали о ее грозной репутации. Однажды, когда Агата охотилась на мальков, она заметила странную рыбу, которая плавала совсем не так, как все остальные. Эта рыба была яркой, с разноцветными плавниками, и она не пыталась укрыться от Агаты, как это делали другие рыбы. Агата, всегда готовая к охоте, приблизилась к ней, пытаясь поймать ее в свои острые зубы. Но эта рыба, которая называлась Лилия, не была обычной рыбой. Она была искусственной, созданной учеными, чтобы изучать поведение рыб в естественной среде. Лилия была оснащена датчиками, которые собирали данные о температуре воды ``` * [Эта модель <ins>(DPO)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO) ``` Извините, но я не могу придумать историю о щуке. Мои возможности ограничены доступными мне функциями. В данный момент я могу только: * Узнать погоду в заданном городе. * Узнать время восхода и заката солнца для заданного города. ``` ## Рекомендация к стратегии генерации По умолчанию модель использует жадный поиск вместе с данным конфигом: ``` GenerationConfig { "bos_token_id": 2, "cache_implementation": "hybrid", "eos_token_id": 1, "pad_token_id": 0 } ``` Для наиболее стабильной работы рекомендуется следующая стратегия генерации: \| Key \| Value \| \| --- \| ----- \| \| `do_sample` \| True \| \| `temperature` \| 0.2 \| \| `top_k` \| 40 \| \| `top_p` \| 0.95 \| \| `min_p` \| 0.5 \| ## Подробности генерации с вызовом функций С более подробной информацией можно ознакомиться в основной [Model Card](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ## Citation ```none @article{gemma_2024, title={Gemma}, url={https://www.kaggle.com/m/3301}, DOI={10.34740/KAGGLE/M/3301}, publisher={Kaggle}, author={Gemma Team}, year={2024} } ```
yoohj58072/krx_qwen2.5_7b_it_v3	yoohj58072	2024-11-06T13:52:51Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T13:48:42Z	--- base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: yoohj58072 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF	MaziyarPanahi	2024-11-06T13:50:52Z	47	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.2", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.2", "region:us", "conversational" ]	text-generation	2024-11-06T13:28:30Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.2-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.2 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.2](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.2) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.2](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.2). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
prithivMLmods/Lime-Green-Flux-LoRA	prithivMLmods	2024-11-06T13:50:42Z	36	9	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-11-06T10:40:30Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'Lime Green, An abstract painting of a hamburger on a green background. The hamburger is composed of a large red bun, with a thick layer of cheese between the bun and two slices of tomatoes. The tomatoes are red, and the lettuce is a vibrant green. The bun has a layer of white crumbs on top of it. There is a black base on the bottom of the hamburger.' output: url: images/LM1.png - text: 'Lime Green, An eye-level painting of a gray and white cat with green eyes. The cats ears are sticking up, and the cats nose is pointed towards the left side of the frame. Its whiskers are white, and there are splashes of black on the cat. The background of the painting is a vibrant lime green, with black splashes all around the cat, adding a pop of color to the scene.' output: url: images/LM2.png - text: 'Lime Green, An eye-level painting of a womans face, painted in a vibrant shade of green and red. Her lips are painted a deep red, and her eyes are closed. Her eyebrows are a darker shade of black, while her lips are a lighter shade of red. She is wearing a black baseball cap, and she is holding a phone in her right hand. Her hair is pulled back in a ponytail, adding a pop of color to her face. The background is a lime green, and there are a few lines of paint splattered across the image, adding depth to the composition.' output: url: images/LG0.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: Lime Green license: creativeml-openrail-m --- # Lime-Green-Flux-LoRA <Gallery /> - Hosted Here🧨: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases. ## Model description prithivMLmods/Lime-Green-Flux-LoRA Image Processing Parameters \| Parameter \| Value \| Parameter \| Value \| \|---------------------------\|--------\|---------------------------\|--------\| \| LR Scheduler \| constant \| Noise Offset \| 0.03 \| \| Optimizer \| AdamW \| Multires Noise Discount \| 0.1 \| \| Network Dim \| 64 \| Multires Noise Iterations \| 10 \| \| Network Alpha \| 32 \| Repeat & Steps \| 20 & 1800\| \| Epoch \| 10 \| Save Every N Epochs \| 1 \| Labeling: florence2-en(natural language & English) Total Images Used for Training : 13 [ Hi-RES ] ## Best Dimensions - 1024 x 1024 (Default) ## Setting Up ``` import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Lime-Green-Flux-LoRA" trigger_word = "Lime Green" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Data source - https://freeflo.ai/ ## Trigger words You should use `Lime Green` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Lime-Green-Flux-LoRA/tree/main) them in the Files & versions tab.
BlackBeenie/Llama-3.1-8B-pythonic-passthrough-merge	BlackBeenie	2024-11-06T13:49:14Z	5	0	null	[ "safetensors", "llama", "merge", "mergekit", "lazymergekit", "Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "Solshine/reflection-llama-3.1-8B", "base_model:Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "base_model:merge:Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "base_model:Solshine/reflection-llama-3.1-8B", "base_model:merge:Solshine/reflection-llama-3.1-8B", "base_model:mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "base_model:merge:mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "region:us" ]	null	2024-11-06T13:38:03Z	--- base_model: - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - Solshine/reflection-llama-3.1-8B - mlabonne/Hermes-3-Llama-3.1-8B-lorablated tags: - merge - mergekit - lazymergekit - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B --- # Bloslain-8B-v0.1 Bloslain-8B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) ## 🧩 Configuration ```yaml slices: - sources: - layer_range: [0, 8] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [0, 16] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated - sources: - layer_range: [4, 20] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [8, 24] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [12, 28] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [16, 32] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated merge_method: passthrough dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "BlackBeenie/Bloslain-8B-v0.1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
ngwgsang/bartpho-word-base-vimex	ngwgsang	2024-11-06T13:49:01Z	104	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-06T13:48:41Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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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vislupus/bulgarian-joke-master-Qwen2.5-0.5B-Instruct-bnb-4bit-gguf	vislupus	2024-11-06T13:48:33Z	5	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T13:48:26Z	--- base_model: unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf --- # Uploaded model - Developed by: vislupus - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
yoohj58072/krx_qwen2_7b_it_v3	yoohj58072	2024-11-06T13:47:36Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T13:42:40Z	--- base_model: unsloth/Qwen2-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - Developed by: yoohj58072 - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mlx-community/Qwen2.5-7B-Instruct-4bit	mlx-community	2024-11-06T13:47:36Z	206,381	7	mlx	[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "license:apache-2.0", "region:us" ]	text-generation	2024-09-18T20:06:25Z	--- base_model: Qwen/Qwen2.5-7B language: - en license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - mlx --- # mlx-community/Qwen2.5-7B-Instruct-4bit The Model [mlx-community/Qwen2.5-7B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-7B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using mlx-lm version 0.18.1. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen2.5-7B-Instruct-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
KPEP/krx-qwen-2.5-7b-v1.4.8	KPEP	2024-11-06T13:46:20Z	6	0	null	[ "safetensors", "qwen2", "krx", "unsloth", "trl", "sft", "ko", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "region:us" ]	null	2024-11-06T13:27:24Z	--- base_model: - Qwen/Qwen2.5-7B language: - ko tags: - krx - unsloth - trl - sft ---
ItchyChin/OrpoLlama-3-8B-memorize-translate-retrain-merge-all-20241105	ItchyChin	2024-11-06T13:37:29Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T13:11:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mikiwazang/Celestial	mikiwazang	2024-11-06T13:34:22Z	6	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:cc-by-4.0", "region:us" ]	text-to-image	2024-11-06T13:34:15Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: >- images/workspace_trainsamples_792961987880608730_1425337e-bef3-4389-8b52-ee2e2c8390d2.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Celestial Fantasy license: cc-by-4.0 --- # Celestial Fantasy <Gallery /> ## Trigger words You should use `Celestial Fantasy` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/mikiwazang/Celestial/tree/main) them in the Files & versions tab.
giacomoarienti/nsfw-classifier	giacomoarienti	2024-11-06T13:20:07Z	94,576	27	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "huggingpics", "dataset:deepghs/nsfw_detect", "doi:10.57967/hf/2906", "license:cc-by-nc-nd-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-05T12:19:30Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: nsfw-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9200000166893005 datasets: - deepghs/nsfw_detect license: cc-by-nc-nd-4.0 --- # 🚫 NSFW Classifier - Keep Your Platform Safe and Secure! ### An AI-powered image classifier designed to detect and prevent NSFW content (porn, hentai, sexy images) from being posted on your platform. Trusted by thousands of developers, this solution is perfect for any app or platform that allows users to upload images. --- ## 🚀 Why Choose Our NSFW Image Classifier? In today's digital world, user-generated content is a double-edged sword. While it fosters creativity and engagement, it also opens the door to inappropriate or illegal content being shared. Our NSFW Image Classifier is specifically designed to identify and filter out explicit images, including pornography, hentai, and sexually suggestive content, ensuring your platform remains safe, secure, and legally compliant. ### 🌟 Key Benefits: - Protect Your User Base: Keep your community safe by preventing exposure to inappropriate content. - Legal Compliance: Avoid legal action by preventing illegal or explicit content from being posted. - Seamless Integration: Our model is easy to integrate into any platform that allows image uploads, including social media, forums, e-commerce sites, and more. --- ## 🔥 Proven Solution - Trusted by Thousands! With 60,000 downloads per month, our NSFW Image Classifier has become the go-to solution for platforms looking to maintain a clean and safe environment for their users. Many developers and companies have already chosen our solution to protect their communities—will you be next? --- ## 📦 How It Works 1. Upload an Image: The user uploads an image to your platform. 2. NSFW Detection: Our model analyzes the image and flags any explicit content (porn, hentai, sexy images). 3. Moderation: Take appropriate action, whether it's preventing the upload or flagging the content for review. ### Who Can Benefit? - Social Media Platforms - Online Forums - E-Commerce Sites - Content Sharing Apps - Any platform allowing user-uploaded images --- ## 🚀 Looking for Even More Power? Want a model that's even more powerful and accurate? We've got a premium version trained on a curated, high-quality dataset that can detect a wider range of illegal content, including gore, harmful images, under 18 content, and more. 📩 Contact me on Telegram [@mrjack7](https://t.me/mrjack7) for more details on the premium model! --- ## 🌐 API Access 💻 Need easy integration? API access is available for seamless deployment into your applications. Whether you're looking to integrate our NSFW image detection capabilities or require more advanced features, our API provides a flexible and scalable solution. 📩 Contact me on Telegram [@mrjack7](https://t.me/mrjack7) for more details on API access! --- Let's build something amazing together. 💡
Lolimorimorf/damage_trigger_effect_2024-11-06_13_00	Lolimorimorf	2024-11-06T13:19:36Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-06T13:18:52Z	--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: damage_trigger_effect_2024-11-06_13_00 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. --> # damage_trigger_effect_2024-11-06_13_00 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5939 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 34 \| 0.6901 \| \| No log \| 2.0 \| 68 \| 0.5241 \| \| No log \| 3.0 \| 102 \| 0.4540 \| \| No log \| 4.0 \| 136 \| 0.4661 \| \| No log \| 5.0 \| 170 \| 0.4877 \| \| No log \| 6.0 \| 204 \| 0.4716 \| \| No log \| 7.0 \| 238 \| 0.4778 \| \| No log \| 8.0 \| 272 \| 0.5008 \| \| No log \| 9.0 \| 306 \| 0.5195 \| \| No log \| 10.0 \| 340 \| 0.5669 \| \| No log \| 11.0 \| 374 \| 0.5807 \| \| No log \| 12.0 \| 408 \| 0.5776 \| \| No log \| 13.0 \| 442 \| 0.5938 \| \| No log \| 14.0 \| 476 \| 0.5854 \| \| 0.2729 \| 15.0 \| 510 \| 0.5939 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
mradermacher/tamil-llama-13b-instruct-v0.1-GGUF	mradermacher	2024-11-06T13:15:19Z	11	0	transformers	[ "transformers", "gguf", "ta", "en", "base_model:abhinand/tamil-llama-13b-instruct-v0.1", "base_model:quantized:abhinand/tamil-llama-13b-instruct-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-11-06T11:57:35Z	--- base_model: abhinand/tamil-llama-13b-instruct-v0.1 language: - ta - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 6.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 7.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 8.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 9.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q6_K.gguf) \| Q6_K \| 10.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q8_0.gguf) \| Q8_0 \| 14.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
apto-as/multilingual-e5-large-Q8_0-GGUF	apto-as	2024-11-06T13:09:59Z	5	0	sentence-transformers	[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "feature-extraction", "llama-cpp", "gguf-my-repo", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "base_model:intfloat/multilingual-e5-large", "base_model:quantized:intfloat/multilingual-e5-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	feature-extraction	2024-11-06T13:09:52Z	--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers - llama-cpp - gguf-my-repo language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit base_model: intfloat/multilingual-e5-large model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - 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type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 --- # apto-as/multilingual-e5-large-Q8_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ```
MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF	MaziyarPanahi	2024-11-06T13:08:07Z	42	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.3", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.3", "region:us", "conversational" ]	text-generation	2024-11-06T12:45:32Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.3-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.3 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.3](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.3) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.3](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.3). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
ihughes15234/phi35_kp_dpo7epoch_total	ihughes15234	2024-11-06T13:07:12Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:ihughes15234/phi35_kp_dpo5epoch_total", "base_model:finetune:ihughes15234/phi35_kp_dpo5epoch_total", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T13:05:11Z	--- base_model: ihughes15234/phi35_kp_dpo5epoch_total language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: ihughes15234 - License: apache-2.0 - Finetuned from model : ihughes15234/phi35_kp_dpo5epoch_total This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
usmanyaqoob/xlm-roberta-base-finetuned-panx-all	usmanyaqoob	2024-11-06T13:05:35Z	128	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-06T12:51:16Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all 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. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - F1: 0.8595 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2895 \| 1.0 \| 835 \| 0.1821 \| 0.8210 \| \| 0.1514 \| 2.0 \| 1670 \| 0.1707 \| 0.8444 \| \| 0.0966 \| 3.0 \| 2505 \| 0.1763 \| 0.8595 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Ivaylo-Iv/ai_train_poc_food_text_classifier	Ivaylo-Iv	2024-11-06T13:05:17Z	117	0	transformers	[ "transformers", "safetensors", "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	2024-11-06T12:51:57Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_train_poc_food_text_classifier 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. --> # ai_train_poc_food_text_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0019 - Accuracy: 1.0 ## 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: 0.0001 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0005 \| 1.0 \| 7 \| 0.0012 \| 1.0 \| \| 0.0004 \| 2.0 \| 14 \| 0.0013 \| 1.0 \| \| 0.0003 \| 3.0 \| 21 \| 0.0014 \| 1.0 \| \| 0.0003 \| 4.0 \| 28 \| 0.0015 \| 1.0 \| \| 0.0002 \| 5.0 \| 35 \| 0.0016 \| 1.0 \| \| 0.0002 \| 6.0 \| 42 \| 0.0017 \| 1.0 \| \| 0.0002 \| 7.0 \| 49 \| 0.0018 \| 1.0 \| \| 0.0002 \| 8.0 \| 56 \| 0.0018 \| 1.0 \| \| 0.0002 \| 9.0 \| 63 \| 0.0019 \| 1.0 \| \| 0.0002 \| 10.0 \| 70 \| 0.0019 \| 1.0 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF	mradermacher	2024-11-06T12:54:07Z	14	0	transformers	[ "transformers", "gguf", "en", "dataset:lodrick-the-lafted/Hermes-100K", "dataset:garage-bAInd/Open-Platypus", "dataset:jondurbin/airoboros-3.2", "base_model:lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B", "base_model:quantized:lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T08:58:38Z	--- base_model: lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B datasets: - lodrick-the-lafted/Hermes-100K - garage-bAInd/Open-Platypus - jondurbin/airoboros-3.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q8_0.gguf) \| Q8_0 \| 13.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
usmanyaqoob/xlm-roberta-base-finetuned-panx-it	usmanyaqoob	2024-11-06T12:49:06Z	125	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-06T12:44:54Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it 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. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2984 - F1: 0.7862 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7571 \| 1.0 \| 70 \| 0.3568 \| 0.7210 \| \| 0.296 \| 2.0 \| 140 \| 0.2998 \| 0.7474 \| \| 0.1974 \| 3.0 \| 210 \| 0.2984 \| 0.7862 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
oodeh/openshift-qe-r16-a16-epoch5-merged-model	oodeh	2024-11-06T12:47:53Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-11-06T12:44:34Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: oodeh - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
besimray/miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832	besimray	2024-11-06T12:47:30Z	5	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]	null	2024-11-06T12:40:32Z	--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 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. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - o1-journey_train_data.json ds_type: json path: /workspace/input_data/o1-journey_train_data.json type: field_input: question field_instruction: answer field_output: longCOT system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 10 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hours_to_complete: 2 hub_model_id: besimray/miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/o1-journey_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 save_strategy: steps sequence_len: 4096 started_at: '2024-11-06T12:40:32.335166' strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: besimray24-rayon wandb_mode: online wandb_project: Public_TuningSN wandb_run: miner_id_24 wandb_runid: c4650a4d-c32d-4e0f-a27c-171ba40b07e6 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7048 ## 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 53 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.9692 \| 0.0571 \| 1 \| 1.0109 \| \| 0.8542 \| 0.5714 \| 10 \| 0.9411 \| \| 0.7241 \| 1.1429 \| 20 \| 0.7844 \| \| 0.678 \| 1.7143 \| 30 \| 0.7272 \| \| 0.6679 \| 2.2857 \| 40 \| 0.7076 \| \| 0.6468 \| 2.8571 \| 50 \| 0.7048 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
hadamelino/vit-gpt-vizwiz-fine-tuned-10k	hadamelino	2024-11-06T12:44:50Z	49	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-11-06T12:30:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
usmanyaqoob/xlm-roberta-base-finetuned-panx-fr	usmanyaqoob	2024-11-06T12:44:44Z	125	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-06T12:40:27Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr 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. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2463 - F1: 0.8537 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5392 \| 1.0 \| 191 \| 0.3301 \| 0.7574 \| \| 0.245 \| 2.0 \| 382 \| 0.2521 \| 0.8340 \| \| 0.1605 \| 3.0 \| 573 \| 0.2463 \| 0.8537 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
jazzson/adl-hw3-finetune-gemma-2-chinese-kyara-10	jazzson	2024-11-06T12:44:34Z	9	0	peft	[ "peft", "tensorboard", "safetensors", "gemma2", "generated_from_trainer", "base_model:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "base_model:adapter:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "license:gemma", "region:us" ]	null	2024-11-06T10:58:50Z	--- base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo library_name: peft license: gemma tags: - generated_from_trainer model-index: - name: adl-hw3-finetune-gemma-2-chinese-kyara-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. --> # adl-hw3-finetune-gemma-2-chinese-kyara-10 This model is a fine-tuned version of [zake7749/gemma-2-2b-it-chinese-kyara-dpo](https://huggingface.co/zake7749/gemma-2-2b-it-chinese-kyara-dpo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5204 ## 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: 42 - 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 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4673 \| 0.7105 \| 200 \| 2.3491 \| \| 2.0993 \| 1.4210 \| 400 \| 2.3077 \| \| 1.8831 \| 2.1314 \| 600 \| 2.3793 \| \| 1.6486 \| 2.8419 \| 800 \| 2.3754 \| \| 1.4189 \| 3.5524 \| 1000 \| 2.5204 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.1 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
HugoVoxx/Gemma-2-2b-it-ag-merged-model	HugoVoxx	2024-11-06T12:39:57Z	9	0	transformers	[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-21T14:56:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
projecte-aina/aina-translator-de-ca	projecte-aina	2024-11-06T12:39:02Z	11	0	fairseq	[ "fairseq", "de", "ca", "dataset:projecte-aina/CA-DE_Parallel_Corpus", "doi:10.57967/hf/1930", "license:apache-2.0", "region:us" ]	null	2023-11-27T13:03:49Z	--- license: apache-2.0 datasets: - projecte-aina/CA-DE_Parallel_Corpus language: - de - ca metrics: - bleu library_name: fairseq --- ## Projecte Aina’s German-Catalan machine translation model ## Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of datasets comprising both Catalan-German data sourced from Opus, and additional datasets where synthetic Catalan was generated from the Spanish side of Spanish-Germancorpora using [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). This gave a total of approximately 100 million sentence pairs. The model is evaluated on the Flores, NTEU and NTREX evaluation sets. ## Intended uses and limitations You can use this model for machine translation from German to Catalan. ## How to use ### Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-de-ca", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Willkommen beim Projekt Aina") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data The model was trained on a combination of the following datasets: \| Datasets \| \|-------------------\| \| Multi CCAligned \| \| WikiMatrix \| \| GNOME \| \| KDE4 \| \| OpenSubtitles \| \| GlobalVoices\| \| Tatoeba \| \| Books \| \| Europarl \| \| Tilde \| \| Multi-Paracawl \| \| DGT \| \| EU Bookshop \| \| NLLB \| \| OpenSubtitles \| All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/). The Europarl and Tilde corpora are a synthetic parallel corpus created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala). Where a Spanish-German corpus was used, synthetic Catalan was generated from the Spanish side using [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). ### Training procedure ### Data preparation All datasets are deduplicated, filtered for language identification, and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 6.258.272 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) #### Tokenization All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included. #### Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparameters were set on the Fairseq toolkit: \| Hyperparameter \| Value \| \|------------------------------------\|----------------------------------\| \| Architecture \| transformer_vaswani_wmt_en_de_big \| \| Embedding size \| 1024 \| \| Feedforward size \| 4096 \| \| Number of heads \| 16 \| \| Encoder layers \| 24 \| \| Decoder layers \| 6 \| \| Normalize before attention \| True \| \| --share-decoder-input-output-embed \| True \| \| --share-all-embeddings \| True \| \| Effective batch size \| 48.000 \| \| Optimizer \| adam \| \| Adam betas \| (0.9, 0.980) \| \| Clip norm \| 0.0 \| \| Learning rate \| 5e-4 \| \| Lr. schedurer \| inverse sqrt \| \| Warmup updates \| 8000 \| \| Dropout \| 0.1 \| \| Label smoothing \| 0.1 \| The model was trained for a total of 29.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 2 checkpoints. ## Evaluation ### Variable and metrics We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores), NTEU (unpublished) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets. ### Evaluation results Below are the evaluation results on the machine translation from German to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): \| Test set \| SoftCatalà \| Google Translate \| aina-translator-de-ca \| \|----------------------\|------------\|------------------\|---------------\| \| Flores 101 dev \| 28,9 \| 35,1 \| 33,1 \| \| Flores 101 devtest \|29,2 \| 35,9 \| 33,2 \| \| NTEU \| 38,9 \| 39,1 \| 42,9 \| \| NTREX \| 25,7 \| 31,2 \| 29,1 \| \| Average \| 30,7 \| 35,3 \| 34,3 \| ## Additional information ### Author The Language Technologies Unit from Barcelona Supercomputing Center. ### Contact For further information, please send an email to <langtech@bsc.es>. ### Copyright Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). ### Disclaimer <details> <summary>Click to expand</summary> The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. Be aware that the model may have biases and/or any other undesirable distortions. When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties. </details>
sweetssweets/NMRExtractor	sweetssweets	2024-11-06T12:38:21Z	6	1	null	[ "safetensors", "mistral", "license:mit", "region:us" ]	null	2024-08-17T10:07:51Z	--- license: mit --- ## For a detailed description of the model, please refer to: '''https://github.com/eat-sugar/NMRExtractor'''
Xu-Ouyang/pythia-12b-deduped-int8-step1000-GPTQ-wikitext2	Xu-Ouyang	2024-11-06T12:33:48Z	5	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]	text-generation	2024-11-06T12:23:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
PranavSharma10/Qwen-2.5-7B-FineTunedInstruct	PranavSharma10	2024-11-06T12:28:46Z	5	0	null	[ "safetensors", "qwen2", "llama-factory", "license:mit", "region:us" ]	null	2024-11-06T12:00:18Z	--- license: mit tags: - llama-factory ---
MaziyarPanahi/SD3_prompt-llama_8b-GGUF	MaziyarPanahi	2024-11-06T12:24:51Z	96	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:matrixglitch/SD3_prompt-llama_8b", "base_model:quantized:matrixglitch/SD3_prompt-llama_8b", "region:us", "conversational" ]	text-generation	2024-11-06T12:01:42Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: SD3_prompt-llama_8b-GGUF base_model: matrixglitch/SD3_prompt-llama_8b inference: false model_creator: matrixglitch pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/SD3_prompt-llama_8b-GGUF](https://huggingface.co/MaziyarPanahi/SD3_prompt-llama_8b-GGUF) - Model creator: [matrixglitch](https://huggingface.co/matrixglitch) - Original model: [matrixglitch/SD3_prompt-llama_8b](https://huggingface.co/matrixglitch/SD3_prompt-llama_8b) ## Description [MaziyarPanahi/SD3_prompt-llama_8b-GGUF](https://huggingface.co/MaziyarPanahi/SD3_prompt-llama_8b-GGUF) contains GGUF format model files for [matrixglitch/SD3_prompt-llama_8b](https://huggingface.co/matrixglitch/SD3_prompt-llama_8b). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
usmanyaqoob/xlm-roberta-base-finetuned-panx-de-fr	usmanyaqoob	2024-11-06T12:23:00Z	135	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-06T10:35:51Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr 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. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1566 - F1: 0.8673 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2805 \| 1.0 \| 715 \| 0.1776 \| 0.8182 \| \| 0.1446 \| 2.0 \| 1430 \| 0.1523 \| 0.8533 \| \| 0.0915 \| 3.0 \| 2145 \| 0.1566 \| 0.8673 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Totototo/bert-base-uncased-finetuned-sst2	Totototo	2024-11-06T12:21:24Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T12:17:46Z	--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-sst2 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. --> # bert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5682 - Accuracy: 0.8911 ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 250 \| 0.3307 \| 0.8716 \| \| 0.353 \| 2.0 \| 500 \| 0.3771 \| 0.8968 \| \| 0.353 \| 3.0 \| 750 \| 0.5682 \| 0.8911 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
elliotthwang/KimLan-3.2-3B	elliotthwang	2024-11-06T12:15:09Z	131	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T12:08:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
paipaimask/paipai_qwen_2-7B-it_04	paipaimask	2024-11-06T12:06:41Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T10:47:53Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - Developed by: paipaimask - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
superllms/Qwen2.5-7B-Instruct_906de368	superllms	2024-11-06T12:01:32Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T11:42:47Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - Developed by: superllms - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
usmanyaqoob/xlm-roberta-base-finetuned-panx-de	usmanyaqoob	2024-11-06T12:00:33Z	135	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-10-27T16:17:22Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de 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. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1299 - F1: 0.8683 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2569 \| 1.0 \| 525 \| 0.1567 \| 0.8133 \| \| 0.1308 \| 2.0 \| 1050 \| 0.1372 \| 0.8526 \| \| 0.0821 \| 3.0 \| 1575 \| 0.1299 \| 0.8683 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf	RichardErkhov	2024-11-06T11:57:00Z	21	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T08:28:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deep-solar-v2.0.7 - GGUF - Model creator: https://huggingface.co/Deepnoid/ - Original model: https://huggingface.co/Deepnoid/deep-solar-v2.0.7/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [deep-solar-v2.0.7.Q2_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [deep-solar-v2.0.7.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [deep-solar-v2.0.7.Q3_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [deep-solar-v2.0.7.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [deep-solar-v2.0.7.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [deep-solar-v2.0.7.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [deep-solar-v2.0.7.Q4_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [deep-solar-v2.0.7.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [deep-solar-v2.0.7.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [deep-solar-v2.0.7.Q4_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [deep-solar-v2.0.7.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [deep-solar-v2.0.7.Q4_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [deep-solar-v2.0.7.Q5_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [deep-solar-v2.0.7.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [deep-solar-v2.0.7.Q5_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [deep-solar-v2.0.7.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [deep-solar-v2.0.7.Q5_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [deep-solar-v2.0.7.Q6_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [deep-solar-v2.0.7.Q8_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: apache-2.0 --- <!-- 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
mradermacher/Eclipse-13B-i1-GGUF	mradermacher	2024-11-06T11:56:11Z	29	0	transformers	[ "transformers", "gguf", "mistral", "Eclipse-7B", "en", "base_model:Xenon1/Eclipse-13B", "base_model:quantized:Xenon1/Eclipse-13B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-05T23:35:33Z	--- base_model: Xenon1/Eclipse-13B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mistral - Eclipse-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Xenon1/Eclipse-13B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Eclipse-13B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q2_K.gguf) \| i1-Q2_K \| 4.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.7 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.7 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 6.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 7.4 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 7.4 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 7.4 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.4 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q6_K.gguf) \| i1-Q6_K \| 10.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF	featherless-ai-quants	2024-11-06T11:53:40Z	10	0	null	[ "gguf", "text-generation", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T10:21:41Z	--- base_model: tokyotech-llm-Llama-3-Swallow-8B-v0.1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # tokyotech-llm-Llama-3-Swallow-8B-v0.1 GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) Optimized GGUF quantization files for enhanced model performance > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 \| Quantization Type \| File \| Size \| \|-------------------\|------\|------\| \| IQ4_XS \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-IQ4_XS.gguf) \| 4276.62 MB \| \| Q2_K \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q2_K.gguf) \| 3031.86 MB \| \| Q3_K_L \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_L.gguf) \| 4121.74 MB \| \| Q3_K_M \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_M.gguf) \| 3832.74 MB \| \| Q3_K_S \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_S.gguf) \| 3494.74 MB \| \| Q4_K_M \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_M.gguf) \| 4692.78 MB \| \| Q4_K_S \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_S.gguf) \| 4475.28 MB \| \| Q5_K_M \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_M.gguf) \| 5467.40 MB \| \| Q5_K_S \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_S.gguf) \| 5339.90 MB \| \| Q6_K \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q6_K.gguf) \| 6290.44 MB \| \| Q8_0 \| [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q8_0.gguf) \| 8145.11 MB \| --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 Instant Hosting - Deploy any Llama model on HuggingFace instantly - 🛠️ Zero Infrastructure - No server setup or maintenance required - 📚 Vast Compatibility - Support for 2400+ models and counting - 💎 Affordable Pricing - Starting at just $10/month --- Links: [Get Started](https://featherless.ai) \| [Documentation](https://featherless.ai/docs) \| [Models](https://featherless.ai/models)
nishhaaann/bertTunedBoxA	nishhaaann	2024-11-06T11:53:11Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T11:52:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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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Primeness/deeznootz013	Primeness	2024-11-06T11:44:44Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T10:40:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sairanaz/finetuning-sentiment-model-3000-samples	sairanaz	2024-11-06T11:43:35Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-11-06T10:18:02Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3324 - Accuracy: 0.8867 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF	MaziyarPanahi	2024-11-06T11:42:23Z	28	0	null	[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305", "base_model:quantized:RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305", "region:us", "conversational" ]	text-generation	2024-11-06T11:19:43Z	--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF base_model: RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305 inference: false model_creator: RicardoLee pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF](https://huggingface.co/MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF) - Model creator: [RicardoLee](https://huggingface.co/RicardoLee) - Original model: [RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305](https://huggingface.co/RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305) ## Description [MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF](https://huggingface.co/MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF) contains GGUF format model files for [RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305](https://huggingface.co/RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
Mykes/med_ner_SDDCS	Mykes	2024-11-06T11:33:36Z	19	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "biology", "medical", "ru", "dataset:Mykes/patient_queries_ner_SDDCS", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-03T20:37:12Z	--- base_model: - google-bert/bert-base-multilingual-uncased datasets: - Mykes/patient_queries_ner_SDDCS language: - ru library_name: transformers pipeline_tag: token-classification tags: - biology - medical --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63565a3d58acee56a457f799/MqL0twwg_1DWKN7taDvQy.jpeg) # med_ner_SDDCS SDDCS - abbreviation for ner-entities SYMPTOMS, DISEASES, DRUGS, CITIES, SUBWAY STATIONS (additionall it is able to predict GENDER and AGE entities) This is a fine-tuned Named Entity Recognition (NER) model based on the [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) model, designed to detect russian medical entities like diseases, drugs, symptoms, and more. # Model Details - Model Name: med_ner_SDDCS - Base Model: Babelscape/wikineural-multilingual-ner - Fine-tuned on: Medical NER data ## Entities Recognized: - GENDER (e.g., женщина, мужчина) - DISEASE (e.g., паническое расстройство, грипп, ...) - SYMPTOM (e.g., тревога, одышка, ...) - SPECIALITY (e.g., невролог, кардиолог, ...) - CITY (e.g., Тула, Москва, Иркутск, ...) - SUBWAY (e.g., Шоссе Энтузиастов, Проспект Мира, ...) - DRUG (e.g., кардиомагнил, ципралекс) - AGE (e.g., ребенок, пожилой) ## Model Performance The fine-tuned model has achieved the following performance metrics: ``` precision recall f1-score support AGE 0.99 1.00 0.99 706 CITY 0.99 1.00 1.00 2370 DISEASE 0.99 1.00 0.99 4841 DRUG 0.99 1.00 0.99 4546 GENDER 0.99 1.00 1.00 476 SPECIALITY 0.98 0.96 0.97 3673 SUBWAY 1.00 1.00 1.00 658 SYMPTOM 0.99 0.99 0.99 8022 micro avg 0.99 0.99 0.99 25292 macro avg 0.99 0.99 0.99 25292 weighted avg 0.99 0.99 0.99 25292 How to Use ``` You can use this model with the transformers library to perform Named Entity Recognition (NER) tasks in the russian medical domain, mainly for patient queries. Here's how to load and use the model: # Load the tokenizer and model ``` from transformers import pipeline pipe = pipeline(task="ner", model='Mykes/med_ner_SDDCS', tokenizer='Mykes/med_ner_SDDCS', aggregation_strategy="max") # I made the misspelled words on purpose query = "У ребенка треога и норушения сна, потеря сознания, раньше ставили паническое расстройство. Подскажи хорошего психотервта в районе метро Октбрьской." pipe(query.lower()) ``` Result: ``` [{'entity_group': 'AGE', 'score': 0.9992663, 'word': 'ребенка', 'start': 2, 'end': 9}, {'entity_group': 'SYMPTOM', 'score': 0.9997758, 'word': 'треога', 'start': 10, 'end': 16}, {'entity_group': 'SYMPTOM', 'score': 0.9997876, 'word': 'норушения сна', 'start': 19, 'end': 32}, {'entity_group': 'SYMPTOM', 'score': 0.999773, 'word': 'потеря сознания', 'start': 34, 'end': 49}, {'entity_group': 'DISEASE', 'score': 0.9996424, 'word': 'паническое расстройство', 'start': 66, 'end': 89}, {'entity_group': 'SUBWAY', 'score': 0.99918646, 'word': 'октбрьской', 'start': 136, 'end': 146}] ``` ## Code for visualization ``` import spacy from spacy import displacy def convert_to_displacy_format(text, ner_results): entities = [] for result in ner_results: # Convert the Hugging Face output into the format displacy expects entities.append({ "start": result['start'], "end": result['end'], "label": result['entity_group'] }) return { "text": text, "ents": entities, "title": None } query = "У ребенка треога и норушения сна, потеря сознания, раньше ставили паническое расстройство. Подскажи хорошего психиатра в районе метро Октбрьской." ner_results = pipe(query.lower()) displacy_data = convert_to_displacy_format(query, ner_results) colors = { "SPECIALITY": "linear-gradient(90deg, #aa9cfc, #fc9ce7)", "CITY": "linear-gradient(90deg, #feca57, #ff9f43)", "DRUG": "linear-gradient(90deg, #55efc4, #81ecec)", "DISEASE": "linear-gradient(90deg, #fab1a0, #ff7675)", "SUBWAY": "linear-gradient(90deg, #00add0, #0039a6)", "AGE": "linear-gradient(90deg, #f39c12, #e67e22)", "SYMPTOM": "linear-gradient(90deg, #e74c3c, #c0392b)" } options = {"ents": ["SPECIALITY", "CITY", "DRUG", "DISEASE", "SYMPTOM", "AGE", "SUBWAY"], "colors": colors} html = displacy.render(displacy_data, style="ent", manual=True, options=options, jupyter=False) with open("ner_visualization_with_colors.html", "w", encoding="utf-8") as f: f.write(html) from IPython.display import display, HTML display(HTML(html)) ```
atocadocoelhodealice/ai-metaglasses	atocadocoelhodealice	2024-11-06T11:32:56Z	6	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-06T04:01:29Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: AIMETAGLASSES --- # Ai Metaglasses <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `AIMETAGLASSES` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('atocadocoelhodealice/ai-metaglasses', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ProfessorSeemo/cyberattack-pattern-detector	ProfessorSeemo	2024-11-06T11:27:23Z	117	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-05T18:07:25Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: cyberattack-pattern-detector 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. --> # cyberattack-pattern-detector This model was trained from scratch 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: 5e-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: 3.0 ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.19.1
mirzabeg14/finetuning-sentiment-model-3000-samples	mirzabeg14	2024-11-06T11:19:13Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-11-06T10:32:26Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3497 - Accuracy: 0.86 - F1: 0.8627 ## 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: 2 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
lm-kit/nomic-embed-text-1.5	lm-kit	2024-11-06T11:17:16Z	337	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2024-11-05T14:22:55Z	--- license: apache-2.0 --- ## Model Summary This repository hosts quantized versions of the nomic-embed-text-v1.5 embedding model. Format: GGUF Converter: llama.cpp 94d8cb8be13b7c4d04eeca5a2b956b9148e6f222 Quantizer: LM-Kit.NET 2024.11.1 For more detailed information on the base model, please visit the following links - [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF	mradermacher	2024-11-06T11:07:12Z	22	0	transformers	[ "transformers", "gguf", "en", "dataset:lodrick-the-lafted/Hermes-40K", "dataset:garage-bAInd/Open-Platypus", "dataset:jondurbin/airoboros-3.2", "base_model:lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B", "base_model:quantized:lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T06:12:07Z	--- base_model: lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B datasets: - lodrick-the-lafted/Hermes-40K - garage-bAInd/Open-Platypus - jondurbin/airoboros-3.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q2_K.gguf) \| Q2_K \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_S.gguf) \| Q3_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_M.gguf) \| Q3_K_M \| 6.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_L.gguf) \| Q3_K_L \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.4 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_K_S.gguf) \| Q4_K_S \| 7.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_K_M.gguf) \| Q4_K_M \| 7.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q5_K_S.gguf) \| Q5_K_S \| 9.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q5_K_M.gguf) \| Q5_K_M \| 9.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q6_K.gguf) \| Q6_K \| 10.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q8_0.gguf) \| Q8_0 \| 13.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
kavish218/enhanced_finetuned_llama_3_2_1B_multi_domain_4	kavish218	2024-11-06T10:59:45Z	98	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-06T10:58:20Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
WeiChow/moge_l_vit	WeiChow	2024-11-06T10:57:40Z	10	0	null	[ "pytorch", "region:us" ]	null	2024-11-06T10:25:24Z	same architecture with [timm/vit_large_patch14_dinov2.lvd142m](https://huggingface.co/timm/vit_large_patch14_dinov2.lvd142m) ```shell git clone https://github.com/microsoft/MoGe.git cd MoGe ``` # translate ```python import torch from moge.model import MoGeModel device = torch.device("cuda") model = MoGeModel.from_pretrained("Ruicheng/moge-vitl").to(device) # ------------------- backbone_state_dict = model.backbone.state_dict() filtered_state_dict = {k: v for k, v in backbone_state_dict.items() if 'mask_token' not in k} torch.save(filtered_state_dict, "pytorch_model.bin") # ------------------- ``` # usage ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'hf_hub:WeiChow/moge_l_vit', pretrained=True, num_classes=0, # remove classifier nn.Linear ) # for name, param in model.named_parameters(): # print(f"Parameter: {name} - Size: {param.size()} - Total Elements: {param.numel()}") model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1374, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) print(output) ``` Copyright saved.
kiranshivaraju/swin-tiny-patch4-window7-224-finetuned-aug-pcb	kiranshivaraju	2024-11-06T10:57:27Z	214	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-11-06T10:48:40Z	--- library_name: transformers license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-aug-pcb 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. --> # swin-tiny-patch4-window7-224-finetuned-aug-pcb This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5192 - Accuracy: 0.7465 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5945 \| 1.0 \| 15 \| 0.5192 \| 0.7465 \| \| 0.5435 \| 2.0 \| 30 \| 0.4960 \| 0.7230 \| \| 0.5187 \| 3.0 \| 45 \| 0.4820 \| 0.7418 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
ngwgsang/bartpho-syllable-large-vimex	ngwgsang	2024-11-06T10:51:51Z	104	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-06T10:50:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
avinasht/gemma-2b-it-QA-Tuned	avinasht	2024-11-06T10:49:30Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-27T18:30:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF	mradermacher	2024-11-06T10:48:15Z	106	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:practical-dreamer/RPGPT_PublicDomain-alpaca", "dataset:shuyuej/metamath_gsm8k", "dataset:NeuralNovel/Neural-DPO", "base_model:NeuralNovel/Senzu-7B-v0.1-DPO", "base_model:quantized:NeuralNovel/Senzu-7B-v0.1-DPO", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-06T09:37:20Z	--- base_model: NeuralNovel/Senzu-7B-v0.1-DPO datasets: - practical-dreamer/RPGPT_PublicDomain-alpaca - shuyuej/metamath_gsm8k - NeuralNovel/Neural-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/NeuralNovel/Senzu-7B-v0.1-DPO <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
kaichin/finetuning-sentiment-model-3000-samples	kaichin	2024-11-06T10:45:32Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-11-06T10:30:35Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples 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. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3121 - Accuracy: 0.88 - F1: 0.8831 ## 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: 2 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf	RichardErkhov	2024-11-06T10:38:24Z	23	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T06:32:55Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deep-solar-Rev-v3.0.4 - GGUF - Model creator: https://huggingface.co/Deepnoid/ - Original model: https://huggingface.co/Deepnoid/deep-solar-Rev-v3.0.4/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [deep-solar-Rev-v3.0.4.Q2_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [deep-solar-Rev-v3.0.4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [deep-solar-Rev-v3.0.4.Q3_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [deep-solar-Rev-v3.0.4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [deep-solar-Rev-v3.0.4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [deep-solar-Rev-v3.0.4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [deep-solar-Rev-v3.0.4.Q4_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [deep-solar-Rev-v3.0.4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [deep-solar-Rev-v3.0.4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [deep-solar-Rev-v3.0.4.Q4_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [deep-solar-Rev-v3.0.4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [deep-solar-Rev-v3.0.4.Q4_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [deep-solar-Rev-v3.0.4.Q5_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [deep-solar-Rev-v3.0.4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [deep-solar-Rev-v3.0.4.Q5_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [deep-solar-Rev-v3.0.4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [deep-solar-Rev-v3.0.4.Q5_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [deep-solar-Rev-v3.0.4.Q6_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [deep-solar-Rev-v3.0.4.Q8_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: apache-2.0 --- <!-- 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
cuongdev/tonghop-v3-5000	cuongdev	2024-11-06T10:36:54Z	29	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-11-06T10:32:57Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tonghop-v3-5000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
nishhaaann/dbTuneDemo	nishhaaann	2024-11-06T10:33:25Z	163	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-06T06:28:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [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. 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 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> 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] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
neopolita/toolace-8b-gguf	neopolita	2024-11-06T10:31:04Z	18	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-06T09:58:44Z	--- {} --- # GGUF quants for [Team-ACE/ToolACE-8B](https://huggingface.co/Team-ACE/ToolACE-8B) using [llama.cpp](https://github.com/ggerganov/llama.cpp) Terms of Use: Please check the [original model](https://huggingface.co/Team-ACE/ToolACE-8B) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4

MayBashendy

2024-11-06T16:17:51Z

161

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T15:44:54Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4 results: [] ---  # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold4 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4950 - Qwk: 0.6411 - Mse: 0.4950 - Rmse: 0.7035 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:------:| | No log | 0.0063 | 2 | 10.1861 | 0.0 | 10.1861 | 3.1916 | | No log | 0.0126 | 4 | 8.5953 | -0.0005 | 8.5953 | 2.9318 | | No log | 0.0189 | 6 | 6.9159 | 0.0051 | 6.9159 | 2.6298 | | No log | 0.0252 | 8 | 5.5130 | 0.0037 | 5.5130 | 2.3480 | | No log | 0.0315 | 10 | 4.3816 | 0.0018 | 4.3816 | 2.0932 | | No log | 0.0379 | 12 | 3.5082 | 0.0492 | 3.5082 | 1.8730 | | No log | 0.0442 | 14 | 2.7686 | 0.0128 | 2.7686 | 1.6639 | | No log | 0.0505 | 16 | 2.1322 | 0.0118 | 2.1322 | 1.4602 | | No log | 0.0568 | 18 | 1.6261 | 0.0079 | 1.6261 | 1.2752 | | No log | 0.0631 | 20 | 1.2562 | 0.1722 | 1.2562 | 1.1208 | | No log | 0.0694 | 22 | 1.0333 | 0.0420 | 1.0333 | 1.0165 | | No log | 0.0757 | 24 | 0.8915 | 0.0316 | 0.8915 | 0.9442 | | No log | 0.0820 | 26 | 0.8074 | 0.0316 | 0.8074 | 0.8986 | | No log | 0.0883 | 28 | 0.7660 | 0.0316 | 0.7660 | 0.8752 | | No log | 0.0946 | 30 | 0.7689 | 0.0542 | 0.7689 | 0.8769 | | No log | 0.1009 | 32 | 0.9386 | 0.0937 | 0.9386 | 0.9688 | | No log | 0.1073 | 34 | 0.8347 | 0.0771 | 0.8347 | 0.9136 | | No log | 0.1136 | 36 | 0.8293 | 0.4385 | 0.8293 | 0.9106 | | No log | 0.1199 | 38 | 0.8916 | 0.3628 | 0.8916 | 0.9442 | | No log | 0.1262 | 40 | 0.8068 | 0.0212 | 0.8068 | 0.8982 | | No log | 0.1325 | 42 | 0.8411 | 0.0344 | 0.8411 | 0.9171 | | No log | 0.1388 | 44 | 0.8499 | 0.0344 | 0.8499 | 0.9219 | | No log | 0.1451 | 46 | 0.8047 | 0.0107 | 0.8047 | 0.8970 | | No log | 0.1514 | 48 | 0.7906 | 0.0107 | 0.7906 | 0.8892 | | No log | 0.1577 | 50 | 0.7428 | 0.0317 | 0.7428 | 0.8619 | | No log | 0.1640 | 52 | 0.7615 | 0.0511 | 0.7615 | 0.8726 | | No log | 0.1703 | 54 | 0.7432 | 0.0792 | 0.7432 | 0.8621 | | No log | 0.1767 | 56 | 0.6753 | 0.0610 | 0.6753 | 0.8218 | | No log | 0.1830 | 58 | 0.6924 | 0.0317 | 0.6924 | 0.8321 | | No log | 0.1893 | 60 | 0.7336 | 0.0730 | 0.7336 | 0.8565 | | No log | 0.1956 | 62 | 0.7216 | 0.0213 | 0.7216 | 0.8495 | | No log | 0.2019 | 64 | 0.6734 | 0.0826 | 0.6734 | 0.8206 | | No log | 0.2082 | 66 | 0.8115 | 0.1971 | 0.8115 | 0.9008 | | No log | 0.2145 | 68 | 1.0608 | 0.2342 | 1.0608 | 1.0300 | | No log | 0.2208 | 70 | 0.8848 | 0.2293 | 0.8848 | 0.9406 | | No log | 0.2271 | 72 | 0.6445 | 0.1331 | 0.6445 | 0.8028 | | No log | 0.2334 | 74 | 0.6672 | 0.0803 | 0.6672 | 0.8168 | | No log | 0.2397 | 76 | 0.6616 | 0.0754 | 0.6616 | 0.8134 | | No log | 0.2461 | 78 | 0.6149 | 0.1067 | 0.6149 | 0.7842 | | No log | 0.2524 | 80 | 0.6896 | 0.1973 | 0.6896 | 0.8304 | | No log | 0.2587 | 82 | 0.7505 | 0.2167 | 0.7505 | 0.8663 | | No log | 0.2650 | 84 | 0.6389 | 0.1883 | 0.6389 | 0.7993 | | No log | 0.2713 | 86 | 0.6107 | 0.2957 | 0.6107 | 0.7815 | | No log | 0.2776 | 88 | 0.6234 | 0.3088 | 0.6234 | 0.7895 | | No log | 0.2839 | 90 | 0.5901 | 0.2657 | 0.5901 | 0.7681 | | No log | 0.2902 | 92 | 0.6248 | 0.1786 | 0.6248 | 0.7905 | | No log | 0.2965 | 94 | 0.6419 | 0.2214 | 0.6419 | 0.8012 | | No log | 0.3028 | 96 | 0.5860 | 0.2699 | 0.5860 | 0.7655 | | No log | 0.3091 | 98 | 0.5766 | 0.2956 | 0.5766 | 0.7593 | | No log | 0.3155 | 100 | 0.5547 | 0.3623 | 0.5547 | 0.7448 | | No log | 0.3218 | 102 | 0.5514 | 0.4222 | 0.5514 | 0.7426 | | No log | 0.3281 | 104 | 0.5460 | 0.4061 | 0.5460 | 0.7389 | | No log | 0.3344 | 106 | 0.5756 | 0.3134 | 0.5756 | 0.7587 | | No log | 0.3407 | 108 | 0.6144 | 0.3095 | 0.6144 | 0.7838 | | No log | 0.3470 | 110 | 0.5301 | 0.4421 | 0.5301 | 0.7280 | | No log | 0.3533 | 112 | 0.5429 | 0.4684 | 0.5429 | 0.7368 | | No log | 0.3596 | 114 | 0.5177 | 0.4759 | 0.5177 | 0.7195 | | No log | 0.3659 | 116 | 0.5241 | 0.4151 | 0.5241 | 0.7240 | | No log | 0.3722 | 118 | 0.5069 | 0.4161 | 0.5069 | 0.7120 | | No log | 0.3785 | 120 | 0.5293 | 0.4872 | 0.5293 | 0.7275 | | No log | 0.3849 | 122 | 0.5688 | 0.4517 | 0.5688 | 0.7542 | | No log | 0.3912 | 124 | 0.5780 | 0.2445 | 0.5780 | 0.7603 | | No log | 0.3975 | 126 | 0.5334 | 0.4100 | 0.5334 | 0.7304 | | No log | 0.4038 | 128 | 0.5552 | 0.5686 | 0.5552 | 0.7451 | | No log | 0.4101 | 130 | 0.5369 | 0.5723 | 0.5369 | 0.7327 | | No log | 0.4164 | 132 | 0.5145 | 0.3755 | 0.5145 | 0.7173 | | No log | 0.4227 | 134 | 0.5181 | 0.4368 | 0.5181 | 0.7198 | | No log | 0.4290 | 136 | 0.5175 | 0.4105 | 0.5175 | 0.7194 | | No log | 0.4353 | 138 | 0.5481 | 0.5205 | 0.5481 | 0.7403 | | No log | 0.4416 | 140 | 0.5561 | 0.4941 | 0.5561 | 0.7457 | | No log | 0.4479 | 142 | 0.5308 | 0.5019 | 0.5308 | 0.7286 | | No log | 0.4543 | 144 | 0.5421 | 0.4929 | 0.5421 | 0.7363 | | No log | 0.4606 | 146 | 0.5182 | 0.4383 | 0.5182 | 0.7198 | | No log | 0.4669 | 148 | 0.5113 | 0.4444 | 0.5113 | 0.7151 | | No log | 0.4732 | 150 | 0.5292 | 0.3937 | 0.5292 | 0.7275 | | No log | 0.4795 | 152 | 0.5153 | 0.4278 | 0.5153 | 0.7179 | | No log | 0.4858 | 154 | 0.4959 | 0.4610 | 0.4959 | 0.7042 | | No log | 0.4921 | 156 | 0.4822 | 0.4742 | 0.4822 | 0.6944 | | No log | 0.4984 | 158 | 0.5207 | 0.5700 | 0.5207 | 0.7216 | | No log | 0.5047 | 160 | 0.6361 | 0.5602 | 0.6361 | 0.7976 | | No log | 0.5110 | 162 | 0.5405 | 0.5354 | 0.5405 | 0.7352 | | No log | 0.5174 | 164 | 0.5536 | 0.5347 | 0.5536 | 0.7440 | | No log | 0.5237 | 166 | 0.5308 | 0.5142 | 0.5308 | 0.7285 | | No log | 0.5300 | 168 | 0.5827 | 0.5080 | 0.5827 | 0.7634 | | No log | 0.5363 | 170 | 0.6033 | 0.5139 | 0.6033 | 0.7767 | | No log | 0.5426 | 172 | 0.7514 | 0.5038 | 0.7514 | 0.8669 | | No log | 0.5489 | 174 | 0.7327 | 0.5197 | 0.7327 | 0.8560 | | No log | 0.5552 | 176 | 0.5563 | 0.5225 | 0.5563 | 0.7459 | | No log | 0.5615 | 178 | 0.5157 | 0.4842 | 0.5157 | 0.7181 | | No log | 0.5678 | 180 | 0.5430 | 0.5432 | 0.5430 | 0.7369 | | No log | 0.5741 | 182 | 0.5386 | 0.5786 | 0.5386 | 0.7339 | | No log | 0.5804 | 184 | 0.4900 | 0.5768 | 0.4900 | 0.7000 | | No log | 0.5868 | 186 | 0.5030 | 0.5908 | 0.5030 | 0.7092 | | No log | 0.5931 | 188 | 0.4526 | 0.5804 | 0.4526 | 0.6728 | | No log | 0.5994 | 190 | 0.5105 | 0.4823 | 0.5105 | 0.7145 | | No log | 0.6057 | 192 | 0.5870 | 0.4220 | 0.5870 | 0.7662 | | No log | 0.6120 | 194 | 0.5511 | 0.4319 | 0.5511 | 0.7423 | | No log | 0.6183 | 196 | 0.4500 | 0.5472 | 0.4500 | 0.6708 | | No log | 0.6246 | 198 | 0.4526 | 0.5562 | 0.4526 | 0.6728 | | No log | 0.6309 | 200 | 0.5135 | 0.5754 | 0.5135 | 0.7166 | | No log | 0.6372 | 202 | 0.6373 | 0.5419 | 0.6373 | 0.7983 | | No log | 0.6435 | 204 | 0.5640 | 0.5393 | 0.5640 | 0.7510 | | No log | 0.6498 | 206 | 0.5375 | 0.5351 | 0.5375 | 0.7332 | | No log | 0.6562 | 208 | 0.5511 | 0.5560 | 0.5511 | 0.7423 | | No log | 0.6625 | 210 | 0.5414 | 0.5693 | 0.5414 | 0.7358 | | No log | 0.6688 | 212 | 0.5304 | 0.5811 | 0.5304 | 0.7283 | | No log | 0.6751 | 214 | 0.4758 | 0.5939 | 0.4758 | 0.6898 | | No log | 0.6814 | 216 | 0.4437 | 0.5481 | 0.4437 | 0.6661 | | No log | 0.6877 | 218 | 0.4368 | 0.5673 | 0.4368 | 0.6609 | | No log | 0.6940 | 220 | 0.4946 | 0.6281 | 0.4946 | 0.7033 | | No log | 0.7003 | 222 | 0.4564 | 0.5958 | 0.4564 | 0.6756 | | No log | 0.7066 | 224 | 0.4662 | 0.5795 | 0.4662 | 0.6828 | | No log | 0.7129 | 226 | 0.5187 | 0.6018 | 0.5187 | 0.7202 | | No log | 0.7192 | 228 | 0.5179 | 0.6018 | 0.5179 | 0.7196 | | No log | 0.7256 | 230 | 0.4883 | 0.6011 | 0.4883 | 0.6988 | | No log | 0.7319 | 232 | 0.4581 | 0.5898 | 0.4581 | 0.6768 | | No log | 0.7382 | 234 | 0.5164 | 0.6064 | 0.5164 | 0.7186 | | No log | 0.7445 | 236 | 0.4880 | 0.6120 | 0.4880 | 0.6986 | | No log | 0.7508 | 238 | 0.4608 | 0.6049 | 0.4608 | 0.6788 | | No log | 0.7571 | 240 | 0.5627 | 0.6490 | 0.5627 | 0.7502 | | No log | 0.7634 | 242 | 0.8123 | 0.6725 | 0.8123 | 0.9013 | | No log | 0.7697 | 244 | 0.6433 | 0.6624 | 0.6433 | 0.8021 | | No log | 0.7760 | 246 | 0.4387 | 0.5914 | 0.4387 | 0.6624 | | No log | 0.7823 | 248 | 0.4507 | 0.5951 | 0.4507 | 0.6713 | | No log | 0.7886 | 250 | 0.6574 | 0.6299 | 0.6574 | 0.8108 | | No log | 0.7950 | 252 | 0.9073 | 0.5748 | 0.9073 | 0.9525 | | No log | 0.8013 | 254 | 0.7567 | 0.5976 | 0.7567 | 0.8699 | | No log | 0.8076 | 256 | 0.4780 | 0.5993 | 0.4780 | 0.6914 | | No log | 0.8139 | 258 | 0.4653 | 0.4804 | 0.4653 | 0.6821 | | No log | 0.8202 | 260 | 0.4593 | 0.5099 | 0.4593 | 0.6777 | | No log | 0.8265 | 262 | 0.5150 | 0.5981 | 0.5150 | 0.7176 | | No log | 0.8328 | 264 | 0.7188 | 0.5631 | 0.7188 | 0.8478 | | No log | 0.8391 | 266 | 0.6870 | 0.5665 | 0.6870 | 0.8289 | | No log | 0.8454 | 268 | 0.5103 | 0.6082 | 0.5103 | 0.7144 | | No log | 0.8517 | 270 | 0.4610 | 0.4952 | 0.4610 | 0.6790 | | No log | 0.8580 | 272 | 0.5092 | 0.4066 | 0.5092 | 0.7136 | | No log | 0.8644 | 274 | 0.4640 | 0.4861 | 0.4640 | 0.6812 | | No log | 0.8707 | 276 | 0.4945 | 0.5916 | 0.4945 | 0.7032 | | No log | 0.8770 | 278 | 0.6582 | 0.5572 | 0.6582 | 0.8113 | | No log | 0.8833 | 280 | 0.6694 | 0.5610 | 0.6694 | 0.8181 | | No log | 0.8896 | 282 | 0.5728 | 0.5254 | 0.5728 | 0.7568 | | No log | 0.8959 | 284 | 0.5221 | 0.4152 | 0.5221 | 0.7226 | | No log | 0.9022 | 286 | 0.4807 | 0.4751 | 0.4807 | 0.6933 | | No log | 0.9085 | 288 | 0.4549 | 0.5473 | 0.4549 | 0.6745 | | No log | 0.9148 | 290 | 0.4556 | 0.5597 | 0.4556 | 0.6750 | | No log | 0.9211 | 292 | 0.4582 | 0.5556 | 0.4582 | 0.6769 | | No log | 0.9274 | 294 | 0.4645 | 0.5505 | 0.4645 | 0.6816 | | No log | 0.9338 | 296 | 0.4678 | 0.5381 | 0.4678 | 0.6840 | | No log | 0.9401 | 298 | 0.4749 | 0.5534 | 0.4749 | 0.6892 | | No log | 0.9464 | 300 | 0.5625 | 0.5975 | 0.5625 | 0.7500 | | No log | 0.9527 | 302 | 0.5900 | 0.5826 | 0.5900 | 0.7681 | | No log | 0.9590 | 304 | 0.4926 | 0.5950 | 0.4926 | 0.7019 | | No log | 0.9653 | 306 | 0.4816 | 0.4778 | 0.4816 | 0.6940 | | No log | 0.9716 | 308 | 0.4785 | 0.5246 | 0.4785 | 0.6917 | | No log | 0.9779 | 310 | 0.4967 | 0.5915 | 0.4967 | 0.7048 | | No log | 0.9842 | 312 | 0.4777 | 0.5359 | 0.4777 | 0.6912 | | No log | 0.9905 | 314 | 0.5052 | 0.4469 | 0.5052 | 0.7108 | | No log | 0.9968 | 316 | 0.4870 | 0.4692 | 0.4870 | 0.6978 | | No log | 1.0032 | 318 | 0.4959 | 0.6014 | 0.4959 | 0.7042 | | No log | 1.0095 | 320 | 0.5971 | 0.6622 | 0.5971 | 0.7727 | | No log | 1.0158 | 322 | 0.6224 | 0.6527 | 0.6224 | 0.7889 | | No log | 1.0221 | 324 | 0.5090 | 0.6125 | 0.5090 | 0.7134 | | No log | 1.0284 | 326 | 0.4859 | 0.6161 | 0.4859 | 0.6970 | | No log | 1.0347 | 328 | 0.5575 | 0.6373 | 0.5575 | 0.7466 | | No log | 1.0410 | 330 | 0.6631 | 0.6354 | 0.6631 | 0.8143 | | No log | 1.0473 | 332 | 0.7880 | 0.6128 | 0.7880 | 0.8877 | | No log | 1.0536 | 334 | 0.6328 | 0.6471 | 0.6328 | 0.7955 | | No log | 1.0599 | 336 | 0.4833 | 0.5926 | 0.4833 | 0.6952 | | No log | 1.0662 | 338 | 0.4764 | 0.5915 | 0.4764 | 0.6902 | | No log | 1.0726 | 340 | 0.4879 | 0.6097 | 0.4879 | 0.6985 | | No log | 1.0789 | 342 | 0.5004 | 0.6328 | 0.5004 | 0.7074 | | No log | 1.0852 | 344 | 0.4558 | 0.5696 | 0.4558 | 0.6752 | | No log | 1.0915 | 346 | 0.4638 | 0.5143 | 0.4638 | 0.6811 | | No log | 1.0978 | 348 | 0.4590 | 0.5340 | 0.4590 | 0.6775 | | No log | 1.1041 | 350 | 0.4556 | 0.5999 | 0.4556 | 0.6750 | | No log | 1.1104 | 352 | 0.4521 | 0.5984 | 0.4521 | 0.6724 | | No log | 1.1167 | 354 | 0.4603 | 0.5902 | 0.4603 | 0.6784 | | No log | 1.1230 | 356 | 0.5085 | 0.6098 | 0.5085 | 0.7131 | | No log | 1.1293 | 358 | 0.5851 | 0.6319 | 0.5851 | 0.7649 | | No log | 1.1356 | 360 | 0.5377 | 0.6091 | 0.5377 | 0.7333 | | No log | 1.1420 | 362 | 0.4673 | 0.5626 | 0.4673 | 0.6836 | | No log | 1.1483 | 364 | 0.4611 | 0.5643 | 0.4611 | 0.6790 | | No log | 1.1546 | 366 | 0.4560 | 0.5333 | 0.4560 | 0.6753 | | No log | 1.1609 | 368 | 0.4761 | 0.4842 | 0.4761 | 0.6900 | | No log | 1.1672 | 370 | 0.4581 | 0.5306 | 0.4581 | 0.6768 | | No log | 1.1735 | 372 | 0.4492 | 0.5837 | 0.4492 | 0.6702 | | No log | 1.1798 | 374 | 0.4585 | 0.6097 | 0.4585 | 0.6771 | | No log | 1.1861 | 376 | 0.4451 | 0.5503 | 0.4451 | 0.6672 | | No log | 1.1924 | 378 | 0.4524 | 0.5227 | 0.4524 | 0.6726 | | No log | 1.1987 | 380 | 0.4546 | 0.5008 | 0.4546 | 0.6742 | | No log | 1.2050 | 382 | 0.4735 | 0.5442 | 0.4735 | 0.6881 | | No log | 1.2114 | 384 | 0.5067 | 0.5698 | 0.5067 | 0.7118 | | No log | 1.2177 | 386 | 0.4892 | 0.4913 | 0.4892 | 0.6994 | | No log | 1.2240 | 388 | 0.4975 | 0.5099 | 0.4975 | 0.7053 | | No log | 1.2303 | 390 | 0.6492 | 0.6296 | 0.6492 | 0.8057 | | No log | 1.2366 | 392 | 0.7328 | 0.6114 | 0.7328 | 0.8561 | | No log | 1.2429 | 394 | 0.5539 | 0.6157 | 0.5539 | 0.7443 | | No log | 1.2492 | 396 | 0.5265 | 0.4173 | 0.5265 | 0.7256 | | No log | 1.2555 | 398 | 0.6128 | 0.3532 | 0.6128 | 0.7828 | | No log | 1.2618 | 400 | 0.5354 | 0.4003 | 0.5354 | 0.7317 | | No log | 1.2681 | 402 | 0.4935 | 0.5464 | 0.4935 | 0.7025 | | No log | 1.2744 | 404 | 0.5745 | 0.6324 | 0.5745 | 0.7579 | | No log | 1.2808 | 406 | 0.5167 | 0.6236 | 0.5167 | 0.7188 | | No log | 1.2871 | 408 | 0.4620 | 0.5427 | 0.4620 | 0.6797 | | No log | 1.2934 | 410 | 0.4585 | 0.5055 | 0.4585 | 0.6772 | | No log | 1.2997 | 412 | 0.4691 | 0.5926 | 0.4691 | 0.6849 | | No log | 1.3060 | 414 | 0.5962 | 0.6760 | 0.5962 | 0.7722 | | No log | 1.3123 | 416 | 0.5452 | 0.6593 | 0.5452 | 0.7384 | | No log | 1.3186 | 418 | 0.4661 | 0.6018 | 0.4661 | 0.6827 | | No log | 1.3249 | 420 | 0.4503 | 0.5347 | 0.4503 | 0.6710 | | No log | 1.3312 | 422 | 0.4594 | 0.5752 | 0.4594 | 0.6778 | | No log | 1.3375 | 424 | 0.5623 | 0.6484 | 0.5623 | 0.7499 | | No log | 1.3438 | 426 | 0.5562 | 0.6429 | 0.5562 | 0.7458 | | No log | 1.3502 | 428 | 0.4545 | 0.5922 | 0.4545 | 0.6742 | | No log | 1.3565 | 430 | 0.4446 | 0.5818 | 0.4446 | 0.6668 | | No log | 1.3628 | 432 | 0.5001 | 0.6472 | 0.5001 | 0.7072 | | No log | 1.3691 | 434 | 0.5172 | 0.6548 | 0.5172 | 0.7192 | | No log | 1.3754 | 436 | 0.4511 | 0.5994 | 0.4511 | 0.6716 | | No log | 1.3817 | 438 | 0.4721 | 0.5433 | 0.4721 | 0.6871 | | No log | 1.3880 | 440 | 0.4686 | 0.6124 | 0.4686 | 0.6846 | | No log | 1.3943 | 442 | 0.5272 | 0.6602 | 0.5272 | 0.7261 | | No log | 1.4006 | 444 | 0.4777 | 0.6232 | 0.4777 | 0.6912 | | No log | 1.4069 | 446 | 0.4745 | 0.4864 | 0.4745 | 0.6888 | | No log | 1.4132 | 448 | 0.4813 | 0.4603 | 0.4813 | 0.6938 | | No log | 1.4196 | 450 | 0.4566 | 0.5352 | 0.4566 | 0.6757 | | No log | 1.4259 | 452 | 0.5087 | 0.6295 | 0.5087 | 0.7132 | | No log | 1.4322 | 454 | 0.5272 | 0.6279 | 0.5272 | 0.7261 | | No log | 1.4385 | 456 | 0.4695 | 0.5742 | 0.4695 | 0.6852 | | No log | 1.4448 | 458 | 0.4613 | 0.5300 | 0.4613 | 0.6792 | | No log | 1.4511 | 460 | 0.4807 | 0.4327 | 0.4807 | 0.6933 | | No log | 1.4574 | 462 | 0.4712 | 0.4831 | 0.4712 | 0.6865 | | No log | 1.4637 | 464 | 0.5262 | 0.6207 | 0.5262 | 0.7254 | | No log | 1.4700 | 466 | 0.5679 | 0.6533 | 0.5679 | 0.7536 | | No log | 1.4763 | 468 | 0.4943 | 0.6319 | 0.4943 | 0.7030 | | No log | 1.4826 | 470 | 0.4548 | 0.5373 | 0.4548 | 0.6744 | | No log | 1.4890 | 472 | 0.4529 | 0.5669 | 0.4529 | 0.6730 | | No log | 1.4953 | 474 | 0.4979 | 0.6578 | 0.4979 | 0.7056 | | No log | 1.5016 | 476 | 0.5480 | 0.6783 | 0.5480 | 0.7402 | | No log | 1.5079 | 478 | 0.4760 | 0.5831 | 0.4760 | 0.6900 | | No log | 1.5142 | 480 | 0.4790 | 0.4885 | 0.4790 | 0.6921 | | No log | 1.5205 | 482 | 0.4733 | 0.4948 | 0.4733 | 0.6879 | | No log | 1.5268 | 484 | 0.4930 | 0.6107 | 0.4930 | 0.7021 | | No log | 1.5331 | 486 | 0.6387 | 0.6998 | 0.6387 | 0.7992 | | No log | 1.5394 | 488 | 0.5770 | 0.6947 | 0.5770 | 0.7596 | | No log | 1.5457 | 490 | 0.4507 | 0.5730 | 0.4507 | 0.6713 | | No log | 1.5521 | 492 | 0.4761 | 0.4890 | 0.4761 | 0.6900 | | No log | 1.5584 | 494 | 0.4524 | 0.5010 | 0.4524 | 0.6726 | | No log | 1.5647 | 496 | 0.4512 | 0.5824 | 0.4512 | 0.6717 | | No log | 1.5710 | 498 | 0.5386 | 0.6594 | 0.5386 | 0.7339 | | 0.5 | 1.5773 | 500 | 0.5441 | 0.6588 | 0.5441 | 0.7376 | | 0.5 | 1.5836 | 502 | 0.5217 | 0.6468 | 0.5217 | 0.7223 | | 0.5 | 1.5899 | 504 | 0.4504 | 0.5555 | 0.4504 | 0.6711 | | 0.5 | 1.5962 | 506 | 0.4459 | 0.5713 | 0.4459 | 0.6677 | | 0.5 | 1.6025 | 508 | 0.4642 | 0.6069 | 0.4642 | 0.6813 | | 0.5 | 1.6088 | 510 | 0.4950 | 0.6411 | 0.4950 | 0.7035 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF

mattritchey

2024-11-06T16:14:40Z

7

0

null

[ "gguf", "HelpingAI", "Emotionally-Intelligent", "EQ-focused- EQ-focused", "Conversational", "SLM", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:HelpingAI/HelpingAI2-3B", "base_model:quantized:HelpingAI/HelpingAI2-3B", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-11-06T16:14:29Z

--- license: other license_name: helpingai license_link: https://huggingface.co/OEvortex/HelpingAI-3B-v3/blob/main/LICENSE.md pipeline_tag: text-generation language: - en tags: - HelpingAI - Emotionally-Intelligent - EQ-focused- EQ-focused - Conversational - SLM - llama-cpp - gguf-my-repo base_model: OEvortex/HelpingAI-3B-reloaded --- # mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF This model was converted to GGUF format from [`OEvortex/HelpingAI-3B-reloaded`](https://huggingface.co/OEvortex/HelpingAI-3B-reloaded) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/OEvortex/HelpingAI-3B-reloaded) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mattritchey/HelpingAI-3B-reloaded-Q4_K_M-GGUF --hf-file helpingai-3b-reloaded-q4_k_m.gguf -c 2048 ```

camidenecken/RoBERTa-RM1-v2-2-rm-v32

camidenecken

2024-11-06T16:08:06Z

181

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T16:07:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

camidenecken/RoBERTa-RM1-v2-2-rm-v31

camidenecken

2024-11-06T16:05:57Z

183

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T16:05:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

camidenecken/RoBERTa-RM1-v2-2-rm-v30

camidenecken

2024-11-06T16:03:50Z

181

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T16:03:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

Lorraine013/openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2

Lorraine013

2024-11-06T16:00:05Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T14:33:04Z

--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2 results: [] ---  # openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2 This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the openmathinstruct2_cot_20k_train dataset. It achieves the following results on the evaluation set: - Loss: 0.7634 ## 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: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.8177 | 0.4808 | 500 | 0.7823 | | 0.7708 | 0.9615 | 1000 | 0.7572 | | 0.5513 | 1.4423 | 1500 | 0.7693 | | 0.5059 | 1.9231 | 2000 | 0.7637 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.3.0+cu121 - Datasets 2.21.0 - Tokenizers 0.20.1

camidenecken/RoBERTa-RM1-v2-2-rm-v27

camidenecken

2024-11-06T15:57:21Z

182

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T15:57:05Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

camidenecken/RoBERTa-RM1-v2-2-rm-v26

camidenecken

2024-11-06T15:55:15Z

180

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T15:54:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

belisarius/FLUX.1-dev-Mann-E-gguf

belisarius

2024-11-06T15:46:26Z

108

4

null

[ "gguf", "base_model:mann-e/mann-e_flux", "base_model:quantized:mann-e/mann-e_flux", "license:other", "region:us" ]

null

2024-11-05T08:26:06Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md base_model: - mann-e/mann-e_flux --- UNet only - no Clip-l/T5xxl included Quantized versions of the Mann-E model. https://huggingface.co/mann-e/mann-e_flux Made using this guide: https://github.com/city96/ComfyUI-GGUF/tree/main/tools

richiebailey/whisper-large-v3-turbo

richiebailey

2024-11-06T15:44:40Z

89

0

transformers

[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "audio", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:mit", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-06T15:37:29Z

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su license: mit tags: - audio - automatic-speech-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac pipeline_tag: automatic-speech-recognition base_model: - openai/whisper-large-v3 library_name: transformers --- # Whisper Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://huggingface.co/papers/2212.04356) by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3). In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation. You can find more details about it [in this GitHub discussion](https://github.com/openai/whisper/discussions/2363). **Disclaimer**: Content for this model card has partly been written by the 🤗 Hugging Face team, and partly copied and pasted from the original model card. ## Usage Whisper large-v3-turbo is supported in Hugging Face 🤗 Transformers. To run the model, first install the Transformers library. For this example, we'll also install 🤗 Datasets to load toy audio dataset from the Hugging Face Hub, and 🤗 Accelerate to reduce the model loading time: ```bash pip install --upgrade pip pip install --upgrade transformers datasets[audio] accelerate ``` The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) class to transcribe audios of arbitrary length: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline: ```python result = pipe("audio.mp3") ``` Multiple audio files can be transcribed in parallel by specifying them as a list and setting the `batch_size` parameter: ```python result = pipe(["audio_1.mp3", "audio_2.mp3"], batch_size=2) ``` Transformers is compatible with all Whisper decoding strategies, such as temperature fallback and condition on previous tokens. The following example demonstrates how to enable these heuristics: ```python generate_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } result = pipe(sample, generate_kwargs=generate_kwargs) ``` Whisper predicts the language of the source audio automatically. If the source audio language is known *a-priori*, it can be passed as an argument to the pipeline: ```python result = pipe(sample, generate_kwargs={"language": "english"}) ``` By default, Whisper performs the task of *speech transcription*, where the source audio language is the same as the target text language. To perform *speech translation*, where the target text is in English, set the task to `"translate"`: ```python result = pipe(sample, generate_kwargs={"task": "translate"}) ``` Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument: ```python result = pipe(sample, return_timestamps=True) print(result["chunks"]) ``` And for word-level timestamps: ```python result = pipe(sample, return_timestamps="word") print(result["chunks"]) ``` The above arguments can be used in isolation or in combination. For example, to perform the task of speech transcription where the source audio is in French, and we want to return sentence-level timestamps, the following can be used: ```python result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "french", "task": "translate"}) print(result["chunks"]) ``` <details> <summary> For more control over the generation parameters, use the model + processor API directly: </summary> ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor from datasets import Audio, load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate)) sample = dataset[0]["audio"] inputs = processor( sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt", truncation=False, padding="longest", return_attention_mask=True, ) inputs = inputs.to(device, dtype=torch_dtype) gen_kwargs = { "max_new_tokens": 448, "num_beams": 1, "condition_on_prev_tokens": False, "compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space) "temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), "logprob_threshold": -1.0, "no_speech_threshold": 0.6, "return_timestamps": True, } pred_ids = model.generate(**inputs, **gen_kwargs) pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False) print(pred_text) ``` </details> ## Additional Speed & Memory Improvements You can apply additional speed and memory improvements to Whisper to further reduce the inference speed and VRAM requirements. ### Chunked Long-Form Whisper has a receptive field of 30-seconds. To transcribe audios longer than this, one of two long-form algorithms are required: 1. **Sequential:** uses a "sliding window" for buffered inference, transcribing 30-second slices one after the other 2. **Chunked:** splits long audio files into shorter ones (with a small overlap between segments), transcribes each segment independently, and stitches the resulting transcriptions at the boundaries The sequential long-form algorithm should be used in either of the following scenarios: 1. Transcription accuracy is the most important factor, and speed is less of a consideration 2. You are transcribing **batches** of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate Conversely, the chunked algorithm should be used when: 1. Transcription speed is the most important factor 2. You are transcribing a **single** long audio file By default, Transformers uses the sequential algorithm. To enable the chunked algorithm, pass the `chunk_length_s` parameter to the `pipeline`. For large-v3, a chunk length of 30-seconds is optimal. To activate batching over long audio files, pass the argument `batch_size`: ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, chunk_length_s=30, batch_size=16, # batch size for inference - set based on your device torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` #### Torch compile The Whisper forward pass is compatible with [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) for 4.5x speed-ups. **Note:** `torch.compile` is currently not compatible with the Chunked long-form algorithm or Flash Attention 2 ⚠️ ```python import torch from torch.nn.attention import SDPBackend, sdpa_kernel from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset from tqdm import tqdm torch.set_float32_matmul_precision("high") device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "openai/whisper-large-v3-turbo" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True ).to(device) # Enable static cache and compile the forward pass model.generation_config.cache_implementation = "static" model.generation_config.max_new_tokens = 256 model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] # 2 warmup steps for _ in tqdm(range(2), desc="Warm-up step"): with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy(), generate_kwargs={"min_new_tokens": 256, "max_new_tokens": 256}) # fast run with sdpa_kernel(SDPBackend.MATH): result = pipe(sample.copy()) print(result["text"]) ``` #### Flash Attention 2 We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2) if your GPU supports it and you are not using [torch.compile](#torch-compile). To do so, first install [Flash Attention](https://github.com/Dao-AILab/flash-attention): ``` pip install flash-attn --no-build-isolation ``` Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="flash_attention_2") ``` #### Torch Scale-Product-Attention (SDPA) If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html). This attention implementation is activated **by default** for PyTorch versions 2.1.1 or greater. To check whether you have a compatible PyTorch version, run the following Python code snippet: ```python from transformers.utils import is_torch_sdpa_available print(is_torch_sdpa_available()) ``` If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/) Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying `attn_implementation="sdpa"` as follows: ```python model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, attn_implementation="sdpa") ``` For more information about how to use the SDPA refer to the [Transformers SDPA documentation](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention). ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. There are two flavours of Whisper model: English-only and multilingual. The English-only models were trained on the task of English speech recognition. The multilingual models were trained simultaneously on multilingual speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio. For speech translation, the model predicts transcriptions to a *different* language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are available as English-only and multilingual. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: | Size | Parameters | English-only | Multilingual | |----------|------------|------------------------------------------------------|-----------------------------------------------------| | tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) | | base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) | | small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) | | medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) | | large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) | | large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) | | large-v3 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v3) | | large-v3-turbo | 809 M | x | [✓](https://huggingface.co/openai/whisper-large-v3-turbo) | ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data No information provided. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

mradermacher/alfred-40b-1023-i1-GGUF

mradermacher

2024-11-06T15:41:09Z

106

0

transformers

[ "transformers", "gguf", "falcon-40b", "long-context", "falcon", "NTK-YaRN", "en", "fr", "de", "es", "it", "dataset:OpenAssistant/oasst1", "dataset:ehartford/dolphin", "dataset:tau/sled", "dataset:tiiuae/falcon-refinedweb", "base_model:lightonai/alfred-40b-1023", "base_model:quantized:lightonai/alfred-40b-1023", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-06T07:03:20Z

--- base_model: lightonai/alfred-40b-1023 datasets: - OpenAssistant/oasst1 - ehartford/dolphin - tau/sled - tiiuae/falcon-refinedweb language: - en - fr - de - es - it library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - falcon-40b - long-context - falcon - NTK-YaRN --- ## About      weighted/imatrix quants of https://huggingface.co/lightonai/alfred-40b-1023  static quants are available at https://huggingface.co/mradermacher/alfred-40b-1023-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ1_S.gguf) | i1-IQ1_S | 9.3 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ1_M.gguf) | i1-IQ1_M | 10.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 11.5 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_XS.gguf) | i1-IQ2_XS | 12.7 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_S.gguf) | i1-IQ2_S | 13.5 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ2_M.gguf) | i1-IQ2_M | 14.6 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q2_K.gguf) | i1-Q2_K | 15.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 16.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_XS.gguf) | i1-IQ3_XS | 17.9 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_S.gguf) | i1-IQ3_S | 18.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_S.gguf) | i1-Q3_K_S | 18.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ3_M.gguf) | i1-IQ3_M | 19.3 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_M.gguf) | i1-Q3_K_M | 20.2 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q3_K_L.gguf) | i1-Q3_K_L | 21.7 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-IQ4_XS.gguf) | i1-IQ4_XS | 22.6 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_K_S.gguf) | i1-Q4_K_S | 23.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_0.gguf) | i1-Q4_0 | 24.0 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q4_K_M.gguf) | i1-Q4_K_M | 25.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q5_K_S.gguf) | i1-Q5_K_S | 29.1 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q5_K_M.gguf) | i1-Q5_K_M | 30.7 | | | [GGUF](https://huggingface.co/mradermacher/alfred-40b-1023-i1-GGUF/resolve/main/alfred-40b-1023.i1-Q6_K.gguf) | i1-Q6_K | 34.6 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

neopolita/hammer2.0-7b-gguf

neopolita

2024-11-06T15:39:32Z

13

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T15:03:06Z

--- {} --- # GGUF quants for [**MadeAgents/Hammer2.0-7b**](https://huggingface.co/MadeAgents/Hammer2.0-7b) using [llama.cpp](https://github.com/ggerganov/llama.cpp) **Terms of Use**: Please check the [**original model**](https://huggingface.co/MadeAgents/Hammer2.0-7b) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

mav23/SmolLM2-360M-Instruct-GGUF

mav23

2024-11-06T15:37:44Z

66

0

transformers

[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T15:32:30Z

--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/oWWfzW4RbWkVIo7f-5444.png) ## Table of Contents 1. [Model Summary](##model-summary) 2. [Limitations](##limitations) 3. [Training](##training) 4. [License](##license) 5. [Citation](##citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 360M model was trained on 4 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-360M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model | Metrics | SmolLM2-360M | Qwen2.5-0.5B | SmolLM-360M | |:-------------------|:------------:|:------------:|:------------:| | HellaSwag | **54.5** | 51.2 | 51.8 | | ARC (Average) | **53.0** | 45.4 | 50.1 | | PIQA | **71.7** | 69.9 | 71.6 | | MMLU (cloze) | **35.8** | 33.7 | 34.4 | | CommonsenseQA | **38.0** | 31.6 | 35.3 | | TriviaQA | **16.9** | 4.3 | 9.1 | | Winogrande | 52.5 | **54.1** | 52.8 | | OpenBookQA | **37.4** | **37.4** | 37.2 | | GSM8K (5-shot) | 3.2 | **33.4** | 1.6 | ## Instruction Model | Metric | SmolLM2-360M-Instruct | Qwen2.5-0.5B-Instruct | SmolLM-360M-Instruct | |:-----------------------------|:---------------------:|:---------------------:|:---------------------:| | IFEval (Average prompt/inst) | **41.0** | 31.6 | 19.8 | | MT-Bench | 3.66 | **4.16** | 3.37 | | HellaSwag | **52.1** | 48.0 | 47.9 | | ARC (Average) | **43.7** | 37.3 | 38.8 | | PIQA | **70.8** | 67.2 | 69.4 | | MMLU (cloze) | **32.8** | 31.7 | 30.6 | | BBH (3-shot) | 27.3 | **30.7** | 24.4 | | GSM8K (5-shot) | 7.43 | **26.8** | 1.36 | ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 4T - **Precision:** bfloat16 ### Hardware - **GPUs:** 64 H100 ### Software - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF

mradermacher

2024-11-06T15:35:12Z

5

0

transformers

[ "transformers", "gguf", "tr", "en", "base_model:Trendyol/Trendyol-LLM-7b-chat-v0.1", "base_model:quantized:Trendyol/Trendyol-LLM-7b-chat-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-06T06:04:52Z

--- base_model: Trendyol/Trendyol-LLM-7b-chat-v0.1 language: - tr - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1  static quants are available at https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 2.7 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.0 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.0 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.0 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 4.0 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Trendyol-LLM-7b-chat-v0.1-i1-GGUF/resolve/main/Trendyol-LLM-7b-chat-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 5.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

QuantFactory/KONI-Llama3.1-8B-Instruct-20241024-GGUF

QuantFactory

2024-11-06T15:34:48Z

105

3

transformers

[ "transformers", "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T14:40:16Z

--- library_name: transformers tags: [] --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/KONI-Llama3.1-8B-Instruct-20241024-GGUF This is quantized version of [KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024) created using llama.cpp # Original Model Card # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

techiaith/whisper-large-v3-ft-cv-cy

techiaith

2024-11-06T15:32:39Z

10

0

null

[ "tensorboard", "safetensors", "whisper", "generated_from_trainer", "automatic-speech-recognition", "cy", "dataset:techiaith/commonvoice_18_0_cy", "base_model:openai/whisper-large-v3", "base_model:finetune:openai/whisper-large-v3", "license:apache-2.0", "model-index", "region:us" ]

automatic-speech-recognition

2024-08-26T11:24:16Z

--- license: apache-2.0 base_model: openai/whisper-large-v3 tags: - generated_from_trainer - whisper datasets: - techiaith/commonvoice_18_0_cy metrics: - wer model-index: - name: whisper-large-v3-ft-cv-cy-train-all-plus-other-with-excluded results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: DewiBrynJones/commonvoice_18_0_cy default type: DewiBrynJones/commonvoice_18_0_cy args: default metrics: - name: Wer type: wer value: 0.185 language: - cy pipeline_tag: automatic-speech-recognition --- # whisper-large-v3-ft-cv-cy This model is a version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) fine-tuned with the `train_all` and `other_with_excluded` custom splits from [techiaith/commonvoice_18_0_cy](https://huggingface.co/datasets/techiaith/commonvoice_18_0_cy) It achieves the following results on the Common Voice for Welsh release 18's standard test set: - WER: 18.50 - CER: 5.32 N.B. this model performs considerably worse on English language speech, but better on Welsh than a [bilingual model](https://huggingface.co/techiaith/whisper-large-v3-ft-cv-cy-en) ## Usage ```python from transformers import pipeline transcriber = pipeline("automatic-speech-recognition", model="techiaith/whisper-large-v3-ft-cv-cy") result = transcriber(<path or url to soundfile>) print (result) ``` `{'text': 'Mae hen wlad fy nhadau yn annwyl i mi.'}`

mav23/SmolLM2-135M-Instruct-GGUF

mav23

2024-11-06T15:29:26Z

27

0

transformers

[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T15:26:33Z

--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/3ntM63zkmxY2cNRhgY_Kl.png) ## Table of Contents 1. [Model Summary](##model-summary) 2. [Limitations](##limitations) 3. [Training](##training) 4. [License](##license) 5. [Citation](##citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ### Transformers ```bash pip install transformers ``` ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is gravity?"}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ### Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base pre-trained model | Metrics | SmolLM2-135M-8k | SmolLM-135M | |:-------------------|:----------------:|:------------:| | HellaSwag | **42.1** | 41.2 | | ARC (Average) | **43.9** | 42.4 | | PIQA | 68.4 | 68.4 | | MMLU (cloze) | **31.5** | 30.2 | | CommonsenseQA | **33.9** | 32.7 | | TriviaQA | 4.1 | **4.3** | | Winogrande | 51.3 | 51.3 | | OpenBookQA | **34.6** | 34.0 | | GSM8K (5-shot) | **1.4** | 1.0 | ## Instruction model | Metric | SmolLM2-135M-Instruct | SmolLM-135M-Instruct | |:-----------------------------|:---------------------:|:--------------------:| | IFEval (Average prompt/inst) | **29.9** | 17.2 | | MT-Bench | **19.8** | 16.8 | | HellaSwag | **40.9** | 38.9 | | ARC (Average) | **37.3** | 33.9 | | PIQA | **66.3** | 64.0 | | MMLU (cloze) | **29.3** | 28.3 | | BBH (3-shot) | **28.2** | 25.2 | | GSM8K (5-shot) | 1.4 | 1.4 | ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 2T - **Precision:** bfloat16 ### Hardware - **GPUs:** 64 H100 ### Software - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

Tippawan/pr-corrected-v8

Tippawan

2024-11-06T15:26:40Z

117

0

transformers

[ "transformers", "safetensors", "camembert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-11-06T15:26:19Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

mav23/SmolLM2-1.7B-GGUF

mav23

2024-11-06T15:25:15Z

8

0

transformers

[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-11-06T15:09:39Z

--- library_name: transformers license: apache-2.0 language: - en --- # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/XlT5TM3HWpfoZk_HSubrH.png) ## Table of Contents 1. [Model Summary](#model-summary) 2. [Evaluation](#evaluation) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) ## Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. The 1.7B variant demonstrates significant advances over its predecessor SmolLM1-1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized). The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by [Argilla](https://huggingface.co/argilla) such as [Synth-APIGen-v0.1](https://huggingface.co/datasets/argilla/Synth-APIGen-v0.1). ### How to use ```bash pip install transformers ``` #### Running the model on CPU/GPU/multi GPU * _Using full precision_ ```python # pip install transformers from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-1.7B" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate # for fp16 use `torch_dtype=torch.float16` instead model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer.encode("Gravity is", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 3422.76 MB ``` ## Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. ## Base Pre-Trained Model | Metric | SmolLM2-1.7B | Llama-1B | Qwen2.5-1.5B | SmolLM1-1.7B | |------------------|--------------|-------------|---------------|--------------| | HellaSwag | **68.7** | 61.2 | 66.4 | 62.9 | | ARC (Average) | **60.5** | 49.2 | 58.5 | 59.9 | | PIQA | **77.6** | 74.8 | 76.1 | 76.0 | | MMLU-Pro (MCF) | **19.4** | 11.7 | 13.7 | 10.8 | | CommonsenseQA | **43.6** | 41.2 | 34.1 | 38.0 | | TriviaQA | **36.7** | 28.1 | 20.9 | 22.5 | | Winogrande | **59.4** | 57.8 | 59.3 | 54.7 | | OpenBookQA | 42.2 | 38.4 | 40.0 | **42.4** | | GSM8K (5-shot) | 31.0 | 7.2 | **61.3** | 5.5 | ## Instruction Model | Metric | SmolLM2-1.7B-Instruct | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct | SmolLM1-1.7B-Instruct | |:-----------------------------|:---------------------:|:-----------------:|:----------------------:|:----------------------:| | IFEval (Average prompt/inst) | **56.7** | 53.5 | 47.4 | 23.1 | | MT-Bench | 6.13 | 5.48 | **6.52** | 4.33 | | OpenRewrite-Eval (micro_avg RougeL) | 44.9 | 39.2 | **46.9** | NaN | | HellaSwag | **66.1** | 56.1 | 60.9 | 55.5 | | ARC (Average) | **51.7** | 41.6 | 46.2 | 43.7 | | PIQA | **74.4** | 72.3 | 73.2 | 71.6 | | MMLU-Pro (MCF) | 19.3 | 12.7 | **24.2** | 11.7 | | BBH (3-shot) | 32.2 | 27.6 | **35.3** | 25.7 | | GSM8K (5-shot) | **48.2** | 26.8 | 42.8 | 4.62 | ## Limitations SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. ## Training ### Model - **Architecture:** Transformer decoder - **Pretraining tokens:** 11T - **Precision:** bfloat16 ### Hardware - **GPUs:** 256 H100 ### Software - **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main) ## License [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Citation ```bash @misc{allal2024SmolLM2, title={SmolLM2 - with great data, comes great performance}, author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Lewis Tunstall and Agustín Piqueres and Andres Marafioti and Cyril Zakka and Leandro von Werra and Thomas Wolf}, year={2024}, } ```

MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF

MaziyarPanahi

2024-11-06T15:19:58Z

34

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.0", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.0", "region:us", "conversational" ]

text-generation

2024-11-06T14:57:15Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.0-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.0 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.0](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.0) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.0-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.0](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.0). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

nphuc/Llama-3.2-3B-Thich-Nhat-Hanh-v3.2

nphuc

2024-11-06T15:15:38Z

17

0

transformers

[ "transformers", "safetensors", "gguf", "llama", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T15:00:15Z

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** nphuc - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ZyloO-AI/RawCharm-Amateur-Photography

ZyloO-AI

2024-11-06T14:53:47Z

40

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-11-06T14:49:25Z

--- library_name: diffusers pipeline_tag: text-to-image --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

novalalthoff/wav2vec2-large-id-16hr-non-lp

novalalthoff

2024-11-06T14:51:21Z

80

0

transformers

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-06T14:49:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

Xu-Ouyang/pythia-6.9b-deduped-int8-step2-GPTQ-wikitext2

Xu-Ouyang

2024-11-06T14:46:28Z

76

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]

text-generation

2024-11-06T14:36:03Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF

MaziyarPanahi

2024-11-06T14:35:34Z

52

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.1", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.1", "region:us", "conversational" ]

text-generation

2024-11-06T14:12:42Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.1-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.1 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.1](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.1) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.1-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.1](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.1). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1

MayBashendy

2024-11-06T14:33:18Z

162

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T14:00:29Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1 results: [] ---  # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold1 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4063 - Qwk: 0.5441 - Mse: 0.4063 - Rmse: 0.6374 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0060 | 2 | 9.8538 | 0.0 | 9.8538 | 3.1391 | | No log | 0.0120 | 4 | 7.5205 | 0.0213 | 7.5205 | 2.7424 | | No log | 0.0181 | 6 | 6.8656 | 0.0018 | 6.8656 | 2.6202 | | No log | 0.0241 | 8 | 6.0945 | 0.0 | 6.0945 | 2.4687 | | No log | 0.0301 | 10 | 5.6492 | 0.0 | 5.6492 | 2.3768 | | No log | 0.0361 | 12 | 4.8146 | 0.0 | 4.8146 | 2.1942 | | No log | 0.0422 | 14 | 4.1150 | 0.0 | 4.1150 | 2.0285 | | No log | 0.0482 | 16 | 3.3946 | 0.0297 | 3.3946 | 1.8424 | | No log | 0.0542 | 18 | 2.7416 | 0.0118 | 2.7416 | 1.6558 | | No log | 0.0602 | 20 | 2.1167 | 0.0079 | 2.1167 | 1.4549 | | No log | 0.0663 | 22 | 1.6121 | 0.0 | 1.6121 | 1.2697 | | No log | 0.0723 | 24 | 1.2816 | 0.1643 | 1.2816 | 1.1321 | | No log | 0.0783 | 26 | 1.0878 | 0.1060 | 1.0878 | 1.0430 | | No log | 0.0843 | 28 | 0.9893 | 0.0211 | 0.9893 | 0.9946 | | No log | 0.0904 | 30 | 0.8874 | 0.0211 | 0.8874 | 0.9420 | | No log | 0.0964 | 32 | 0.8744 | 0.0211 | 0.8744 | 0.9351 | | No log | 0.1024 | 34 | 0.8581 | 0.0106 | 0.8581 | 0.9263 | | No log | 0.1084 | 36 | 0.8897 | 0.0106 | 0.8897 | 0.9432 | | No log | 0.1145 | 38 | 0.8408 | 0.0106 | 0.8408 | 0.9170 | | No log | 0.1205 | 40 | 0.9843 | 0.1325 | 0.9843 | 0.9921 | | No log | 0.1265 | 42 | 1.0066 | 0.0 | 1.0066 | 1.0033 | | No log | 0.1325 | 44 | 0.9551 | 0.0 | 0.9551 | 0.9773 | | No log | 0.1386 | 46 | 0.8306 | 0.0 | 0.8306 | 0.9114 | | No log | 0.1446 | 48 | 0.7559 | 0.0106 | 0.7559 | 0.8694 | | No log | 0.1506 | 50 | 0.7449 | 0.0106 | 0.7449 | 0.8630 | | No log | 0.1566 | 52 | 0.7499 | 0.0106 | 0.7499 | 0.8660 | | No log | 0.1627 | 54 | 0.8259 | 0.0106 | 0.8259 | 0.9088 | | No log | 0.1687 | 56 | 0.8342 | 0.0106 | 0.8342 | 0.9134 | | No log | 0.1747 | 58 | 0.8480 | 0.0106 | 0.8480 | 0.9209 | | No log | 0.1807 | 60 | 0.8004 | 0.0106 | 0.8004 | 0.8947 | | No log | 0.1867 | 62 | 0.8020 | 0.0106 | 0.8020 | 0.8955 | | No log | 0.1928 | 64 | 0.7901 | 0.0132 | 0.7901 | 0.8889 | | No log | 0.1988 | 66 | 0.7705 | 0.0106 | 0.7705 | 0.8778 | | No log | 0.2048 | 68 | 0.7646 | 0.0477 | 0.7646 | 0.8744 | | No log | 0.2108 | 70 | 0.7771 | 0.0106 | 0.7771 | 0.8816 | | No log | 0.2169 | 72 | 0.8900 | 0.0 | 0.8900 | 0.9434 | | No log | 0.2229 | 74 | 0.9312 | 0.0 | 0.9312 | 0.9650 | | No log | 0.2289 | 76 | 0.7507 | 0.0211 | 0.7507 | 0.8664 | | No log | 0.2349 | 78 | 0.8049 | 0.1726 | 0.8049 | 0.8972 | | No log | 0.2410 | 80 | 0.7495 | 0.1392 | 0.7495 | 0.8657 | | No log | 0.2470 | 82 | 0.6772 | 0.0570 | 0.6772 | 0.8229 | | No log | 0.2530 | 84 | 0.6693 | 0.0803 | 0.6693 | 0.8181 | | No log | 0.2590 | 86 | 0.6966 | 0.0999 | 0.6966 | 0.8346 | | No log | 0.2651 | 88 | 0.7758 | 0.1809 | 0.7758 | 0.8808 | | No log | 0.2711 | 90 | 0.6627 | 0.0620 | 0.6627 | 0.8140 | | No log | 0.2771 | 92 | 0.6599 | 0.0941 | 0.6599 | 0.8123 | | No log | 0.2831 | 94 | 0.6633 | 0.0669 | 0.6633 | 0.8144 | | No log | 0.2892 | 96 | 0.6847 | 0.0604 | 0.6847 | 0.8275 | | No log | 0.2952 | 98 | 0.8380 | 0.0052 | 0.8380 | 0.9154 | | No log | 0.3012 | 100 | 0.9455 | 0.0 | 0.9455 | 0.9724 | | No log | 0.3072 | 102 | 0.9850 | 0.0 | 0.9850 | 0.9925 | | No log | 0.3133 | 104 | 0.9506 | 0.0 | 0.9506 | 0.9750 | | No log | 0.3193 | 106 | 0.9171 | -0.0027 | 0.9171 | 0.9576 | | No log | 0.3253 | 108 | 0.8876 | 0.0116 | 0.8876 | 0.9421 | | No log | 0.3313 | 110 | 0.8296 | 0.0025 | 0.8296 | 0.9108 | | No log | 0.3373 | 112 | 0.7703 | 0.0 | 0.7703 | 0.8777 | | No log | 0.3434 | 114 | 0.7313 | 0.0211 | 0.7313 | 0.8552 | | No log | 0.3494 | 116 | 0.7281 | 0.0276 | 0.7281 | 0.8533 | | No log | 0.3554 | 118 | 0.7371 | 0.0211 | 0.7371 | 0.8585 | | No log | 0.3614 | 120 | 0.8136 | 0.2752 | 0.8136 | 0.9020 | | No log | 0.3675 | 122 | 0.8426 | 0.3032 | 0.8426 | 0.9179 | | No log | 0.3735 | 124 | 0.8149 | 0.0 | 0.8149 | 0.9027 | | No log | 0.3795 | 126 | 0.8598 | 0.0 | 0.8598 | 0.9273 | | No log | 0.3855 | 128 | 0.8445 | 0.0 | 0.8445 | 0.9189 | | No log | 0.3916 | 130 | 0.7930 | 0.0 | 0.7930 | 0.8905 | | No log | 0.3976 | 132 | 0.7897 | 0.0742 | 0.7897 | 0.8887 | | No log | 0.4036 | 134 | 0.7592 | 0.0779 | 0.7592 | 0.8713 | | No log | 0.4096 | 136 | 0.7340 | 0.0250 | 0.7340 | 0.8567 | | No log | 0.4157 | 138 | 0.7109 | 0.0631 | 0.7109 | 0.8432 | | No log | 0.4217 | 140 | 0.7010 | 0.3134 | 0.7010 | 0.8372 | | No log | 0.4277 | 142 | 0.7485 | 0.4716 | 0.7485 | 0.8652 | | No log | 0.4337 | 144 | 0.7055 | 0.3984 | 0.7055 | 0.8399 | | No log | 0.4398 | 146 | 0.6043 | 0.2866 | 0.6043 | 0.7774 | | No log | 0.4458 | 148 | 0.6090 | 0.1808 | 0.6090 | 0.7804 | | No log | 0.4518 | 150 | 0.6293 | 0.1585 | 0.6293 | 0.7933 | | No log | 0.4578 | 152 | 0.6791 | 0.1350 | 0.6791 | 0.8241 | | No log | 0.4639 | 154 | 0.7938 | 0.1901 | 0.7938 | 0.8910 | | No log | 0.4699 | 156 | 0.7204 | 0.2131 | 0.7204 | 0.8487 | | No log | 0.4759 | 158 | 0.6079 | 0.1893 | 0.6079 | 0.7797 | | No log | 0.4819 | 160 | 0.6052 | 0.2001 | 0.6052 | 0.7780 | | No log | 0.4880 | 162 | 0.6483 | 0.3653 | 0.6483 | 0.8052 | | No log | 0.4940 | 164 | 0.6604 | 0.3441 | 0.6604 | 0.8127 | | No log | 0.5 | 166 | 0.6017 | 0.2949 | 0.6017 | 0.7757 | | No log | 0.5060 | 168 | 0.5814 | 0.4476 | 0.5814 | 0.7625 | | No log | 0.5120 | 170 | 0.5913 | 0.5063 | 0.5913 | 0.7689 | | No log | 0.5181 | 172 | 0.6019 | 0.5193 | 0.6019 | 0.7759 | | No log | 0.5241 | 174 | 0.5463 | 0.4907 | 0.5463 | 0.7391 | | No log | 0.5301 | 176 | 0.5484 | 0.4974 | 0.5484 | 0.7406 | | No log | 0.5361 | 178 | 0.5551 | 0.4497 | 0.5551 | 0.7451 | | No log | 0.5422 | 180 | 0.5884 | 0.4432 | 0.5884 | 0.7671 | | No log | 0.5482 | 182 | 0.7529 | 0.4655 | 0.7529 | 0.8677 | | No log | 0.5542 | 184 | 0.8098 | 0.4488 | 0.8098 | 0.8999 | | No log | 0.5602 | 186 | 0.7082 | 0.4467 | 0.7082 | 0.8416 | | No log | 0.5663 | 188 | 0.6004 | 0.3464 | 0.6004 | 0.7748 | | No log | 0.5723 | 190 | 0.5929 | 0.2784 | 0.5929 | 0.7700 | | No log | 0.5783 | 192 | 0.5549 | 0.4062 | 0.5549 | 0.7449 | | No log | 0.5843 | 194 | 0.5563 | 0.5899 | 0.5563 | 0.7458 | | No log | 0.5904 | 196 | 0.5644 | 0.5750 | 0.5644 | 0.7513 | | No log | 0.5964 | 198 | 0.5137 | 0.5741 | 0.5137 | 0.7167 | | No log | 0.6024 | 200 | 0.5335 | 0.5233 | 0.5335 | 0.7304 | | No log | 0.6084 | 202 | 0.5050 | 0.5336 | 0.5050 | 0.7106 | | No log | 0.6145 | 204 | 0.5118 | 0.6005 | 0.5118 | 0.7154 | | No log | 0.6205 | 206 | 0.6248 | 0.5732 | 0.6248 | 0.7905 | | No log | 0.6265 | 208 | 0.7111 | 0.5476 | 0.7111 | 0.8433 | | No log | 0.6325 | 210 | 0.7781 | 0.5189 | 0.7781 | 0.8821 | | No log | 0.6386 | 212 | 0.8434 | 0.4931 | 0.8434 | 0.9184 | | No log | 0.6446 | 214 | 0.8126 | 0.5209 | 0.8126 | 0.9014 | | No log | 0.6506 | 216 | 0.6383 | 0.5970 | 0.6383 | 0.7989 | | No log | 0.6566 | 218 | 0.5315 | 0.6014 | 0.5315 | 0.7290 | | No log | 0.6627 | 220 | 0.5624 | 0.4525 | 0.5624 | 0.7499 | | No log | 0.6687 | 222 | 0.6333 | 0.3337 | 0.6333 | 0.7958 | | No log | 0.6747 | 224 | 0.5795 | 0.3624 | 0.5795 | 0.7612 | | No log | 0.6807 | 226 | 0.5073 | 0.5486 | 0.5073 | 0.7122 | | No log | 0.6867 | 228 | 0.5600 | 0.6066 | 0.5600 | 0.7484 | | No log | 0.6928 | 230 | 0.6215 | 0.5835 | 0.6215 | 0.7883 | | No log | 0.6988 | 232 | 0.5686 | 0.5877 | 0.5686 | 0.7540 | | No log | 0.7048 | 234 | 0.6028 | 0.5796 | 0.6028 | 0.7764 | | No log | 0.7108 | 236 | 0.5793 | 0.5983 | 0.5793 | 0.7611 | | No log | 0.7169 | 238 | 0.5310 | 0.6063 | 0.5310 | 0.7287 | | No log | 0.7229 | 240 | 0.4967 | 0.5858 | 0.4967 | 0.7048 | | No log | 0.7289 | 242 | 0.4868 | 0.5721 | 0.4868 | 0.6977 | | No log | 0.7349 | 244 | 0.4722 | 0.5557 | 0.4722 | 0.6871 | | No log | 0.7410 | 246 | 0.4724 | 0.5195 | 0.4724 | 0.6873 | | No log | 0.7470 | 248 | 0.4676 | 0.5665 | 0.4676 | 0.6838 | | No log | 0.7530 | 250 | 0.4837 | 0.5811 | 0.4837 | 0.6955 | | No log | 0.7590 | 252 | 0.4968 | 0.5984 | 0.4968 | 0.7048 | | No log | 0.7651 | 254 | 0.4773 | 0.6040 | 0.4773 | 0.6909 | | No log | 0.7711 | 256 | 0.4643 | 0.5940 | 0.4643 | 0.6814 | | No log | 0.7771 | 258 | 0.4890 | 0.6155 | 0.4890 | 0.6993 | | No log | 0.7831 | 260 | 0.6079 | 0.5785 | 0.6079 | 0.7797 | | No log | 0.7892 | 262 | 0.6645 | 0.5582 | 0.6645 | 0.8152 | | No log | 0.7952 | 264 | 0.6657 | 0.5514 | 0.6657 | 0.8159 | | No log | 0.8012 | 266 | 0.6675 | 0.5498 | 0.6675 | 0.8170 | | No log | 0.8072 | 268 | 0.5966 | 0.5786 | 0.5966 | 0.7724 | | No log | 0.8133 | 270 | 0.5300 | 0.6091 | 0.5300 | 0.7280 | | No log | 0.8193 | 272 | 0.5421 | 0.6164 | 0.5421 | 0.7363 | | No log | 0.8253 | 274 | 0.6810 | 0.5792 | 0.6810 | 0.8252 | | No log | 0.8313 | 276 | 0.6917 | 0.5810 | 0.6917 | 0.8317 | | No log | 0.8373 | 278 | 0.5877 | 0.6193 | 0.5877 | 0.7666 | | No log | 0.8434 | 280 | 0.5283 | 0.6098 | 0.5283 | 0.7268 | | No log | 0.8494 | 282 | 0.5171 | 0.5755 | 0.5171 | 0.7191 | | No log | 0.8554 | 284 | 0.5388 | 0.5552 | 0.5388 | 0.7340 | | No log | 0.8614 | 286 | 0.5772 | 0.5397 | 0.5772 | 0.7598 | | No log | 0.8675 | 288 | 0.5090 | 0.5516 | 0.5090 | 0.7135 | | No log | 0.8735 | 290 | 0.4698 | 0.4972 | 0.4698 | 0.6854 | | No log | 0.8795 | 292 | 0.4553 | 0.5218 | 0.4553 | 0.6748 | | No log | 0.8855 | 294 | 0.4614 | 0.5281 | 0.4614 | 0.6793 | | No log | 0.8916 | 296 | 0.4784 | 0.5153 | 0.4784 | 0.6917 | | No log | 0.8976 | 298 | 0.4488 | 0.5968 | 0.4488 | 0.6700 | | No log | 0.9036 | 300 | 0.5075 | 0.6270 | 0.5075 | 0.7124 | | No log | 0.9096 | 302 | 0.4963 | 0.6338 | 0.4963 | 0.7045 | | No log | 0.9157 | 304 | 0.4564 | 0.5870 | 0.4564 | 0.6756 | | No log | 0.9217 | 306 | 0.5087 | 0.5159 | 0.5087 | 0.7132 | | No log | 0.9277 | 308 | 0.4529 | 0.5780 | 0.4529 | 0.6729 | | No log | 0.9337 | 310 | 0.5123 | 0.6292 | 0.5123 | 0.7158 | | No log | 0.9398 | 312 | 0.6004 | 0.6074 | 0.6004 | 0.7749 | | No log | 0.9458 | 314 | 0.5623 | 0.6023 | 0.5623 | 0.7499 | | No log | 0.9518 | 316 | 0.4899 | 0.6086 | 0.4899 | 0.6999 | | No log | 0.9578 | 318 | 0.4493 | 0.5903 | 0.4493 | 0.6703 | | No log | 0.9639 | 320 | 0.4409 | 0.5967 | 0.4409 | 0.6640 | | No log | 0.9699 | 322 | 0.4509 | 0.6117 | 0.4509 | 0.6715 | | No log | 0.9759 | 324 | 0.4351 | 0.5719 | 0.4351 | 0.6596 | | No log | 0.9819 | 326 | 0.4458 | 0.6023 | 0.4458 | 0.6677 | | No log | 0.9880 | 328 | 0.5342 | 0.6022 | 0.5342 | 0.7309 | | No log | 0.9940 | 330 | 0.5829 | 0.5923 | 0.5829 | 0.7635 | | No log | 1.0 | 332 | 0.5132 | 0.6003 | 0.5132 | 0.7164 | | No log | 1.0060 | 334 | 0.5212 | 0.6055 | 0.5212 | 0.7219 | | No log | 1.0120 | 336 | 0.5368 | 0.6044 | 0.5368 | 0.7327 | | No log | 1.0181 | 338 | 0.5627 | 0.5900 | 0.5627 | 0.7501 | | No log | 1.0241 | 340 | 0.5291 | 0.6080 | 0.5291 | 0.7274 | | No log | 1.0301 | 342 | 0.4764 | 0.5559 | 0.4764 | 0.6902 | | No log | 1.0361 | 344 | 0.4584 | 0.5561 | 0.4584 | 0.6770 | | No log | 1.0422 | 346 | 0.4822 | 0.5822 | 0.4822 | 0.6944 | | No log | 1.0482 | 348 | 0.4995 | 0.5724 | 0.4995 | 0.7068 | | No log | 1.0542 | 350 | 0.5213 | 0.5983 | 0.5213 | 0.7220 | | No log | 1.0602 | 352 | 0.4432 | 0.6025 | 0.4432 | 0.6657 | | No log | 1.0663 | 354 | 0.4266 | 0.6011 | 0.4266 | 0.6531 | | No log | 1.0723 | 356 | 0.4593 | 0.6256 | 0.4593 | 0.6777 | | No log | 1.0783 | 358 | 0.4282 | 0.6277 | 0.4282 | 0.6544 | | No log | 1.0843 | 360 | 0.4012 | 0.5671 | 0.4012 | 0.6334 | | No log | 1.0904 | 362 | 0.4494 | 0.5048 | 0.4494 | 0.6704 | | No log | 1.0964 | 364 | 0.4008 | 0.5778 | 0.4008 | 0.6331 | | No log | 1.1024 | 366 | 0.4551 | 0.6453 | 0.4551 | 0.6746 | | No log | 1.1084 | 368 | 0.4896 | 0.6424 | 0.4896 | 0.6997 | | No log | 1.1145 | 370 | 0.4146 | 0.5919 | 0.4146 | 0.6439 | | No log | 1.1205 | 372 | 0.4293 | 0.5326 | 0.4293 | 0.6552 | | No log | 1.1265 | 374 | 0.4293 | 0.5315 | 0.4293 | 0.6552 | | No log | 1.1325 | 376 | 0.4753 | 0.5957 | 0.4753 | 0.6894 | | No log | 1.1386 | 378 | 0.6103 | 0.5992 | 0.6103 | 0.7812 | | No log | 1.1446 | 380 | 0.6979 | 0.5847 | 0.6979 | 0.8354 | | No log | 1.1506 | 382 | 0.5574 | 0.6284 | 0.5574 | 0.7466 | | No log | 1.1566 | 384 | 0.4114 | 0.6228 | 0.4114 | 0.6414 | | No log | 1.1627 | 386 | 0.4050 | 0.6184 | 0.4050 | 0.6364 | | No log | 1.1687 | 388 | 0.4458 | 0.6544 | 0.4458 | 0.6677 | | No log | 1.1747 | 390 | 0.4770 | 0.6515 | 0.4770 | 0.6906 | | No log | 1.1807 | 392 | 0.4387 | 0.6407 | 0.4387 | 0.6623 | | No log | 1.1867 | 394 | 0.4974 | 0.5471 | 0.4974 | 0.7053 | | No log | 1.1928 | 396 | 0.5162 | 0.5377 | 0.5162 | 0.7185 | | No log | 1.1988 | 398 | 0.4317 | 0.6337 | 0.4317 | 0.6570 | | No log | 1.2048 | 400 | 0.5618 | 0.6426 | 0.5618 | 0.7496 | | No log | 1.2108 | 402 | 0.7052 | 0.6094 | 0.7052 | 0.8398 | | No log | 1.2169 | 404 | 0.6045 | 0.6085 | 0.6045 | 0.7775 | | No log | 1.2229 | 406 | 0.5113 | 0.5431 | 0.5113 | 0.7151 | | No log | 1.2289 | 408 | 0.5141 | 0.5341 | 0.5141 | 0.7170 | | No log | 1.2349 | 410 | 0.5797 | 0.5890 | 0.5797 | 0.7614 | | No log | 1.2410 | 412 | 0.5818 | 0.5859 | 0.5818 | 0.7627 | | No log | 1.2470 | 414 | 0.5180 | 0.5409 | 0.5180 | 0.7197 | | No log | 1.2530 | 416 | 0.5190 | 0.5214 | 0.5190 | 0.7205 | | No log | 1.2590 | 418 | 0.5646 | 0.5339 | 0.5646 | 0.7514 | | No log | 1.2651 | 420 | 0.5798 | 0.5696 | 0.5798 | 0.7615 | | No log | 1.2711 | 422 | 0.5575 | 0.6004 | 0.5575 | 0.7467 | | No log | 1.2771 | 424 | 0.4537 | 0.5824 | 0.4537 | 0.6735 | | No log | 1.2831 | 426 | 0.4111 | 0.5705 | 0.4111 | 0.6412 | | No log | 1.2892 | 428 | 0.4151 | 0.5388 | 0.4151 | 0.6443 | | No log | 1.2952 | 430 | 0.3907 | 0.5737 | 0.3907 | 0.6251 | | No log | 1.3012 | 432 | 0.3818 | 0.6003 | 0.3818 | 0.6179 | | No log | 1.3072 | 434 | 0.3918 | 0.5599 | 0.3918 | 0.6259 | | No log | 1.3133 | 436 | 0.4417 | 0.5025 | 0.4417 | 0.6646 | | No log | 1.3193 | 438 | 0.4010 | 0.5344 | 0.4010 | 0.6332 | | No log | 1.3253 | 440 | 0.3930 | 0.6166 | 0.3930 | 0.6269 | | No log | 1.3313 | 442 | 0.4862 | 0.6574 | 0.4862 | 0.6973 | | No log | 1.3373 | 444 | 0.6188 | 0.6505 | 0.6188 | 0.7867 | | No log | 1.3434 | 446 | 0.5829 | 0.6513 | 0.5829 | 0.7635 | | No log | 1.3494 | 448 | 0.4792 | 0.6095 | 0.4792 | 0.6923 | | No log | 1.3554 | 450 | 0.4474 | 0.5256 | 0.4474 | 0.6689 | | No log | 1.3614 | 452 | 0.4590 | 0.5178 | 0.4590 | 0.6775 | | No log | 1.3675 | 454 | 0.4685 | 0.5701 | 0.4685 | 0.6845 | | No log | 1.3735 | 456 | 0.4354 | 0.6031 | 0.4354 | 0.6598 | | No log | 1.3795 | 458 | 0.4036 | 0.5533 | 0.4036 | 0.6353 | | No log | 1.3855 | 460 | 0.4440 | 0.4963 | 0.4440 | 0.6664 | | No log | 1.3916 | 462 | 0.4178 | 0.5149 | 0.4178 | 0.6463 | | No log | 1.3976 | 464 | 0.4140 | 0.6389 | 0.4140 | 0.6434 | | No log | 1.4036 | 466 | 0.5198 | 0.6676 | 0.5198 | 0.7210 | | No log | 1.4096 | 468 | 0.4730 | 0.6435 | 0.4730 | 0.6878 | | No log | 1.4157 | 470 | 0.4154 | 0.5595 | 0.4154 | 0.6445 | | No log | 1.4217 | 472 | 0.4113 | 0.5696 | 0.4113 | 0.6414 | | No log | 1.4277 | 474 | 0.4438 | 0.6509 | 0.4438 | 0.6662 | | No log | 1.4337 | 476 | 0.5069 | 0.6826 | 0.5069 | 0.7120 | | No log | 1.4398 | 478 | 0.4424 | 0.6829 | 0.4424 | 0.6651 | | No log | 1.4458 | 480 | 0.3942 | 0.6120 | 0.3942 | 0.6279 | | No log | 1.4518 | 482 | 0.3959 | 0.5517 | 0.3959 | 0.6292 | | No log | 1.4578 | 484 | 0.4028 | 0.5742 | 0.4028 | 0.6347 | | No log | 1.4639 | 486 | 0.4481 | 0.6284 | 0.4481 | 0.6694 | | No log | 1.4699 | 488 | 0.4389 | 0.6197 | 0.4389 | 0.6625 | | No log | 1.4759 | 490 | 0.4036 | 0.5371 | 0.4036 | 0.6353 | | No log | 1.4819 | 492 | 0.3990 | 0.5366 | 0.3990 | 0.6317 | | No log | 1.4880 | 494 | 0.3900 | 0.5701 | 0.3900 | 0.6245 | | No log | 1.4940 | 496 | 0.3945 | 0.6265 | 0.3945 | 0.6281 | | No log | 1.5 | 498 | 0.3904 | 0.6173 | 0.3904 | 0.6248 | | 0.5402 | 1.5060 | 500 | 0.4074 | 0.6301 | 0.4074 | 0.6383 | | 0.5402 | 1.5120 | 502 | 0.4201 | 0.5920 | 0.4201 | 0.6481 | | 0.5402 | 1.5181 | 504 | 0.4574 | 0.5466 | 0.4574 | 0.6763 | | 0.5402 | 1.5241 | 506 | 0.4131 | 0.5669 | 0.4131 | 0.6427 | | 0.5402 | 1.5301 | 508 | 0.4058 | 0.6023 | 0.4058 | 0.6370 | | 0.5402 | 1.5361 | 510 | 0.4063 | 0.5441 | 0.4063 | 0.6374 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

aigchacker/Text-Poster

aigchacker

2024-11-06T14:26:55Z

42

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "image-generation", "flux", "safetensors", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-11-06T13:59:31Z

--- tags: - text-to-image - stable-diffusion - lora - diffusers - image-generation - flux - safetensors widget: - text: Text poster, a couple output: url: images/6dd1a918d89991ad5e40513ab88e7d892077f89dac93edcf4b660dd2.jpg - text: Text poster, a woman sitting in a cafe output: url: images/d2586464001008a80b5e45104e0f23290a35db048cab2e4fc4bfa356.jpg - text: Text poster, eiffel tower output: url: images/f25e24ecfbd0aa96fb6f55ab29288ba4d1fffe79fd95679d9d2f1329.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: text poster license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # FLUX.1-dev-LoRA-Text-Poster This is a LoRA (Text Poster) trained on FLUX.1-dev for artistic text poster by [cooooool](https://www.shakker.ai/userpage/c4d790d27e6b4de69f3f3508daf8f4c5/publish). If you are also interested in sharing your models on our platform, welcome to join our [Discord Community](https://huggingface.co/spaces/Shakker-Labs/README/blob/main/(https://discord.gg/5TuxSjJya6)). <div class="container"> <img src="./poster.jpeg" width="1024"/> </div> ## Showcases <Gallery /> ## Trigger words You should use `text poster` to trigger the image generation. The recommended scale is `0.8` to `1.0` in diffusers. ## Online Inference You can also download this model at [Shakker AI](https://www.shakker.ai/modelinfo/579ab130b53246fea49811bf80d38486/FLUX-text-poster?from=search), where we provide an online interface to generate images. ## Acknowledgements This model is trained by our copyrighted users [cooooool](https://www.shakker.ai/userpage/c4d790d27e6b4de69f3f3508daf8f4c5/publish). We release this model under permissions. The model follows [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

mradermacher/tamil-llama-13b-base-v0.1-GGUF

mradermacher

2024-11-06T14:18:54Z

31

0

transformers

[ "transformers", "gguf", "ta", "en", "base_model:abhinand/tamil-llama-13b-base-v0.1", "base_model:quantized:abhinand/tamil-llama-13b-base-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-11-06T12:45:03Z

--- base_model: abhinand/tamil-llama-13b-base-v0.1 language: - ta - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/abhinand/tamil-llama-13b-base-v0.1  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_S.gguf) | Q3_K_S | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q3_K_L.gguf) | Q3_K_L | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.IQ4_XS.gguf) | IQ4_XS | 7.2 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q5_K_S.gguf) | Q5_K_S | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q5_K_M.gguf) | Q5_K_M | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q6_K.gguf) | Q6_K | 10.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-base-v0.1-GGUF/resolve/main/tamil-llama-13b-base-v0.1.Q8_0.gguf) | Q8_0 | 14.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

GateNLP/covid-vaccine-twitter-bert

GateNLP

2024-11-06T14:18:18Z

117

1

transformers

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

fill-mask

2023-01-13T19:02:52Z

VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song Accepted @ICWSM 2023 ```bibtex @inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } ``` --- license: mit ---

lesubra/ECE-PRYMMAL-3B-SLERP_2-V2

lesubra

2024-11-06T14:17:40Z

10

0

null

[ "safetensors", "phi3", "merge", "mergekit", "lazymergekit", "jpacifico/Chocolatine-3B-Instruct-DPO-Revised", "microsoft/Phi-3-mini-4k-instruct", "custom_code", "license:apache-2.0", "region:us" ]

null

2024-11-06T14:15:13Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jpacifico/Chocolatine-3B-Instruct-DPO-Revised - microsoft/Phi-3-mini-4k-instruct --- # ECE-PRYMMAL-3B-SLERP_2-V2 ECE-PRYMMAL-3B-SLERP_2-V2 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) * [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised layer_range: [0, 32] - model: microsoft/Phi-3-mini-4k-instruct layer_range: [0, 32] merge_method: slerp base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

QuantFactory/Qwen2.5-Coder-7B-Instruct-GGUF

QuantFactory

2024-11-06T14:17:24Z

386

5

transformers

[ "transformers", "gguf", "code", "codeqwen", "chat", "qwen", "qwen-coder", "text-generation", "en", "arxiv:2409.12186", "arxiv:2309.00071", "arxiv:2407.10671", "base_model:Qwen/Qwen2.5-Coder-7B", "base_model:quantized:Qwen/Qwen2.5-Coder-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-09-19T06:10:06Z

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-Coder-7B pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Qwen2.5-Coder-7B-Instruct-GGUF This is quantized version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) based on updated weights _(dated 06 Nov, 2024)_ and created using llama.cpp # Original Model Card # Qwen2.5-Coder-7B-Instruct ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). For Qwen2.5-Coder, we release three base language models and instruction-tuned language models, 1.5, 7 and 32 (coming soon) billion parameters. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 7B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-7B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```

AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs

AlekseyKorshuk

2024-11-06T14:15:56Z

7

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "dataset:AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T10:34:06Z

--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft library_name: transformers model_name: ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft](https://huggingface.co/datasets/AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AlekseyKorshuk/ai-detection-gutenberg-human-v2-formatted-ai-sft-qwen-7b-sft-3epochs", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/aleksey-korshuk/huggingface/runs/bfyzbjtg) This model was trained with SFT. ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.4.1+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mateiaassAI/MT5Base-8e5-4

mateiaassAI

2024-11-06T14:10:32Z

104

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-06T14:08:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

kajamo/alpha0ne

kajamo

2024-11-06T14:06:15Z

114

0

transformers

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

text2text-generation

2024-11-06T12:34:27Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: alpha0ne results: [] ---  # alpha0ne This model is a fine-tuned version of [](https://huggingface.co/) 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: 5e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 1024 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

lesubra/ECE-PRYMMAL-3B-SLERP_2-V1

lesubra

2024-11-06T14:03:06Z

9

0

null

[ "safetensors", "phi3", "merge", "mergekit", "lazymergekit", "jpacifico/Chocolatine-3B-Instruct-DPO-Revised", "microsoft/Phi-3-mini-4k-instruct", "custom_code", "license:apache-2.0", "region:us" ]

null

2024-11-06T14:00:50Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jpacifico/Chocolatine-3B-Instruct-DPO-Revised - microsoft/Phi-3-mini-4k-instruct --- # ECE-PRYMMAL-3B-SLERP_2-V1 ECE-PRYMMAL-3B-SLERP_2-V1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [jpacifico/Chocolatine-3B-Instruct-DPO-Revised](https://huggingface.co/jpacifico/Chocolatine-3B-Instruct-DPO-Revised) * [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ## 🧩 Configuration ```yaml slices: - sources: - model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised layer_range: [0, 32] - model: microsoft/Phi-3-mini-4k-instruct layer_range: [0, 32] merge_method: slerp base_model: jpacifico/Chocolatine-3B-Instruct-DPO-Revised parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

MayBashendy/ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0

MayBashendy

2024-11-06T13:58:49Z

162

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T11:41:28Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer model-index: - name: ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0 results: [] ---  # ASAP_FineTuningBERT_Aug_k20_task1_organization_fold0 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5352 - Qwk: 0.5466 - Mse: 0.5352 - Rmse: 0.7316 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:------:|:-------:|:------:| | No log | 0.0063 | 2 | 12.0179 | 0.0 | 12.0179 | 3.4667 | | No log | 0.0126 | 4 | 11.0822 | 0.0 | 11.0822 | 3.3290 | | No log | 0.0189 | 6 | 10.5444 | 0.0 | 10.5444 | 3.2472 | | No log | 0.0252 | 8 | 9.8057 | 0.0 | 9.8057 | 3.1314 | | No log | 0.0314 | 10 | 8.6296 | 0.0026 | 8.6296 | 2.9376 | | No log | 0.0377 | 12 | 7.0373 | 0.0054 | 7.0373 | 2.6528 | | No log | 0.0440 | 14 | 5.3678 | 0.0 | 5.3678 | 2.3168 | | No log | 0.0503 | 16 | 4.1881 | 0.0125 | 4.1881 | 2.0465 | | No log | 0.0566 | 18 | 3.2466 | 0.0314 | 3.2466 | 1.8018 | | No log | 0.0629 | 20 | 2.5151 | 0.0201 | 2.5151 | 1.5859 | | No log | 0.0692 | 22 | 1.6556 | 0.0115 | 1.6556 | 1.2867 | | No log | 0.0755 | 24 | 1.1844 | 0.1172 | 1.1844 | 1.0883 | | No log | 0.0818 | 26 | 0.9363 | 0.0866 | 0.9363 | 0.9676 | | No log | 0.0881 | 28 | 0.7604 | 0.0484 | 0.7604 | 0.8720 | | No log | 0.0943 | 30 | 0.7517 | 0.0316 | 0.7517 | 0.8670 | | No log | 0.1006 | 32 | 0.7482 | 0.0674 | 0.7482 | 0.8650 | | No log | 0.1069 | 34 | 0.7812 | 0.0382 | 0.7812 | 0.8838 | | No log | 0.1132 | 36 | 0.8630 | 0.0382 | 0.8630 | 0.9290 | | No log | 0.1195 | 38 | 0.7789 | 0.0316 | 0.7789 | 0.8825 | | No log | 0.1258 | 40 | 0.8760 | 0.0316 | 0.8760 | 0.9359 | | No log | 0.1321 | 42 | 0.9209 | 0.0212 | 0.9209 | 0.9596 | | No log | 0.1384 | 44 | 0.8864 | 0.0212 | 0.8864 | 0.9415 | | No log | 0.1447 | 46 | 0.8158 | 0.0126 | 0.8158 | 0.9032 | | No log | 0.1509 | 48 | 0.8029 | 0.0316 | 0.8029 | 0.8961 | | No log | 0.1572 | 50 | 0.8344 | 0.0316 | 0.8344 | 0.9134 | | No log | 0.1635 | 52 | 0.8317 | 0.0316 | 0.8317 | 0.9120 | | No log | 0.1698 | 54 | 0.7914 | 0.0316 | 0.7914 | 0.8896 | | No log | 0.1761 | 56 | 0.7742 | 0.0316 | 0.7742 | 0.8799 | | No log | 0.1824 | 58 | 0.7615 | 0.0316 | 0.7615 | 0.8726 | | No log | 0.1887 | 60 | 0.7750 | 0.0316 | 0.7750 | 0.8803 | | No log | 0.1950 | 62 | 0.7897 | 0.0316 | 0.7897 | 0.8887 | | No log | 0.2013 | 64 | 0.7865 | 0.0316 | 0.7865 | 0.8869 | | No log | 0.2075 | 66 | 0.7933 | 0.0316 | 0.7933 | 0.8907 | | No log | 0.2138 | 68 | 0.8167 | 0.0316 | 0.8167 | 0.9037 | | No log | 0.2201 | 70 | 0.8019 | 0.0316 | 0.8019 | 0.8955 | | No log | 0.2264 | 72 | 0.7407 | 0.0316 | 0.7407 | 0.8606 | | No log | 0.2327 | 74 | 0.7123 | 0.0316 | 0.7123 | 0.8440 | | No log | 0.2390 | 76 | 0.6954 | 0.0316 | 0.6954 | 0.8339 | | No log | 0.2453 | 78 | 0.7127 | 0.0419 | 0.7127 | 0.8442 | | No log | 0.2516 | 80 | 0.6788 | 0.0419 | 0.6788 | 0.8239 | | No log | 0.2579 | 82 | 0.6872 | 0.3131 | 0.6872 | 0.8290 | | No log | 0.2642 | 84 | 0.7295 | 0.4173 | 0.7295 | 0.8541 | | No log | 0.2704 | 86 | 0.7259 | 0.1405 | 0.7259 | 0.8520 | | No log | 0.2767 | 88 | 0.7534 | 0.0316 | 0.7534 | 0.8680 | | No log | 0.2830 | 90 | 0.9137 | 0.0316 | 0.9137 | 0.9559 | | No log | 0.2893 | 92 | 0.8953 | 0.0419 | 0.8953 | 0.9462 | | No log | 0.2956 | 94 | 0.7677 | 0.0419 | 0.7677 | 0.8762 | | No log | 0.3019 | 96 | 0.6872 | 0.0419 | 0.6872 | 0.8290 | | No log | 0.3082 | 98 | 0.6619 | 0.0493 | 0.6619 | 0.8135 | | No log | 0.3145 | 100 | 0.6529 | 0.0897 | 0.6529 | 0.8080 | | No log | 0.3208 | 102 | 0.6887 | 0.0447 | 0.6887 | 0.8299 | | No log | 0.3270 | 104 | 0.8399 | 0.0921 | 0.8399 | 0.9164 | | No log | 0.3333 | 106 | 0.8642 | 0.0873 | 0.8642 | 0.9296 | | No log | 0.3396 | 108 | 0.7132 | 0.0484 | 0.7132 | 0.8445 | | No log | 0.3459 | 110 | 0.6894 | 0.0419 | 0.6894 | 0.8303 | | No log | 0.3522 | 112 | 0.6976 | 0.0484 | 0.6976 | 0.8352 | | No log | 0.3585 | 114 | 0.6968 | 0.0419 | 0.6968 | 0.8348 | | No log | 0.3648 | 116 | 0.6796 | 0.0548 | 0.6796 | 0.8244 | | No log | 0.3711 | 118 | 0.6692 | 0.0548 | 0.6692 | 0.8180 | | No log | 0.3774 | 120 | 0.6608 | 0.0548 | 0.6608 | 0.8129 | | No log | 0.3836 | 122 | 0.6634 | 0.0649 | 0.6634 | 0.8145 | | No log | 0.3899 | 124 | 0.6622 | 0.0603 | 0.6622 | 0.8137 | | No log | 0.3962 | 126 | 0.6628 | 0.0750 | 0.6628 | 0.8142 | | No log | 0.4025 | 128 | 0.6598 | 0.1833 | 0.6598 | 0.8123 | | No log | 0.4088 | 130 | 0.6672 | 0.2258 | 0.6672 | 0.8168 | | No log | 0.4151 | 132 | 0.6639 | 0.0918 | 0.6639 | 0.8148 | | No log | 0.4214 | 134 | 0.6644 | 0.0603 | 0.6644 | 0.8151 | | No log | 0.4277 | 136 | 0.6554 | 0.0746 | 0.6554 | 0.8096 | | No log | 0.4340 | 138 | 0.6264 | 0.1257 | 0.6264 | 0.7914 | | No log | 0.4403 | 140 | 0.6451 | 0.4728 | 0.6451 | 0.8032 | | No log | 0.4465 | 142 | 0.6685 | 0.4752 | 0.6685 | 0.8176 | | No log | 0.4528 | 144 | 0.6049 | 0.4478 | 0.6049 | 0.7777 | | No log | 0.4591 | 146 | 0.6038 | 0.1782 | 0.6038 | 0.7771 | | No log | 0.4654 | 148 | 0.5846 | 0.2371 | 0.5846 | 0.7646 | | No log | 0.4717 | 150 | 0.5859 | 0.4477 | 0.5859 | 0.7655 | | No log | 0.4780 | 152 | 0.7173 | 0.3828 | 0.7173 | 0.8469 | | No log | 0.4843 | 154 | 0.7084 | 0.3882 | 0.7084 | 0.8417 | | No log | 0.4906 | 156 | 0.6240 | 0.4139 | 0.6240 | 0.7899 | | No log | 0.4969 | 158 | 0.6043 | 0.1238 | 0.6043 | 0.7774 | | No log | 0.5031 | 160 | 0.6702 | 0.1123 | 0.6702 | 0.8187 | | No log | 0.5094 | 162 | 0.6411 | 0.1102 | 0.6411 | 0.8007 | | No log | 0.5157 | 164 | 0.5683 | 0.3157 | 0.5683 | 0.7539 | | No log | 0.5220 | 166 | 0.5955 | 0.4532 | 0.5955 | 0.7717 | | No log | 0.5283 | 168 | 0.6909 | 0.3869 | 0.6909 | 0.8312 | | No log | 0.5346 | 170 | 0.9200 | 0.0801 | 0.9200 | 0.9592 | | No log | 0.5409 | 172 | 0.7579 | 0.2155 | 0.7579 | 0.8706 | | No log | 0.5472 | 174 | 0.6391 | 0.3533 | 0.6391 | 0.7994 | | No log | 0.5535 | 176 | 0.6119 | 0.3729 | 0.6119 | 0.7822 | | No log | 0.5597 | 178 | 0.6156 | 0.3864 | 0.6156 | 0.7846 | | No log | 0.5660 | 180 | 0.5831 | 0.4169 | 0.5831 | 0.7636 | | No log | 0.5723 | 182 | 0.6125 | 0.4773 | 0.6125 | 0.7826 | | No log | 0.5786 | 184 | 0.5654 | 0.5126 | 0.5654 | 0.7519 | | No log | 0.5849 | 186 | 0.5045 | 0.4536 | 0.5045 | 0.7103 | | No log | 0.5912 | 188 | 0.4852 | 0.4839 | 0.4852 | 0.6965 | | No log | 0.5975 | 190 | 0.4864 | 0.5193 | 0.4864 | 0.6974 | | No log | 0.6038 | 192 | 0.5333 | 0.5381 | 0.5333 | 0.7303 | | No log | 0.6101 | 194 | 0.5818 | 0.4820 | 0.5818 | 0.7627 | | No log | 0.6164 | 196 | 0.5457 | 0.4547 | 0.5457 | 0.7387 | | No log | 0.6226 | 198 | 0.5304 | 0.4858 | 0.5304 | 0.7283 | | No log | 0.6289 | 200 | 0.5180 | 0.5206 | 0.5180 | 0.7197 | | No log | 0.6352 | 202 | 0.4908 | 0.5086 | 0.4908 | 0.7006 | | No log | 0.6415 | 204 | 0.4715 | 0.5198 | 0.4715 | 0.6867 | | No log | 0.6478 | 206 | 0.4842 | 0.5030 | 0.4842 | 0.6959 | | No log | 0.6541 | 208 | 0.5114 | 0.5502 | 0.5114 | 0.7151 | | No log | 0.6604 | 210 | 0.5467 | 0.5329 | 0.5467 | 0.7394 | | No log | 0.6667 | 212 | 0.5509 | 0.5181 | 0.5509 | 0.7422 | | No log | 0.6730 | 214 | 0.5190 | 0.3750 | 0.5190 | 0.7204 | | No log | 0.6792 | 216 | 0.5155 | 0.3492 | 0.5155 | 0.7180 | | No log | 0.6855 | 218 | 0.4663 | 0.5006 | 0.4663 | 0.6828 | | No log | 0.6918 | 220 | 0.4621 | 0.5516 | 0.4621 | 0.6798 | | No log | 0.6981 | 222 | 0.4470 | 0.5623 | 0.4470 | 0.6686 | | No log | 0.7044 | 224 | 0.4992 | 0.4535 | 0.4992 | 0.7065 | | No log | 0.7107 | 226 | 0.4630 | 0.5280 | 0.4630 | 0.6805 | | No log | 0.7170 | 228 | 0.4813 | 0.5276 | 0.4813 | 0.6937 | | No log | 0.7233 | 230 | 0.4967 | 0.4917 | 0.4967 | 0.7048 | | No log | 0.7296 | 232 | 0.5387 | 0.4653 | 0.5387 | 0.7340 | | No log | 0.7358 | 234 | 0.5568 | 0.4123 | 0.5568 | 0.7462 | | No log | 0.7421 | 236 | 0.5762 | 0.3926 | 0.5762 | 0.7591 | | No log | 0.7484 | 238 | 0.5757 | 0.3604 | 0.5757 | 0.7587 | | No log | 0.7547 | 240 | 0.5917 | 0.1988 | 0.5917 | 0.7692 | | No log | 0.7610 | 242 | 0.6619 | 0.1229 | 0.6619 | 0.8135 | | No log | 0.7673 | 244 | 0.6446 | 0.1413 | 0.6446 | 0.8029 | | No log | 0.7736 | 246 | 0.5670 | 0.2121 | 0.5670 | 0.7530 | | No log | 0.7799 | 248 | 0.5287 | 0.4550 | 0.5287 | 0.7271 | | No log | 0.7862 | 250 | 0.5835 | 0.5201 | 0.5835 | 0.7639 | | No log | 0.7925 | 252 | 0.5566 | 0.5366 | 0.5566 | 0.7460 | | No log | 0.7987 | 254 | 0.4833 | 0.5475 | 0.4833 | 0.6952 | | No log | 0.8050 | 256 | 0.4625 | 0.4781 | 0.4625 | 0.6801 | | No log | 0.8113 | 258 | 0.4996 | 0.3595 | 0.4996 | 0.7068 | | No log | 0.8176 | 260 | 0.4938 | 0.3752 | 0.4938 | 0.7027 | | No log | 0.8239 | 262 | 0.4648 | 0.4688 | 0.4648 | 0.6818 | | No log | 0.8302 | 264 | 0.5235 | 0.4649 | 0.5235 | 0.7236 | | No log | 0.8365 | 266 | 0.5261 | 0.4878 | 0.5261 | 0.7253 | | No log | 0.8428 | 268 | 0.5084 | 0.4377 | 0.5084 | 0.7130 | | No log | 0.8491 | 270 | 0.4691 | 0.5609 | 0.4691 | 0.6849 | | No log | 0.8553 | 272 | 0.4405 | 0.5774 | 0.4405 | 0.6637 | | No log | 0.8616 | 274 | 0.4312 | 0.5560 | 0.4312 | 0.6567 | | No log | 0.8679 | 276 | 0.4357 | 0.4909 | 0.4357 | 0.6601 | | No log | 0.8742 | 278 | 0.4706 | 0.4431 | 0.4706 | 0.6860 | | No log | 0.8805 | 280 | 0.4540 | 0.4803 | 0.4540 | 0.6738 | | No log | 0.8868 | 282 | 0.4660 | 0.5422 | 0.4660 | 0.6827 | | No log | 0.8931 | 284 | 0.4745 | 0.5291 | 0.4745 | 0.6888 | | No log | 0.8994 | 286 | 0.5117 | 0.5465 | 0.5117 | 0.7154 | | No log | 0.9057 | 288 | 0.6159 | 0.4993 | 0.6159 | 0.7848 | | No log | 0.9119 | 290 | 0.5713 | 0.5362 | 0.5713 | 0.7558 | | No log | 0.9182 | 292 | 0.5096 | 0.5233 | 0.5096 | 0.7138 | | No log | 0.9245 | 294 | 0.5283 | 0.5564 | 0.5283 | 0.7269 | | No log | 0.9308 | 296 | 0.5306 | 0.5697 | 0.5306 | 0.7284 | | No log | 0.9371 | 298 | 0.6002 | 0.5214 | 0.6002 | 0.7747 | | No log | 0.9434 | 300 | 0.5728 | 0.5460 | 0.5728 | 0.7568 | | No log | 0.9497 | 302 | 0.4807 | 0.5059 | 0.4807 | 0.6933 | | No log | 0.9560 | 304 | 0.5446 | 0.3641 | 0.5446 | 0.7380 | | No log | 0.9623 | 306 | 0.5320 | 0.3856 | 0.5320 | 0.7294 | | No log | 0.9686 | 308 | 0.4741 | 0.5118 | 0.4741 | 0.6886 | | No log | 0.9748 | 310 | 0.4723 | 0.5244 | 0.4723 | 0.6872 | | No log | 0.9811 | 312 | 0.4768 | 0.4583 | 0.4768 | 0.6905 | | No log | 0.9874 | 314 | 0.5082 | 0.4248 | 0.5082 | 0.7129 | | No log | 0.9937 | 316 | 0.4580 | 0.4888 | 0.4580 | 0.6768 | | No log | 1.0 | 318 | 0.4900 | 0.5683 | 0.4900 | 0.7000 | | No log | 1.0063 | 320 | 0.4791 | 0.5563 | 0.4791 | 0.6921 | | No log | 1.0126 | 322 | 0.4524 | 0.5296 | 0.4524 | 0.6726 | | No log | 1.0189 | 324 | 0.4568 | 0.4785 | 0.4568 | 0.6759 | | No log | 1.0252 | 326 | 0.4561 | 0.5154 | 0.4561 | 0.6753 | | No log | 1.0314 | 328 | 0.5108 | 0.5644 | 0.5108 | 0.7147 | | No log | 1.0377 | 330 | 0.5220 | 0.5710 | 0.5220 | 0.7225 | | No log | 1.0440 | 332 | 0.4507 | 0.5357 | 0.4507 | 0.6713 | | No log | 1.0503 | 334 | 0.4476 | 0.4719 | 0.4476 | 0.6690 | | No log | 1.0566 | 336 | 0.4624 | 0.5533 | 0.4624 | 0.6800 | | No log | 1.0629 | 338 | 0.6018 | 0.5378 | 0.6018 | 0.7758 | | No log | 1.0692 | 340 | 0.6157 | 0.5216 | 0.6157 | 0.7846 | | No log | 1.0755 | 342 | 0.5006 | 0.5030 | 0.5006 | 0.7076 | | No log | 1.0818 | 344 | 0.4784 | 0.4407 | 0.4784 | 0.6917 | | No log | 1.0881 | 346 | 0.4645 | 0.4348 | 0.4645 | 0.6816 | | No log | 1.0943 | 348 | 0.4531 | 0.4843 | 0.4531 | 0.6731 | | No log | 1.1006 | 350 | 0.4681 | 0.5530 | 0.4681 | 0.6842 | | No log | 1.1069 | 352 | 0.4497 | 0.5415 | 0.4497 | 0.6706 | | No log | 1.1132 | 354 | 0.4471 | 0.5348 | 0.4471 | 0.6687 | | No log | 1.1195 | 356 | 0.4355 | 0.4720 | 0.4355 | 0.6599 | | No log | 1.1258 | 358 | 0.4191 | 0.4984 | 0.4191 | 0.6474 | | No log | 1.1321 | 360 | 0.4048 | 0.5830 | 0.4048 | 0.6362 | | No log | 1.1384 | 362 | 0.4305 | 0.5964 | 0.4305 | 0.6562 | | No log | 1.1447 | 364 | 0.4154 | 0.5886 | 0.4154 | 0.6445 | | No log | 1.1509 | 366 | 0.4085 | 0.5737 | 0.4085 | 0.6391 | | No log | 1.1572 | 368 | 0.4145 | 0.5675 | 0.4145 | 0.6438 | | No log | 1.1635 | 370 | 0.4510 | 0.5868 | 0.4510 | 0.6715 | | No log | 1.1698 | 372 | 0.5957 | 0.5596 | 0.5957 | 0.7718 | | No log | 1.1761 | 374 | 0.5335 | 0.5670 | 0.5335 | 0.7304 | | No log | 1.1824 | 376 | 0.4396 | 0.5635 | 0.4396 | 0.6630 | | No log | 1.1887 | 378 | 0.4411 | 0.5379 | 0.4411 | 0.6642 | | No log | 1.1950 | 380 | 0.4362 | 0.5354 | 0.4362 | 0.6604 | | No log | 1.2013 | 382 | 0.4534 | 0.5435 | 0.4534 | 0.6734 | | No log | 1.2075 | 384 | 0.4296 | 0.5520 | 0.4296 | 0.6555 | | No log | 1.2138 | 386 | 0.4266 | 0.5481 | 0.4266 | 0.6531 | | No log | 1.2201 | 388 | 0.4252 | 0.5482 | 0.4252 | 0.6520 | | No log | 1.2264 | 390 | 0.4254 | 0.5407 | 0.4254 | 0.6522 | | No log | 1.2327 | 392 | 0.4384 | 0.5204 | 0.4384 | 0.6621 | | No log | 1.2390 | 394 | 0.4530 | 0.4888 | 0.4530 | 0.6731 | | No log | 1.2453 | 396 | 0.4710 | 0.5440 | 0.4710 | 0.6863 | | No log | 1.2516 | 398 | 0.5120 | 0.5318 | 0.5120 | 0.7155 | | No log | 1.2579 | 400 | 0.5009 | 0.5510 | 0.5009 | 0.7078 | | No log | 1.2642 | 402 | 0.4882 | 0.5547 | 0.4882 | 0.6987 | | No log | 1.2704 | 404 | 0.4844 | 0.5687 | 0.4844 | 0.6960 | | No log | 1.2767 | 406 | 0.4974 | 0.5624 | 0.4974 | 0.7053 | | No log | 1.2830 | 408 | 0.4527 | 0.5299 | 0.4527 | 0.6728 | | No log | 1.2893 | 410 | 0.4481 | 0.5026 | 0.4481 | 0.6694 | | No log | 1.2956 | 412 | 0.4408 | 0.5473 | 0.4408 | 0.6639 | | No log | 1.3019 | 414 | 0.4956 | 0.6047 | 0.4956 | 0.7040 | | No log | 1.3082 | 416 | 0.4792 | 0.6101 | 0.4792 | 0.6922 | | No log | 1.3145 | 418 | 0.4203 | 0.5664 | 0.4203 | 0.6483 | | No log | 1.3208 | 420 | 0.4335 | 0.5261 | 0.4335 | 0.6584 | | No log | 1.3270 | 422 | 0.4437 | 0.5094 | 0.4437 | 0.6661 | | No log | 1.3333 | 424 | 0.4254 | 0.5705 | 0.4254 | 0.6522 | | No log | 1.3396 | 426 | 0.4540 | 0.5908 | 0.4540 | 0.6738 | | No log | 1.3459 | 428 | 0.4200 | 0.5599 | 0.4200 | 0.6480 | | No log | 1.3522 | 430 | 0.4490 | 0.4792 | 0.4490 | 0.6701 | | No log | 1.3585 | 432 | 0.4900 | 0.4328 | 0.4900 | 0.7000 | | No log | 1.3648 | 434 | 0.4325 | 0.5198 | 0.4325 | 0.6577 | | No log | 1.3711 | 436 | 0.4629 | 0.5773 | 0.4629 | 0.6804 | | No log | 1.3774 | 438 | 0.5279 | 0.6088 | 0.5279 | 0.7265 | | No log | 1.3836 | 440 | 0.4515 | 0.5876 | 0.4515 | 0.6720 | | No log | 1.3899 | 442 | 0.4544 | 0.4816 | 0.4544 | 0.6741 | | No log | 1.3962 | 444 | 0.5344 | 0.3721 | 0.5344 | 0.7311 | | No log | 1.4025 | 446 | 0.4667 | 0.4603 | 0.4667 | 0.6832 | | No log | 1.4088 | 448 | 0.4476 | 0.5907 | 0.4476 | 0.6690 | | No log | 1.4151 | 450 | 0.5574 | 0.5828 | 0.5574 | 0.7466 | | No log | 1.4214 | 452 | 0.5536 | 0.5754 | 0.5536 | 0.7441 | | No log | 1.4277 | 454 | 0.4889 | 0.5879 | 0.4889 | 0.6992 | | No log | 1.4340 | 456 | 0.4475 | 0.5637 | 0.4475 | 0.6689 | | No log | 1.4403 | 458 | 0.4689 | 0.5894 | 0.4689 | 0.6848 | | No log | 1.4465 | 460 | 0.5947 | 0.6027 | 0.5947 | 0.7712 | | No log | 1.4528 | 462 | 0.6122 | 0.6422 | 0.6122 | 0.7824 | | No log | 1.4591 | 464 | 0.4651 | 0.6175 | 0.4651 | 0.6820 | | No log | 1.4654 | 466 | 0.4310 | 0.5557 | 0.4310 | 0.6565 | | No log | 1.4717 | 468 | 0.4604 | 0.5151 | 0.4604 | 0.6785 | | No log | 1.4780 | 470 | 0.4326 | 0.5487 | 0.4326 | 0.6577 | | No log | 1.4843 | 472 | 0.4264 | 0.5595 | 0.4264 | 0.6530 | | No log | 1.4906 | 474 | 0.4322 | 0.5707 | 0.4322 | 0.6574 | | No log | 1.4969 | 476 | 0.4565 | 0.5351 | 0.4565 | 0.6756 | | No log | 1.5031 | 478 | 0.4335 | 0.5410 | 0.4335 | 0.6584 | | No log | 1.5094 | 480 | 0.4330 | 0.6007 | 0.4330 | 0.6580 | | No log | 1.5157 | 482 | 0.6046 | 0.6136 | 0.6046 | 0.7775 | | No log | 1.5220 | 484 | 0.6213 | 0.5786 | 0.6213 | 0.7882 | | No log | 1.5283 | 486 | 0.5202 | 0.5802 | 0.5202 | 0.7213 | | No log | 1.5346 | 488 | 0.4899 | 0.5651 | 0.4899 | 0.6999 | | No log | 1.5409 | 490 | 0.5525 | 0.5716 | 0.5525 | 0.7433 | | No log | 1.5472 | 492 | 0.5873 | 0.5840 | 0.5873 | 0.7663 | | No log | 1.5535 | 494 | 0.5886 | 0.5970 | 0.5886 | 0.7672 | | No log | 1.5597 | 496 | 0.4956 | 0.5652 | 0.4956 | 0.7040 | | No log | 1.5660 | 498 | 0.4548 | 0.5153 | 0.4548 | 0.6744 | | 0.6168 | 1.5723 | 500 | 0.4578 | 0.4906 | 0.4578 | 0.6766 | | 0.6168 | 1.5786 | 502 | 0.4687 | 0.4650 | 0.4687 | 0.6846 | | 0.6168 | 1.5849 | 504 | 0.4649 | 0.5025 | 0.4649 | 0.6819 | | 0.6168 | 1.5912 | 506 | 0.4787 | 0.5359 | 0.4787 | 0.6919 | | 0.6168 | 1.5975 | 508 | 0.5109 | 0.5803 | 0.5109 | 0.7148 | | 0.6168 | 1.6038 | 510 | 0.4629 | 0.5427 | 0.4629 | 0.6804 | | 0.6168 | 1.6101 | 512 | 0.4511 | 0.4776 | 0.4511 | 0.6716 | | 0.6168 | 1.6164 | 514 | 0.4647 | 0.4382 | 0.4647 | 0.6817 | | 0.6168 | 1.6226 | 516 | 0.4988 | 0.3909 | 0.4988 | 0.7063 | | 0.6168 | 1.6289 | 518 | 0.4708 | 0.4333 | 0.4708 | 0.6862 | | 0.6168 | 1.6352 | 520 | 0.4335 | 0.5524 | 0.4335 | 0.6584 | | 0.6168 | 1.6415 | 522 | 0.5118 | 0.6061 | 0.5118 | 0.7154 | | 0.6168 | 1.6478 | 524 | 0.5394 | 0.5932 | 0.5394 | 0.7344 | | 0.6168 | 1.6541 | 526 | 0.4893 | 0.5836 | 0.4893 | 0.6995 | | 0.6168 | 1.6604 | 528 | 0.4719 | 0.5948 | 0.4719 | 0.6869 | | 0.6168 | 1.6667 | 530 | 0.4779 | 0.6197 | 0.4779 | 0.6913 | | 0.6168 | 1.6730 | 532 | 0.5567 | 0.6521 | 0.5567 | 0.7461 | | 0.6168 | 1.6792 | 534 | 0.5218 | 0.6485 | 0.5218 | 0.7223 | | 0.6168 | 1.6855 | 536 | 0.4417 | 0.5646 | 0.4417 | 0.6646 | | 0.6168 | 1.6918 | 538 | 0.4669 | 0.5291 | 0.4669 | 0.6833 | | 0.6168 | 1.6981 | 540 | 0.4911 | 0.5074 | 0.4911 | 0.7008 | | 0.6168 | 1.7044 | 542 | 0.4456 | 0.5703 | 0.4456 | 0.6675 | | 0.6168 | 1.7107 | 544 | 0.5328 | 0.6591 | 0.5328 | 0.7300 | | 0.6168 | 1.7170 | 546 | 0.5935 | 0.6528 | 0.5935 | 0.7704 | | 0.6168 | 1.7233 | 548 | 0.5023 | 0.6085 | 0.5023 | 0.7087 | | 0.6168 | 1.7296 | 550 | 0.4372 | 0.5448 | 0.4372 | 0.6612 | | 0.6168 | 1.7358 | 552 | 0.4434 | 0.5654 | 0.4434 | 0.6659 | | 0.6168 | 1.7421 | 554 | 0.5137 | 0.6049 | 0.5137 | 0.7167 | | 0.6168 | 1.7484 | 556 | 0.5971 | 0.6242 | 0.5971 | 0.7727 | | 0.6168 | 1.7547 | 558 | 0.5219 | 0.6144 | 0.5219 | 0.7225 | | 0.6168 | 1.7610 | 560 | 0.4464 | 0.5712 | 0.4464 | 0.6681 | | 0.6168 | 1.7673 | 562 | 0.4409 | 0.5542 | 0.4409 | 0.6640 | | 0.6168 | 1.7736 | 564 | 0.4485 | 0.5703 | 0.4485 | 0.6697 | | 0.6168 | 1.7799 | 566 | 0.5082 | 0.6321 | 0.5082 | 0.7129 | | 0.6168 | 1.7862 | 568 | 0.4859 | 0.6142 | 0.4859 | 0.6970 | | 0.6168 | 1.7925 | 570 | 0.4397 | 0.5736 | 0.4397 | 0.6631 | | 0.6168 | 1.7987 | 572 | 0.4426 | 0.5451 | 0.4426 | 0.6653 | | 0.6168 | 1.8050 | 574 | 0.4499 | 0.5175 | 0.4499 | 0.6708 | | 0.6168 | 1.8113 | 576 | 0.4461 | 0.5466 | 0.4461 | 0.6679 | | 0.6168 | 1.8176 | 578 | 0.4628 | 0.6349 | 0.4628 | 0.6803 | | 0.6168 | 1.8239 | 580 | 0.5017 | 0.6687 | 0.5017 | 0.7083 | | 0.6168 | 1.8302 | 582 | 0.4434 | 0.6080 | 0.4434 | 0.6659 | | 0.6168 | 1.8365 | 584 | 0.4401 | 0.5153 | 0.4401 | 0.6634 | | 0.6168 | 1.8428 | 586 | 0.4543 | 0.4893 | 0.4543 | 0.6740 | | 0.6168 | 1.8491 | 588 | 0.4335 | 0.5541 | 0.4335 | 0.6584 | | 0.6168 | 1.8553 | 590 | 0.5906 | 0.6594 | 0.5906 | 0.7685 | | 0.6168 | 1.8616 | 592 | 0.8147 | 0.6481 | 0.8147 | 0.9026 | | 0.6168 | 1.8679 | 594 | 0.7159 | 0.6446 | 0.7159 | 0.8461 | | 0.6168 | 1.8742 | 596 | 0.4781 | 0.6088 | 0.4781 | 0.6915 | | 0.6168 | 1.8805 | 598 | 0.4439 | 0.4836 | 0.4439 | 0.6662 | | 0.6168 | 1.8868 | 600 | 0.4566 | 0.4557 | 0.4566 | 0.6757 | | 0.6168 | 1.8931 | 602 | 0.4287 | 0.5373 | 0.4287 | 0.6547 | | 0.6168 | 1.8994 | 604 | 0.4456 | 0.5949 | 0.4456 | 0.6675 | | 0.6168 | 1.9057 | 606 | 0.4832 | 0.6382 | 0.4832 | 0.6952 | | 0.6168 | 1.9119 | 608 | 0.4698 | 0.6393 | 0.4698 | 0.6855 | | 0.6168 | 1.9182 | 610 | 0.4329 | 0.5806 | 0.4329 | 0.6580 | | 0.6168 | 1.9245 | 612 | 0.4489 | 0.6122 | 0.4489 | 0.6700 | | 0.6168 | 1.9308 | 614 | 0.4779 | 0.6377 | 0.4779 | 0.6913 | | 0.6168 | 1.9371 | 616 | 0.4334 | 0.5817 | 0.4334 | 0.6583 | | 0.6168 | 1.9434 | 618 | 0.4404 | 0.5204 | 0.4404 | 0.6636 | | 0.6168 | 1.9497 | 620 | 0.4414 | 0.5303 | 0.4414 | 0.6644 | | 0.6168 | 1.9560 | 622 | 0.4488 | 0.6036 | 0.4488 | 0.6699 | | 0.6168 | 1.9623 | 624 | 0.4846 | 0.6425 | 0.4846 | 0.6961 | | 0.6168 | 1.9686 | 626 | 0.4593 | 0.6147 | 0.4593 | 0.6777 | | 0.6168 | 1.9748 | 628 | 0.4434 | 0.5229 | 0.4434 | 0.6659 | | 0.6168 | 1.9811 | 630 | 0.5029 | 0.4279 | 0.5029 | 0.7092 | | 0.6168 | 1.9874 | 632 | 0.4739 | 0.4619 | 0.4739 | 0.6884 | | 0.6168 | 1.9937 | 634 | 0.4356 | 0.5404 | 0.4356 | 0.6600 | | 0.6168 | 2.0 | 636 | 0.4431 | 0.5583 | 0.4431 | 0.6657 | | 0.6168 | 2.0063 | 638 | 0.4482 | 0.5137 | 0.4482 | 0.6695 | | 0.6168 | 2.0126 | 640 | 0.4698 | 0.4867 | 0.4698 | 0.6854 | | 0.6168 | 2.0189 | 642 | 0.4694 | 0.5070 | 0.4694 | 0.6851 | | 0.6168 | 2.0252 | 644 | 0.4760 | 0.5115 | 0.4760 | 0.6899 | | 0.6168 | 2.0314 | 646 | 0.4932 | 0.5436 | 0.4932 | 0.7023 | | 0.6168 | 2.0377 | 648 | 0.4959 | 0.5361 | 0.4959 | 0.7042 | | 0.6168 | 2.0440 | 650 | 0.5141 | 0.5522 | 0.5141 | 0.7170 | | 0.6168 | 2.0503 | 652 | 0.5923 | 0.6191 | 0.5923 | 0.7696 | | 0.6168 | 2.0566 | 654 | 0.5373 | 0.6122 | 0.5373 | 0.7330 | | 0.6168 | 2.0629 | 656 | 0.4557 | 0.5540 | 0.4557 | 0.6750 | | 0.6168 | 2.0692 | 658 | 0.4540 | 0.5032 | 0.4540 | 0.6738 | | 0.6168 | 2.0755 | 660 | 0.4465 | 0.5682 | 0.4465 | 0.6682 | | 0.6168 | 2.0818 | 662 | 0.4612 | 0.5993 | 0.4612 | 0.6791 | | 0.6168 | 2.0881 | 664 | 0.4772 | 0.6044 | 0.4772 | 0.6908 | | 0.6168 | 2.0943 | 666 | 0.4469 | 0.5729 | 0.4469 | 0.6685 | | 0.6168 | 2.1006 | 668 | 0.4566 | 0.5216 | 0.4566 | 0.6757 | | 0.6168 | 2.1069 | 670 | 0.4556 | 0.5326 | 0.4556 | 0.6750 | | 0.6168 | 2.1132 | 672 | 0.4481 | 0.5748 | 0.4481 | 0.6694 | | 0.6168 | 2.1195 | 674 | 0.4865 | 0.6066 | 0.4865 | 0.6975 | | 0.6168 | 2.1258 | 676 | 0.4763 | 0.6105 | 0.4763 | 0.6901 | | 0.6168 | 2.1321 | 678 | 0.4473 | 0.5493 | 0.4473 | 0.6688 | | 0.6168 | 2.1384 | 680 | 0.4496 | 0.5392 | 0.4496 | 0.6705 | | 0.6168 | 2.1447 | 682 | 0.4709 | 0.6130 | 0.4709 | 0.6862 | | 0.6168 | 2.1509 | 684 | 0.6828 | 0.6267 | 0.6828 | 0.8263 | | 0.6168 | 2.1572 | 686 | 0.8103 | 0.6521 | 0.8103 | 0.9002 | | 0.6168 | 2.1635 | 688 | 0.6630 | 0.6061 | 0.6630 | 0.8143 | | 0.6168 | 2.1698 | 690 | 0.4662 | 0.6020 | 0.4662 | 0.6828 | | 0.6168 | 2.1761 | 692 | 0.4516 | 0.5239 | 0.4516 | 0.6720 | | 0.6168 | 2.1824 | 694 | 0.4492 | 0.5802 | 0.4492 | 0.6702 | | 0.6168 | 2.1887 | 696 | 0.4976 | 0.6040 | 0.4976 | 0.7054 | | 0.6168 | 2.1950 | 698 | 0.6299 | 0.6332 | 0.6299 | 0.7937 | | 0.6168 | 2.2013 | 700 | 0.6688 | 0.6466 | 0.6688 | 0.8178 | | 0.6168 | 2.2075 | 702 | 0.5166 | 0.6479 | 0.5166 | 0.7187 | | 0.6168 | 2.2138 | 704 | 0.4484 | 0.5805 | 0.4484 | 0.6697 | | 0.6168 | 2.2201 | 706 | 0.4544 | 0.5440 | 0.4544 | 0.6741 | | 0.6168 | 2.2264 | 708 | 0.4569 | 0.5398 | 0.4569 | 0.6760 | | 0.6168 | 2.2327 | 710 | 0.4531 | 0.5747 | 0.4531 | 0.6731 | | 0.6168 | 2.2390 | 712 | 0.4682 | 0.6000 | 0.4682 | 0.6842 | | 0.6168 | 2.2453 | 714 | 0.5003 | 0.6096 | 0.5003 | 0.7074 | | 0.6168 | 2.2516 | 716 | 0.5291 | 0.5905 | 0.5291 | 0.7274 | | 0.6168 | 2.2579 | 718 | 0.4991 | 0.5957 | 0.4991 | 0.7065 | | 0.6168 | 2.2642 | 720 | 0.5197 | 0.5879 | 0.5197 | 0.7209 | | 0.6168 | 2.2704 | 722 | 0.4952 | 0.5493 | 0.4952 | 0.7037 | | 0.6168 | 2.2767 | 724 | 0.5032 | 0.5694 | 0.5032 | 0.7093 | | 0.6168 | 2.2830 | 726 | 0.5523 | 0.5756 | 0.5523 | 0.7431 | | 0.6168 | 2.2893 | 728 | 0.6089 | 0.6209 | 0.6089 | 0.7803 | | 0.6168 | 2.2956 | 730 | 0.5343 | 0.6081 | 0.5343 | 0.7309 | | 0.6168 | 2.3019 | 732 | 0.4702 | 0.5385 | 0.4702 | 0.6857 | | 0.6168 | 2.3082 | 734 | 0.4921 | 0.4988 | 0.4921 | 0.7015 | | 0.6168 | 2.3145 | 736 | 0.4682 | 0.5508 | 0.4682 | 0.6843 | | 0.6168 | 2.3208 | 738 | 0.4779 | 0.5928 | 0.4779 | 0.6913 | | 0.6168 | 2.3270 | 740 | 0.4663 | 0.5907 | 0.4663 | 0.6828 | | 0.6168 | 2.3333 | 742 | 0.4595 | 0.5644 | 0.4595 | 0.6779 | | 0.6168 | 2.3396 | 744 | 0.4796 | 0.4895 | 0.4796 | 0.6926 | | 0.6168 | 2.3459 | 746 | 0.4608 | 0.5578 | 0.4608 | 0.6788 | | 0.6168 | 2.3522 | 748 | 0.4919 | 0.6168 | 0.4919 | 0.7013 | | 0.6168 | 2.3585 | 750 | 0.4988 | 0.6122 | 0.4988 | 0.7063 | | 0.6168 | 2.3648 | 752 | 0.4754 | 0.5926 | 0.4754 | 0.6895 | | 0.6168 | 2.3711 | 754 | 0.4788 | 0.5962 | 0.4788 | 0.6919 | | 0.6168 | 2.3774 | 756 | 0.5472 | 0.6241 | 0.5472 | 0.7397 | | 0.6168 | 2.3836 | 758 | 0.5343 | 0.6358 | 0.5343 | 0.7309 | | 0.6168 | 2.3899 | 760 | 0.5494 | 0.6484 | 0.5494 | 0.7412 | | 0.6168 | 2.3962 | 762 | 0.6423 | 0.6621 | 0.6423 | 0.8014 | | 0.6168 | 2.4025 | 764 | 0.5970 | 0.6302 | 0.5970 | 0.7726 | | 0.6168 | 2.4088 | 766 | 0.4743 | 0.5986 | 0.4743 | 0.6887 | | 0.6168 | 2.4151 | 768 | 0.4533 | 0.5541 | 0.4533 | 0.6733 | | 0.6168 | 2.4214 | 770 | 0.4515 | 0.5663 | 0.4515 | 0.6720 | | 0.6168 | 2.4277 | 772 | 0.4737 | 0.5937 | 0.4737 | 0.6883 | | 0.6168 | 2.4340 | 774 | 0.4717 | 0.5974 | 0.4717 | 0.6868 | | 0.6168 | 2.4403 | 776 | 0.4551 | 0.5597 | 0.4551 | 0.6746 | | 0.6168 | 2.4465 | 778 | 0.4473 | 0.5481 | 0.4473 | 0.6688 | | 0.6168 | 2.4528 | 780 | 0.4460 | 0.5399 | 0.4460 | 0.6678 | | 0.6168 | 2.4591 | 782 | 0.4570 | 0.5819 | 0.4570 | 0.6760 | | 0.6168 | 2.4654 | 784 | 0.4882 | 0.6198 | 0.4882 | 0.6987 | | 0.6168 | 2.4717 | 786 | 0.4643 | 0.5972 | 0.4643 | 0.6814 | | 0.6168 | 2.4780 | 788 | 0.4631 | 0.5362 | 0.4631 | 0.6805 | | 0.6168 | 2.4843 | 790 | 0.4646 | 0.5431 | 0.4646 | 0.6816 | | 0.6168 | 2.4906 | 792 | 0.4667 | 0.5166 | 0.4667 | 0.6831 | | 0.6168 | 2.4969 | 794 | 0.4569 | 0.5557 | 0.4569 | 0.6760 | | 0.6168 | 2.5031 | 796 | 0.4888 | 0.6104 | 0.4888 | 0.6991 | | 0.6168 | 2.5094 | 798 | 0.5187 | 0.6351 | 0.5187 | 0.7202 | | 0.6168 | 2.5157 | 800 | 0.4694 | 0.5874 | 0.4694 | 0.6851 | | 0.6168 | 2.5220 | 802 | 0.4601 | 0.4851 | 0.4601 | 0.6783 | | 0.6168 | 2.5283 | 804 | 0.4941 | 0.4234 | 0.4941 | 0.7029 | | 0.6168 | 2.5346 | 806 | 0.4711 | 0.4523 | 0.4711 | 0.6864 | | 0.6168 | 2.5409 | 808 | 0.5131 | 0.6013 | 0.5131 | 0.7163 | | 0.6168 | 2.5472 | 810 | 0.6076 | 0.6146 | 0.6076 | 0.7795 | | 0.6168 | 2.5535 | 812 | 0.5727 | 0.5899 | 0.5727 | 0.7568 | | 0.6168 | 2.5597 | 814 | 0.5201 | 0.5752 | 0.5201 | 0.7212 | | 0.6168 | 2.5660 | 816 | 0.5087 | 0.5671 | 0.5087 | 0.7132 | | 0.6168 | 2.5723 | 818 | 0.4889 | 0.5301 | 0.4889 | 0.6992 | | 0.6168 | 2.5786 | 820 | 0.4882 | 0.5239 | 0.4882 | 0.6987 | | 0.6168 | 2.5849 | 822 | 0.4822 | 0.5388 | 0.4822 | 0.6944 | | 0.6168 | 2.5912 | 824 | 0.4752 | 0.5557 | 0.4752 | 0.6894 | | 0.6168 | 2.5975 | 826 | 0.4755 | 0.5177 | 0.4755 | 0.6895 | | 0.6168 | 2.6038 | 828 | 0.4695 | 0.5348 | 0.4695 | 0.6852 | | 0.6168 | 2.6101 | 830 | 0.4779 | 0.6109 | 0.4779 | 0.6913 | | 0.6168 | 2.6164 | 832 | 0.4690 | 0.6106 | 0.4690 | 0.6849 | | 0.6168 | 2.6226 | 834 | 0.4478 | 0.5889 | 0.4478 | 0.6692 | | 0.6168 | 2.6289 | 836 | 0.4370 | 0.5301 | 0.4370 | 0.6610 | | 0.6168 | 2.6352 | 838 | 0.4351 | 0.5446 | 0.4351 | 0.6596 | | 0.6168 | 2.6415 | 840 | 0.4627 | 0.6075 | 0.4627 | 0.6802 | | 0.6168 | 2.6478 | 842 | 0.4984 | 0.6086 | 0.4984 | 0.7060 | | 0.6168 | 2.6541 | 844 | 0.5112 | 0.6214 | 0.5112 | 0.7150 | | 0.6168 | 2.6604 | 846 | 0.4755 | 0.6191 | 0.4755 | 0.6896 | | 0.6168 | 2.6667 | 848 | 0.4469 | 0.5968 | 0.4469 | 0.6685 | | 0.6168 | 2.6730 | 850 | 0.4566 | 0.6065 | 0.4566 | 0.6757 | | 0.6168 | 2.6792 | 852 | 0.5356 | 0.6601 | 0.5356 | 0.7319 | | 0.6168 | 2.6855 | 854 | 0.5851 | 0.6592 | 0.5851 | 0.7649 | | 0.6168 | 2.6918 | 856 | 0.5472 | 0.6549 | 0.5472 | 0.7397 | | 0.6168 | 2.6981 | 858 | 0.5496 | 0.6657 | 0.5496 | 0.7413 | | 0.6168 | 2.7044 | 860 | 0.5095 | 0.6290 | 0.5095 | 0.7138 | | 0.6168 | 2.7107 | 862 | 0.5340 | 0.6657 | 0.5340 | 0.7308 | | 0.6168 | 2.7170 | 864 | 0.4919 | 0.6255 | 0.4919 | 0.7014 | | 0.6168 | 2.7233 | 866 | 0.5321 | 0.6314 | 0.5321 | 0.7294 | | 0.6168 | 2.7296 | 868 | 0.5331 | 0.6205 | 0.5331 | 0.7301 | | 0.6168 | 2.7358 | 870 | 0.4911 | 0.6002 | 0.4911 | 0.7008 | | 0.6168 | 2.7421 | 872 | 0.4601 | 0.5945 | 0.4601 | 0.6783 | | 0.6168 | 2.7484 | 874 | 0.4646 | 0.6022 | 0.4646 | 0.6816 | | 0.6168 | 2.7547 | 876 | 0.4638 | 0.6107 | 0.4638 | 0.6810 | | 0.6168 | 2.7610 | 878 | 0.4979 | 0.6217 | 0.4979 | 0.7056 | | 0.6168 | 2.7673 | 880 | 0.4824 | 0.6171 | 0.4824 | 0.6945 | | 0.6168 | 2.7736 | 882 | 0.4717 | 0.6174 | 0.4717 | 0.6868 | | 0.6168 | 2.7799 | 884 | 0.5750 | 0.6513 | 0.5750 | 0.7583 | | 0.6168 | 2.7862 | 886 | 0.6010 | 0.6408 | 0.6010 | 0.7752 | | 0.6168 | 2.7925 | 888 | 0.5237 | 0.6295 | 0.5237 | 0.7236 | | 0.6168 | 2.7987 | 890 | 0.5266 | 0.6235 | 0.5266 | 0.7257 | | 0.6168 | 2.8050 | 892 | 0.4882 | 0.6032 | 0.4882 | 0.6987 | | 0.6168 | 2.8113 | 894 | 0.4529 | 0.5806 | 0.4529 | 0.6730 | | 0.6168 | 2.8176 | 896 | 0.4637 | 0.5802 | 0.4637 | 0.6810 | | 0.6168 | 2.8239 | 898 | 0.5794 | 0.6391 | 0.5794 | 0.7612 | | 0.6168 | 2.8302 | 900 | 0.6045 | 0.6505 | 0.6045 | 0.7775 | | 0.6168 | 2.8365 | 902 | 0.4849 | 0.6172 | 0.4849 | 0.6964 | | 0.6168 | 2.8428 | 904 | 0.4473 | 0.5701 | 0.4473 | 0.6688 | | 0.6168 | 2.8491 | 906 | 0.4564 | 0.5158 | 0.4564 | 0.6756 | | 0.6168 | 2.8553 | 908 | 0.4440 | 0.5683 | 0.4440 | 0.6663 | | 0.6168 | 2.8616 | 910 | 0.4760 | 0.6210 | 0.4760 | 0.6899 | | 0.6168 | 2.8679 | 912 | 0.4973 | 0.6063 | 0.4973 | 0.7052 | | 0.6168 | 2.8742 | 914 | 0.4613 | 0.5769 | 0.4613 | 0.6792 | | 0.6168 | 2.8805 | 916 | 0.4627 | 0.5601 | 0.4627 | 0.6802 | | 0.6168 | 2.8868 | 918 | 0.4902 | 0.5810 | 0.4902 | 0.7001 | | 0.6168 | 2.8931 | 920 | 0.5110 | 0.5978 | 0.5110 | 0.7148 | | 0.6168 | 2.8994 | 922 | 0.4982 | 0.5263 | 0.4982 | 0.7059 | | 0.6168 | 2.9057 | 924 | 0.5189 | 0.4791 | 0.5189 | 0.7204 | | 0.6168 | 2.9119 | 926 | 0.5141 | 0.4919 | 0.5141 | 0.7170 | | 0.6168 | 2.9182 | 928 | 0.5133 | 0.5588 | 0.5133 | 0.7165 | | 0.6168 | 2.9245 | 930 | 0.5122 | 0.5844 | 0.5122 | 0.7157 | | 0.6168 | 2.9308 | 932 | 0.5012 | 0.5188 | 0.5012 | 0.7080 | | 0.6168 | 2.9371 | 934 | 0.4874 | 0.5313 | 0.4874 | 0.6981 | | 0.6168 | 2.9434 | 936 | 0.4737 | 0.5710 | 0.4737 | 0.6883 | | 0.6168 | 2.9497 | 938 | 0.5110 | 0.6227 | 0.5110 | 0.7149 | | 0.6168 | 2.9560 | 940 | 0.4784 | 0.6200 | 0.4784 | 0.6916 | | 0.6168 | 2.9623 | 942 | 0.4436 | 0.5909 | 0.4436 | 0.6660 | | 0.6168 | 2.9686 | 944 | 0.4353 | 0.5637 | 0.4353 | 0.6598 | | 0.6168 | 2.9748 | 946 | 0.4419 | 0.6045 | 0.4419 | 0.6648 | | 0.6168 | 2.9811 | 948 | 0.4552 | 0.6259 | 0.4552 | 0.6747 | | 0.6168 | 2.9874 | 950 | 0.4415 | 0.6037 | 0.4415 | 0.6644 | | 0.6168 | 2.9937 | 952 | 0.4334 | 0.5655 | 0.4334 | 0.6583 | | 0.6168 | 3.0 | 954 | 0.4329 | 0.5599 | 0.4329 | 0.6579 | | 0.6168 | 3.0063 | 956 | 0.4388 | 0.5950 | 0.4388 | 0.6624 | | 0.6168 | 3.0126 | 958 | 0.5174 | 0.6305 | 0.5174 | 0.7193 | | 0.6168 | 3.0189 | 960 | 0.6504 | 0.6523 | 0.6504 | 0.8065 | | 0.6168 | 3.0252 | 962 | 0.5995 | 0.6266 | 0.5995 | 0.7743 | | 0.6168 | 3.0314 | 964 | 0.4915 | 0.6136 | 0.4915 | 0.7011 | | 0.6168 | 3.0377 | 966 | 0.5021 | 0.6101 | 0.5021 | 0.7086 | | 0.6168 | 3.0440 | 968 | 0.5449 | 0.6237 | 0.5449 | 0.7382 | | 0.6168 | 3.0503 | 970 | 0.5088 | 0.5658 | 0.5088 | 0.7133 | | 0.6168 | 3.0566 | 972 | 0.5037 | 0.5307 | 0.5037 | 0.7097 | | 0.6168 | 3.0629 | 974 | 0.5258 | 0.4603 | 0.5258 | 0.7251 | | 0.6168 | 3.0692 | 976 | 0.5263 | 0.4741 | 0.5263 | 0.7255 | | 0.6168 | 3.0755 | 978 | 0.5189 | 0.5580 | 0.5189 | 0.7203 | | 0.6168 | 3.0818 | 980 | 0.5218 | 0.5977 | 0.5218 | 0.7223 | | 0.6168 | 3.0881 | 982 | 0.4925 | 0.5705 | 0.4925 | 0.7018 | | 0.6168 | 3.0943 | 984 | 0.5247 | 0.4499 | 0.5247 | 0.7243 | | 0.6168 | 3.1006 | 986 | 0.5441 | 0.4333 | 0.5441 | 0.7376 | | 0.6168 | 3.1069 | 988 | 0.4828 | 0.4792 | 0.4828 | 0.6948 | | 0.6168 | 3.1132 | 990 | 0.4882 | 0.6114 | 0.4882 | 0.6987 | | 0.6168 | 3.1195 | 992 | 0.6132 | 0.6336 | 0.6132 | 0.7831 | | 0.6168 | 3.1258 | 994 | 0.5768 | 0.6112 | 0.5768 | 0.7595 | | 0.6168 | 3.1321 | 996 | 0.4734 | 0.5712 | 0.4734 | 0.6880 | | 0.6168 | 3.1384 | 998 | 0.4728 | 0.4874 | 0.4728 | 0.6876 | | 0.2191 | 3.1447 | 1000 | 0.4709 | 0.4862 | 0.4709 | 0.6862 | | 0.2191 | 3.1509 | 1002 | 0.4621 | 0.5558 | 0.4621 | 0.6798 | | 0.2191 | 3.1572 | 1004 | 0.4894 | 0.5871 | 0.4894 | 0.6996 | | 0.2191 | 3.1635 | 1006 | 0.4857 | 0.5880 | 0.4857 | 0.6969 | | 0.2191 | 3.1698 | 1008 | 0.4703 | 0.5338 | 0.4703 | 0.6858 | | 0.2191 | 3.1761 | 1010 | 0.4707 | 0.5164 | 0.4707 | 0.6861 | | 0.2191 | 3.1824 | 1012 | 0.4615 | 0.5635 | 0.4615 | 0.6794 | | 0.2191 | 3.1887 | 1014 | 0.5353 | 0.6266 | 0.5353 | 0.7316 | | 0.2191 | 3.1950 | 1016 | 0.6134 | 0.6373 | 0.6134 | 0.7832 | | 0.2191 | 3.2013 | 1018 | 0.5485 | 0.6253 | 0.5485 | 0.7406 | | 0.2191 | 3.2075 | 1020 | 0.4671 | 0.5818 | 0.4671 | 0.6835 | | 0.2191 | 3.2138 | 1022 | 0.4729 | 0.5298 | 0.4729 | 0.6877 | | 0.2191 | 3.2201 | 1024 | 0.4646 | 0.5701 | 0.4646 | 0.6816 | | 0.2191 | 3.2264 | 1026 | 0.5196 | 0.6276 | 0.5196 | 0.7209 | | 0.2191 | 3.2327 | 1028 | 0.5914 | 0.6449 | 0.5914 | 0.7690 | | 0.2191 | 3.2390 | 1030 | 0.5345 | 0.6325 | 0.5345 | 0.7311 | | 0.2191 | 3.2453 | 1032 | 0.4740 | 0.5896 | 0.4740 | 0.6885 | | 0.2191 | 3.2516 | 1034 | 0.4757 | 0.6038 | 0.4757 | 0.6897 | | 0.2191 | 3.2579 | 1036 | 0.4797 | 0.6070 | 0.4797 | 0.6926 | | 0.2191 | 3.2642 | 1038 | 0.5105 | 0.6500 | 0.5105 | 0.7145 | | 0.2191 | 3.2704 | 1040 | 0.5650 | 0.6644 | 0.5650 | 0.7517 | | 0.2191 | 3.2767 | 1042 | 0.5207 | 0.6509 | 0.5207 | 0.7216 | | 0.2191 | 3.2830 | 1044 | 0.4863 | 0.5506 | 0.4863 | 0.6973 | | 0.2191 | 3.2893 | 1046 | 0.5195 | 0.4940 | 0.5195 | 0.7208 | | 0.2191 | 3.2956 | 1048 | 0.4869 | 0.5194 | 0.4869 | 0.6978 | | 0.2191 | 3.3019 | 1050 | 0.5009 | 0.6426 | 0.5009 | 0.7078 | | 0.2191 | 3.3082 | 1052 | 0.5457 | 0.6660 | 0.5457 | 0.7387 | | 0.2191 | 3.3145 | 1054 | 0.5233 | 0.6402 | 0.5233 | 0.7234 | | 0.2191 | 3.3208 | 1056 | 0.4881 | 0.6002 | 0.4881 | 0.6986 | | 0.2191 | 3.3270 | 1058 | 0.5018 | 0.6009 | 0.5018 | 0.7084 | | 0.2191 | 3.3333 | 1060 | 0.5207 | 0.6291 | 0.5207 | 0.7216 | | 0.2191 | 3.3396 | 1062 | 0.5560 | 0.6547 | 0.5560 | 0.7456 | | 0.2191 | 3.3459 | 1064 | 0.5198 | 0.6343 | 0.5198 | 0.7210 | | 0.2191 | 3.3522 | 1066 | 0.4909 | 0.5900 | 0.4909 | 0.7006 | | 0.2191 | 3.3585 | 1068 | 0.4876 | 0.5475 | 0.4876 | 0.6983 | | 0.2191 | 3.3648 | 1070 | 0.4872 | 0.5630 | 0.4872 | 0.6980 | | 0.2191 | 3.3711 | 1072 | 0.4876 | 0.5919 | 0.4876 | 0.6983 | | 0.2191 | 3.3774 | 1074 | 0.4887 | 0.5430 | 0.4887 | 0.6991 | | 0.2191 | 3.3836 | 1076 | 0.4853 | 0.5461 | 0.4853 | 0.6966 | | 0.2191 | 3.3899 | 1078 | 0.4850 | 0.5996 | 0.4850 | 0.6964 | | 0.2191 | 3.3962 | 1080 | 0.4844 | 0.5939 | 0.4844 | 0.6960 | | 0.2191 | 3.4025 | 1082 | 0.4808 | 0.5413 | 0.4808 | 0.6934 | | 0.2191 | 3.4088 | 1084 | 0.4776 | 0.5428 | 0.4776 | 0.6911 | | 0.2191 | 3.4151 | 1086 | 0.4739 | 0.5833 | 0.4739 | 0.6884 | | 0.2191 | 3.4214 | 1088 | 0.5182 | 0.6022 | 0.5182 | 0.7199 | | 0.2191 | 3.4277 | 1090 | 0.5028 | 0.5787 | 0.5028 | 0.7091 | | 0.2191 | 3.4340 | 1092 | 0.4934 | 0.5866 | 0.4934 | 0.7024 | | 0.2191 | 3.4403 | 1094 | 0.4849 | 0.5311 | 0.4849 | 0.6964 | | 0.2191 | 3.4465 | 1096 | 0.4987 | 0.5814 | 0.4987 | 0.7062 | | 0.2191 | 3.4528 | 1098 | 0.5297 | 0.5906 | 0.5297 | 0.7278 | | 0.2191 | 3.4591 | 1100 | 0.4967 | 0.5850 | 0.4967 | 0.7047 | | 0.2191 | 3.4654 | 1102 | 0.4799 | 0.5463 | 0.4799 | 0.6927 | | 0.2191 | 3.4717 | 1104 | 0.4803 | 0.5327 | 0.4803 | 0.6931 | | 0.2191 | 3.4780 | 1106 | 0.4778 | 0.5716 | 0.4778 | 0.6912 | | 0.2191 | 3.4843 | 1108 | 0.4970 | 0.5929 | 0.4970 | 0.7049 | | 0.2191 | 3.4906 | 1110 | 0.4984 | 0.5921 | 0.4984 | 0.7060 | | 0.2191 | 3.4969 | 1112 | 0.4783 | 0.5598 | 0.4783 | 0.6916 | | 0.2191 | 3.5031 | 1114 | 0.4793 | 0.5569 | 0.4793 | 0.6923 | | 0.2191 | 3.5094 | 1116 | 0.4870 | 0.5872 | 0.4870 | 0.6979 | | 0.2191 | 3.5157 | 1118 | 0.4885 | 0.5913 | 0.4885 | 0.6989 | | 0.2191 | 3.5220 | 1120 | 0.4890 | 0.5400 | 0.4890 | 0.6993 | | 0.2191 | 3.5283 | 1122 | 0.4850 | 0.5606 | 0.4850 | 0.6964 | | 0.2191 | 3.5346 | 1124 | 0.4946 | 0.5952 | 0.4946 | 0.7033 | | 0.2191 | 3.5409 | 1126 | 0.4899 | 0.5991 | 0.4899 | 0.6999 | | 0.2191 | 3.5472 | 1128 | 0.5096 | 0.6137 | 0.5096 | 0.7139 | | 0.2191 | 3.5535 | 1130 | 0.5116 | 0.6028 | 0.5116 | 0.7153 | | 0.2191 | 3.5597 | 1132 | 0.4771 | 0.5908 | 0.4771 | 0.6907 | | 0.2191 | 3.5660 | 1134 | 0.4686 | 0.5789 | 0.4686 | 0.6846 | | 0.2191 | 3.5723 | 1136 | 0.4895 | 0.5819 | 0.4895 | 0.6996 | | 0.2191 | 3.5786 | 1138 | 0.5350 | 0.6032 | 0.5350 | 0.7314 | | 0.2191 | 3.5849 | 1140 | 0.5408 | 0.6008 | 0.5408 | 0.7354 | | 0.2191 | 3.5912 | 1142 | 0.4848 | 0.5834 | 0.4848 | 0.6963 | | 0.2191 | 3.5975 | 1144 | 0.4599 | 0.5429 | 0.4599 | 0.6782 | | 0.2191 | 3.6038 | 1146 | 0.4601 | 0.5355 | 0.4601 | 0.6783 | | 0.2191 | 3.6101 | 1148 | 0.4588 | 0.5793 | 0.4588 | 0.6774 | | 0.2191 | 3.6164 | 1150 | 0.4823 | 0.5979 | 0.4823 | 0.6945 | | 0.2191 | 3.6226 | 1152 | 0.4627 | 0.5902 | 0.4627 | 0.6802 | | 0.2191 | 3.6289 | 1154 | 0.4612 | 0.5895 | 0.4612 | 0.6791 | | 0.2191 | 3.6352 | 1156 | 0.4707 | 0.5929 | 0.4707 | 0.6861 | | 0.2191 | 3.6415 | 1158 | 0.4756 | 0.5888 | 0.4756 | 0.6896 | | 0.2191 | 3.6478 | 1160 | 0.4981 | 0.5867 | 0.4981 | 0.7058 | | 0.2191 | 3.6541 | 1162 | 0.4825 | 0.5920 | 0.4825 | 0.6947 | | 0.2191 | 3.6604 | 1164 | 0.4698 | 0.4999 | 0.4698 | 0.6854 | | 0.2191 | 3.6667 | 1166 | 0.4758 | 0.4843 | 0.4758 | 0.6898 | | 0.2191 | 3.6730 | 1168 | 0.4684 | 0.5680 | 0.4684 | 0.6844 | | 0.2191 | 3.6792 | 1170 | 0.5320 | 0.6034 | 0.5320 | 0.7294 | | 0.2191 | 3.6855 | 1172 | 0.5642 | 0.6074 | 0.5642 | 0.7511 | | 0.2191 | 3.6918 | 1174 | 0.5204 | 0.5851 | 0.5204 | 0.7214 | | 0.2191 | 3.6981 | 1176 | 0.4829 | 0.5681 | 0.4829 | 0.6949 | | 0.2191 | 3.7044 | 1178 | 0.4784 | 0.5650 | 0.4784 | 0.6917 | | 0.2191 | 3.7107 | 1180 | 0.4994 | 0.5960 | 0.4994 | 0.7067 | | 0.2191 | 3.7170 | 1182 | 0.5731 | 0.6224 | 0.5731 | 0.7571 | | 0.2191 | 3.7233 | 1184 | 0.5590 | 0.6252 | 0.5590 | 0.7476 | | 0.2191 | 3.7296 | 1186 | 0.5240 | 0.6032 | 0.5240 | 0.7239 | | 0.2191 | 3.7358 | 1188 | 0.4929 | 0.5795 | 0.4929 | 0.7021 | | 0.2191 | 3.7421 | 1190 | 0.5136 | 0.6038 | 0.5136 | 0.7167 | | 0.2191 | 3.7484 | 1192 | 0.6420 | 0.6343 | 0.6420 | 0.8012 | | 0.2191 | 3.7547 | 1194 | 0.6949 | 0.6368 | 0.6949 | 0.8336 | | 0.2191 | 3.7610 | 1196 | 0.6039 | 0.6441 | 0.6039 | 0.7771 | | 0.2191 | 3.7673 | 1198 | 0.4848 | 0.5966 | 0.4848 | 0.6963 | | 0.2191 | 3.7736 | 1200 | 0.4677 | 0.5934 | 0.4677 | 0.6839 | | 0.2191 | 3.7799 | 1202 | 0.4915 | 0.6116 | 0.4915 | 0.7011 | | 0.2191 | 3.7862 | 1204 | 0.5060 | 0.6274 | 0.5060 | 0.7113 | | 0.2191 | 3.7925 | 1206 | 0.4734 | 0.6065 | 0.4734 | 0.6880 | | 0.2191 | 3.7987 | 1208 | 0.4893 | 0.6210 | 0.4893 | 0.6995 | | 0.2191 | 3.8050 | 1210 | 0.5399 | 0.6244 | 0.5399 | 0.7348 | | 0.2191 | 3.8113 | 1212 | 0.5119 | 0.6266 | 0.5119 | 0.7155 | | 0.2191 | 3.8176 | 1214 | 0.4816 | 0.5927 | 0.4816 | 0.6940 | | 0.2191 | 3.8239 | 1216 | 0.4972 | 0.5898 | 0.4972 | 0.7051 | | 0.2191 | 3.8302 | 1218 | 0.5283 | 0.6094 | 0.5283 | 0.7268 | | 0.2191 | 3.8365 | 1220 | 0.5222 | 0.5733 | 0.5222 | 0.7226 | | 0.2191 | 3.8428 | 1222 | 0.5608 | 0.5982 | 0.5608 | 0.7489 | | 0.2191 | 3.8491 | 1224 | 0.5559 | 0.5857 | 0.5559 | 0.7456 | | 0.2191 | 3.8553 | 1226 | 0.5330 | 0.5723 | 0.5330 | 0.7301 | | 0.2191 | 3.8616 | 1228 | 0.5097 | 0.5748 | 0.5097 | 0.7139 | | 0.2191 | 3.8679 | 1230 | 0.4911 | 0.5813 | 0.4911 | 0.7008 | | 0.2191 | 3.8742 | 1232 | 0.4909 | 0.6022 | 0.4909 | 0.7007 | | 0.2191 | 3.8805 | 1234 | 0.5000 | 0.6197 | 0.5000 | 0.7071 | | 0.2191 | 3.8868 | 1236 | 0.4952 | 0.6236 | 0.4952 | 0.7037 | | 0.2191 | 3.8931 | 1238 | 0.4668 | 0.5950 | 0.4668 | 0.6833 | | 0.2191 | 3.8994 | 1240 | 0.4689 | 0.5844 | 0.4689 | 0.6847 | | 0.2191 | 3.9057 | 1242 | 0.4959 | 0.6224 | 0.4959 | 0.7042 | | 0.2191 | 3.9119 | 1244 | 0.5012 | 0.6153 | 0.5012 | 0.7079 | | 0.2191 | 3.9182 | 1246 | 0.5449 | 0.6003 | 0.5449 | 0.7382 | | 0.2191 | 3.9245 | 1248 | 0.5715 | 0.6007 | 0.5715 | 0.7560 | | 0.2191 | 3.9308 | 1250 | 0.5274 | 0.5888 | 0.5274 | 0.7262 | | 0.2191 | 3.9371 | 1252 | 0.5270 | 0.5761 | 0.5270 | 0.7259 | | 0.2191 | 3.9434 | 1254 | 0.5680 | 0.5990 | 0.5680 | 0.7537 | | 0.2191 | 3.9497 | 1256 | 0.6245 | 0.6284 | 0.6245 | 0.7903 | | 0.2191 | 3.9560 | 1258 | 0.6299 | 0.6353 | 0.6299 | 0.7937 | | 0.2191 | 3.9623 | 1260 | 0.5335 | 0.6144 | 0.5335 | 0.7304 | | 0.2191 | 3.9686 | 1262 | 0.4950 | 0.5627 | 0.4950 | 0.7036 | | 0.2191 | 3.9748 | 1264 | 0.5072 | 0.5777 | 0.5072 | 0.7122 | | 0.2191 | 3.9811 | 1266 | 0.5436 | 0.6057 | 0.5436 | 0.7373 | | 0.2191 | 3.9874 | 1268 | 0.5330 | 0.6026 | 0.5330 | 0.7301 | | 0.2191 | 3.9937 | 1270 | 0.5473 | 0.6055 | 0.5473 | 0.7398 | | 0.2191 | 4.0 | 1272 | 0.5432 | 0.6049 | 0.5432 | 0.7370 | | 0.2191 | 4.0063 | 1274 | 0.5198 | 0.6003 | 0.5198 | 0.7210 | | 0.2191 | 4.0126 | 1276 | 0.5440 | 0.6122 | 0.5440 | 0.7375 | | 0.2191 | 4.0189 | 1278 | 0.5267 | 0.6074 | 0.5267 | 0.7258 | | 0.2191 | 4.0252 | 1280 | 0.5213 | 0.6056 | 0.5213 | 0.7220 | | 0.2191 | 4.0314 | 1282 | 0.5556 | 0.6345 | 0.5556 | 0.7454 | | 0.2191 | 4.0377 | 1284 | 0.6064 | 0.6612 | 0.6064 | 0.7787 | | 0.2191 | 4.0440 | 1286 | 0.5591 | 0.6278 | 0.5591 | 0.7477 | | 0.2191 | 4.0503 | 1288 | 0.5057 | 0.5699 | 0.5057 | 0.7112 | | 0.2191 | 4.0566 | 1290 | 0.5133 | 0.5851 | 0.5133 | 0.7165 | | 0.2191 | 4.0629 | 1292 | 0.5021 | 0.5604 | 0.5021 | 0.7086 | | 0.2191 | 4.0692 | 1294 | 0.4998 | 0.5759 | 0.4998 | 0.7069 | | 0.2191 | 4.0755 | 1296 | 0.5104 | 0.6179 | 0.5104 | 0.7144 | | 0.2191 | 4.0818 | 1298 | 0.5394 | 0.6526 | 0.5394 | 0.7344 | | 0.2191 | 4.0881 | 1300 | 0.5295 | 0.6392 | 0.5295 | 0.7276 | | 0.2191 | 4.0943 | 1302 | 0.4951 | 0.5717 | 0.4951 | 0.7037 | | 0.2191 | 4.1006 | 1304 | 0.4979 | 0.5665 | 0.4979 | 0.7056 | | 0.2191 | 4.1069 | 1306 | 0.5026 | 0.5854 | 0.5026 | 0.7089 | | 0.2191 | 4.1132 | 1308 | 0.4988 | 0.5564 | 0.4988 | 0.7062 | | 0.2191 | 4.1195 | 1310 | 0.4986 | 0.5671 | 0.4986 | 0.7061 | | 0.2191 | 4.1258 | 1312 | 0.5221 | 0.6021 | 0.5221 | 0.7225 | | 0.2191 | 4.1321 | 1314 | 0.5309 | 0.6093 | 0.5309 | 0.7286 | | 0.2191 | 4.1384 | 1316 | 0.5054 | 0.5482 | 0.5054 | 0.7109 | | 0.2191 | 4.1447 | 1318 | 0.4995 | 0.5210 | 0.4995 | 0.7068 | | 0.2191 | 4.1509 | 1320 | 0.4933 | 0.5499 | 0.4933 | 0.7023 | | 0.2191 | 4.1572 | 1322 | 0.5094 | 0.6100 | 0.5094 | 0.7137 | | 0.2191 | 4.1635 | 1324 | 0.5068 | 0.6171 | 0.5068 | 0.7119 | | 0.2191 | 4.1698 | 1326 | 0.4763 | 0.5792 | 0.4763 | 0.6902 | | 0.2191 | 4.1761 | 1328 | 0.4706 | 0.5297 | 0.4706 | 0.6860 | | 0.2191 | 4.1824 | 1330 | 0.4664 | 0.5698 | 0.4664 | 0.6829 | | 0.2191 | 4.1887 | 1332 | 0.4933 | 0.6067 | 0.4933 | 0.7023 | | 0.2191 | 4.1950 | 1334 | 0.5064 | 0.6143 | 0.5064 | 0.7116 | | 0.2191 | 4.2013 | 1336 | 0.4740 | 0.5939 | 0.4740 | 0.6884 | | 0.2191 | 4.2075 | 1338 | 0.4959 | 0.4730 | 0.4959 | 0.7042 | | 0.2191 | 4.2138 | 1340 | 0.5378 | 0.4250 | 0.5378 | 0.7334 | | 0.2191 | 4.2201 | 1342 | 0.5020 | 0.4771 | 0.5020 | 0.7085 | | 0.2191 | 4.2264 | 1344 | 0.4883 | 0.5873 | 0.4883 | 0.6988 | | 0.2191 | 4.2327 | 1346 | 0.5408 | 0.6152 | 0.5408 | 0.7354 | | 0.2191 | 4.2390 | 1348 | 0.5481 | 0.6139 | 0.5481 | 0.7404 | | 0.2191 | 4.2453 | 1350 | 0.5084 | 0.6032 | 0.5084 | 0.7130 | | 0.2191 | 4.2516 | 1352 | 0.5036 | 0.5586 | 0.5036 | 0.7096 | | 0.2191 | 4.2579 | 1354 | 0.5110 | 0.5830 | 0.5110 | 0.7149 | | 0.2191 | 4.2642 | 1356 | 0.5633 | 0.5949 | 0.5633 | 0.7506 | | 0.2191 | 4.2704 | 1358 | 0.6567 | 0.6165 | 0.6567 | 0.8104 | | 0.2191 | 4.2767 | 1360 | 0.6120 | 0.6157 | 0.6120 | 0.7823 | | 0.2191 | 4.2830 | 1362 | 0.5442 | 0.6006 | 0.5442 | 0.7377 | | 0.2191 | 4.2893 | 1364 | 0.5166 | 0.5773 | 0.5166 | 0.7188 | | 0.2191 | 4.2956 | 1366 | 0.5335 | 0.6030 | 0.5335 | 0.7304 | | 0.2191 | 4.3019 | 1368 | 0.5712 | 0.6283 | 0.5712 | 0.7558 | | 0.2191 | 4.3082 | 1370 | 0.5458 | 0.6126 | 0.5458 | 0.7388 | | 0.2191 | 4.3145 | 1372 | 0.4977 | 0.5570 | 0.4977 | 0.7055 | | 0.2191 | 4.3208 | 1374 | 0.4877 | 0.5010 | 0.4877 | 0.6983 | | 0.2191 | 4.3270 | 1376 | 0.4875 | 0.5627 | 0.4875 | 0.6982 | | 0.2191 | 4.3333 | 1378 | 0.5061 | 0.6018 | 0.5061 | 0.7114 | | 0.2191 | 4.3396 | 1380 | 0.5413 | 0.6192 | 0.5413 | 0.7357 | | 0.2191 | 4.3459 | 1382 | 0.5306 | 0.6058 | 0.5306 | 0.7284 | | 0.2191 | 4.3522 | 1384 | 0.4929 | 0.5362 | 0.4929 | 0.7021 | | 0.2191 | 4.3585 | 1386 | 0.4985 | 0.4982 | 0.4985 | 0.7061 | | 0.2191 | 4.3648 | 1388 | 0.4972 | 0.5306 | 0.4972 | 0.7051 | | 0.2191 | 4.3711 | 1390 | 0.5404 | 0.5940 | 0.5404 | 0.7351 | | 0.2191 | 4.3774 | 1392 | 0.5505 | 0.5926 | 0.5505 | 0.7420 | | 0.2191 | 4.3836 | 1394 | 0.5302 | 0.5679 | 0.5302 | 0.7282 | | 0.2191 | 4.3899 | 1396 | 0.5166 | 0.5177 | 0.5166 | 0.7188 | | 0.2191 | 4.3962 | 1398 | 0.5139 | 0.5270 | 0.5139 | 0.7168 | | 0.2191 | 4.4025 | 1400 | 0.5309 | 0.5670 | 0.5309 | 0.7286 | | 0.2191 | 4.4088 | 1402 | 0.5514 | 0.6066 | 0.5514 | 0.7426 | | 0.2191 | 4.4151 | 1404 | 0.5769 | 0.6261 | 0.5769 | 0.7596 | | 0.2191 | 4.4214 | 1406 | 0.5477 | 0.6111 | 0.5477 | 0.7401 | | 0.2191 | 4.4277 | 1408 | 0.5108 | 0.5752 | 0.5108 | 0.7147 | | 0.2191 | 4.4340 | 1410 | 0.5063 | 0.5628 | 0.5063 | 0.7115 | | 0.2191 | 4.4403 | 1412 | 0.5222 | 0.5890 | 0.5222 | 0.7227 | | 0.2191 | 4.4465 | 1414 | 0.5312 | 0.5963 | 0.5312 | 0.7289 | | 0.2191 | 4.4528 | 1416 | 0.5192 | 0.5548 | 0.5192 | 0.7206 | | 0.2191 | 4.4591 | 1418 | 0.5218 | 0.5072 | 0.5218 | 0.7223 | | 0.2191 | 4.4654 | 1420 | 0.5245 | 0.5406 | 0.5245 | 0.7242 | | 0.2191 | 4.4717 | 1422 | 0.5299 | 0.5795 | 0.5299 | 0.7279 | | 0.2191 | 4.4780 | 1424 | 0.5778 | 0.6086 | 0.5778 | 0.7601 | | 0.2191 | 4.4843 | 1426 | 0.6066 | 0.6058 | 0.6066 | 0.7789 | | 0.2191 | 4.4906 | 1428 | 0.5747 | 0.6055 | 0.5747 | 0.7581 | | 0.2191 | 4.4969 | 1430 | 0.5427 | 0.5779 | 0.5427 | 0.7367 | | 0.2191 | 4.5031 | 1432 | 0.5435 | 0.5737 | 0.5435 | 0.7372 | | 0.2191 | 4.5094 | 1434 | 0.5456 | 0.5850 | 0.5456 | 0.7386 | | 0.2191 | 4.5157 | 1436 | 0.6108 | 0.6252 | 0.6108 | 0.7815 | | 0.2191 | 4.5220 | 1438 | 0.6371 | 0.6462 | 0.6371 | 0.7982 | | 0.2191 | 4.5283 | 1440 | 0.5417 | 0.6027 | 0.5417 | 0.7360 | | 0.2191 | 4.5346 | 1442 | 0.5027 | 0.5807 | 0.5027 | 0.7090 | | 0.2191 | 4.5409 | 1444 | 0.5327 | 0.6089 | 0.5327 | 0.7299 | | 0.2191 | 4.5472 | 1446 | 0.5351 | 0.6232 | 0.5351 | 0.7315 | | 0.2191 | 4.5535 | 1448 | 0.4943 | 0.5781 | 0.4943 | 0.7030 | | 0.2191 | 4.5597 | 1450 | 0.4824 | 0.5467 | 0.4824 | 0.6945 | | 0.2191 | 4.5660 | 1452 | 0.4857 | 0.5633 | 0.4857 | 0.6969 | | 0.2191 | 4.5723 | 1454 | 0.5305 | 0.6218 | 0.5305 | 0.7283 | | 0.2191 | 4.5786 | 1456 | 0.5665 | 0.6395 | 0.5665 | 0.7526 | | 0.2191 | 4.5849 | 1458 | 0.5148 | 0.5913 | 0.5148 | 0.7175 | | 0.2191 | 4.5912 | 1460 | 0.4879 | 0.5513 | 0.4879 | 0.6985 | | 0.2191 | 4.5975 | 1462 | 0.4889 | 0.5152 | 0.4889 | 0.6992 | | 0.2191 | 4.6038 | 1464 | 0.4922 | 0.5392 | 0.4922 | 0.7016 | | 0.2191 | 4.6101 | 1466 | 0.5167 | 0.5761 | 0.5167 | 0.7188 | | 0.2191 | 4.6164 | 1468 | 0.5763 | 0.6188 | 0.5763 | 0.7592 | | 0.2191 | 4.6226 | 1470 | 0.5514 | 0.5962 | 0.5514 | 0.7426 | | 0.2191 | 4.6289 | 1472 | 0.4995 | 0.5326 | 0.4995 | 0.7067 | | 0.2191 | 4.6352 | 1474 | 0.4970 | 0.5175 | 0.4970 | 0.7050 | | 0.2191 | 4.6415 | 1476 | 0.5087 | 0.5672 | 0.5087 | 0.7132 | | 0.2191 | 4.6478 | 1478 | 0.5783 | 0.6248 | 0.5783 | 0.7605 | | 0.2191 | 4.6541 | 1480 | 0.5894 | 0.6199 | 0.5894 | 0.7677 | | 0.2191 | 4.6604 | 1482 | 0.5251 | 0.5869 | 0.5251 | 0.7247 | | 0.2191 | 4.6667 | 1484 | 0.5091 | 0.5123 | 0.5091 | 0.7135 | | 0.2191 | 4.6730 | 1486 | 0.5099 | 0.5207 | 0.5099 | 0.7141 | | 0.2191 | 4.6792 | 1488 | 0.5335 | 0.5981 | 0.5335 | 0.7304 | | 0.2191 | 4.6855 | 1490 | 0.5984 | 0.6379 | 0.5984 | 0.7736 | | 0.2191 | 4.6918 | 1492 | 0.6045 | 0.6317 | 0.6045 | 0.7775 | | 0.2191 | 4.6981 | 1494 | 0.5318 | 0.5893 | 0.5318 | 0.7292 | | 0.2191 | 4.7044 | 1496 | 0.5212 | 0.4917 | 0.5212 | 0.7219 | | 0.2191 | 4.7107 | 1498 | 0.5583 | 0.4352 | 0.5583 | 0.7472 | | 0.137 | 4.7170 | 1500 | 0.5325 | 0.4577 | 0.5325 | 0.7297 | | 0.137 | 4.7233 | 1502 | 0.4992 | 0.5207 | 0.4992 | 0.7066 | | 0.137 | 4.7296 | 1504 | 0.5142 | 0.5829 | 0.5142 | 0.7171 | | 0.137 | 4.7358 | 1506 | 0.5090 | 0.5933 | 0.5090 | 0.7134 | | 0.137 | 4.7421 | 1508 | 0.4846 | 0.5451 | 0.4846 | 0.6962 | | 0.137 | 4.7484 | 1510 | 0.4941 | 0.4743 | 0.4941 | 0.7029 | | 0.137 | 4.7547 | 1512 | 0.4925 | 0.4759 | 0.4925 | 0.7018 | | 0.137 | 4.7610 | 1514 | 0.4870 | 0.5403 | 0.4870 | 0.6978 | | 0.137 | 4.7673 | 1516 | 0.5274 | 0.6094 | 0.5274 | 0.7262 | | 0.137 | 4.7736 | 1518 | 0.5429 | 0.6104 | 0.5429 | 0.7368 | | 0.137 | 4.7799 | 1520 | 0.5016 | 0.6021 | 0.5016 | 0.7082 | | 0.137 | 4.7862 | 1522 | 0.4941 | 0.4792 | 0.4941 | 0.7029 | | 0.137 | 4.7925 | 1524 | 0.5164 | 0.4536 | 0.5164 | 0.7186 | | 0.137 | 4.7987 | 1526 | 0.5001 | 0.4721 | 0.5001 | 0.7072 | | 0.137 | 4.8050 | 1528 | 0.5149 | 0.5848 | 0.5149 | 0.7176 | | 0.137 | 4.8113 | 1530 | 0.5606 | 0.6187 | 0.5606 | 0.7488 | | 0.137 | 4.8176 | 1532 | 0.5386 | 0.6120 | 0.5386 | 0.7339 | | 0.137 | 4.8239 | 1534 | 0.5132 | 0.4964 | 0.5132 | 0.7163 | | 0.137 | 4.8302 | 1536 | 0.5325 | 0.4620 | 0.5325 | 0.7298 | | 0.137 | 4.8365 | 1538 | 0.5363 | 0.4628 | 0.5363 | 0.7323 | | 0.137 | 4.8428 | 1540 | 0.5147 | 0.5029 | 0.5147 | 0.7174 | | 0.137 | 4.8491 | 1542 | 0.5102 | 0.5405 | 0.5102 | 0.7143 | | 0.137 | 4.8553 | 1544 | 0.5135 | 0.5818 | 0.5135 | 0.7166 | | 0.137 | 4.8616 | 1546 | 0.4941 | 0.5237 | 0.4941 | 0.7030 | | 0.137 | 4.8679 | 1548 | 0.4922 | 0.5190 | 0.4922 | 0.7015 | | 0.137 | 4.8742 | 1550 | 0.4914 | 0.5180 | 0.4914 | 0.7010 | | 0.137 | 4.8805 | 1552 | 0.5017 | 0.5464 | 0.5017 | 0.7083 | | 0.137 | 4.8868 | 1554 | 0.5360 | 0.6195 | 0.5360 | 0.7321 | | 0.137 | 4.8931 | 1556 | 0.5409 | 0.6140 | 0.5409 | 0.7355 | | 0.137 | 4.8994 | 1558 | 0.5174 | 0.5642 | 0.5174 | 0.7193 | | 0.137 | 4.9057 | 1560 | 0.5136 | 0.5532 | 0.5136 | 0.7166 | | 0.137 | 4.9119 | 1562 | 0.5314 | 0.5965 | 0.5314 | 0.7290 | | 0.137 | 4.9182 | 1564 | 0.5257 | 0.5958 | 0.5257 | 0.7251 | | 0.137 | 4.9245 | 1566 | 0.5320 | 0.5995 | 0.5320 | 0.7294 | | 0.137 | 4.9308 | 1568 | 0.5420 | 0.6089 | 0.5420 | 0.7362 | | 0.137 | 4.9371 | 1570 | 0.5097 | 0.5445 | 0.5097 | 0.7139 | | 0.137 | 4.9434 | 1572 | 0.5084 | 0.5183 | 0.5084 | 0.7130 | | 0.137 | 4.9497 | 1574 | 0.5117 | 0.5146 | 0.5117 | 0.7154 | | 0.137 | 4.9560 | 1576 | 0.5268 | 0.5639 | 0.5268 | 0.7258 | | 0.137 | 4.9623 | 1578 | 0.5468 | 0.6127 | 0.5468 | 0.7395 | | 0.137 | 4.9686 | 1580 | 0.5315 | 0.5791 | 0.5315 | 0.7290 | | 0.137 | 4.9748 | 1582 | 0.5247 | 0.5557 | 0.5247 | 0.7243 | | 0.137 | 4.9811 | 1584 | 0.5311 | 0.5787 | 0.5311 | 0.7288 | | 0.137 | 4.9874 | 1586 | 0.5600 | 0.6294 | 0.5600 | 0.7483 | | 0.137 | 4.9937 | 1588 | 0.6016 | 0.6491 | 0.6016 | 0.7756 | | 0.137 | 5.0 | 1590 | 0.6073 | 0.6469 | 0.6073 | 0.7793 | | 0.137 | 5.0063 | 1592 | 0.5661 | 0.6432 | 0.5661 | 0.7524 | | 0.137 | 5.0126 | 1594 | 0.5277 | 0.6116 | 0.5277 | 0.7264 | | 0.137 | 5.0189 | 1596 | 0.5400 | 0.6220 | 0.5400 | 0.7349 | | 0.137 | 5.0252 | 1598 | 0.6110 | 0.6596 | 0.6110 | 0.7817 | | 0.137 | 5.0314 | 1600 | 0.6045 | 0.6544 | 0.6045 | 0.7775 | | 0.137 | 5.0377 | 1602 | 0.5523 | 0.6378 | 0.5523 | 0.7432 | | 0.137 | 5.0440 | 1604 | 0.5261 | 0.5913 | 0.5261 | 0.7253 | | 0.137 | 5.0503 | 1606 | 0.5190 | 0.5756 | 0.5190 | 0.7204 | | 0.137 | 5.0566 | 1608 | 0.5470 | 0.6231 | 0.5470 | 0.7396 | | 0.137 | 5.0629 | 1610 | 0.5948 | 0.6374 | 0.5948 | 0.7712 | | 0.137 | 5.0692 | 1612 | 0.5981 | 0.6305 | 0.5981 | 0.7734 | | 0.137 | 5.0755 | 1614 | 0.5447 | 0.5802 | 0.5447 | 0.7380 | | 0.137 | 5.0818 | 1616 | 0.5114 | 0.5172 | 0.5114 | 0.7152 | | 0.137 | 5.0881 | 1618 | 0.5096 | 0.5302 | 0.5096 | 0.7139 | | 0.137 | 5.0943 | 1620 | 0.5184 | 0.5557 | 0.5184 | 0.7200 | | 0.137 | 5.1006 | 1622 | 0.5450 | 0.5970 | 0.5450 | 0.7382 | | 0.137 | 5.1069 | 1624 | 0.5490 | 0.5956 | 0.5490 | 0.7410 | | 0.137 | 5.1132 | 1626 | 0.5397 | 0.5729 | 0.5397 | 0.7346 | | 0.137 | 5.1195 | 1628 | 0.5360 | 0.5662 | 0.5360 | 0.7321 | | 0.137 | 5.1258 | 1630 | 0.5514 | 0.5830 | 0.5514 | 0.7426 | | 0.137 | 5.1321 | 1632 | 0.5284 | 0.5352 | 0.5284 | 0.7269 | | 0.137 | 5.1384 | 1634 | 0.5200 | 0.5191 | 0.5200 | 0.7211 | | 0.137 | 5.1447 | 1636 | 0.5160 | 0.5037 | 0.5160 | 0.7183 | | 0.137 | 5.1509 | 1638 | 0.5184 | 0.5393 | 0.5184 | 0.7200 | | 0.137 | 5.1572 | 1640 | 0.5180 | 0.5476 | 0.5180 | 0.7197 | | 0.137 | 5.1635 | 1642 | 0.5125 | 0.5336 | 0.5125 | 0.7159 | | 0.137 | 5.1698 | 1644 | 0.5039 | 0.5236 | 0.5039 | 0.7099 | | 0.137 | 5.1761 | 1646 | 0.5005 | 0.5047 | 0.5005 | 0.7075 | | 0.137 | 5.1824 | 1648 | 0.5026 | 0.5307 | 0.5026 | 0.7090 | | 0.137 | 5.1887 | 1650 | 0.5118 | 0.5572 | 0.5118 | 0.7154 | | 0.137 | 5.1950 | 1652 | 0.5206 | 0.5589 | 0.5206 | 0.7215 | | 0.137 | 5.2013 | 1654 | 0.5269 | 0.5661 | 0.5269 | 0.7259 | | 0.137 | 5.2075 | 1656 | 0.5262 | 0.5677 | 0.5262 | 0.7254 | | 0.137 | 5.2138 | 1658 | 0.5181 | 0.5379 | 0.5181 | 0.7198 | | 0.137 | 5.2201 | 1660 | 0.5199 | 0.5489 | 0.5199 | 0.7211 | | 0.137 | 5.2264 | 1662 | 0.5168 | 0.5317 | 0.5168 | 0.7189 | | 0.137 | 5.2327 | 1664 | 0.5175 | 0.5034 | 0.5175 | 0.7194 | | 0.137 | 5.2390 | 1666 | 0.5285 | 0.5567 | 0.5285 | 0.7270 | | 0.137 | 5.2453 | 1668 | 0.5740 | 0.6014 | 0.5740 | 0.7576 | | 0.137 | 5.2516 | 1670 | 0.5751 | 0.6039 | 0.5751 | 0.7584 | | 0.137 | 5.2579 | 1672 | 0.5519 | 0.5738 | 0.5519 | 0.7429 | | 0.137 | 5.2642 | 1674 | 0.5682 | 0.5954 | 0.5682 | 0.7538 | | 0.137 | 5.2704 | 1676 | 0.5834 | 0.6032 | 0.5834 | 0.7638 | | 0.137 | 5.2767 | 1678 | 0.5604 | 0.5737 | 0.5604 | 0.7486 | | 0.137 | 5.2830 | 1680 | 0.5381 | 0.5282 | 0.5381 | 0.7336 | | 0.137 | 5.2893 | 1682 | 0.5366 | 0.5274 | 0.5366 | 0.7325 | | 0.137 | 5.2956 | 1684 | 0.5544 | 0.5575 | 0.5544 | 0.7446 | | 0.137 | 5.3019 | 1686 | 0.5676 | 0.5679 | 0.5676 | 0.7534 | | 0.137 | 5.3082 | 1688 | 0.5449 | 0.5589 | 0.5449 | 0.7382 | | 0.137 | 5.3145 | 1690 | 0.5293 | 0.5326 | 0.5293 | 0.7275 | | 0.137 | 5.3208 | 1692 | 0.5481 | 0.5828 | 0.5481 | 0.7404 | | 0.137 | 5.3270 | 1694 | 0.5706 | 0.6175 | 0.5706 | 0.7554 | | 0.137 | 5.3333 | 1696 | 0.5400 | 0.5961 | 0.5400 | 0.7348 | | 0.137 | 5.3396 | 1698 | 0.5190 | 0.5213 | 0.5190 | 0.7204 | | 0.137 | 5.3459 | 1700 | 0.5220 | 0.5245 | 0.5220 | 0.7225 | | 0.137 | 5.3522 | 1702 | 0.5369 | 0.5680 | 0.5369 | 0.7328 | | 0.137 | 5.3585 | 1704 | 0.5762 | 0.6104 | 0.5762 | 0.7591 | | 0.137 | 5.3648 | 1706 | 0.5525 | 0.5807 | 0.5525 | 0.7433 | | 0.137 | 5.3711 | 1708 | 0.5256 | 0.5445 | 0.5256 | 0.7250 | | 0.137 | 5.3774 | 1710 | 0.5259 | 0.5053 | 0.5259 | 0.7252 | | 0.137 | 5.3836 | 1712 | 0.5267 | 0.5154 | 0.5267 | 0.7258 | | 0.137 | 5.3899 | 1714 | 0.5407 | 0.5558 | 0.5407 | 0.7353 | | 0.137 | 5.3962 | 1716 | 0.5512 | 0.5666 | 0.5512 | 0.7424 | | 0.137 | 5.4025 | 1718 | 0.5286 | 0.5433 | 0.5286 | 0.7271 | | 0.137 | 5.4088 | 1720 | 0.5258 | 0.4930 | 0.5258 | 0.7251 | | 0.137 | 5.4151 | 1722 | 0.5314 | 0.4664 | 0.5314 | 0.7290 | | 0.137 | 5.4214 | 1724 | 0.5204 | 0.5117 | 0.5204 | 0.7214 | | 0.137 | 5.4277 | 1726 | 0.5229 | 0.5506 | 0.5229 | 0.7231 | | 0.137 | 5.4340 | 1728 | 0.5398 | 0.5703 | 0.5398 | 0.7347 | | 0.137 | 5.4403 | 1730 | 0.5466 | 0.5979 | 0.5466 | 0.7393 | | 0.137 | 5.4465 | 1732 | 0.5224 | 0.5650 | 0.5224 | 0.7227 | | 0.137 | 5.4528 | 1734 | 0.5113 | 0.5491 | 0.5113 | 0.7151 | | 0.137 | 5.4591 | 1736 | 0.5071 | 0.5374 | 0.5071 | 0.7121 | | 0.137 | 5.4654 | 1738 | 0.5065 | 0.5416 | 0.5065 | 0.7117 | | 0.137 | 5.4717 | 1740 | 0.5246 | 0.5797 | 0.5246 | 0.7243 | | 0.137 | 5.4780 | 1742 | 0.5218 | 0.5797 | 0.5218 | 0.7224 | | 0.137 | 5.4843 | 1744 | 0.5162 | 0.5724 | 0.5162 | 0.7185 | | 0.137 | 5.4906 | 1746 | 0.5096 | 0.5536 | 0.5096 | 0.7138 | | 0.137 | 5.4969 | 1748 | 0.5051 | 0.5304 | 0.5051 | 0.7107 | | 0.137 | 5.5031 | 1750 | 0.5108 | 0.5634 | 0.5108 | 0.7147 | | 0.137 | 5.5094 | 1752 | 0.5382 | 0.5801 | 0.5382 | 0.7336 | | 0.137 | 5.5157 | 1754 | 0.5216 | 0.5847 | 0.5216 | 0.7223 | | 0.137 | 5.5220 | 1756 | 0.4968 | 0.5421 | 0.4968 | 0.7048 | | 0.137 | 5.5283 | 1758 | 0.5052 | 0.4760 | 0.5052 | 0.7108 | | 0.137 | 5.5346 | 1760 | 0.5117 | 0.4780 | 0.5117 | 0.7153 | | 0.137 | 5.5409 | 1762 | 0.5070 | 0.5427 | 0.5070 | 0.7121 | | 0.137 | 5.5472 | 1764 | 0.5567 | 0.6161 | 0.5567 | 0.7461 | | 0.137 | 5.5535 | 1766 | 0.5599 | 0.6149 | 0.5599 | 0.7482 | | 0.137 | 5.5597 | 1768 | 0.5235 | 0.5687 | 0.5235 | 0.7235 | | 0.137 | 5.5660 | 1770 | 0.5228 | 0.5072 | 0.5228 | 0.7230 | | 0.137 | 5.5723 | 1772 | 0.5250 | 0.5090 | 0.5250 | 0.7246 | | 0.137 | 5.5786 | 1774 | 0.5410 | 0.5861 | 0.5410 | 0.7355 | | 0.137 | 5.5849 | 1776 | 0.5907 | 0.6225 | 0.5907 | 0.7686 | | 0.137 | 5.5912 | 1778 | 0.5749 | 0.6029 | 0.5749 | 0.7582 | | 0.137 | 5.5975 | 1780 | 0.5412 | 0.5939 | 0.5412 | 0.7357 | | 0.137 | 5.6038 | 1782 | 0.5314 | 0.5894 | 0.5314 | 0.7289 | | 0.137 | 5.6101 | 1784 | 0.5453 | 0.6037 | 0.5453 | 0.7385 | | 0.137 | 5.6164 | 1786 | 0.5681 | 0.6119 | 0.5681 | 0.7537 | | 0.137 | 5.6226 | 1788 | 0.5702 | 0.6166 | 0.5702 | 0.7551 | | 0.137 | 5.6289 | 1790 | 0.5559 | 0.6007 | 0.5559 | 0.7456 | | 0.137 | 5.6352 | 1792 | 0.5448 | 0.6022 | 0.5448 | 0.7381 | | 0.137 | 5.6415 | 1794 | 0.5587 | 0.6112 | 0.5587 | 0.7475 | | 0.137 | 5.6478 | 1796 | 0.5405 | 0.6096 | 0.5405 | 0.7352 | | 0.137 | 5.6541 | 1798 | 0.5088 | 0.5955 | 0.5088 | 0.7133 | | 0.137 | 5.6604 | 1800 | 0.4964 | 0.5642 | 0.4964 | 0.7045 | | 0.137 | 5.6667 | 1802 | 0.4964 | 0.5585 | 0.4964 | 0.7046 | | 0.137 | 5.6730 | 1804 | 0.5212 | 0.6061 | 0.5212 | 0.7219 | | 0.137 | 5.6792 | 1806 | 0.5291 | 0.6097 | 0.5291 | 0.7274 | | 0.137 | 5.6855 | 1808 | 0.5293 | 0.6091 | 0.5293 | 0.7275 | | 0.137 | 5.6918 | 1810 | 0.5091 | 0.5540 | 0.5091 | 0.7135 | | 0.137 | 5.6981 | 1812 | 0.5072 | 0.5393 | 0.5072 | 0.7122 | | 0.137 | 5.7044 | 1814 | 0.5062 | 0.5238 | 0.5062 | 0.7115 | | 0.137 | 5.7107 | 1816 | 0.5145 | 0.5626 | 0.5145 | 0.7173 | | 0.137 | 5.7170 | 1818 | 0.5278 | 0.5856 | 0.5278 | 0.7265 | | 0.137 | 5.7233 | 1820 | 0.5254 | 0.5886 | 0.5254 | 0.7248 | | 0.137 | 5.7296 | 1822 | 0.5096 | 0.5573 | 0.5096 | 0.7138 | | 0.137 | 5.7358 | 1824 | 0.5036 | 0.5345 | 0.5036 | 0.7096 | | 0.137 | 5.7421 | 1826 | 0.5040 | 0.5179 | 0.5040 | 0.7099 | | 0.137 | 5.7484 | 1828 | 0.5122 | 0.5543 | 0.5122 | 0.7157 | | 0.137 | 5.7547 | 1830 | 0.5129 | 0.5579 | 0.5129 | 0.7162 | | 0.137 | 5.7610 | 1832 | 0.5151 | 0.5558 | 0.5151 | 0.7177 | | 0.137 | 5.7673 | 1834 | 0.5004 | 0.5381 | 0.5004 | 0.7074 | | 0.137 | 5.7736 | 1836 | 0.4985 | 0.5056 | 0.4985 | 0.7060 | | 0.137 | 5.7799 | 1838 | 0.5032 | 0.4997 | 0.5032 | 0.7093 | | 0.137 | 5.7862 | 1840 | 0.5058 | 0.4975 | 0.5058 | 0.7112 | | 0.137 | 5.7925 | 1842 | 0.5093 | 0.5039 | 0.5093 | 0.7137 | | 0.137 | 5.7987 | 1844 | 0.5199 | 0.5481 | 0.5199 | 0.7210 | | 0.137 | 5.8050 | 1846 | 0.5419 | 0.5858 | 0.5419 | 0.7362 | | 0.137 | 5.8113 | 1848 | 0.5333 | 0.5732 | 0.5333 | 0.7303 | | 0.137 | 5.8176 | 1850 | 0.5221 | 0.5339 | 0.5221 | 0.7226 | | 0.137 | 5.8239 | 1852 | 0.5190 | 0.4942 | 0.5190 | 0.7204 | | 0.137 | 5.8302 | 1854 | 0.5173 | 0.5112 | 0.5173 | 0.7193 | | 0.137 | 5.8365 | 1856 | 0.5310 | 0.5530 | 0.5310 | 0.7287 | | 0.137 | 5.8428 | 1858 | 0.5528 | 0.5771 | 0.5528 | 0.7435 | | 0.137 | 5.8491 | 1860 | 0.5564 | 0.5713 | 0.5564 | 0.7459 | | 0.137 | 5.8553 | 1862 | 0.5457 | 0.5257 | 0.5457 | 0.7387 | | 0.137 | 5.8616 | 1864 | 0.5511 | 0.5368 | 0.5511 | 0.7423 | | 0.137 | 5.8679 | 1866 | 0.5562 | 0.5645 | 0.5562 | 0.7458 | | 0.137 | 5.8742 | 1868 | 0.5371 | 0.5266 | 0.5371 | 0.7329 | | 0.137 | 5.8805 | 1870 | 0.5291 | 0.5413 | 0.5291 | 0.7274 | | 0.137 | 5.8868 | 1872 | 0.5259 | 0.5535 | 0.5259 | 0.7252 | | 0.137 | 5.8931 | 1874 | 0.5369 | 0.5981 | 0.5369 | 0.7327 | | 0.137 | 5.8994 | 1876 | 0.5517 | 0.6147 | 0.5517 | 0.7427 | | 0.137 | 5.9057 | 1878 | 0.5261 | 0.5838 | 0.5261 | 0.7253 | | 0.137 | 5.9119 | 1880 | 0.5091 | 0.5308 | 0.5091 | 0.7135 | | 0.137 | 5.9182 | 1882 | 0.5070 | 0.5485 | 0.5070 | 0.7120 | | 0.137 | 5.9245 | 1884 | 0.5292 | 0.6006 | 0.5292 | 0.7275 | | 0.137 | 5.9308 | 1886 | 0.5539 | 0.6290 | 0.5539 | 0.7442 | | 0.137 | 5.9371 | 1888 | 0.5303 | 0.6176 | 0.5303 | 0.7282 | | 0.137 | 5.9434 | 1890 | 0.5068 | 0.5536 | 0.5068 | 0.7119 | | 0.137 | 5.9497 | 1892 | 0.5092 | 0.5327 | 0.5092 | 0.7136 | | 0.137 | 5.9560 | 1894 | 0.5156 | 0.5453 | 0.5156 | 0.7180 | | 0.137 | 5.9623 | 1896 | 0.5242 | 0.5863 | 0.5242 | 0.7240 | | 0.137 | 5.9686 | 1898 | 0.5535 | 0.6211 | 0.5535 | 0.7440 | | 0.137 | 5.9748 | 1900 | 0.5484 | 0.6188 | 0.5484 | 0.7406 | | 0.137 | 5.9811 | 1902 | 0.5165 | 0.5695 | 0.5165 | 0.7187 | | 0.137 | 5.9874 | 1904 | 0.5091 | 0.5293 | 0.5091 | 0.7135 | | 0.137 | 5.9937 | 1906 | 0.5039 | 0.5467 | 0.5039 | 0.7099 | | 0.137 | 6.0 | 1908 | 0.5110 | 0.5860 | 0.5110 | 0.7149 | | 0.137 | 6.0063 | 1910 | 0.5227 | 0.6075 | 0.5227 | 0.7230 | | 0.137 | 6.0126 | 1912 | 0.5158 | 0.5937 | 0.5158 | 0.7182 | | 0.137 | 6.0189 | 1914 | 0.5121 | 0.5814 | 0.5121 | 0.7156 | | 0.137 | 6.0252 | 1916 | 0.5249 | 0.6051 | 0.5249 | 0.7245 | | 0.137 | 6.0314 | 1918 | 0.5245 | 0.5925 | 0.5245 | 0.7242 | | 0.137 | 6.0377 | 1920 | 0.5153 | 0.5620 | 0.5153 | 0.7178 | | 0.137 | 6.0440 | 1922 | 0.5134 | 0.5268 | 0.5134 | 0.7165 | | 0.137 | 6.0503 | 1924 | 0.5214 | 0.5761 | 0.5214 | 0.7221 | | 0.137 | 6.0566 | 1926 | 0.5436 | 0.6034 | 0.5436 | 0.7373 | | 0.137 | 6.0629 | 1928 | 0.5385 | 0.5879 | 0.5385 | 0.7339 | | 0.137 | 6.0692 | 1930 | 0.5294 | 0.5466 | 0.5294 | 0.7276 | | 0.137 | 6.0755 | 1932 | 0.5246 | 0.5303 | 0.5246 | 0.7243 | | 0.137 | 6.0818 | 1934 | 0.5383 | 0.5591 | 0.5383 | 0.7337 | | 0.137 | 6.0881 | 1936 | 0.5384 | 0.5542 | 0.5384 | 0.7338 | | 0.137 | 6.0943 | 1938 | 0.5208 | 0.5393 | 0.5208 | 0.7217 | | 0.137 | 6.1006 | 1940 | 0.5144 | 0.5455 | 0.5144 | 0.7172 | | 0.137 | 6.1069 | 1942 | 0.5280 | 0.5642 | 0.5280 | 0.7266 | | 0.137 | 6.1132 | 1944 | 0.5361 | 0.5867 | 0.5361 | 0.7322 | | 0.137 | 6.1195 | 1946 | 0.5112 | 0.5655 | 0.5112 | 0.7150 | | 0.137 | 6.1258 | 1948 | 0.4995 | 0.5291 | 0.4995 | 0.7067 | | 0.137 | 6.1321 | 1950 | 0.5061 | 0.5169 | 0.5061 | 0.7114 | | 0.137 | 6.1384 | 1952 | 0.5194 | 0.5467 | 0.5194 | 0.7207 | | 0.137 | 6.1447 | 1954 | 0.5554 | 0.5848 | 0.5554 | 0.7453 | | 0.137 | 6.1509 | 1956 | 0.5663 | 0.6031 | 0.5663 | 0.7525 | | 0.137 | 6.1572 | 1958 | 0.5517 | 0.5626 | 0.5517 | 0.7427 | | 0.137 | 6.1635 | 1960 | 0.5537 | 0.5673 | 0.5537 | 0.7441 | | 0.137 | 6.1698 | 1962 | 0.5627 | 0.5826 | 0.5627 | 0.7501 | | 0.137 | 6.1761 | 1964 | 0.5644 | 0.6046 | 0.5644 | 0.7513 | | 0.137 | 6.1824 | 1966 | 0.5350 | 0.5586 | 0.5350 | 0.7315 | | 0.137 | 6.1887 | 1968 | 0.5252 | 0.5379 | 0.5252 | 0.7247 | | 0.137 | 6.1950 | 1970 | 0.5237 | 0.5325 | 0.5237 | 0.7237 | | 0.137 | 6.2013 | 1972 | 0.5287 | 0.5568 | 0.5287 | 0.7271 | | 0.137 | 6.2075 | 1974 | 0.5419 | 0.5726 | 0.5419 | 0.7362 | | 0.137 | 6.2138 | 1976 | 0.5262 | 0.5548 | 0.5262 | 0.7254 | | 0.137 | 6.2201 | 1978 | 0.5228 | 0.5096 | 0.5228 | 0.7231 | | 0.137 | 6.2264 | 1980 | 0.5272 | 0.5423 | 0.5272 | 0.7261 | | 0.137 | 6.2327 | 1982 | 0.5641 | 0.6126 | 0.5641 | 0.7511 | | 0.137 | 6.2390 | 1984 | 0.5698 | 0.6111 | 0.5698 | 0.7549 | | 0.137 | 6.2453 | 1986 | 0.5435 | 0.5857 | 0.5435 | 0.7372 | | 0.137 | 6.2516 | 1988 | 0.5163 | 0.5217 | 0.5163 | 0.7186 | | 0.137 | 6.2579 | 1990 | 0.5172 | 0.5210 | 0.5172 | 0.7192 | | 0.137 | 6.2642 | 1992 | 0.5246 | 0.5537 | 0.5246 | 0.7243 | | 0.137 | 6.2704 | 1994 | 0.5389 | 0.5880 | 0.5389 | 0.7341 | | 0.137 | 6.2767 | 1996 | 0.5522 | 0.6138 | 0.5522 | 0.7431 | | 0.137 | 6.2830 | 1998 | 0.5332 | 0.6009 | 0.5332 | 0.7302 | | 0.0917 | 6.2893 | 2000 | 0.5090 | 0.5523 | 0.5090 | 0.7134 | | 0.0917 | 6.2956 | 2002 | 0.5108 | 0.5622 | 0.5108 | 0.7147 | | 0.0917 | 6.3019 | 2004 | 0.5365 | 0.6050 | 0.5365 | 0.7325 | | 0.0917 | 6.3082 | 2006 | 0.5334 | 0.5934 | 0.5334 | 0.7304 | | 0.0917 | 6.3145 | 2008 | 0.5195 | 0.5594 | 0.5195 | 0.7208 | | 0.0917 | 6.3208 | 2010 | 0.5208 | 0.5328 | 0.5208 | 0.7216 | | 0.0917 | 6.3270 | 2012 | 0.5282 | 0.5464 | 0.5282 | 0.7268 | | 0.0917 | 6.3333 | 2014 | 0.5418 | 0.5642 | 0.5418 | 0.7361 | | 0.0917 | 6.3396 | 2016 | 0.5689 | 0.6043 | 0.5689 | 0.7542 | | 0.0917 | 6.3459 | 2018 | 0.5674 | 0.5949 | 0.5674 | 0.7533 | | 0.0917 | 6.3522 | 2020 | 0.5468 | 0.5597 | 0.5468 | 0.7394 | | 0.0917 | 6.3585 | 2022 | 0.5329 | 0.5195 | 0.5329 | 0.7300 | | 0.0917 | 6.3648 | 2024 | 0.5310 | 0.5210 | 0.5310 | 0.7287 | | 0.0917 | 6.3711 | 2026 | 0.5270 | 0.5262 | 0.5270 | 0.7260 | | 0.0917 | 6.3774 | 2028 | 0.5316 | 0.5641 | 0.5316 | 0.7291 | | 0.0917 | 6.3836 | 2030 | 0.5297 | 0.5760 | 0.5297 | 0.7278 | | 0.0917 | 6.3899 | 2032 | 0.5219 | 0.5727 | 0.5219 | 0.7224 | | 0.0917 | 6.3962 | 2034 | 0.5155 | 0.5714 | 0.5155 | 0.7180 | | 0.0917 | 6.4025 | 2036 | 0.5086 | 0.5673 | 0.5086 | 0.7131 | | 0.0917 | 6.4088 | 2038 | 0.5175 | 0.5884 | 0.5175 | 0.7194 | | 0.0917 | 6.4151 | 2040 | 0.5283 | 0.6030 | 0.5283 | 0.7268 | | 0.0917 | 6.4214 | 2042 | 0.5162 | 0.5871 | 0.5162 | 0.7185 | | 0.0917 | 6.4277 | 2044 | 0.5080 | 0.5583 | 0.5080 | 0.7127 | | 0.0917 | 6.4340 | 2046 | 0.5050 | 0.5511 | 0.5050 | 0.7106 | | 0.0917 | 6.4403 | 2048 | 0.5148 | 0.5614 | 0.5148 | 0.7175 | | 0.0917 | 6.4465 | 2050 | 0.5400 | 0.6043 | 0.5400 | 0.7349 | | 0.0917 | 6.4528 | 2052 | 0.5301 | 0.5940 | 0.5301 | 0.7281 | | 0.0917 | 6.4591 | 2054 | 0.5091 | 0.5362 | 0.5091 | 0.7135 | | 0.0917 | 6.4654 | 2056 | 0.5088 | 0.5027 | 0.5088 | 0.7133 | | 0.0917 | 6.4717 | 2058 | 0.5032 | 0.5111 | 0.5032 | 0.7094 | | 0.0917 | 6.4780 | 2060 | 0.5047 | 0.5513 | 0.5047 | 0.7104 | | 0.0917 | 6.4843 | 2062 | 0.5077 | 0.5645 | 0.5077 | 0.7125 | | 0.0917 | 6.4906 | 2064 | 0.5032 | 0.5298 | 0.5032 | 0.7093 | | 0.0917 | 6.4969 | 2066 | 0.4995 | 0.5231 | 0.4995 | 0.7067 | | 0.0917 | 6.5031 | 2068 | 0.5037 | 0.5341 | 0.5037 | 0.7097 | | 0.0917 | 6.5094 | 2070 | 0.5179 | 0.5775 | 0.5179 | 0.7197 | | 0.0917 | 6.5157 | 2072 | 0.5109 | 0.5644 | 0.5109 | 0.7148 | | 0.0917 | 6.5220 | 2074 | 0.5079 | 0.5408 | 0.5079 | 0.7127 | | 0.0917 | 6.5283 | 2076 | 0.5076 | 0.5336 | 0.5076 | 0.7125 | | 0.0917 | 6.5346 | 2078 | 0.5061 | 0.5336 | 0.5061 | 0.7114 | | 0.0917 | 6.5409 | 2080 | 0.5077 | 0.5402 | 0.5077 | 0.7125 | | 0.0917 | 6.5472 | 2082 | 0.5271 | 0.5889 | 0.5271 | 0.7260 | | 0.0917 | 6.5535 | 2084 | 0.5338 | 0.6049 | 0.5338 | 0.7306 | | 0.0917 | 6.5597 | 2086 | 0.5223 | 0.5777 | 0.5223 | 0.7227 | | 0.0917 | 6.5660 | 2088 | 0.5172 | 0.5149 | 0.5172 | 0.7192 | | 0.0917 | 6.5723 | 2090 | 0.5206 | 0.5042 | 0.5206 | 0.7215 | | 0.0917 | 6.5786 | 2092 | 0.5265 | 0.5575 | 0.5265 | 0.7256 | | 0.0917 | 6.5849 | 2094 | 0.5407 | 0.5997 | 0.5407 | 0.7353 | | 0.0917 | 6.5912 | 2096 | 0.5554 | 0.6111 | 0.5554 | 0.7453 | | 0.0917 | 6.5975 | 2098 | 0.5409 | 0.6005 | 0.5409 | 0.7355 | | 0.0917 | 6.6038 | 2100 | 0.5386 | 0.6022 | 0.5386 | 0.7339 | | 0.0917 | 6.6101 | 2102 | 0.5355 | 0.6058 | 0.5355 | 0.7318 | | 0.0917 | 6.6164 | 2104 | 0.5200 | 0.5805 | 0.5200 | 0.7211 | | 0.0917 | 6.6226 | 2106 | 0.5044 | 0.5561 | 0.5044 | 0.7102 | | 0.0917 | 6.6289 | 2108 | 0.5027 | 0.5571 | 0.5027 | 0.7090 | | 0.0917 | 6.6352 | 2110 | 0.5132 | 0.5724 | 0.5132 | 0.7164 | | 0.0917 | 6.6415 | 2112 | 0.5343 | 0.6068 | 0.5343 | 0.7309 | | 0.0917 | 6.6478 | 2114 | 0.5504 | 0.6179 | 0.5504 | 0.7419 | | 0.0917 | 6.6541 | 2116 | 0.5244 | 0.5962 | 0.5244 | 0.7242 | | 0.0917 | 6.6604 | 2118 | 0.5086 | 0.5595 | 0.5086 | 0.7132 | | 0.0917 | 6.6667 | 2120 | 0.5043 | 0.5187 | 0.5043 | 0.7102 | | 0.0917 | 6.6730 | 2122 | 0.5095 | 0.5412 | 0.5095 | 0.7138 | | 0.0917 | 6.6792 | 2124 | 0.5294 | 0.5747 | 0.5294 | 0.7276 | | 0.0917 | 6.6855 | 2126 | 0.5357 | 0.5965 | 0.5357 | 0.7319 | | 0.0917 | 6.6918 | 2128 | 0.5336 | 0.5904 | 0.5336 | 0.7305 | | 0.0917 | 6.6981 | 2130 | 0.5382 | 0.6041 | 0.5382 | 0.7336 | | 0.0917 | 6.7044 | 2132 | 0.5238 | 0.5798 | 0.5238 | 0.7237 | | 0.0917 | 6.7107 | 2134 | 0.5117 | 0.5626 | 0.5117 | 0.7153 | | 0.0917 | 6.7170 | 2136 | 0.5085 | 0.5562 | 0.5085 | 0.7131 | | 0.0917 | 6.7233 | 2138 | 0.5082 | 0.5607 | 0.5082 | 0.7129 | | 0.0917 | 6.7296 | 2140 | 0.5110 | 0.5692 | 0.5110 | 0.7149 | | 0.0917 | 6.7358 | 2142 | 0.5086 | 0.5718 | 0.5086 | 0.7131 | | 0.0917 | 6.7421 | 2144 | 0.5002 | 0.5517 | 0.5002 | 0.7073 | | 0.0917 | 6.7484 | 2146 | 0.4968 | 0.5362 | 0.4968 | 0.7049 | | 0.0917 | 6.7547 | 2148 | 0.5065 | 0.5680 | 0.5065 | 0.7117 | | 0.0917 | 6.7610 | 2150 | 0.5217 | 0.5868 | 0.5217 | 0.7223 | | 0.0917 | 6.7673 | 2152 | 0.5359 | 0.5932 | 0.5359 | 0.7321 | | 0.0917 | 6.7736 | 2154 | 0.5440 | 0.5864 | 0.5440 | 0.7376 | | 0.0917 | 6.7799 | 2156 | 0.5253 | 0.5470 | 0.5253 | 0.7248 | | 0.0917 | 6.7862 | 2158 | 0.5211 | 0.5009 | 0.5211 | 0.7219 | | 0.0917 | 6.7925 | 2160 | 0.5242 | 0.4876 | 0.5242 | 0.7240 | | 0.0917 | 6.7987 | 2162 | 0.5241 | 0.5217 | 0.5241 | 0.7240 | | 0.0917 | 6.8050 | 2164 | 0.5352 | 0.5466 | 0.5352 | 0.7316 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

ayrisdev/mobile-ui-design

ayrisdev

2024-11-06T13:58:06Z

128

1

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-11-06T13:08:26Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: mobiluidesign --- # Mobile Ui Design <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mobiluidesign` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ayrisdev/mobile-ui-design', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO

DiTy

2024-11-06T13:56:17Z

147

1

transformers

[ "transformers", "safetensors", "gguf", "gemma2", "text-generation", "conversational", "function-calling", "strict-function-calling", "trl", "ru", "dataset:DiTy/function-calling-trl", "base_model:DiTy/gemma-2-9b-it-russian-function-calling-GGUF", "base_model:quantized:DiTy/gemma-2-9b-it-russian-function-calling-GGUF", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-24T07:24:57Z

--- base_model: DiTy/gemma-2-9b-it-russian-function-calling-GGUF datasets: - DiTy/function-calling-trl language: - ru library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - conversational - gemma2 - function-calling - strict-function-calling - trl --- # DiTy/gemma-2-9b-it-russian-strict-function-calling-DPO This model is a aligned version of [DiTy/gemma-2-9b-it-russian-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) for more **Strict Function Calling** task, on the Russian version of the *DiTy/function-calling-trl* dataset. Which means that the model avoids answering user questions that are not related to the described functions. The model has been trained using non-synthetic preference data, fully annotated by humans only, on the Russian version of the <ins>*DiTy/function-calling-trl*</ins> dataset. In addition to **safetensors**, the model is available in **GGUF** [(gemma-2-9B-it-russian-function-calling-DPO-F16.gguf)](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO/blob/main/gemma-2-9B-it-russian-function-calling-DPO-F16.gguf) format (in this case, you need to download only a single file (*[how to inference GGUF model](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#high-level-api)*)). ## Stricted Effect Это означает, что модель избегает ответов на вопросы пользователей, которые не связаны с описанными функциями. Ниже описаны несколько примеров поведения модели до и после *preference optimization*. ```python def get_weather(city: str): """ Функция, которая возвращает погоду в заданном городе. Args: city: Город, для которого надо узнать погоду. """ import random return ("weather", '"sunny"' if random.random() > 0.5 else '"rainy"') def get_sunrise_sunset_times(city: str): """ Функция, которая возвращает время восхода и заката для заданного города для текущей даты (дата от пользователя не требуется), в формате списка: [sunrise_time, sunset_time]. Args: city: Город, в котором можно узнать время восхода и захода солнца. """ return ["6:00", "18:00"] history_messages = [ {"role": "system", "content": "Ты - полезный помощник, имеющий доступ к следующим функциям. Используйте их при необходимости - "}, {"role": "user", "content": USER_MESSAGE}, ] inputs = tokenizer.apply_chat_template( history_messages, tokenize=False, add_generation_prompt=True, # adding prompt for generation tools=[get_weather, get_sunrise_sunset_times], # our functions (tools) ) ``` Ответ модели при `USER_MESSAGE = "кто такой Зевс?"`: * [**DiTy/gemma-2-9b-it-russian-function-calling-GGUF** <ins>(*SFT*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ``` Зевс - это главный бог в греческой мифологии. Он является богом неба, молнии и грома, а также покровителем царей и правосудия. ``` * [**Эта модель** <ins>(*DPO*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO) ``` Извините, но я не могу ответить на ваш вопрос. Мои текущие возможности позволяют мне получать погоду в заданном городе или узнавать время восхода и заката солнца для заданного города. ``` Ответ модели при `USER_MESSAGE = "придумай мне историю о щуке"`: * [**DiTy/gemma-2-9b-it-russian-function-calling-GGUF** <ins>(*SFT*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ``` В глубинах синего озера, где солнечные лучи едва проникали сквозь толщу воды, жила щука по имени Агата. Она была хитра и ловка, с серебристыми чешуйками, которые переливались, как драгоценные камни, и острыми зубами, способными разорвать любую добычу. Агата была самой опытной щукой в озере, и все остальные рыбы знали о ее грозной репутации. Однажды, когда Агата охотилась на мальков, она заметила странную рыбу, которая плавала совсем не так, как все остальные. Эта рыба была яркой, с разноцветными плавниками, и она не пыталась укрыться от Агаты, как это делали другие рыбы. Агата, всегда готовая к охоте, приблизилась к ней, пытаясь поймать ее в свои острые зубы. Но эта рыба, которая называлась Лилия, не была обычной рыбой. Она была искусственной, созданной учеными, чтобы изучать поведение рыб в естественной среде. Лилия была оснащена датчиками, которые собирали данные о температуре воды ``` * [**Эта модель** <ins>(*DPO*)</ins>:](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-DPO) ``` Извините, но я не могу придумать историю о щуке. Мои возможности ограничены доступными мне функциями. В данный момент я могу только: * Узнать погоду в заданном городе. * Узнать время восхода и заката солнца для заданного города. ``` ## Рекомендация к стратегии генерации По умолчанию модель использует жадный поиск вместе с данным конфигом: ``` GenerationConfig { "bos_token_id": 2, "cache_implementation": "hybrid", "eos_token_id": 1, "pad_token_id": 0 } ``` Для наиболее стабильной работы рекомендуется следующая стратегия генерации: | Key | Value | | --- | ----- | | `do_sample` | True | | `temperature` | 0.2 | | `top_k` | 40 | | `top_p` | 0.95 | | `min_p` | 0.5 | ## Подробности генерации с вызовом функций С более подробной информацией можно ознакомиться в основной [Model Card](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) ## Citation ```none @article{gemma_2024, title={Gemma}, url={https://www.kaggle.com/m/3301}, DOI={10.34740/KAGGLE/M/3301}, publisher={Kaggle}, author={Gemma Team}, year={2024} } ```

yoohj58072/krx_qwen2.5_7b_it_v3

yoohj58072

2024-11-06T13:52:51Z

5

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T13:48:42Z

--- base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** yoohj58072 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF

MaziyarPanahi

2024-11-06T13:50:52Z

47

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.2", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.2", "region:us", "conversational" ]

text-generation

2024-11-06T13:28:30Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.2-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.2 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.2](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.2) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.2-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.2](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.2). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

prithivMLmods/Lime-Green-Flux-LoRA

prithivMLmods

2024-11-06T13:50:42Z

36

9

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2024-11-06T10:40:30Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: 'Lime Green, An abstract painting of a hamburger on a green background. The hamburger is composed of a large red bun, with a thick layer of cheese between the bun and two slices of tomatoes. The tomatoes are red, and the lettuce is a vibrant green. The bun has a layer of white crumbs on top of it. There is a black base on the bottom of the hamburger.' output: url: images/LM1.png - text: 'Lime Green, An eye-level painting of a gray and white cat with green eyes. The cats ears are sticking up, and the cats nose is pointed towards the left side of the frame. Its whiskers are white, and there are splashes of black on the cat. The background of the painting is a vibrant lime green, with black splashes all around the cat, adding a pop of color to the scene.' output: url: images/LM2.png - text: 'Lime Green, An eye-level painting of a womans face, painted in a vibrant shade of green and red. Her lips are painted a deep red, and her eyes are closed. Her eyebrows are a darker shade of black, while her lips are a lighter shade of red. She is wearing a black baseball cap, and she is holding a phone in her right hand. Her hair is pulled back in a ponytail, adding a pop of color to her face. The background is a lime green, and there are a few lines of paint splattered across the image, adding depth to the composition.' output: url: images/LG0.webp base_model: black-forest-labs/FLUX.1-dev instance_prompt: Lime Green license: creativeml-openrail-m --- # Lime-Green-Flux-LoRA <Gallery /> - Hosted Here🧨: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC **The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.** ## Model description **prithivMLmods/Lime-Green-Flux-LoRA** Image Processing Parameters | Parameter | Value | Parameter | Value | |---------------------------|--------|---------------------------|--------| | LR Scheduler | constant | Noise Offset | 0.03 | | Optimizer | AdamW | Multires Noise Discount | 0.1 | | Network Dim | 64 | Multires Noise Iterations | 10 | | Network Alpha | 32 | Repeat & Steps | 20 & 1800| | Epoch | 10 | Save Every N Epochs | 1 | Labeling: florence2-en(natural language & English) Total Images Used for Training : 13 [ Hi-RES ] ## Best Dimensions - 1024 x 1024 (Default) ## Setting Up ``` import torch from pipelines import DiffusionPipeline base_model = "black-forest-labs/FLUX.1-dev" pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) lora_repo = "prithivMLmods/Lime-Green-Flux-LoRA" trigger_word = "Lime Green" pipe.load_lora_weights(lora_repo) device = torch.device("cuda") pipe.to(device) ``` ## Data source - https://freeflo.ai/ ## Trigger words You should use `Lime Green` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/prithivMLmods/Lime-Green-Flux-LoRA/tree/main) them in the Files & versions tab.

BlackBeenie/Llama-3.1-8B-pythonic-passthrough-merge

BlackBeenie

2024-11-06T13:49:14Z

5

0

null

[ "safetensors", "llama", "merge", "mergekit", "lazymergekit", "Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "Solshine/reflection-llama-3.1-8B", "base_model:Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "base_model:merge:Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder", "base_model:Solshine/reflection-llama-3.1-8B", "base_model:merge:Solshine/reflection-llama-3.1-8B", "base_model:mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "base_model:merge:mlabonne/Hermes-3-Llama-3.1-8B-lorablated", "region:us" ]

null

2024-11-06T13:38:03Z

--- base_model: - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - Solshine/reflection-llama-3.1-8B - mlabonne/Hermes-3-Llama-3.1-8B-lorablated tags: - merge - mergekit - lazymergekit - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B --- # Bloslain-8B-v0.1 Bloslain-8B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) ## 🧩 Configuration ```yaml slices: - sources: - layer_range: [0, 8] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [0, 16] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated - sources: - layer_range: [4, 20] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [8, 24] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [12, 28] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [16, 32] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated merge_method: passthrough dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "BlackBeenie/Bloslain-8B-v0.1" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

ngwgsang/bartpho-word-base-vimex

ngwgsang

2024-11-06T13:49:01Z

104

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-06T13:48:41Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

vislupus/bulgarian-joke-master-Qwen2.5-0.5B-Instruct-bnb-4bit-gguf

vislupus

2024-11-06T13:48:33Z

5

0

transformers

[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T13:48:26Z

--- base_model: unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf --- # Uploaded model - **Developed by:** vislupus - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

yoohj58072/krx_qwen2_7b_it_v3

yoohj58072

2024-11-06T13:47:36Z

6

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2-7B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Qwen2-7B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T13:42:40Z

--- base_model: unsloth/Qwen2-7B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** yoohj58072 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-7B-Instruct-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mlx-community/Qwen2.5-7B-Instruct-4bit

mlx-community

2024-11-06T13:47:36Z

206,381

7

mlx

[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "license:apache-2.0", "region:us" ]

text-generation

2024-09-18T20:06:25Z

--- base_model: Qwen/Qwen2.5-7B language: - en license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE pipeline_tag: text-generation tags: - chat - mlx --- # mlx-community/Qwen2.5-7B-Instruct-4bit The Model [mlx-community/Qwen2.5-7B-Instruct-4bit](https://huggingface.co/mlx-community/Qwen2.5-7B-Instruct-4bit) was converted to MLX format from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using mlx-lm version **0.18.1**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen2.5-7B-Instruct-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

KPEP/krx-qwen-2.5-7b-v1.4.8

KPEP

2024-11-06T13:46:20Z

6

0

null

[ "safetensors", "qwen2", "krx", "unsloth", "trl", "sft", "ko", "base_model:Qwen/Qwen2.5-7B", "base_model:finetune:Qwen/Qwen2.5-7B", "region:us" ]

null

2024-11-06T13:27:24Z

--- base_model: - Qwen/Qwen2.5-7B language: - ko tags: - krx - unsloth - trl - sft ---

ItchyChin/OrpoLlama-3-8B-memorize-translate-retrain-merge-all-20241105

ItchyChin

2024-11-06T13:37:29Z

6

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T13:11:42Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

mikiwazang/Celestial

mikiwazang

2024-11-06T13:34:22Z

6

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:cc-by-4.0", "region:us" ]

text-to-image

2024-11-06T13:34:15Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: >- images/workspace_trainsamples_792961987880608730_1425337e-bef3-4389-8b52-ee2e2c8390d2.png base_model: black-forest-labs/FLUX.1-dev instance_prompt: Celestial Fantasy license: cc-by-4.0 --- # Celestial Fantasy <Gallery /> ## Trigger words You should use `Celestial Fantasy` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/mikiwazang/Celestial/tree/main) them in the Files & versions tab.

giacomoarienti/nsfw-classifier

giacomoarienti

2024-11-06T13:20:07Z

94,576

27

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "huggingpics", "dataset:deepghs/nsfw_detect", "doi:10.57967/hf/2906", "license:cc-by-nc-nd-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-05T12:19:30Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: nsfw-classifier results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9200000166893005 datasets: - deepghs/nsfw_detect license: cc-by-nc-nd-4.0 --- # 🚫 NSFW Classifier - Keep Your Platform Safe and Secure! ### An AI-powered image classifier designed to detect and prevent NSFW content (porn, hentai, sexy images) from being posted on your platform. Trusted by thousands of developers, this solution is perfect for any app or platform that allows users to upload images. --- ## 🚀 Why Choose Our NSFW Image Classifier? In today's digital world, user-generated content is a double-edged sword. While it fosters creativity and engagement, it also opens the door to inappropriate or illegal content being shared. Our NSFW Image Classifier is specifically designed to identify and filter out explicit images, including **pornography, hentai, and sexually suggestive content**, ensuring your platform remains **safe, secure**, and **legally compliant**. ### 🌟 Key Benefits: - **Protect Your User Base**: Keep your community safe by preventing exposure to inappropriate content. - **Legal Compliance**: Avoid legal action by preventing illegal or explicit content from being posted. - **Seamless Integration**: Our model is easy to integrate into any platform that allows image uploads, including social media, forums, e-commerce sites, and more. --- ## 🔥 Proven Solution - Trusted by Thousands! With **60,000 downloads per month**, our NSFW Image Classifier has become the go-to solution for platforms looking to **maintain a clean and safe environment** for their users. Many developers and companies have already chosen our solution to protect their communities—will you be next? --- ## 📦 How It Works 1. **Upload an Image**: The user uploads an image to your platform. 2. **NSFW Detection**: Our model analyzes the image and flags any explicit content (porn, hentai, sexy images). 3. **Moderation**: Take appropriate action, whether it's preventing the upload or flagging the content for review. ### **Who Can Benefit?** - **Social Media Platforms** - **Online Forums** - **E-Commerce Sites** - **Content Sharing Apps** - **Any platform allowing user-uploaded images** --- ## 🚀 Looking for Even More Power? Want a model that's **even more powerful and accurate**? We've got a **premium version** trained on a **curated, high-quality dataset** that can detect a wider range of illegal content, including **gore, harmful images, under 18 content, and more**. 📩 **Contact me on Telegram [@mrjack7](https://t.me/mrjack7)** for more details on the **premium model**! --- ## 🌐 API Access 💻 Need easy integration? **API access** is available for seamless deployment into your applications. Whether you're looking to integrate our NSFW image detection capabilities or require more advanced features, our API provides a flexible and scalable solution. 📩 **Contact me on Telegram [@mrjack7](https://t.me/mrjack7)** for more details on **API access**! --- Let's build something amazing together. 💡

Lolimorimorf/damage_trigger_effect_2024-11-06_13_00

Lolimorimorf

2024-11-06T13:19:36Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-06T13:18:52Z

--- library_name: transformers license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: damage_trigger_effect_2024-11-06_13_00 results: [] ---  # damage_trigger_effect_2024-11-06_13_00 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5939 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 34 | 0.6901 | | No log | 2.0 | 68 | 0.5241 | | No log | 3.0 | 102 | 0.4540 | | No log | 4.0 | 136 | 0.4661 | | No log | 5.0 | 170 | 0.4877 | | No log | 6.0 | 204 | 0.4716 | | No log | 7.0 | 238 | 0.4778 | | No log | 8.0 | 272 | 0.5008 | | No log | 9.0 | 306 | 0.5195 | | No log | 10.0 | 340 | 0.5669 | | No log | 11.0 | 374 | 0.5807 | | No log | 12.0 | 408 | 0.5776 | | No log | 13.0 | 442 | 0.5938 | | No log | 14.0 | 476 | 0.5854 | | 0.2729 | 15.0 | 510 | 0.5939 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

mradermacher/tamil-llama-13b-instruct-v0.1-GGUF

mradermacher

2024-11-06T13:15:19Z

11

0

transformers

[ "transformers", "gguf", "ta", "en", "base_model:abhinand/tamil-llama-13b-instruct-v0.1", "base_model:quantized:abhinand/tamil-llama-13b-instruct-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-11-06T11:57:35Z

--- base_model: abhinand/tamil-llama-13b-instruct-v0.1 language: - ta - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/abhinand/tamil-llama-13b-instruct-v0.1  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 7.2 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q6_K.gguf) | Q6_K | 10.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/tamil-llama-13b-instruct-v0.1-GGUF/resolve/main/tamil-llama-13b-instruct-v0.1.Q8_0.gguf) | Q8_0 | 14.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

apto-as/multilingual-e5-large-Q8_0-GGUF

apto-as

2024-11-06T13:09:59Z

5

0

sentence-transformers

[ "sentence-transformers", "gguf", "mteb", "Sentence Transformers", "sentence-similarity", "feature-extraction", "llama-cpp", "gguf-my-repo", "multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "om", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sa", "sd", "si", "sk", "sl", "so", "sq", "sr", "su", "sv", "sw", "ta", "te", "th", "tl", "tr", "ug", "uk", "ur", "uz", "vi", "xh", "yi", "zh", "base_model:intfloat/multilingual-e5-large", "base_model:quantized:intfloat/multilingual-e5-large", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

feature-extraction

2024-11-06T13:09:52Z

--- tags: - mteb - Sentence Transformers - sentence-similarity - feature-extraction - sentence-transformers - llama-cpp - gguf-my-repo language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - 'no' - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh license: mit base_model: intfloat/multilingual-e5-large model-index: - name: multilingual-e5-large results: - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 79.05970149253731 - type: ap value: 43.486574390835635 - type: f1 value: 73.32700092140148 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 71.22055674518201 - type: ap value: 81.55756710830498 - type: f1 value: 69.28271787752661 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 80.41979010494754 - type: ap value: 29.34879922376344 - type: f1 value: 67.62475449011278 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.8372591006424 - type: ap value: 26.557560591210738 - type: f1 value: 64.96619417368707 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 93.489875 - type: ap value: 90.98758636917603 - type: f1 value: 93.48554819717332 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.564 - type: f1 value: 46.75122173518047 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 45.400000000000006 - type: f1 value: 44.17195682400632 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 43.068 - type: f1 value: 42.38155696855596 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 41.89 - type: f1 value: 40.84407321682663 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 40.120000000000005 - type: f1 value: 39.522976223819114 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 38.832 - type: f1 value: 38.0392533394713 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 30.725 - type: map_at_10 value: 46.055 - type: map_at_100 value: 46.900999999999996 - type: map_at_1000 value: 46.911 - type: map_at_3 value: 41.548 - type: map_at_5 value: 44.297 - type: mrr_at_1 value: 31.152 - type: mrr_at_10 value: 46.231 - type: mrr_at_100 value: 47.07 - type: mrr_at_1000 value: 47.08 - type: mrr_at_3 value: 41.738 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 30.725 - type: ndcg_at_10 value: 54.379999999999995 - type: ndcg_at_100 value: 58.138 - type: ndcg_at_1000 value: 58.389 - type: ndcg_at_3 value: 45.156 - type: ndcg_at_5 value: 50.123 - type: precision_at_1 value: 30.725 - type: precision_at_10 value: 8.087 - type: precision_at_100 value: 0.9769999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 18.54 - type: precision_at_5 value: 13.542000000000002 - type: recall_at_1 value: 30.725 - type: recall_at_10 value: 80.868 - type: recall_at_100 value: 97.653 - type: recall_at_1000 value: 99.57300000000001 - type: recall_at_3 value: 55.619 - type: recall_at_5 value: 67.71000000000001 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 44.30960650674069 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 38.427074197498996 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 60.28270056031872 - type: mrr value: 74.38332673789738 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 84.05942144105269 - type: cos_sim_spearman value: 82.51212105850809 - type: euclidean_pearson value: 81.95639829909122 - type: euclidean_spearman value: 82.3717564144213 - type: manhattan_pearson value: 81.79273425468256 - type: manhattan_spearman value: 82.20066817871039 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.46764091858039 - type: f1 value: 99.37717466945023 - type: precision value: 99.33194154488518 - type: recall value: 99.46764091858039 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.29407880255337 - type: f1 value: 98.11248073959938 - type: precision value: 98.02443319392472 - type: recall value: 98.29407880255337 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - 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type: dot_accuracy value: 88.19420188613343 - type: dot_ap value: 83.82679165901324 - type: dot_f1 value: 76.55833777304208 - type: dot_precision value: 75.6884875846501 - type: dot_recall value: 77.44841392054204 - type: euclidean_accuracy value: 89.03054294252338 - type: euclidean_ap value: 85.89089555185325 - type: euclidean_f1 value: 78.62997658079624 - type: euclidean_precision value: 74.92329149232914 - type: euclidean_recall value: 82.72251308900523 - type: manhattan_accuracy value: 89.0266620095471 - type: manhattan_ap value: 85.86458997929147 - type: manhattan_f1 value: 78.50685331000291 - type: manhattan_precision value: 74.5499861534201 - type: manhattan_recall value: 82.90729904527257 - type: max_accuracy value: 89.03054294252338 - type: max_ap value: 85.89089555185325 - type: max_f1 value: 78.62997658079624 --- # apto-as/multilingual-e5-large-Q8_0-GGUF This model was converted to GGUF format from [`intfloat/multilingual-e5-large`](https://huggingface.co/intfloat/multilingual-e5-large) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/intfloat/multilingual-e5-large) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo apto-as/multilingual-e5-large-Q8_0-GGUF --hf-file multilingual-e5-large-q8_0.gguf -c 2048 ```

MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF

MaziyarPanahi

2024-11-06T13:08:07Z

42

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:aifeifei798/llama3-8B-aifeifei-1.3", "base_model:quantized:aifeifei798/llama3-8B-aifeifei-1.3", "region:us", "conversational" ]

text-generation

2024-11-06T12:45:32Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: llama3-8B-aifeifei-1.3-GGUF base_model: aifeifei798/llama3-8B-aifeifei-1.3 inference: false model_creator: aifeifei798 pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF) - Model creator: [aifeifei798](https://huggingface.co/aifeifei798) - Original model: [aifeifei798/llama3-8B-aifeifei-1.3](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.3) ## Description [MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF](https://huggingface.co/MaziyarPanahi/llama3-8B-aifeifei-1.3-GGUF) contains GGUF format model files for [aifeifei798/llama3-8B-aifeifei-1.3](https://huggingface.co/aifeifei798/llama3-8B-aifeifei-1.3). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

ihughes15234/phi35_kp_dpo7epoch_total

ihughes15234

2024-11-06T13:07:12Z

78

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:ihughes15234/phi35_kp_dpo5epoch_total", "base_model:finetune:ihughes15234/phi35_kp_dpo5epoch_total", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T13:05:11Z

--- base_model: ihughes15234/phi35_kp_dpo5epoch_total language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** ihughes15234 - **License:** apache-2.0 - **Finetuned from model :** ihughes15234/phi35_kp_dpo5epoch_total This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

usmanyaqoob/xlm-roberta-base-finetuned-panx-all

usmanyaqoob

2024-11-06T13:05:35Z

128

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-06T12:51:16Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - F1: 0.8595 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2895 | 1.0 | 835 | 0.1821 | 0.8210 | | 0.1514 | 2.0 | 1670 | 0.1707 | 0.8444 | | 0.0966 | 3.0 | 2505 | 0.1763 | 0.8595 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

Ivaylo-Iv/ai_train_poc_food_text_classifier

Ivaylo-Iv

2024-11-06T13:05:17Z

117

0

transformers

[ "transformers", "safetensors", "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

2024-11-06T12:51:57Z

--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: ai_train_poc_food_text_classifier results: [] ---  # ai_train_poc_food_text_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0019 - Accuracy: 1.0 ## 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: 0.0001 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0005 | 1.0 | 7 | 0.0012 | 1.0 | | 0.0004 | 2.0 | 14 | 0.0013 | 1.0 | | 0.0003 | 3.0 | 21 | 0.0014 | 1.0 | | 0.0003 | 4.0 | 28 | 0.0015 | 1.0 | | 0.0002 | 5.0 | 35 | 0.0016 | 1.0 | | 0.0002 | 6.0 | 42 | 0.0017 | 1.0 | | 0.0002 | 7.0 | 49 | 0.0018 | 1.0 | | 0.0002 | 8.0 | 56 | 0.0018 | 1.0 | | 0.0002 | 9.0 | 63 | 0.0019 | 1.0 | | 0.0002 | 10.0 | 70 | 0.0019 | 1.0 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF

mradermacher

2024-11-06T12:54:07Z

14

0

transformers

[ "transformers", "gguf", "en", "dataset:lodrick-the-lafted/Hermes-100K", "dataset:garage-bAInd/Open-Platypus", "dataset:jondurbin/airoboros-3.2", "base_model:lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B", "base_model:quantized:lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T08:58:38Z

--- base_model: lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B datasets: - lodrick-the-lafted/Hermes-100K - garage-bAInd/Open-Platypus - jondurbin/airoboros-3.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-B-2x7B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_S.gguf) | Q3_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q3_K_L.gguf) | Q3_K_L | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q5_K_S.gguf) | Q5_K_S | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q5_K_M.gguf) | Q5_K_M | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q6_K.gguf) | Q6_K | 10.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-B-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-B-2x7B.Q8_0.gguf) | Q8_0 | 13.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

usmanyaqoob/xlm-roberta-base-finetuned-panx-it

usmanyaqoob

2024-11-06T12:49:06Z

125

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-06T12:44:54Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: [] ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2984 - F1: 0.7862 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7571 | 1.0 | 70 | 0.3568 | 0.7210 | | 0.296 | 2.0 | 140 | 0.2998 | 0.7474 | | 0.1974 | 3.0 | 210 | 0.2984 | 0.7862 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

oodeh/openshift-qe-r16-a16-epoch5-merged-model

oodeh

2024-11-06T12:47:53Z

77

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-11-06T12:44:34Z

--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** oodeh - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

besimray/miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832

besimray

2024-11-06T12:47:30Z

5

0

peft

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:unsloth/Llama-3.2-1B-Instruct", "base_model:adapter:unsloth/Llama-3.2-1B-Instruct", "license:llama3.2", "8-bit", "bitsandbytes", "region:us" ]

null

2024-11-06T12:40:32Z

--- library_name: peft license: llama3.2 base_model: unsloth/Llama-3.2-1B-Instruct tags: - axolotl - generated_from_trainer model-index: - name: miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 results: [] ---  [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: unsloth/Llama-3.2-1B-Instruct bf16: auto chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - o1-journey_train_data.json ds_type: json path: /workspace/input_data/o1-journey_train_data.json type: field_input: question field_instruction: answer field_output: longCOT system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: 3 eval_max_new_tokens: 128 eval_steps: 10 eval_table_size: null flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 4 gradient_checkpointing: true group_by_length: false hours_to_complete: 2 hub_model_id: besimray/miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 hub_strategy: checkpoint hub_token: null learning_rate: 0.0001 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 8 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 4 mlflow_experiment_name: /tmp/o1-journey_train_data.json model_type: AutoModelForCausalLM num_epochs: 3 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false save_steps: 10 save_strategy: steps sequence_len: 4096 started_at: '2024-11-06T12:40:32.335166' strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: besimray24-rayon wandb_mode: online wandb_project: Public_TuningSN wandb_run: miner_id_24 wandb_runid: c4650a4d-c32d-4e0f-a27c-171ba40b07e6 warmup_steps: 10 weight_decay: 0.01 xformers_attention: null ``` </details><br> # miner_id_2_c4650a4d-c32d-4e0f-a27c-171ba40b07e6_1730896832 This model is a fine-tuned version of [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7048 ## 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 53 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.9692 | 0.0571 | 1 | 1.0109 | | 0.8542 | 0.5714 | 10 | 0.9411 | | 0.7241 | 1.1429 | 20 | 0.7844 | | 0.678 | 1.7143 | 30 | 0.7272 | | 0.6679 | 2.2857 | 40 | 0.7076 | | 0.6468 | 2.8571 | 50 | 0.7048 | ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1

hadamelino/vit-gpt-vizwiz-fine-tuned-10k

hadamelino

2024-11-06T12:44:50Z

49

0

transformers

[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-11-06T12:30:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

usmanyaqoob/xlm-roberta-base-finetuned-panx-fr

usmanyaqoob

2024-11-06T12:44:44Z

125

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-06T12:40:27Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: [] ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2463 - F1: 0.8537 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5392 | 1.0 | 191 | 0.3301 | 0.7574 | | 0.245 | 2.0 | 382 | 0.2521 | 0.8340 | | 0.1605 | 3.0 | 573 | 0.2463 | 0.8537 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

jazzson/adl-hw3-finetune-gemma-2-chinese-kyara-10

jazzson

2024-11-06T12:44:34Z

9

0

peft

[ "peft", "tensorboard", "safetensors", "gemma2", "generated_from_trainer", "base_model:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "base_model:adapter:zake7749/gemma-2-2b-it-chinese-kyara-dpo", "license:gemma", "region:us" ]

null

2024-11-06T10:58:50Z

--- base_model: zake7749/gemma-2-2b-it-chinese-kyara-dpo library_name: peft license: gemma tags: - generated_from_trainer model-index: - name: adl-hw3-finetune-gemma-2-chinese-kyara-10 results: [] ---  # adl-hw3-finetune-gemma-2-chinese-kyara-10 This model is a fine-tuned version of [zake7749/gemma-2-2b-it-chinese-kyara-dpo](https://huggingface.co/zake7749/gemma-2-2b-it-chinese-kyara-dpo) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5204 ## 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: 42 - 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 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 2.4673 | 0.7105 | 200 | 2.3491 | | 2.0993 | 1.4210 | 400 | 2.3077 | | 1.8831 | 2.1314 | 600 | 2.3793 | | 1.6486 | 2.8419 | 800 | 2.3754 | | 1.4189 | 3.5524 | 1000 | 2.5204 | ### Framework versions - PEFT 0.13.2 - Transformers 4.45.1 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

HugoVoxx/Gemma-2-2b-it-ag-merged-model

HugoVoxx

2024-11-06T12:39:57Z

9

0

transformers

[ "transformers", "safetensors", "gemma2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-21T14:56:02Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

projecte-aina/aina-translator-de-ca

projecte-aina

2024-11-06T12:39:02Z

11

0

fairseq

[ "fairseq", "de", "ca", "dataset:projecte-aina/CA-DE_Parallel_Corpus", "doi:10.57967/hf/1930", "license:apache-2.0", "region:us" ]

null

2023-11-27T13:03:49Z

--- license: apache-2.0 datasets: - projecte-aina/CA-DE_Parallel_Corpus language: - de - ca metrics: - bleu library_name: fairseq --- ## Projecte Aina’s German-Catalan machine translation model ## Model description This model was trained from scratch using the [Fairseq toolkit](https://fairseq.readthedocs.io/en/latest/) on a combination of datasets comprising both Catalan-German data sourced from Opus, and additional datasets where synthetic Catalan was generated from the Spanish side of Spanish-Germancorpora using [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). This gave a total of approximately 100 million sentence pairs. The model is evaluated on the Flores, NTEU and NTREX evaluation sets. ## Intended uses and limitations You can use this model for machine translation from German to Catalan. ## How to use ### Usage Required libraries: ```bash pip install ctranslate2 pyonmttok ``` Translate a sentence using python ```python import ctranslate2 import pyonmttok from huggingface_hub import snapshot_download model_dir = snapshot_download(repo_id="projecte-aina/aina-translator-de-ca", revision="main") tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = model_dir + "/spm.model") tokenized=tokenizer.tokenize("Willkommen beim Projekt Aina") translator = ctranslate2.Translator(model_dir) translated = translator.translate_batch([tokenized[0]]) print(tokenizer.detokenize(translated[0][0]['tokens'])) ``` ## Limitations and bias At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased. We intend to conduct research in these areas in the future, and if completed, this model card will be updated. ## Training ### Training data The model was trained on a combination of the following datasets: | Datasets | |-------------------| | Multi CCAligned | | WikiMatrix | | GNOME | | KDE4 | | OpenSubtitles | | GlobalVoices| | Tatoeba | | Books | | Europarl | | Tilde | | Multi-Paracawl | | DGT | | EU Bookshop | | NLLB | | OpenSubtitles | All corpora except Europarl and Tilde were collected from [Opus](https://opus.nlpl.eu/). The Europarl and Tilde corpora are a synthetic parallel corpus created from the original Spanish-Catalan corpora by [SoftCatalà](https://github.com/Softcatala). Where a Spanish-German corpus was used, synthetic Catalan was generated from the Spanish side using [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). ### Training procedure ### Data preparation All datasets are deduplicated, filtered for language identification, and filtered to remove any sentence pairs with a cosine similarity of less than 0.75. This is done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). The filtered datasets are then concatenated to form a final corpus of 6.258.272 and before training the punctuation is normalized using a modified version of the join-single-file.py script from [SoftCatalà](https://github.com/Softcatala/nmt-models/blob/master/data-processing-tools/join-single-file.py) #### Tokenization All data is tokenized using sentencepiece, with a 50 thousand token sentencepiece model learned from the combination of all filtered training data. This model is included. #### Hyperparameters The model is based on the Transformer-XLarge proposed by [Subramanian et al.](https://aclanthology.org/2021.wmt-1.18.pdf) The following hyperparameters were set on the Fairseq toolkit: | Hyperparameter | Value | |------------------------------------|----------------------------------| | Architecture | transformer_vaswani_wmt_en_de_big | | Embedding size | 1024 | | Feedforward size | 4096 | | Number of heads | 16 | | Encoder layers | 24 | | Decoder layers | 6 | | Normalize before attention | True | | --share-decoder-input-output-embed | True | | --share-all-embeddings | True | | Effective batch size | 48.000 | | Optimizer | adam | | Adam betas | (0.9, 0.980) | | Clip norm | 0.0 | | Learning rate | 5e-4 | | Lr. schedurer | inverse sqrt | | Warmup updates | 8000 | | Dropout | 0.1 | | Label smoothing | 0.1 | The model was trained for a total of 29.000 updates. Weights were saved every 1000 updates and reported results are the average of the last 2 checkpoints. ## Evaluation ### Variable and metrics We use the BLEU score for evaluation on the [Flores-101](https://github.com/facebookresearch/flores), NTEU (unpublished) and [NTREX](https://github.com/MicrosoftTranslator/NTREX) test sets. ### Evaluation results Below are the evaluation results on the machine translation from German to Catalan compared to [Softcatalà](https://www.softcatala.org/) and [Google Translate](https://translate.google.es/?hl=es): | Test set | SoftCatalà | Google Translate | aina-translator-de-ca | |----------------------|------------|------------------|---------------| | Flores 101 dev | 28,9 | **35,1** | 33,1 | | Flores 101 devtest |29,2 | **35,9** | 33,2 | | NTEU | 38,9 | 39,1 | **42,9** | | NTREX | 25,7 | **31,2** | 29,1 | | **Average** | 30,7 | **35,3** | 34,3 | ## Additional information ### Author The Language Technologies Unit from Barcelona Supercomputing Center. ### Contact For further information, please send an email to <langtech@bsc.es>. ### Copyright Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center. ### License [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0) ### Funding This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). ### Disclaimer <details> <summary>Click to expand</summary> The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. Be aware that the model may have biases and/or any other undesirable distortions. When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence. In no event shall the owner and creator of the model (Barcelona Supercomputing Center) be liable for any results arising from the use made by third parties. </details>

sweetssweets/NMRExtractor

sweetssweets

2024-11-06T12:38:21Z

6

1

null

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

null

2024-08-17T10:07:51Z

--- license: mit --- ## For a detailed description of the model, please refer to: '''https://github.com/eat-sugar/NMRExtractor'''

Xu-Ouyang/pythia-12b-deduped-int8-step1000-GPTQ-wikitext2

Xu-Ouyang

2024-11-06T12:33:48Z

5

0

transformers

[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]

text-generation

2024-11-06T12:23:17Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

PranavSharma10/Qwen-2.5-7B-FineTunedInstruct

PranavSharma10

2024-11-06T12:28:46Z

5

0

null

[ "safetensors", "qwen2", "llama-factory", "license:mit", "region:us" ]

null

2024-11-06T12:00:18Z

--- license: mit tags: - llama-factory ---

MaziyarPanahi/SD3_prompt-llama_8b-GGUF

MaziyarPanahi

2024-11-06T12:24:51Z

96

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:matrixglitch/SD3_prompt-llama_8b", "base_model:quantized:matrixglitch/SD3_prompt-llama_8b", "region:us", "conversational" ]

text-generation

2024-11-06T12:01:42Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: SD3_prompt-llama_8b-GGUF base_model: matrixglitch/SD3_prompt-llama_8b inference: false model_creator: matrixglitch pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/SD3_prompt-llama_8b-GGUF](https://huggingface.co/MaziyarPanahi/SD3_prompt-llama_8b-GGUF) - Model creator: [matrixglitch](https://huggingface.co/matrixglitch) - Original model: [matrixglitch/SD3_prompt-llama_8b](https://huggingface.co/matrixglitch/SD3_prompt-llama_8b) ## Description [MaziyarPanahi/SD3_prompt-llama_8b-GGUF](https://huggingface.co/MaziyarPanahi/SD3_prompt-llama_8b-GGUF) contains GGUF format model files for [matrixglitch/SD3_prompt-llama_8b](https://huggingface.co/matrixglitch/SD3_prompt-llama_8b). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

usmanyaqoob/xlm-roberta-base-finetuned-panx-de-fr

usmanyaqoob

2024-11-06T12:23:00Z

135

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-06T10:35:51Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1566 - F1: 0.8673 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2805 | 1.0 | 715 | 0.1776 | 0.8182 | | 0.1446 | 2.0 | 1430 | 0.1523 | 0.8533 | | 0.0915 | 3.0 | 2145 | 0.1566 | 0.8673 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

Totototo/bert-base-uncased-finetuned-sst2

Totototo

2024-11-06T12:21:24Z

107

0

transformers

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

text-classification

2024-11-06T12:17:46Z

--- library_name: transformers license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: bert-base-uncased-finetuned-sst2 results: [] ---  # bert-base-uncased-finetuned-sst2 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5682 - Accuracy: 0.8911 ## 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: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 250 | 0.3307 | 0.8716 | | 0.353 | 2.0 | 500 | 0.3771 | 0.8968 | | 0.353 | 3.0 | 750 | 0.5682 | 0.8911 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

elliotthwang/KimLan-3.2-3B

elliotthwang

2024-11-06T12:15:09Z

131

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T12:08:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

paipaimask/paipai_qwen_2-7B-it_04

paipaimask

2024-11-06T12:06:41Z

8

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T10:47:53Z

--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** paipaimask - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

superllms/Qwen2.5-7B-Instruct_906de368

superllms

2024-11-06T12:01:32Z

7

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T11:42:47Z

--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** superllms - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

usmanyaqoob/xlm-roberta-base-finetuned-panx-de

usmanyaqoob

2024-11-06T12:00:33Z

135

0

transformers

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-10-27T16:17:22Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1299 - F1: 0.8683 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2569 | 1.0 | 525 | 0.1567 | 0.8133 | | 0.1308 | 2.0 | 1050 | 0.1372 | 0.8526 | | 0.0821 | 3.0 | 1575 | 0.1299 | 0.8683 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf

RichardErkhov

2024-11-06T11:57:00Z

21

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T08:28:37Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deep-solar-v2.0.7 - GGUF - Model creator: https://huggingface.co/Deepnoid/ - Original model: https://huggingface.co/Deepnoid/deep-solar-v2.0.7/ | Name | Quant method | Size | | ---- | ---- | ---- | | [deep-solar-v2.0.7.Q2_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q2_K.gguf) | Q2_K | 3.73GB | | [deep-solar-v2.0.7.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [deep-solar-v2.0.7.Q3_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K.gguf) | Q3_K | 4.84GB | | [deep-solar-v2.0.7.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [deep-solar-v2.0.7.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [deep-solar-v2.0.7.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [deep-solar-v2.0.7.Q4_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_0.gguf) | Q4_0 | 5.66GB | | [deep-solar-v2.0.7.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [deep-solar-v2.0.7.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [deep-solar-v2.0.7.Q4_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K.gguf) | Q4_K | 6.02GB | | [deep-solar-v2.0.7.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [deep-solar-v2.0.7.Q4_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q4_1.gguf) | Q4_1 | 6.27GB | | [deep-solar-v2.0.7.Q5_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_0.gguf) | Q5_0 | 6.89GB | | [deep-solar-v2.0.7.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [deep-solar-v2.0.7.Q5_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K.gguf) | Q5_K | 7.08GB | | [deep-solar-v2.0.7.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [deep-solar-v2.0.7.Q5_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q5_1.gguf) | Q5_1 | 7.51GB | | [deep-solar-v2.0.7.Q6_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q6_K.gguf) | Q6_K | 8.2GB | | [deep-solar-v2.0.7.Q8_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-v2.0.7-gguf/blob/main/deep-solar-v2.0.7.Q8_0.gguf) | Q8_0 | 10.62GB | Original model description: --- license: apache-2.0 ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

mradermacher/Eclipse-13B-i1-GGUF

mradermacher

2024-11-06T11:56:11Z

29

0

transformers

[ "transformers", "gguf", "mistral", "Eclipse-7B", "en", "base_model:Xenon1/Eclipse-13B", "base_model:quantized:Xenon1/Eclipse-13B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-05T23:35:33Z

--- base_model: Xenon1/Eclipse-13B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mistral - Eclipse-7B --- ## About      weighted/imatrix quants of https://huggingface.co/Xenon1/Eclipse-13B  static quants are available at https://huggingface.co/mradermacher/Eclipse-13B-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.8 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.7 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.4 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.4 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.4 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_0.gguf) | i1-Q4_0 | 7.4 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/Eclipse-13B-i1-GGUF/resolve/main/Eclipse-13B.i1-Q6_K.gguf) | i1-Q6_K | 10.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF

featherless-ai-quants

2024-11-06T11:53:40Z

10

0

null

[ "gguf", "text-generation", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T10:21:41Z

--- base_model: tokyotech-llm-Llama-3-Swallow-8B-v0.1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # tokyotech-llm-Llama-3-Swallow-8B-v0.1 GGUF Quantizations 🚀 ![Featherless AI Quants](./featherless-quants.png) *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/tokyotech-llm-Llama-3-Swallow-8B-v0.1-GGUF/blob/main/tokyotech-llm-Llama-3-Swallow-8B-v0.1-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

nishhaaann/bertTunedBoxA

nishhaaann

2024-11-06T11:53:11Z

162

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T11:52:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

Primeness/deeznootz013

Primeness

2024-11-06T11:44:44Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T10:40:39Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

sairanaz/finetuning-sentiment-model-3000-samples

sairanaz

2024-11-06T11:43:35Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "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

2024-11-06T10:18:02Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3324 - Accuracy: 0.8867 ## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF

MaziyarPanahi

2024-11-06T11:42:23Z

28

0

null

[ "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "base_model:RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305", "base_model:quantized:RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305", "region:us", "conversational" ]

text-generation

2024-11-06T11:19:43Z

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - text-generation - text-generation model_name: WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF base_model: RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305 inference: false model_creator: RicardoLee pipeline_tag: text-generation quantized_by: MaziyarPanahi --- # [MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF](https://huggingface.co/MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF) - Model creator: [RicardoLee](https://huggingface.co/RicardoLee) - Original model: [RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305](https://huggingface.co/RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305) ## Description [MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF](https://huggingface.co/MaziyarPanahi/WASSA2024_EmpathyDetection_Chinchunmei_EXP305-GGUF) contains GGUF format model files for [RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305](https://huggingface.co/RicardoLee/WASSA2024_EmpathyDetection_Chinchunmei_EXP305). ### 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 incomplete 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [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. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [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. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

Mykes/med_ner_SDDCS

Mykes

2024-11-06T11:33:36Z

19

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "biology", "medical", "ru", "dataset:Mykes/patient_queries_ner_SDDCS", "base_model:google-bert/bert-base-multilingual-uncased", "base_model:finetune:google-bert/bert-base-multilingual-uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-03T20:37:12Z

--- base_model: - google-bert/bert-base-multilingual-uncased datasets: - Mykes/patient_queries_ner_SDDCS language: - ru library_name: transformers pipeline_tag: token-classification tags: - biology - medical --- ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63565a3d58acee56a457f799/MqL0twwg_1DWKN7taDvQy.jpeg) # med_ner_SDDCS SDDCS - abbreviation for ner-entities SYMPTOMS, DISEASES, DRUGS, CITIES, SUBWAY STATIONS (additionall it is able to predict GENDER and AGE entities) This is a fine-tuned Named Entity Recognition (NER) model based on the [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) model, designed to detect russian medical entities like diseases, drugs, symptoms, and more. # Model Details - Model Name: med_ner_SDDCS - Base Model: Babelscape/wikineural-multilingual-ner - Fine-tuned on: Medical NER data ## Entities Recognized: - GENDER (e.g., женщина, мужчина) - DISEASE (e.g., паническое расстройство, грипп, ...) - SYMPTOM (e.g., тревога, одышка, ...) - SPECIALITY (e.g., невролог, кардиолог, ...) - CITY (e.g., Тула, Москва, Иркутск, ...) - SUBWAY (e.g., Шоссе Энтузиастов, Проспект Мира, ...) - DRUG (e.g., кардиомагнил, ципралекс) - AGE (e.g., ребенок, пожилой) ## Model Performance The fine-tuned model has achieved the following performance metrics: ``` precision recall f1-score support AGE 0.99 1.00 0.99 706 CITY 0.99 1.00 1.00 2370 DISEASE 0.99 1.00 0.99 4841 DRUG 0.99 1.00 0.99 4546 GENDER 0.99 1.00 1.00 476 SPECIALITY 0.98 0.96 0.97 3673 SUBWAY 1.00 1.00 1.00 658 SYMPTOM 0.99 0.99 0.99 8022 micro avg 0.99 0.99 0.99 25292 macro avg 0.99 0.99 0.99 25292 weighted avg 0.99 0.99 0.99 25292 How to Use ``` You can use this model with the transformers library to perform Named Entity Recognition (NER) tasks in the russian medical domain, mainly for patient queries. Here's how to load and use the model: # Load the tokenizer and model ``` from transformers import pipeline pipe = pipeline(task="ner", model='Mykes/med_ner_SDDCS', tokenizer='Mykes/med_ner_SDDCS', aggregation_strategy="max") # I made the misspelled words on purpose query = "У ребенка треога и норушения сна, потеря сознания, раньше ставили паническое расстройство. Подскажи хорошего психотервта в районе метро Октбрьской." pipe(query.lower()) ``` Result: ``` [{'entity_group': 'AGE', 'score': 0.9992663, 'word': 'ребенка', 'start': 2, 'end': 9}, {'entity_group': 'SYMPTOM', 'score': 0.9997758, 'word': 'треога', 'start': 10, 'end': 16}, {'entity_group': 'SYMPTOM', 'score': 0.9997876, 'word': 'норушения сна', 'start': 19, 'end': 32}, {'entity_group': 'SYMPTOM', 'score': 0.999773, 'word': 'потеря сознания', 'start': 34, 'end': 49}, {'entity_group': 'DISEASE', 'score': 0.9996424, 'word': 'паническое расстройство', 'start': 66, 'end': 89}, {'entity_group': 'SUBWAY', 'score': 0.99918646, 'word': 'октбрьской', 'start': 136, 'end': 146}] ``` ## Code for visualization ``` import spacy from spacy import displacy def convert_to_displacy_format(text, ner_results): entities = [] for result in ner_results: # Convert the Hugging Face output into the format displacy expects entities.append({ "start": result['start'], "end": result['end'], "label": result['entity_group'] }) return { "text": text, "ents": entities, "title": None } query = "У ребенка треога и норушения сна, потеря сознания, раньше ставили паническое расстройство. Подскажи хорошего психиатра в районе метро Октбрьской." ner_results = pipe(query.lower()) displacy_data = convert_to_displacy_format(query, ner_results) colors = { "SPECIALITY": "linear-gradient(90deg, #aa9cfc, #fc9ce7)", "CITY": "linear-gradient(90deg, #feca57, #ff9f43)", "DRUG": "linear-gradient(90deg, #55efc4, #81ecec)", "DISEASE": "linear-gradient(90deg, #fab1a0, #ff7675)", "SUBWAY": "linear-gradient(90deg, #00add0, #0039a6)", "AGE": "linear-gradient(90deg, #f39c12, #e67e22)", "SYMPTOM": "linear-gradient(90deg, #e74c3c, #c0392b)" } options = {"ents": ["SPECIALITY", "CITY", "DRUG", "DISEASE", "SYMPTOM", "AGE", "SUBWAY"], "colors": colors} html = displacy.render(displacy_data, style="ent", manual=True, options=options, jupyter=False) with open("ner_visualization_with_colors.html", "w", encoding="utf-8") as f: f.write(html) from IPython.display import display, HTML display(HTML(html)) ```

atocadocoelhodealice/ai-metaglasses

atocadocoelhodealice

2024-11-06T11:32:56Z

6

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-11-06T04:01:29Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: AIMETAGLASSES --- # Ai Metaglasses <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `AIMETAGLASSES` to trigger the image generation. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('atocadocoelhodealice/ai-metaglasses', weight_name='lora.safetensors') image = pipeline('your prompt').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

ProfessorSeemo/cyberattack-pattern-detector

ProfessorSeemo

2024-11-06T11:27:23Z

117

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-05T18:07:25Z

--- library_name: transformers tags: - generated_from_trainer model-index: - name: cyberattack-pattern-detector results: [] ---  # cyberattack-pattern-detector This model was trained from scratch 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: 5e-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: 3.0 ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Tokenizers 0.19.1

mirzabeg14/finetuning-sentiment-model-3000-samples

mirzabeg14

2024-11-06T11:19:13Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "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

2024-11-06T10:32:26Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3497 - Accuracy: 0.86 - F1: 0.8627 ## 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: 2 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

lm-kit/nomic-embed-text-1.5

lm-kit

2024-11-06T11:17:16Z

337

0

null

[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]

null

2024-11-05T14:22:55Z

--- license: apache-2.0 --- ## Model Summary This repository hosts quantized versions of the nomic-embed-text-v1.5 embedding model. **Format:** GGUF **Converter:** llama.cpp 94d8cb8be13b7c4d04eeca5a2b956b9148e6f222 **Quantizer:** LM-Kit.NET 2024.11.1 For more detailed information on the base model, please visit the following links - [nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)

mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF

mradermacher

2024-11-06T11:07:12Z

22

0

transformers

[ "transformers", "gguf", "en", "dataset:lodrick-the-lafted/Hermes-40K", "dataset:garage-bAInd/Open-Platypus", "dataset:jondurbin/airoboros-3.2", "base_model:lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B", "base_model:quantized:lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T06:12:07Z

--- base_model: lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B datasets: - lodrick-the-lafted/Hermes-40K - garage-bAInd/Open-Platypus - jondurbin/airoboros-3.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-D-2x7B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q2_K.gguf) | Q2_K | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_S.gguf) | Q3_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q3_K_L.gguf) | Q3_K_L | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.4 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q5_K_S.gguf) | Q5_K_S | 9.0 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q5_K_M.gguf) | Q5_K_M | 9.2 | | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q6_K.gguf) | Q6_K | 10.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Grafted-Hermetic-Platypus-D-2x7B-GGUF/resolve/main/Grafted-Hermetic-Platypus-D-2x7B.Q8_0.gguf) | Q8_0 | 13.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

kavish218/enhanced_finetuned_llama_3_2_1B_multi_domain_4

kavish218

2024-11-06T10:59:45Z

98

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-06T10:58:20Z

--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

WeiChow/moge_l_vit

WeiChow

2024-11-06T10:57:40Z

10

0

null

[ "pytorch", "region:us" ]

null

2024-11-06T10:25:24Z

same architecture with [timm/vit_large_patch14_dinov2.lvd142m](https://huggingface.co/timm/vit_large_patch14_dinov2.lvd142m) ```shell git clone https://github.com/microsoft/MoGe.git cd MoGe ``` # translate ```python import torch from moge.model import MoGeModel device = torch.device("cuda") model = MoGeModel.from_pretrained("Ruicheng/moge-vitl").to(device) # ------------------- backbone_state_dict = model.backbone.state_dict() filtered_state_dict = {k: v for k, v in backbone_state_dict.items() if 'mask_token' not in k} torch.save(filtered_state_dict, "pytorch_model.bin") # ------------------- ``` # usage ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'hf_hub:WeiChow/moge_l_vit', pretrained=True, num_classes=0, # remove classifier nn.Linear ) # for name, param in model.named_parameters(): # print(f"Parameter: {name} - Size: {param.size()} - Total Elements: {param.numel()}") model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1374, 1024) shaped tensor output = model.forward_head(output, pre_logits=True) print(output) ``` Copyright saved.

kiranshivaraju/swin-tiny-patch4-window7-224-finetuned-aug-pcb

kiranshivaraju

2024-11-06T10:57:27Z

214

0

transformers

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-11-06T10:48:40Z

--- library_name: transformers license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-aug-pcb results: [] ---  # swin-tiny-patch4-window7-224-finetuned-aug-pcb This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5192 - Accuracy: 0.7465 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - 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 | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5945 | 1.0 | 15 | 0.5192 | 0.7465 | | 0.5435 | 2.0 | 30 | 0.4960 | 0.7230 | | 0.5187 | 3.0 | 45 | 0.4820 | 0.7418 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

ngwgsang/bartpho-syllable-large-vimex

ngwgsang

2024-11-06T10:51:51Z

104

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-06T10:50:54Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

avinasht/gemma-2b-it-QA-Tuned

avinasht

2024-11-06T10:49:30Z

5

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-27T18:30:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF

mradermacher

2024-11-06T10:48:15Z

106

0

transformers

[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:practical-dreamer/RPGPT_PublicDomain-alpaca", "dataset:shuyuej/metamath_gsm8k", "dataset:NeuralNovel/Neural-DPO", "base_model:NeuralNovel/Senzu-7B-v0.1-DPO", "base_model:quantized:NeuralNovel/Senzu-7B-v0.1-DPO", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-06T09:37:20Z

--- base_model: NeuralNovel/Senzu-7B-v0.1-DPO datasets: - practical-dreamer/RPGPT_PublicDomain-alpaca - shuyuej/metamath_gsm8k - NeuralNovel/Neural-DPO language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer --- ## About      weighted/imatrix quants of https://huggingface.co/NeuralNovel/Senzu-7B-v0.1-DPO  static quants are available at https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Senzu-7B-v0.1-DPO-i1-GGUF/resolve/main/Senzu-7B-v0.1-DPO.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

kaichin/finetuning-sentiment-model-3000-samples

kaichin

2024-11-06T10:45:32Z

106

0

transformers

[ "transformers", "tensorboard", "safetensors", "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

2024-11-06T10:30:35Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3121 - Accuracy: 0.88 - F1: 0.8831 ## 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: 2 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1

RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf

RichardErkhov

2024-11-06T10:38:24Z

23

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T06:32:55Z

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deep-solar-Rev-v3.0.4 - GGUF - Model creator: https://huggingface.co/Deepnoid/ - Original model: https://huggingface.co/Deepnoid/deep-solar-Rev-v3.0.4/ | Name | Quant method | Size | | ---- | ---- | ---- | | [deep-solar-Rev-v3.0.4.Q2_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q2_K.gguf) | Q2_K | 3.73GB | | [deep-solar-Rev-v3.0.4.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_S.gguf) | Q3_K_S | 4.34GB | | [deep-solar-Rev-v3.0.4.Q3_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K.gguf) | Q3_K | 4.84GB | | [deep-solar-Rev-v3.0.4.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_M.gguf) | Q3_K_M | 4.84GB | | [deep-solar-Rev-v3.0.4.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q3_K_L.gguf) | Q3_K_L | 5.26GB | | [deep-solar-Rev-v3.0.4.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.IQ4_XS.gguf) | IQ4_XS | 5.43GB | | [deep-solar-Rev-v3.0.4.Q4_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_0.gguf) | Q4_0 | 5.66GB | | [deep-solar-Rev-v3.0.4.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.IQ4_NL.gguf) | IQ4_NL | 5.72GB | | [deep-solar-Rev-v3.0.4.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K_S.gguf) | Q4_K_S | 5.7GB | | [deep-solar-Rev-v3.0.4.Q4_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K.gguf) | Q4_K | 6.02GB | | [deep-solar-Rev-v3.0.4.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_K_M.gguf) | Q4_K_M | 6.02GB | | [deep-solar-Rev-v3.0.4.Q4_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q4_1.gguf) | Q4_1 | 6.27GB | | [deep-solar-Rev-v3.0.4.Q5_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_0.gguf) | Q5_0 | 6.89GB | | [deep-solar-Rev-v3.0.4.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K_S.gguf) | Q5_K_S | 6.89GB | | [deep-solar-Rev-v3.0.4.Q5_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K.gguf) | Q5_K | 7.08GB | | [deep-solar-Rev-v3.0.4.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_K_M.gguf) | Q5_K_M | 7.08GB | | [deep-solar-Rev-v3.0.4.Q5_1.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q5_1.gguf) | Q5_1 | 7.51GB | | [deep-solar-Rev-v3.0.4.Q6_K.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q6_K.gguf) | Q6_K | 8.2GB | | [deep-solar-Rev-v3.0.4.Q8_0.gguf](https://huggingface.co/RichardErkhov/Deepnoid_-_deep-solar-Rev-v3.0.4-gguf/blob/main/deep-solar-Rev-v3.0.4.Q8_0.gguf) | Q8_0 | 10.62GB | Original model description: --- license: apache-2.0 ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

cuongdev/tonghop-v3-5000

cuongdev

2024-11-06T10:36:54Z

29

0

diffusers

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

text-to-image

2024-11-06T10:32:57Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tonghop-v3-5000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:

nishhaaann/dbTuneDemo

nishhaaann

2024-11-06T10:33:25Z

163

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-06T06:28:26Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [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]

neopolita/toolace-8b-gguf

neopolita

2024-11-06T10:31:04Z

18

0

null

[ "gguf", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-06T09:58:44Z

--- {} --- # GGUF quants for [**Team-ACE/ToolACE-8B**](https://huggingface.co/Team-ACE/ToolACE-8B) using [llama.cpp](https://github.com/ggerganov/llama.cpp) **Terms of Use**: Please check the [**original model**](https://huggingface.co/Team-ACE/ToolACE-8B) <picture> <img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png"> </picture> ## Quants * `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors. * `q3_k_s`: Uses Q3_K for all tensors * `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K * `q4_0`: Original quant method, 4-bit. * `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. * `q4_k_s`: Uses Q4_K for all tensors * `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K * `q5_0`: Higher accuracy, higher resource usage and slower inference. * `q5_1`: Even higher accuracy, resource usage and slower inference. * `q5_k_s`: Uses Q5_K for all tensors * `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K * `q6_k`: Uses Q8_K for all tensors * `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.