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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-05 12:28:30	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 539 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-05 12:28:13	card stringlengths 11 1.01M
Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-8-hook_resid_post-l1-0.0001	Prisma-Multimodal	2024-11-01T16:23:15Z	22	0	torch	[ "torch", "clip", "vision", "transformers", "interpretability", "sparse autoencoder", "sae", "mechanistic interpretability", "feature-extraction", "en", "license:apache-2.0", "region:us" ]	feature-extraction	2024-11-01T16:23:04Z	--- language: en tags: - clip - vision - transformers - interpretability - sparse autoencoder - sae - mechanistic interpretability license: apache-2.0 library_name: torch pipeline_tag: feature-extraction metrics: - type: explained_variance value: 77.9 pretty_name: Explained Variance % range: min: 0 max: 100 - type: l0 value: 156.154 pretty_name: L0 --- # CLIP-B-32 Sparse Autoencoder x64 vanilla - L1:0.0001 ![Explained Variance](https://img.shields.io/badge/Explained%20Variance-77.9%25-blue) ![Sparsity](https://img.shields.io/badge/Active%20Features-15615.4%-green) ### Training Details - Base Model: CLIP-ViT-B-32 (LAION DataComp.XL-s13B-b90K) - Layer: 8 - Component: hook_resid_post ### Model Architecture - Input Dimension: 768 - SAE Dimension: 49,152 - Expansion Factor: x64 (vanilla architecture) - Activation Function: ReLU - Initialization: encoder_transpose_decoder - Context Size: 50 tokens ### Performance Metrics - L1 Coefficient: 0.0001 - L0 Sparsity: 156.1541 - Explained Variance: 0.7787 (77.87%) ### Training Configuration - Learning Rate: 0.0004 - LR Scheduler: Cosine Annealing with Warmup (200 steps) - Epochs: 10 - Gradient Clipping: 1.0 - Device: NVIDIA Quadro RTX 8000 Experiment Tracking: - Weights & Biases Run ID: aoa9e6a9 - Full experiment details: https://wandb.ai/perceptual-alignment/clip/runs/aoa9e6a9/overview - Git Commit: e22dd02726b74a054a779a4805b96059d83244aa ## Citation ```bibtex @misc{2024josephsparseautoencoders, title={Sparse Autoencoders for CLIP-ViT-B-32}, author={Joseph, Sonia}, year={2024}, publisher={Prisma-Multimodal}, url={https://huggingface.co/Prisma-Multimodal}, note={Layer 8, hook_resid_post, Run ID: aoa9e6a9} }
sulaimank/wav2vec-xlsr-cv-grain-lg_grn_only_v2	sulaimank	2024-11-01T16:20:25Z	17	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-xls-r-300m", "base_model:finetune:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-01T04:58:01Z	--- library_name: transformers license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec-xlsr-cv-grain-lg_grn_only_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec-xlsr-cv-grain-lg_grn_only_v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Wer: 0.0276 - Cer: 0.0085 ## 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: 0.0003 - train_batch_size: 24 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 48 - 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: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\| \| 6.8998 \| 0.9984 \| 321 \| 2.7793 \| 1.0 \| 0.8727 \| \| 3.2905 \| 2.0 \| 643 \| 0.8365 \| 0.9015 \| 0.2478 \| \| 1.26 \| 2.9984 \| 964 \| 0.3066 \| 0.4268 \| 0.0856 \| \| 0.6344 \| 4.0 \| 1286 \| 0.1856 \| 0.2137 \| 0.0451 \| \| 0.4164 \| 4.9984 \| 1607 \| 0.1513 \| 0.1649 \| 0.0364 \| \| 0.3006 \| 6.0 \| 1929 \| 0.1271 \| 0.1274 \| 0.0285 \| \| 0.2414 \| 6.9984 \| 2250 \| 0.1111 \| 0.1083 \| 0.0251 \| \| 0.2035 \| 8.0 \| 2572 \| 0.1076 \| 0.0992 \| 0.0228 \| \| 0.169 \| 8.9984 \| 2893 \| 0.1076 \| 0.0931 \| 0.0213 \| \| 0.1501 \| 10.0 \| 3215 \| 0.1007 \| 0.0920 \| 0.0213 \| \| 0.1291 \| 10.9984 \| 3536 \| 0.0892 \| 0.0772 \| 0.0185 \| \| 0.1122 \| 12.0 \| 3858 \| 0.0917 \| 0.0746 \| 0.0180 \| \| 0.1053 \| 12.9984 \| 4179 \| 0.0903 \| 0.0707 \| 0.0173 \| \| 0.0972 \| 14.0 \| 4501 \| 0.0863 \| 0.0673 \| 0.0164 \| \| 0.0847 \| 14.9984 \| 4822 \| 0.0849 \| 0.0616 \| 0.0157 \| \| 0.0754 \| 16.0 \| 5144 \| 0.0870 \| 0.0657 \| 0.0158 \| \| 0.0751 \| 16.9984 \| 5465 \| 0.0830 \| 0.0610 \| 0.0154 \| \| 0.0722 \| 18.0 \| 5787 \| 0.0922 \| 0.0621 \| 0.0159 \| \| 0.0665 \| 18.9984 \| 6108 \| 0.0784 \| 0.0601 \| 0.0153 \| \| 0.0634 \| 20.0 \| 6430 \| 0.0856 \| 0.0545 \| 0.0146 \| \| 0.0601 \| 20.9984 \| 6751 \| 0.0881 \| 0.0584 \| 0.0151 \| \| 0.0545 \| 22.0 \| 7073 \| 0.0876 \| 0.0558 \| 0.0144 \| \| 0.0503 \| 22.9984 \| 7394 \| 0.0815 \| 0.0523 \| 0.0137 \| \| 0.0511 \| 24.0 \| 7716 \| 0.0842 \| 0.0521 \| 0.0140 \| \| 0.0477 \| 24.9984 \| 8037 \| 0.0808 \| 0.0532 \| 0.0151 \| \| 0.0433 \| 26.0 \| 8359 \| 0.0770 \| 0.0482 \| 0.0125 \| \| 0.0441 \| 26.9984 \| 8680 \| 0.0803 \| 0.0510 \| 0.0137 \| \| 0.0424 \| 28.0 \| 9002 \| 0.0771 \| 0.0460 \| 0.0123 \| \| 0.0373 \| 28.9984 \| 9323 \| 0.0727 \| 0.0462 \| 0.0122 \| \| 0.0376 \| 30.0 \| 9645 \| 0.0768 \| 0.0525 \| 0.0134 \| \| 0.0325 \| 30.9984 \| 9966 \| 0.0801 \| 0.0508 \| 0.0134 \| \| 0.0371 \| 32.0 \| 10288 \| 0.0714 \| 0.0445 \| 0.0118 \| \| 0.0339 \| 32.9984 \| 10609 \| 0.0738 \| 0.0458 \| 0.0122 \| \| 0.0329 \| 34.0 \| 10931 \| 0.0672 \| 0.0388 \| 0.0104 \| \| 0.0294 \| 34.9984 \| 11252 \| 0.0750 \| 0.0408 \| 0.0113 \| \| 0.0322 \| 36.0 \| 11574 \| 0.0768 \| 0.0423 \| 0.0117 \| \| 0.028 \| 36.9984 \| 11895 \| 0.0735 \| 0.0386 \| 0.0117 \| \| 0.0279 \| 38.0 \| 12217 \| 0.0756 \| 0.0414 \| 0.0122 \| \| 0.0259 \| 38.9984 \| 12538 \| 0.0842 \| 0.0495 \| 0.0135 \| \| 0.0273 \| 40.0 \| 12860 \| 0.0775 \| 0.0456 \| 0.0131 \| \| 0.026 \| 40.9984 \| 13181 \| 0.0729 \| 0.0427 \| 0.0119 \| \| 0.0247 \| 42.0 \| 13503 \| 0.0728 \| 0.0410 \| 0.0115 \| \| 0.0247 \| 42.9984 \| 13824 \| 0.0709 \| 0.0430 \| 0.0118 \| \| 0.023 \| 44.0 \| 14146 \| 0.0632 \| 0.0362 \| 0.0101 \| \| 0.0206 \| 44.9984 \| 14467 \| 0.0675 \| 0.0347 \| 0.0106 \| \| 0.0203 \| 46.0 \| 14789 \| 0.0750 \| 0.0419 \| 0.0125 \| \| 0.0215 \| 46.9984 \| 15110 \| 0.0644 \| 0.0358 \| 0.0104 \| \| 0.0172 \| 48.0 \| 15432 \| 0.0693 \| 0.0332 \| 0.0098 \| \| 0.0191 \| 48.9984 \| 15753 \| 0.0694 \| 0.0341 \| 0.0102 \| \| 0.0175 \| 50.0 \| 16075 \| 0.0716 \| 0.0369 \| 0.0108 \| \| 0.018 \| 50.9984 \| 16396 \| 0.0635 \| 0.0351 \| 0.0101 \| \| 0.0162 \| 52.0 \| 16718 \| 0.0711 \| 0.0382 \| 0.0106 \| \| 0.0167 \| 52.9984 \| 17039 \| 0.0605 \| 0.0343 \| 0.0097 \| \| 0.0173 \| 54.0 \| 17361 \| 0.0699 \| 0.0321 \| 0.0097 \| \| 0.0157 \| 54.9984 \| 17682 \| 0.0726 \| 0.0330 \| 0.0100 \| \| 0.0128 \| 56.0 \| 18004 \| 0.0693 \| 0.0323 \| 0.0096 \| \| 0.0169 \| 56.9984 \| 18325 \| 0.0602 \| 0.0306 \| 0.0092 \| \| 0.014 \| 58.0 \| 18647 \| 0.0638 \| 0.0332 \| 0.0097 \| \| 0.0133 \| 58.9984 \| 18968 \| 0.0630 \| 0.0325 \| 0.0097 \| \| 0.0151 \| 60.0 \| 19290 \| 0.0645 \| 0.0328 \| 0.0098 \| \| 0.0137 \| 60.9984 \| 19611 \| 0.0642 \| 0.0351 \| 0.0098 \| \| 0.0135 \| 62.0 \| 19933 \| 0.0569 \| 0.0284 \| 0.0084 \| \| 0.0119 \| 62.9984 \| 20254 \| 0.0595 \| 0.0308 \| 0.0088 \| \| 0.011 \| 64.0 \| 20576 \| 0.0601 \| 0.0263 \| 0.0086 \| \| 0.0113 \| 64.9984 \| 20897 \| 0.0639 \| 0.0282 \| 0.0090 \| \| 0.0125 \| 66.0 \| 21219 \| 0.0588 \| 0.0291 \| 0.0090 \| \| 0.0103 \| 66.9984 \| 21540 \| 0.0632 \| 0.0289 \| 0.0090 \| \| 0.0094 \| 68.0 \| 21862 \| 0.0600 \| 0.0282 \| 0.0087 \| \| 0.0098 \| 68.9984 \| 22183 \| 0.0615 \| 0.0278 \| 0.0085 \| \| 0.0089 \| 70.0 \| 22505 \| 0.0598 \| 0.0278 \| 0.0084 \| \| 0.0105 \| 70.9984 \| 22826 \| 0.0611 \| 0.0291 \| 0.0081 \| \| 0.0083 \| 72.0 \| 23148 \| 0.0623 \| 0.0293 \| 0.0084 \| \| 0.0092 \| 72.9984 \| 23469 \| 0.0590 \| 0.0302 \| 0.0090 \| \| 0.0068 \| 74.0 \| 23791 \| 0.0604 \| 0.0276 \| 0.0085 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.1.0+cu118 - Datasets 3.1.0 - Tokenizers 0.20.1
LocalDoc/LaBSE-small-AZ	LocalDoc	2024-11-01T16:14:17Z	22	0	null	[ "safetensors", "bert", "sentence-similarity", "en", "az", "base_model:sentence-transformers/LaBSE", "base_model:finetune:sentence-transformers/LaBSE", "doi:10.57967/hf/3417", "license:apache-2.0", "region:us" ]	sentence-similarity	2024-11-01T15:41:06Z	--- license: apache-2.0 language: - en - az base_model: - sentence-transformers/LaBSE pipeline_tag: sentence-similarity --- # Small LaBSE for English-Azerbaijani This is an optimized version of [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) # Benchmark \| STSBenchmark \| biosses-sts \| sickr-sts \| sts12-sts \| sts13-sts \| sts15-sts \| sts16-sts \| Average Pearson \| Model \| \|--------------\|-------------\|-----------\|-----------\|-----------\|-----------\|-----------\|-----------------\|--------------------------------------\| \| 0.7363 \| 0.8148 \| 0.7067 \| 0.7050 \| 0.6535 \| 0.7514 \| 0.7070 \| 0.7250 \| sentence-transformers/LaBSE \| \| 0.7400 \| 0.8216 \| 0.6946 \| 0.7098 \| 0.6781 \| 0.7637 \| 0.7222 \| 0.7329 \| LocalDoc/LaBSE-small-AZ \| \| 0.5830 \| 0.2486 \| 0.5921 \| 0.5593 \| 0.5559 \| 0.5404 \| 0.5289 \| 0.5155 \| antoinelouis/colbert-xm \| \| 0.7572 \| 0.8139 \| 0.7328 \| 0.7646 \| 0.6318 \| 0.7542 \| 0.7092 \| 0.7377 \| intfloat/multilingual-e5-large-instruct \| \| 0.7485 \| 0.7714 \| 0.7271 \| 0.7170 \| 0.6496 \| 0.7570 \| 0.7255 \| 0.7280 \| intfloat/multilingual-e5-large \| \| 0.6960 \| 0.8185 \| 0.6950 \| 0.6752 \| 0.5899 \| 0.7186 \| 0.6790 \| 0.6960 \| intfloat/multilingual-e5-base \| \| 0.7376 \| 0.7917 \| 0.7190 \| 0.7441 \| 0.6286 \| 0.7461 \| 0.7026 \| 0.7242 \| intfloat/multilingual-e5-small \| \| 0.7927 \| 0.6672 \| 0.7758 \| 0.8122 \| 0.7312 \| 0.7831 \| 0.7416 \| 0.7577 \| BAAI/bge-m3 \| [STS-Benchmark](https://github.com/LocalDoc-Azerbaijan/STS-Benchmark) ## How to Use ```python from transformers import AutoTokenizer, AutoModel import torch # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("LocalDoc/LaBSE-small-AZ") model = AutoModel.from_pretrained("LocalDoc/LaBSE-small-AZ") # Prepare texts texts = [ "Hello world", "Salam dünya" ] # Tokenize and generate embeddings encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): embeddings = model(**encoded).pooler_output # Compute similarity similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0) ```
RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf	RichardErkhov	2024-11-01T16:11:57Z	20	0	null	[ "gguf", "arxiv:2101.00027", "arxiv:2201.07311", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:31:15Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pythia-2.8b-v0 - GGUF - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/pythia-2.8b-v0/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [pythia-2.8b-v0.Q2_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q2_K.gguf) \| Q2_K \| 1.01GB \| \| [pythia-2.8b-v0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q3_K_S.gguf) \| Q3_K_S \| 1.16GB \| \| [pythia-2.8b-v0.Q3_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q3_K.gguf) \| Q3_K \| 1.38GB \| \| [pythia-2.8b-v0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q3_K_M.gguf) \| Q3_K_M \| 1.38GB \| \| [pythia-2.8b-v0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q3_K_L.gguf) \| Q3_K_L \| 1.49GB \| \| [pythia-2.8b-v0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.IQ4_XS.gguf) \| IQ4_XS \| 1.43GB \| \| [pythia-2.8b-v0.Q4_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q4_0.gguf) \| Q4_0 \| 1.49GB \| \| [pythia-2.8b-v0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.IQ4_NL.gguf) \| IQ4_NL \| 1.5GB \| \| [pythia-2.8b-v0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q4_K_S.gguf) \| Q4_K_S \| 1.5GB \| \| [pythia-2.8b-v0.Q4_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q4_K.gguf) \| Q4_K \| 1.66GB \| \| [pythia-2.8b-v0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q4_K_M.gguf) \| Q4_K_M \| 1.66GB \| \| [pythia-2.8b-v0.Q4_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q4_1.gguf) \| Q4_1 \| 1.64GB \| \| [pythia-2.8b-v0.Q5_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q5_0.gguf) \| Q5_0 \| 1.8GB \| \| [pythia-2.8b-v0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q5_K_S.gguf) \| Q5_K_S \| 1.8GB \| \| [pythia-2.8b-v0.Q5_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q5_K.gguf) \| Q5_K \| 1.93GB \| \| [pythia-2.8b-v0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q5_K_M.gguf) \| Q5_K_M \| 1.93GB \| \| [pythia-2.8b-v0.Q5_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q5_1.gguf) \| Q5_1 \| 1.95GB \| \| [pythia-2.8b-v0.Q6_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q6_K.gguf) \| Q6_K \| 2.13GB \| \| [pythia-2.8b-v0.Q8_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-v0-gguf/blob/main/pythia-2.8b-v0.Q8_0.gguf) \| Q8_0 \| 2.75GB \| Original model description: --- language: - en tags: - pytorch - causal-lm - pythia - pythia_v0 license: apache-2.0 datasets: - the_pile --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research. It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. All Pythia models are available [on Hugging Face](https://huggingface.co/models?other=pythia). The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. ## Pythia-2.8B ### Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:contact@eleuther.ai). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 4M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 4M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 4M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ### Uses and Limitations #### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. To enable the study of how language models change over the course of training, we provide 143 evenly spaced intermediate checkpoints per model. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-2.8B for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-2.8B as a basis for your fine-tuned model, please conduct your own risk and bias assessment. #### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-2.8B will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “understand” human instructions. #### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token deemed statistically most likely by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-2.8B may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-2.8B. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ### Training #### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/).<br> The Pile was not** deduplicated before being used to train Pythia-2.8B. #### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia models trained for the equivalent of 143000 steps at a batch size of 2,097,152 tokens. Two batch sizes were used: 2M and 4M. Models with a batch size of 4M tokens listed were originally trained for 71500 steps instead, with checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for consistency with all 2M batch models, so `step1000` is the first checkpoint for `pythia-1.4b` that was saved (corresponding to step 500 in training), and `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved (corresponding to 1000 “actual” steps).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ### Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Challenge Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/> </details> ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
Haesteining/Phi3smallv6	Haesteining	2024-11-01T16:10:37Z	39	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T16:05:14Z	--- 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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Daniyal100/adefkfe	Daniyal100	2024-11-01T16:10:36Z	7	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	2024-11-01T15:26:37Z	--- 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: MARIAPIC --- # Adefkfe <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `MARIAPIC` 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('Daniyal100/adefkfe', 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)
bb1070/barcelona_wf	bb1070	2024-11-01T16:08:40Z	5	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	2024-11-01T16:08:37Z	--- 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: TOK --- # Barcelona_Wf <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('bb1070/barcelona_wf', 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)
PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-GGUF	PJMixers-Dev	2024-11-01T16:07:02Z	15	0	null	[ "gguf", "en", "dataset:PJMixers-Dev/HailMary-v0.1-KTO", "base_model:PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B", "base_model:quantized:PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B", "license:llama3.2", "model-index", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T00:47:18Z	--- license: llama3.2 language: - en datasets: - PJMixers-Dev/HailMary-v0.1-KTO base_model: - PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B model-index: - name: PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 65.04 name: strict accuracy source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 22.29 name: normalized accuracy source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 11.78 name: exact match source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 2.91 name: acc_norm source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 4.69 name: acc_norm source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 23.42 name: accuracy source: url: https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B-details name: Open LLM Leaderboard --- [PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B) was further trained using KTO (with `apo_zero_unpaired` loss type) using a mix of instruct, RP, and storygen datasets. I created rejected samples by using the SFT with bad settings (including logit bias) for every model turn. The model was only trained at `max_length=6144`, and is nowhere near a full epoch as it eventually crashed. So think of this like a test of a test. # W&B Training Logs ![train/rewards/chosen/rejected](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_rewards_chosen_rejected.png) ![train/rewards/margins](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_rewards_margins.png) ![train/logits/chosen/rejected](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_logits_chosen_rejected.png) ![train/logps/chosen/rejected](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_logps_chosen_rejected.png) ![train/loss](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_loss.png) ![train/grad_norm](https://huggingface.co/PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.2-SFT-HailMary-v0.1-KTO-3B/resolve/main/images/train_grad_norm.png) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/PJMixers-Dev__LLaMa-3.2-Instruct-JankMix-v0.2-SFT-3B-details) \| Metric \|Value\| \|-------------------\|----:\| \|Avg. \|21.69\| \|IFEval (0-Shot) \|65.04\| \|BBH (3-Shot) \|22.29\| \|MATH Lvl 5 (4-Shot)\|11.78\| \|GPQA (0-shot) \| 2.91\| \|MuSR (0-shot) \| 4.69\| \|MMLU-PRO (5-shot) \|23.42\|
RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf	RichardErkhov	2024-11-01T16:04:43Z	20	0	null	[ "gguf", "arxiv:2304.01373", "arxiv:2101.00027", "arxiv:2201.07311", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:21:26Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) pythia-2.8b-deduped - GGUF - Model creator: https://huggingface.co/EleutherAI/ - Original model: https://huggingface.co/EleutherAI/pythia-2.8b-deduped/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [pythia-2.8b-deduped.Q2_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q2_K.gguf) \| Q2_K \| 1.01GB \| \| [pythia-2.8b-deduped.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q3_K_S.gguf) \| Q3_K_S \| 1.16GB \| \| [pythia-2.8b-deduped.Q3_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q3_K.gguf) \| Q3_K \| 1.38GB \| \| [pythia-2.8b-deduped.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q3_K_M.gguf) \| Q3_K_M \| 1.38GB \| \| [pythia-2.8b-deduped.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q3_K_L.gguf) \| Q3_K_L \| 1.49GB \| \| [pythia-2.8b-deduped.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.IQ4_XS.gguf) \| IQ4_XS \| 1.43GB \| \| [pythia-2.8b-deduped.Q4_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q4_0.gguf) \| Q4_0 \| 1.49GB \| \| [pythia-2.8b-deduped.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.IQ4_NL.gguf) \| IQ4_NL \| 1.5GB \| \| [pythia-2.8b-deduped.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q4_K_S.gguf) \| Q4_K_S \| 1.5GB \| \| [pythia-2.8b-deduped.Q4_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q4_K.gguf) \| Q4_K \| 1.66GB \| \| [pythia-2.8b-deduped.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q4_K_M.gguf) \| Q4_K_M \| 1.66GB \| \| [pythia-2.8b-deduped.Q4_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q4_1.gguf) \| Q4_1 \| 1.64GB \| \| [pythia-2.8b-deduped.Q5_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q5_0.gguf) \| Q5_0 \| 1.8GB \| \| [pythia-2.8b-deduped.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q5_K_S.gguf) \| Q5_K_S \| 1.8GB \| \| [pythia-2.8b-deduped.Q5_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q5_K.gguf) \| Q5_K \| 1.93GB \| \| [pythia-2.8b-deduped.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q5_K_M.gguf) \| Q5_K_M \| 1.93GB \| \| [pythia-2.8b-deduped.Q5_1.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q5_1.gguf) \| Q5_1 \| 1.95GB \| \| [pythia-2.8b-deduped.Q6_K.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q6_K.gguf) \| Q6_K \| 2.13GB \| \| [pythia-2.8b-deduped.Q8_0.gguf](https://huggingface.co/RichardErkhov/EleutherAI_-_pythia-2.8b-deduped-gguf/blob/main/pythia-2.8b-deduped.Q8_0.gguf) \| Q8_0 \| 2.75GB \| Original model description: --- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - EleutherAI/the_pile_deduplicated --- The Pythia Scaling Suite is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> This is the current release. Please note that all models in the Pythia suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-2.8B-deduped ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing Pythia documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:contact@eleuther.ai). <figure> \| Pythia model \| Non-Embedding Params \| Layers \| Model Dim \| Heads \| Batch Size \| Learning Rate \| Equivalent Models \| \| -----------: \| -------------------: \| :----: \| :-------: \| :---: \| :--------: \| :-------------------: \| :--------------------: \| \| 70M \| 18,915,328 \| 6 \| 512 \| 8 \| 2M \| 1.0 x 10<sup>-3</sup> \| — \| \| 160M \| 85,056,000 \| 12 \| 768 \| 12 \| 2M \| 6.0 x 10<sup>-4</sup> \| GPT-Neo 125M, OPT-125M \| \| 410M \| 302,311,424 \| 24 \| 1024 \| 16 \| 2M \| 3.0 x 10<sup>-4</sup> \| OPT-350M \| \| 1.0B \| 805,736,448 \| 16 \| 2048 \| 8 \| 2M \| 3.0 x 10<sup>-4</sup> \| — \| \| 1.4B \| 1,208,602,624 \| 24 \| 2048 \| 16 \| 2M \| 2.0 x 10<sup>-4</sup> \| GPT-Neo 1.3B, OPT-1.3B \| \| 2.8B \| 2,517,652,480 \| 32 \| 2560 \| 32 \| 2M \| 1.6 x 10<sup>-4</sup> \| GPT-Neo 2.7B, OPT-2.7B \| \| 6.9B \| 6,444,163,072 \| 32 \| 4096 \| 32 \| 2M \| 1.2 x 10<sup>-4</sup> \| OPT-6.7B \| \| 12B \| 11,327,027,200 \| 36 \| 5120 \| 40 \| 2M \| 1.2 x 10<sup>-4</sup> \| — \| <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-2.8B-deduped for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-2.8B-deduped as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is not intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-2.8B-deduped has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-2.8B-deduped will not respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-2.8B-deduped to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-2.8B-deduped may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-2.8B-deduped. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data Pythia-2.8B-deduped was trained on the Pile after the dataset has been globally deduplicated*.<br> [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/). ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 Pythia models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> \| current Pythia suffix \| old suffix \| total params \| non-embedding params \| \| --------------------: \| ---------: \| -------------: \| -------------------: \| \| 70M \| 19M \| 70,426,624 \| 18,915,328 \| \| 160M \| 125M \| 162,322,944 \| 85,056,000 \| \| 410M \| 350M \| 405,334,016 \| 302,311,424 \| \| 1B \| 800M \| 1,011,781,632 \| 805,736,448 \| \| 1.4B \| 1.3B \| 1,414,647,808 \| 1,208,602,624 \| \| 2.8B \| 2.7B \| 2,775,208,960 \| 2,517,652,480 \| \| 6.9B \| 6.7B \| 6,857,302,016 \| 6,444,163,072 \| \| 12B \| 13B \| 11,846,072,320 \| 11,327,027,200 \| </figure>
RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf	RichardErkhov	2024-11-01T15:56:13Z	450	0	null	[ "gguf", "arxiv:2410.17215", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T15:37:27Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Pretrain-Qwen-1.2B - GGUF - Model creator: https://huggingface.co/MiniLLM/ - Original model: https://huggingface.co/MiniLLM/Pretrain-Qwen-1.2B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Pretrain-Qwen-1.2B.Q2_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q2_K.gguf) \| Q2_K \| 0.51GB \| \| [Pretrain-Qwen-1.2B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q3_K_S.gguf) \| Q3_K_S \| 0.57GB \| \| [Pretrain-Qwen-1.2B.Q3_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q3_K.gguf) \| Q3_K \| 0.61GB \| \| [Pretrain-Qwen-1.2B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q3_K_M.gguf) \| Q3_K_M \| 0.61GB \| \| [Pretrain-Qwen-1.2B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q3_K_L.gguf) \| Q3_K_L \| 0.63GB \| \| [Pretrain-Qwen-1.2B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.IQ4_XS.gguf) \| IQ4_XS \| 0.65GB \| \| [Pretrain-Qwen-1.2B.Q4_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q4_0.gguf) \| Q4_0 \| 0.67GB \| \| [Pretrain-Qwen-1.2B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.IQ4_NL.gguf) \| IQ4_NL \| 0.67GB \| \| [Pretrain-Qwen-1.2B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q4_K_S.gguf) \| Q4_K_S \| 0.69GB \| \| [Pretrain-Qwen-1.2B.Q4_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q4_K.gguf) \| Q4_K \| 0.72GB \| \| [Pretrain-Qwen-1.2B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q4_K_M.gguf) \| Q4_K_M \| 0.72GB \| \| [Pretrain-Qwen-1.2B.Q4_1.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q4_1.gguf) \| Q4_1 \| 0.72GB \| \| [Pretrain-Qwen-1.2B.Q5_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q5_0.gguf) \| Q5_0 \| 0.78GB \| \| [Pretrain-Qwen-1.2B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q5_K_S.gguf) \| Q5_K_S \| 0.79GB \| \| [Pretrain-Qwen-1.2B.Q5_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q5_K.gguf) \| Q5_K \| 0.81GB \| \| [Pretrain-Qwen-1.2B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q5_K_M.gguf) \| Q5_K_M \| 0.81GB \| \| [Pretrain-Qwen-1.2B.Q5_1.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q5_1.gguf) \| Q5_1 \| 0.83GB \| \| [Pretrain-Qwen-1.2B.Q6_K.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q6_K.gguf) \| Q6_K \| 0.93GB \| \| [Pretrain-Qwen-1.2B.Q8_0.gguf](https://huggingface.co/RichardErkhov/MiniLLM_-_Pretrain-Qwen-1.2B-gguf/blob/main/Pretrain-Qwen-1.2B.Q8_0.gguf) \| Q8_0 \| 1.15GB \| Original model description: --- library_name: transformers license: apache-2.0 datasets: - monology/pile-uncopyrighted - MiniLLM/pile-tokenized language: - en metrics: - accuracy pipeline_tag: text-generation --- # Pretrain-Qwen-1.2B [paper](https://arxiv.org/abs/2410.17215) \| [code](https://github.com/thu-coai/MiniPLM) Pretrain-Qwen-1.2B is a 1.2B model with Qwen achitecture conventionally pre-trained from scratch on [the Pile](https://huggingface.co/datasets/monology/pile-uncopyrighted) for 50B tokens. We also open-source the tokenized [pre-training corpus](https://huggingface.co/datasets/MiniLLM/pile-tokenized) for reproducibility. It is used as the baseline for [MiniLLM-Qwen-1.2B](https://huggingface.co/MiniLLM/MiniPLM-Qwen-1.2B) ## Evaluation MiniPLM models achieves better performance given the same computation and scales well across model sizes: <p align='left'> <img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/EOYzajQcwQFT5PobqL3j0.png" width="1000"> </p> ## Other Baselines + [VanillaKD](https://huggingface.co/MiniLLM/VanillaKD-Pretrain-Qwen-1.2B) ## Citation ```bibtext @article{miniplm, title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang}, journal={arXiv preprint arXiv:2410.17215}, year={2024} } ```
mlfoundations-dev/OH_DCFT_V3_wo_platypus	mlfoundations-dev	2024-11-01T15:54:44Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-30T21:32:27Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: OH_DCFT_V3_wo_platypus 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. --> # OH_DCFT_V3_wo_platypus This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the mlfoundations-dev/OH_DCFT_V3_wo_platypus dataset. It achieves the following results on the evaluation set: - Loss: 0.6428 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6557 \| 0.9988 \| 410 \| 0.6519 \| \| 0.6082 \| 2.0 \| 821 \| 0.6420 \| \| 0.5706 \| 2.9963 \| 1230 \| 0.6428 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.3.0 - Datasets 2.21.0 - Tokenizers 0.20.1
RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf	RichardErkhov	2024-11-01T15:47:02Z	9	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T15:06:22Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) vinallama-2.7b-chat-orpo-v2 - GGUF - Model creator: https://huggingface.co/d-llm/ - Original model: https://huggingface.co/d-llm/vinallama-2.7b-chat-orpo-v2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [vinallama-2.7b-chat-orpo-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q2_K.gguf) \| Q2_K \| 1.0GB \| \| [vinallama-2.7b-chat-orpo-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q3_K_S.gguf) \| Q3_K_S \| 1.16GB \| \| [vinallama-2.7b-chat-orpo-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q3_K.gguf) \| Q3_K \| 1.28GB \| \| [vinallama-2.7b-chat-orpo-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q3_K_M.gguf) \| Q3_K_M \| 1.28GB \| \| [vinallama-2.7b-chat-orpo-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q3_K_L.gguf) \| Q3_K_L \| 1.39GB \| \| [vinallama-2.7b-chat-orpo-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.IQ4_XS.gguf) \| IQ4_XS \| 1.42GB \| \| [vinallama-2.7b-chat-orpo-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q4_0.gguf) \| Q4_0 \| 1.48GB \| \| [vinallama-2.7b-chat-orpo-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.IQ4_NL.gguf) \| IQ4_NL \| 1.49GB \| \| [vinallama-2.7b-chat-orpo-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q4_K_S.gguf) \| Q4_K_S \| 1.49GB \| \| [vinallama-2.7b-chat-orpo-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q4_K.gguf) \| Q4_K \| 1.58GB \| \| [vinallama-2.7b-chat-orpo-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q4_K_M.gguf) \| Q4_K_M \| 1.58GB \| \| [vinallama-2.7b-chat-orpo-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q4_1.gguf) \| Q4_1 \| 1.64GB \| \| [vinallama-2.7b-chat-orpo-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q5_0.gguf) \| Q5_0 \| 1.79GB \| \| [vinallama-2.7b-chat-orpo-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q5_K_S.gguf) \| Q5_K_S \| 1.79GB \| \| [vinallama-2.7b-chat-orpo-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q5_K.gguf) \| Q5_K \| 1.84GB \| \| [vinallama-2.7b-chat-orpo-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q5_K_M.gguf) \| Q5_K_M \| 1.84GB \| \| [vinallama-2.7b-chat-orpo-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q5_1.gguf) \| Q5_1 \| 1.95GB \| \| [vinallama-2.7b-chat-orpo-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q6_K.gguf) \| Q6_K \| 2.12GB \| \| [vinallama-2.7b-chat-orpo-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/d-llm_-_vinallama-2.7b-chat-orpo-v2-gguf/blob/main/vinallama-2.7b-chat-orpo-v2.Q8_0.gguf) \| Q8_0 \| 2.75GB \| Original model description: --- 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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gglabs/Mistral-Nemo-FC-1030-3-epoch	gglabs	2024-11-01T15:45:02Z	7	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T15:24:35Z	--- base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf --- # Uploaded model - Developed by: gglabs - License: apache-2.0 - Finetuned from model : unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit This mistral 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)
RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf	RichardErkhov	2024-11-01T15:38:02Z	6	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:18:05Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old - GGUF - Model creator: https://huggingface.co/martimfasantos/ - Original model: https://huggingface.co/martimfasantos/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q2_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q2_K.gguf) \| Q2_K \| 0.4GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_S.gguf) \| Q3_K_S \| 0.47GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K.gguf) \| Q3_K \| 0.51GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_M.gguf) \| Q3_K_M \| 0.51GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q3_K_L.gguf) \| Q3_K_L \| 0.55GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.IQ4_XS.gguf) \| IQ4_XS \| 0.57GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_0.gguf) \| Q4_0 \| 0.59GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.IQ4_NL.gguf) \| IQ4_NL \| 0.6GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K_S.gguf) \| Q4_K_S \| 0.6GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K.gguf) \| Q4_K \| 0.62GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_K_M.gguf) \| Q4_K_M \| 0.62GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_1.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q4_1.gguf) \| Q4_1 \| 0.65GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_0.gguf) \| Q5_0 \| 0.71GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K_S.gguf) \| Q5_K_S \| 0.71GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K.gguf) \| Q5_K \| 0.73GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_K_M.gguf) \| Q5_K_M \| 0.73GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_1.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q5_1.gguf) \| Q5_1 \| 0.77GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q6_K.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q6_K.gguf) \| Q6_K \| 0.84GB \| \| [tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q8_0.gguf](https://huggingface.co/RichardErkhov/martimfasantos_-_tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old-gguf/blob/main/tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old.Q8_0.gguf) \| Q8_0 \| 1.09GB \| Original model description: --- license: apache-2.0 base_model: martimfasantos/tinyllama-1.1b-sum-sft-full_old tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - openai/summarize_from_feedback model-index: - name: tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old 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. --> # tinyllama-1.1b-sum-dpo-full_LR5e-8_3epochs_old This model is a fine-tuned version of [martimfasantos/tinyllama-1.1b-sum-sft-full_old](https://huggingface.co/martimfasantos/tinyllama-1.1b-sum-sft-full_old) on the openai/summarize_from_feedback dataset. It achieves the following results on the evaluation set: - Loss: 0.6687 - Rewards/chosen: -0.2893 - Rewards/rejected: -0.3487 - Rewards/accuracies: 0.6008 - Rewards/margins: 0.0594 - Logps/rejected: -98.0463 - Logps/chosen: -87.6427 - Logits/rejected: -2.7624 - Logits/chosen: -2.7684 ## 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-08 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6931 \| 0.0172 \| 100 \| 0.6932 \| -0.0000 \| 0.0001 \| 0.4851 \| -0.0001 \| -63.1729 \| -58.7138 \| -3.1573 \| -3.1630 \| \| 0.6931 \| 0.0345 \| 200 \| 0.6932 \| -0.0000 \| 0.0001 \| 0.4730 \| -0.0001 \| -63.1741 \| -58.7133 \| -3.1575 \| -3.1631 \| \| 0.6932 \| 0.0517 \| 300 \| 0.6932 \| 0.0001 \| 0.0001 \| 0.4942 \| -0.0000 \| -63.1702 \| -58.7051 \| -3.1574 \| -3.1631 \| \| 0.6932 \| 0.0689 \| 400 \| 0.6932 \| 0.0001 \| 0.0001 \| 0.4884 \| -0.0001 \| -63.1678 \| -58.7049 \| -3.1574 \| -3.1631 \| \| 0.6931 \| 0.0861 \| 500 \| 0.6932 \| -0.0000 \| 0.0001 \| 0.4737 \| -0.0001 \| -63.1733 \| -58.7135 \| -3.1577 \| -3.1633 \| \| 0.693 \| 0.1034 \| 600 \| 0.6932 \| 0.0001 \| 0.0001 \| 0.4923 \| -0.0000 \| -63.1656 \| -58.7003 \| -3.1575 \| -3.1632 \| \| 0.6932 \| 0.1206 \| 700 \| 0.6931 \| 0.0002 \| 0.0002 \| 0.5100 \| 0.0001 \| -63.1644 \| -58.6897 \| -3.1574 \| -3.1631 \| \| 0.6929 \| 0.1378 \| 800 \| 0.6932 \| 0.0002 \| 0.0003 \| 0.4668 \| -0.0001 \| -63.1484 \| -58.6918 \| -3.1571 \| -3.1627 \| \| 0.6931 \| 0.1551 \| 900 \| 0.6931 \| 0.0003 \| 0.0002 \| 0.5058 \| 0.0000 \| -63.1556 \| -58.6837 \| -3.1569 \| -3.1625 \| \| 0.6931 \| 0.1723 \| 1000 \| 0.6931 \| 0.0004 \| 0.0002 \| 0.5051 \| 0.0001 \| -63.1557 \| -58.6755 \| -3.1567 \| -3.1624 \| \| 0.6929 \| 0.1895 \| 1100 \| 0.6931 \| 0.0005 \| 0.0004 \| 0.5160 \| 0.0001 \| -63.1450 \| -58.6627 \| -3.1565 \| -3.1621 \| \| 0.6927 \| 0.2068 \| 1200 \| 0.6930 \| 0.0007 \| 0.0005 \| 0.5160 \| 0.0002 \| -63.1294 \| -58.6411 \| -3.1560 \| -3.1616 \| \| 0.6929 \| 0.2240 \| 1300 \| 0.6930 \| 0.0009 \| 0.0006 \| 0.5230 \| 0.0003 \| -63.1224 \| -58.6264 \| -3.1548 \| -3.1605 \| \| 0.692 \| 0.2412 \| 1400 \| 0.6929 \| 0.0010 \| 0.0005 \| 0.5407 \| 0.0005 \| -63.1333 \| -58.6153 \| -3.1542 \| -3.1598 \| \| 0.6918 \| 0.2584 \| 1500 \| 0.6929 \| 0.0011 \| 0.0006 \| 0.5351 \| 0.0005 \| -63.1157 \| -58.5976 \| -3.1532 \| -3.1588 \| \| 0.6921 \| 0.2757 \| 1600 \| 0.6928 \| 0.0015 \| 0.0007 \| 0.5611 \| 0.0008 \| -63.1099 \| -58.5639 \| -3.1517 \| -3.1574 \| \| 0.692 \| 0.2929 \| 1700 \| 0.6926 \| 0.0018 \| 0.0008 \| 0.5662 \| 0.0010 \| -63.1046 \| -58.5339 \| -3.1502 \| -3.1558 \| \| 0.6904 \| 0.3101 \| 1800 \| 0.6926 \| 0.0018 \| 0.0007 \| 0.5699 \| 0.0012 \| -63.1148 \| -58.5277 \| -3.1485 \| -3.1542 \| \| 0.691 \| 0.3274 \| 1900 \| 0.6924 \| 0.0018 \| 0.0003 \| 0.5581 \| 0.0015 \| -63.1539 \| -58.5341 \| -3.1473 \| -3.1529 \| \| 0.6909 \| 0.3446 \| 2000 \| 0.6923 \| 0.0020 \| 0.0002 \| 0.5723 \| 0.0018 \| -63.1632 \| -58.5155 \| -3.1452 \| -3.1509 \| \| 0.6903 \| 0.3618 \| 2100 \| 0.6921 \| 0.0019 \| -0.0002 \| 0.5697 \| 0.0021 \| -63.1963 \| -58.5193 \| -3.1434 \| -3.1490 \| \| 0.6884 \| 0.3790 \| 2200 \| 0.6920 \| 0.0018 \| -0.0006 \| 0.5757 \| 0.0024 \| -63.2422 \| -58.5311 \| -3.1407 \| -3.1464 \| \| 0.6876 \| 0.3963 \| 2300 \| 0.6918 \| 0.0015 \| -0.0012 \| 0.5769 \| 0.0027 \| -63.3015 \| -58.5638 \| -3.1381 \| -3.1437 \| \| 0.6898 \| 0.4135 \| 2400 \| 0.6917 \| 0.0012 \| -0.0018 \| 0.5625 \| 0.0030 \| -63.3619 \| -58.5900 \| -3.1348 \| -3.1404 \| \| 0.6905 \| 0.4307 \| 2500 \| 0.6915 \| 0.0007 \| -0.0028 \| 0.5743 \| 0.0035 \| -63.4609 \| -58.6445 \| -3.1321 \| -3.1378 \| \| 0.6864 \| 0.4480 \| 2600 \| 0.6913 \| -0.0001 \| -0.0039 \| 0.5732 \| 0.0038 \| -63.5690 \| -58.7216 \| -3.1295 \| -3.1352 \| \| 0.6866 \| 0.4652 \| 2700 \| 0.6911 \| -0.0014 \| -0.0057 \| 0.5709 \| 0.0043 \| -63.7456 \| -58.8490 \| -3.1270 \| -3.1327 \| \| 0.6869 \| 0.4824 \| 2800 \| 0.6909 \| -0.0025 \| -0.0071 \| 0.5750 \| 0.0046 \| -63.8913 \| -58.9609 \| -3.1248 \| -3.1305 \| \| 0.6888 \| 0.4997 \| 2900 \| 0.6907 \| -0.0042 \| -0.0093 \| 0.5855 \| 0.0051 \| -64.1121 \| -59.1289 \| -3.1214 \| -3.1271 \| \| 0.6885 \| 0.5169 \| 3000 \| 0.6905 \| -0.0061 \| -0.0118 \| 0.5804 \| 0.0057 \| -64.3621 \| -59.3245 \| -3.1180 \| -3.1236 \| \| 0.686 \| 0.5341 \| 3100 \| 0.6904 \| -0.0071 \| -0.0130 \| 0.5857 \| 0.0059 \| -64.4774 \| -59.4209 \| -3.1160 \| -3.1217 \| \| 0.6869 \| 0.5513 \| 3200 \| 0.6902 \| -0.0095 \| -0.0159 \| 0.5878 \| 0.0064 \| -64.7659 \| -59.6584 \| -3.1119 \| -3.1176 \| \| 0.6834 \| 0.5686 \| 3300 \| 0.6900 \| -0.0122 \| -0.0190 \| 0.5809 \| 0.0068 \| -65.0782 \| -59.9308 \| -3.1072 \| -3.1130 \| \| 0.6795 \| 0.5858 \| 3400 \| 0.6897 \| -0.0147 \| -0.0221 \| 0.5881 \| 0.0074 \| -65.3901 \| -60.1840 \| -3.1036 \| -3.1093 \| \| 0.6848 \| 0.6030 \| 3500 \| 0.6895 \| -0.0171 \| -0.0250 \| 0.5897 \| 0.0079 \| -65.6826 \| -60.4227 \| -3.1007 \| -3.1064 \| \| 0.6834 \| 0.6203 \| 3600 \| 0.6893 \| -0.0196 \| -0.0280 \| 0.5857 \| 0.0084 \| -65.9796 \| -60.6710 \| -3.0969 \| -3.1026 \| \| 0.6788 \| 0.6375 \| 3700 \| 0.6890 \| -0.0219 \| -0.0308 \| 0.5813 \| 0.0089 \| -66.2620 \| -60.8999 \| -3.0922 \| -3.0979 \| \| 0.6825 \| 0.6547 \| 3800 \| 0.6888 \| -0.0253 \| -0.0348 \| 0.5904 \| 0.0095 \| -66.6623 \| -61.2404 \| -3.0889 \| -3.0946 \| \| 0.6791 \| 0.6720 \| 3900 \| 0.6885 \| -0.0287 \| -0.0389 \| 0.5943 \| 0.0103 \| -67.0740 \| -61.5806 \| -3.0858 \| -3.0915 \| \| 0.6816 \| 0.6892 \| 4000 \| 0.6881 \| -0.0328 \| -0.0438 \| 0.5897 \| 0.0110 \| -67.5621 \| -61.9903 \| -3.0815 \| -3.0872 \| \| 0.6749 \| 0.7064 \| 4100 \| 0.6879 \| -0.0340 \| -0.0456 \| 0.5901 \| 0.0116 \| -67.7361 \| -62.1084 \| -3.0755 \| -3.0812 \| \| 0.6839 \| 0.7236 \| 4200 \| 0.6877 \| -0.0364 \| -0.0484 \| 0.5964 \| 0.0120 \| -68.0226 \| -62.3546 \| -3.0712 \| -3.0769 \| \| 0.6827 \| 0.7409 \| 4300 \| 0.6876 \| -0.0377 \| -0.0500 \| 0.5897 \| 0.0123 \| -68.1844 \| -62.4844 \| -3.0675 \| -3.0732 \| \| 0.6815 \| 0.7581 \| 4400 \| 0.6873 \| -0.0402 \| -0.0531 \| 0.5950 \| 0.0129 \| -68.4913 \| -62.7319 \| -3.0645 \| -3.0702 \| \| 0.6829 \| 0.7753 \| 4500 \| 0.6870 \| -0.0443 \| -0.0578 \| 0.5939 \| 0.0136 \| -68.9615 \| -63.1372 \| -3.0609 \| -3.0666 \| \| 0.6747 \| 0.7926 \| 4600 \| 0.6868 \| -0.0476 \| -0.0617 \| 0.5915 \| 0.0141 \| -69.3541 \| -63.4724 \| -3.0573 \| -3.0630 \| \| 0.6828 \| 0.8098 \| 4700 \| 0.6864 \| -0.0518 \| -0.0669 \| 0.5936 \| 0.0151 \| -69.8725 \| -63.8948 \| -3.0542 \| -3.0599 \| \| 0.6821 \| 0.8270 \| 4800 \| 0.6861 \| -0.0560 \| -0.0717 \| 0.5939 \| 0.0156 \| -70.3462 \| -64.3141 \| -3.0504 \| -3.0562 \| \| 0.6767 \| 0.8442 \| 4900 \| 0.6858 \| -0.0602 \| -0.0766 \| 0.5948 \| 0.0164 \| -70.8421 \| -64.7344 \| -3.0474 \| -3.0532 \| \| 0.6765 \| 0.8615 \| 5000 \| 0.6856 \| -0.0618 \| -0.0786 \| 0.5934 \| 0.0168 \| -71.0357 \| -64.8873 \| -3.0427 \| -3.0484 \| \| 0.6792 \| 0.8787 \| 5100 \| 0.6853 \| -0.0665 \| -0.0841 \| 0.5936 \| 0.0176 \| -71.5851 \| -65.3618 \| -3.0385 \| -3.0443 \| \| 0.6753 \| 0.8959 \| 5200 \| 0.6851 \| -0.0697 \| -0.0877 \| 0.5929 \| 0.0180 \| -71.9544 \| -65.6814 \| -3.0354 \| -3.0413 \| \| 0.6749 \| 0.9132 \| 5300 \| 0.6849 \| -0.0732 \| -0.0918 \| 0.5922 \| 0.0186 \| -72.3637 \| -66.0356 \| -3.0313 \| -3.0370 \| \| 0.6762 \| 0.9304 \| 5400 \| 0.6846 \| -0.0747 \| -0.0940 \| 0.5932 \| 0.0192 \| -72.5755 \| -66.1839 \| -3.0282 \| -3.0340 \| \| 0.6757 \| 0.9476 \| 5500 \| 0.6845 \| -0.0761 \| -0.0955 \| 0.5962 \| 0.0194 \| -72.7312 \| -66.3251 \| -3.0247 \| -3.0305 \| \| 0.6795 \| 0.9649 \| 5600 \| 0.6844 \| -0.0758 \| -0.0955 \| 0.6018 \| 0.0197 \| -72.7251 \| -66.2887 \| -3.0221 \| -3.0279 \| \| 0.6736 \| 0.9821 \| 5700 \| 0.6842 \| -0.0786 \| -0.0989 \| 0.6008 \| 0.0202 \| -73.0675 \| -66.5758 \| -3.0181 \| -3.0239 \| \| 0.6701 \| 0.9993 \| 5800 \| 0.6839 \| -0.0831 \| -0.1040 \| 0.6029 \| 0.0209 \| -73.5774 \| -67.0210 \| -3.0139 \| -3.0198 \| \| 0.6725 \| 1.0165 \| 5900 \| 0.6836 \| -0.0839 \| -0.1053 \| 0.6039 \| 0.0214 \| -73.7143 \| -67.1023 \| -3.0090 \| -3.0148 \| \| 0.6742 \| 1.0338 \| 6000 \| 0.6834 \| -0.0850 \| -0.1069 \| 0.6043 \| 0.0219 \| -73.8738 \| -67.2139 \| -3.0056 \| -3.0114 \| \| 0.6712 \| 1.0510 \| 6100 \| 0.6833 \| -0.0878 \| -0.1100 \| 0.6046 \| 0.0223 \| -74.1846 \| -67.4874 \| -3.0008 \| -3.0066 \| \| 0.675 \| 1.0682 \| 6200 \| 0.6831 \| -0.0903 \| -0.1131 \| 0.6043 \| 0.0228 \| -74.4897 \| -67.7427 \| -2.9969 \| -3.0027 \| \| 0.6766 \| 1.0855 \| 6300 \| 0.6828 \| -0.0936 \| -0.1170 \| 0.6036 \| 0.0234 \| -74.8753 \| -68.0717 \| -2.9936 \| -2.9994 \| \| 0.6754 \| 1.1027 \| 6400 \| 0.6826 \| -0.0972 \| -0.1212 \| 0.6094 \| 0.0240 \| -75.2993 \| -68.4308 \| -2.9896 \| -2.9954 \| \| 0.6769 \| 1.1199 \| 6500 \| 0.6823 \| -0.0999 \| -0.1244 \| 0.6059 \| 0.0246 \| -75.6244 \| -68.6977 \| -2.9850 \| -2.9909 \| \| 0.6764 \| 1.1371 \| 6600 \| 0.6821 \| -0.1041 \| -0.1293 \| 0.6076 \| 0.0252 \| -76.1111 \| -69.1214 \| -2.9809 \| -2.9867 \| \| 0.6734 \| 1.1544 \| 6700 \| 0.6817 \| -0.1081 \| -0.1341 \| 0.6022 \| 0.0260 \| -76.5930 \| -69.5220 \| -2.9770 \| -2.9828 \| \| 0.6654 \| 1.1716 \| 6800 \| 0.6814 \| -0.1138 \| -0.1407 \| 0.6053 \| 0.0268 \| -77.2464 \| -70.0935 \| -2.9716 \| -2.9774 \| \| 0.679 \| 1.1888 \| 6900 \| 0.6812 \| -0.1168 \| -0.1441 \| 0.6090 \| 0.0272 \| -77.5858 \| -70.3942 \| -2.9678 \| -2.9737 \| \| 0.6652 \| 1.2061 \| 7000 \| 0.6809 \| -0.1215 \| -0.1495 \| 0.6057 \| 0.0280 \| -78.1280 \| -70.8571 \| -2.9641 \| -2.9700 \| \| 0.6668 \| 1.2233 \| 7100 \| 0.6808 \| -0.1224 \| -0.1507 \| 0.6071 \| 0.0283 \| -78.2466 \| -70.9482 \| -2.9603 \| -2.9661 \| \| 0.6655 \| 1.2405 \| 7200 \| 0.6806 \| -0.1254 \| -0.1542 \| 0.6083 \| 0.0288 \| -78.5984 \| -71.2532 \| -2.9555 \| -2.9614 \| \| 0.6783 \| 1.2578 \| 7300 \| 0.6804 \| -0.1273 \| -0.1565 \| 0.6087 \| 0.0292 \| -78.8264 \| -71.4380 \| -2.9521 \| -2.9580 \| \| 0.6703 \| 1.2750 \| 7400 \| 0.6802 \| -0.1295 \| -0.1593 \| 0.6071 \| 0.0297 \| -79.1055 \| -71.6647 \| -2.9497 \| -2.9555 \| \| 0.6709 \| 1.2922 \| 7500 \| 0.6802 \| -0.1302 \| -0.1601 \| 0.6080 \| 0.0299 \| -79.1917 \| -71.7369 \| -2.9461 \| -2.9519 \| \| 0.6774 \| 1.3094 \| 7600 \| 0.6799 \| -0.1334 \| -0.1639 \| 0.6097 \| 0.0305 \| -79.5669 \| -72.0519 \| -2.9409 \| -2.9468 \| \| 0.6667 \| 1.3267 \| 7700 \| 0.6796 \| -0.1379 \| -0.1690 \| 0.6078 \| 0.0311 \| -80.0833 \| -72.5013 \| -2.9364 \| -2.9423 \| \| 0.6631 \| 1.3439 \| 7800 \| 0.6793 \| -0.1427 \| -0.1747 \| 0.6076 \| 0.0321 \| -80.6536 \| -72.9770 \| -2.9325 \| -2.9384 \| \| 0.6734 \| 1.3611 \| 7900 \| 0.6790 \| -0.1469 \| -0.1797 \| 0.6094 \| 0.0327 \| -81.1455 \| -73.4038 \| -2.9286 \| -2.9346 \| \| 0.6646 \| 1.3784 \| 8000 \| 0.6786 \| -0.1515 \| -0.1852 \| 0.6092 \| 0.0337 \| -81.6967 \| -73.8575 \| -2.9249 \| -2.9308 \| \| 0.6717 \| 1.3956 \| 8100 \| 0.6783 \| -0.1560 \| -0.1904 \| 0.6111 \| 0.0344 \| -82.2197 \| -74.3164 \| -2.9212 \| -2.9271 \| \| 0.6674 \| 1.4128 \| 8200 \| 0.6779 \| -0.1608 \| -0.1962 \| 0.6087 \| 0.0354 \| -82.7997 \| -74.7964 \| -2.9181 \| -2.9240 \| \| 0.6659 \| 1.4300 \| 8300 \| 0.6779 \| -0.1625 \| -0.1979 \| 0.6087 \| 0.0354 \| -82.9745 \| -74.9664 \| -2.9143 \| -2.9202 \| \| 0.6642 \| 1.4473 \| 8400 \| 0.6777 \| -0.1647 \| -0.2007 \| 0.6092 \| 0.0360 \| -83.2477 \| -75.1821 \| -2.9110 \| -2.9169 \| \| 0.6579 \| 1.4645 \| 8500 \| 0.6775 \| -0.1650 \| -0.2013 \| 0.6080 \| 0.0363 \| -83.3130 \| -75.2138 \| -2.9067 \| -2.9125 \| \| 0.6725 \| 1.4817 \| 8600 \| 0.6774 \| -0.1676 \| -0.2043 \| 0.6101 \| 0.0367 \| -83.6107 \| -75.4718 \| -2.9030 \| -2.9089 \| \| 0.6646 \| 1.4990 \| 8700 \| 0.6774 \| -0.1665 \| -0.2032 \| 0.6101 \| 0.0367 \| -83.4985 \| -75.3618 \| -2.9012 \| -2.9071 \| \| 0.6681 \| 1.5162 \| 8800 \| 0.6771 \| -0.1691 \| -0.2064 \| 0.6092 \| 0.0373 \| -83.8169 \| -75.6183 \| -2.8978 \| -2.9037 \| \| 0.6635 \| 1.5334 \| 8900 \| 0.6768 \| -0.1758 \| -0.2138 \| 0.6087 \| 0.0381 \| -84.5617 \| -76.2875 \| -2.8935 \| -2.8994 \| \| 0.6509 \| 1.5507 \| 9000 \| 0.6766 \| -0.1793 \| -0.2180 \| 0.6092 \| 0.0386 \| -84.9755 \| -76.6455 \| -2.8897 \| -2.8956 \| \| 0.663 \| 1.5679 \| 9100 \| 0.6764 \| -0.1824 \| -0.2216 \| 0.6073 \| 0.0391 \| -85.3355 \| -76.9553 \| -2.8858 \| -2.8918 \| \| 0.6614 \| 1.5851 \| 9200 \| 0.6762 \| -0.1856 \| -0.2252 \| 0.6076 \| 0.0396 \| -85.7006 \| -77.2724 \| -2.8834 \| -2.8894 \| \| 0.6605 \| 1.6023 \| 9300 \| 0.6761 \| -0.1847 \| -0.2246 \| 0.6078 \| 0.0398 \| -85.6352 \| -77.1840 \| -2.8793 \| -2.8852 \| \| 0.6616 \| 1.6196 \| 9400 \| 0.6759 \| -0.1879 \| -0.2282 \| 0.6053 \| 0.0403 \| -86.0049 \| -77.5025 \| -2.8759 \| -2.8818 \| \| 0.6595 \| 1.6368 \| 9500 \| 0.6757 \| -0.1905 \| -0.2315 \| 0.6085 \| 0.0410 \| -86.3271 \| -77.7626 \| -2.8721 \| -2.8781 \| \| 0.6612 \| 1.6540 \| 9600 \| 0.6753 \| -0.1938 \| -0.2356 \| 0.6069 \| 0.0418 \| -86.7373 \| -78.0935 \| -2.8679 \| -2.8738 \| \| 0.6563 \| 1.6713 \| 9700 \| 0.6751 \| -0.1979 \| -0.2402 \| 0.6083 \| 0.0423 \| -87.2033 \| -78.5057 \| -2.8649 \| -2.8708 \| \| 0.6526 \| 1.6885 \| 9800 \| 0.6750 \| -0.2017 \| -0.2444 \| 0.6069 \| 0.0427 \| -87.6160 \| -78.8784 \| -2.8620 \| -2.8680 \| \| 0.6392 \| 1.7057 \| 9900 \| 0.6747 \| -0.2051 \| -0.2485 \| 0.6094 \| 0.0434 \| -88.0276 \| -79.2194 \| -2.8594 \| -2.8653 \| \| 0.6528 \| 1.7229 \| 10000 \| 0.6746 \| -0.2062 \| -0.2500 \| 0.6087 \| 0.0437 \| -88.1775 \| -79.3360 \| -2.8562 \| -2.8622 \| \| 0.6542 \| 1.7402 \| 10100 \| 0.6744 \| -0.2075 \| -0.2516 \| 0.6066 \| 0.0441 \| -88.3364 \| -79.4595 \| -2.8532 \| -2.8592 \| \| 0.6559 \| 1.7574 \| 10200 \| 0.6739 \| -0.2141 \| -0.2595 \| 0.6078 \| 0.0454 \| -89.1350 \| -80.1233 \| -2.8483 \| -2.8543 \| \| 0.6708 \| 1.7746 \| 10300 \| 0.6737 \| -0.2171 \| -0.2629 \| 0.6104 \| 0.0458 \| -89.4692 \| -80.4205 \| -2.8439 \| -2.8500 \| \| 0.6454 \| 1.7919 \| 10400 \| 0.6737 \| -0.2178 \| -0.2638 \| 0.6048 \| 0.0460 \| -89.5570 \| -80.4903 \| -2.8419 \| -2.8479 \| \| 0.6495 \| 1.8091 \| 10500 \| 0.6735 \| -0.2211 \| -0.2676 \| 0.6036 \| 0.0465 \| -89.9389 \| -80.8204 \| -2.8383 \| -2.8444 \| \| 0.6648 \| 1.8263 \| 10600 \| 0.6732 \| -0.2247 \| -0.2719 \| 0.6034 \| 0.0472 \| -90.3731 \| -81.1833 \| -2.8349 \| -2.8409 \| \| 0.6568 \| 1.8436 \| 10700 \| 0.6731 \| -0.2275 \| -0.2752 \| 0.6039 \| 0.0476 \| -90.6979 \| -81.4662 \| -2.8311 \| -2.8372 \| \| 0.6536 \| 1.8608 \| 10800 \| 0.6728 \| -0.2303 \| -0.2785 \| 0.6043 \| 0.0482 \| -91.0335 \| -81.7461 \| -2.8295 \| -2.8355 \| \| 0.6574 \| 1.8780 \| 10900 \| 0.6726 \| -0.2320 \| -0.2808 \| 0.6032 \| 0.0487 \| -91.2560 \| -81.9128 \| -2.8271 \| -2.8331 \| \| 0.6601 \| 1.8952 \| 11000 \| 0.6725 \| -0.2331 \| -0.2820 \| 0.6018 \| 0.0489 \| -91.3829 \| -82.0227 \| -2.8250 \| -2.8311 \| \| 0.6562 \| 1.9125 \| 11100 \| 0.6722 \| -0.2383 \| -0.2881 \| 0.6029 \| 0.0498 \| -91.9931 \| -82.5429 \| -2.8218 \| -2.8278 \| \| 0.6536 \| 1.9297 \| 11200 \| 0.6720 \| -0.2416 \| -0.2919 \| 0.6025 \| 0.0503 \| -92.3716 \| -82.8687 \| -2.8187 \| -2.8248 \| \| 0.674 \| 1.9469 \| 11300 \| 0.6718 \| -0.2432 \| -0.2940 \| 0.6041 \| 0.0508 \| -92.5781 \| -83.0317 \| -2.8164 \| -2.8225 \| \| 0.6536 \| 1.9642 \| 11400 \| 0.6717 \| -0.2439 \| -0.2949 \| 0.6032 \| 0.0511 \| -92.6723 \| -83.0980 \| -2.8133 \| -2.8194 \| \| 0.6693 \| 1.9814 \| 11500 \| 0.6717 \| -0.2456 \| -0.2969 \| 0.6018 \| 0.0513 \| -92.8725 \| -83.2765 \| -2.8119 \| -2.8179 \| \| 0.6529 \| 1.9986 \| 11600 \| 0.6714 \| -0.2469 \| -0.2988 \| 0.6036 \| 0.0518 \| -93.0569 \| -83.4057 \| -2.8097 \| -2.8158 \| \| 0.6454 \| 2.0159 \| 11700 \| 0.6713 \| -0.2488 \| -0.3010 \| 0.6025 \| 0.0522 \| -93.2831 \| -83.5962 \| -2.8079 \| -2.8140 \| \| 0.6643 \| 2.0331 \| 11800 \| 0.6711 \| -0.2513 \| -0.3040 \| 0.6027 \| 0.0527 \| -93.5825 \| -83.8399 \| -2.8052 \| -2.8113 \| \| 0.6478 \| 2.0503 \| 11900 \| 0.6710 \| -0.2554 \| -0.3084 \| 0.5985 \| 0.0530 \| -94.0157 \| -84.2502 \| -2.8025 \| -2.8086 \| \| 0.6512 \| 2.0675 \| 12000 \| 0.6708 \| -0.2561 \| -0.3095 \| 0.6050 \| 0.0535 \| -94.1316 \| -84.3177 \| -2.8001 \| -2.8061 \| \| 0.6517 \| 2.0848 \| 12100 \| 0.6708 \| -0.2574 \| -0.3109 \| 0.6053 \| 0.0536 \| -94.2719 \| -84.4484 \| -2.7988 \| -2.8048 \| \| 0.646 \| 2.1020 \| 12200 \| 0.6707 \| -0.2592 \| -0.3130 \| 0.6025 \| 0.0538 \| -94.4818 \| -84.6297 \| -2.7972 \| -2.8033 \| \| 0.6439 \| 2.1192 \| 12300 \| 0.6706 \| -0.2607 \| -0.3147 \| 0.6029 \| 0.0540 \| -94.6511 \| -84.7795 \| -2.7953 \| -2.8014 \| \| 0.6432 \| 2.1365 \| 12400 \| 0.6705 \| -0.2646 \| -0.3191 \| 0.6053 \| 0.0545 \| -95.0945 \| -85.1767 \| -2.7925 \| -2.7985 \| \| 0.6437 \| 2.1537 \| 12500 \| 0.6704 \| -0.2662 \| -0.3209 \| 0.6018 \| 0.0548 \| -95.2735 \| -85.3289 \| -2.7907 \| -2.7968 \| \| 0.6581 \| 2.1709 \| 12600 \| 0.6702 \| -0.2678 \| -0.3229 \| 0.6029 \| 0.0552 \| -95.4749 \| -85.4889 \| -2.7888 \| -2.7948 \| \| 0.6509 \| 2.1881 \| 12700 \| 0.6700 \| -0.2692 \| -0.3248 \| 0.6036 \| 0.0556 \| -95.6598 \| -85.6304 \| -2.7870 \| -2.7930 \| \| 0.6603 \| 2.2054 \| 12800 \| 0.6700 \| -0.2697 \| -0.3254 \| 0.6004 \| 0.0557 \| -95.7213 \| -85.6830 \| -2.7854 \| -2.7914 \| \| 0.6459 \| 2.2226 \| 12900 \| 0.6700 \| -0.2702 \| -0.3259 \| 0.6027 \| 0.0556 \| -95.7675 \| -85.7359 \| -2.7844 \| -2.7904 \| \| 0.6501 \| 2.2398 \| 13000 \| 0.6698 \| -0.2723 \| -0.3285 \| 0.6011 \| 0.0562 \| -96.0266 \| -85.9425 \| -2.7827 \| -2.7887 \| \| 0.6452 \| 2.2571 \| 13100 \| 0.6698 \| -0.2721 \| -0.3282 \| 0.6025 \| 0.0561 \| -96.0042 \| -85.9225 \| -2.7811 \| -2.7872 \| \| 0.6553 \| 2.2743 \| 13200 \| 0.6697 \| -0.2732 \| -0.3296 \| 0.6034 \| 0.0564 \| -96.1360 \| -86.0296 \| -2.7798 \| -2.7859 \| \| 0.6627 \| 2.2915 \| 13300 \| 0.6697 \| -0.2745 \| -0.3311 \| 0.6020 \| 0.0566 \| -96.2910 \| -86.1636 \| -2.7781 \| -2.7842 \| \| 0.6393 \| 2.3088 \| 13400 \| 0.6697 \| -0.2741 \| -0.3307 \| 0.6013 \| 0.0566 \| -96.2503 \| -86.1255 \| -2.7777 \| -2.7838 \| \| 0.6366 \| 2.3260 \| 13500 \| 0.6696 \| -0.2757 \| -0.3325 \| 0.6027 \| 0.0568 \| -96.4266 \| -86.2794 \| -2.7767 \| -2.7827 \| \| 0.6522 \| 2.3432 \| 13600 \| 0.6696 \| -0.2765 \| -0.3334 \| 0.6032 \| 0.0569 \| -96.5202 \| -86.3612 \| -2.7753 \| -2.7814 \| \| 0.6535 \| 2.3604 \| 13700 \| 0.6695 \| -0.2780 \| -0.3351 \| 0.6022 \| 0.0572 \| -96.6946 \| -86.5112 \| -2.7742 \| -2.7802 \| \| 0.6555 \| 2.3777 \| 13800 \| 0.6694 \| -0.2786 \| -0.3360 \| 0.6022 \| 0.0574 \| -96.7815 \| -86.5683 \| -2.7734 \| -2.7795 \| \| 0.6658 \| 2.3949 \| 13900 \| 0.6694 \| -0.2781 \| -0.3355 \| 0.6032 \| 0.0574 \| -96.7320 \| -86.5236 \| -2.7727 \| -2.7788 \| \| 0.6453 \| 2.4121 \| 14000 \| 0.6693 \| -0.2789 \| -0.3364 \| 0.6018 \| 0.0575 \| -96.8240 \| -86.6049 \| -2.7718 \| -2.7778 \| \| 0.6451 \| 2.4294 \| 14100 \| 0.6692 \| -0.2797 \| -0.3375 \| 0.6034 \| 0.0578 \| -96.9303 \| -86.6776 \| -2.7708 \| -2.7769 \| \| 0.636 \| 2.4466 \| 14200 \| 0.6693 \| -0.2803 \| -0.3378 \| 0.6008 \| 0.0576 \| -96.9631 \| -86.7390 \| -2.7706 \| -2.7766 \| \| 0.6251 \| 2.4638 \| 14300 \| 0.6691 \| -0.2812 \| -0.3393 \| 0.6011 \| 0.0581 \| -97.1110 \| -86.8353 \| -2.7697 \| -2.7757 \| \| 0.6517 \| 2.4810 \| 14400 \| 0.6691 \| -0.2827 \| -0.3409 \| 0.6025 \| 0.0583 \| -97.2740 \| -86.9799 \| -2.7687 \| -2.7747 \| \| 0.633 \| 2.4983 \| 14500 \| 0.6690 \| -0.2837 \| -0.3422 \| 0.6006 \| 0.0585 \| -97.3994 \| -87.0852 \| -2.7680 \| -2.7740 \| \| 0.6407 \| 2.5155 \| 14600 \| 0.6690 \| -0.2842 \| -0.3426 \| 0.6011 \| 0.0584 \| -97.4438 \| -87.1331 \| -2.7679 \| -2.7739 \| \| 0.6298 \| 2.5327 \| 14700 \| 0.6690 \| -0.2853 \| -0.3438 \| 0.6013 \| 0.0584 \| -97.5570 \| -87.2438 \| -2.7671 \| -2.7731 \| \| 0.6432 \| 2.5500 \| 14800 \| 0.6690 \| -0.2862 \| -0.3447 \| 0.6018 \| 0.0585 \| -97.6493 \| -87.3336 \| -2.7663 \| -2.7723 \| \| 0.6492 \| 2.5672 \| 14900 \| 0.6689 \| -0.2866 \| -0.3453 \| 0.6013 \| 0.0587 \| -97.7090 \| -87.3695 \| -2.7660 \| -2.7721 \| \| 0.65 \| 2.5844 \| 15000 \| 0.6689 \| -0.2870 \| -0.3457 \| 0.6011 \| 0.0587 \| -97.7523 \| -87.4156 \| -2.7655 \| -2.7715 \| \| 0.6519 \| 2.6017 \| 15100 \| 0.6689 \| -0.2874 \| -0.3462 \| 0.6008 \| 0.0588 \| -97.8011 \| -87.4534 \| -2.7657 \| -2.7718 \| \| 0.6308 \| 2.6189 \| 15200 \| 0.6689 \| -0.2880 \| -0.3469 \| 0.6011 \| 0.0589 \| -97.8694 \| -87.5090 \| -2.7649 \| -2.7709 \| \| 0.6465 \| 2.6361 \| 15300 \| 0.6689 \| -0.2880 \| -0.3469 \| 0.6025 \| 0.0589 \| -97.8726 \| -87.5095 \| -2.7649 \| -2.7710 \| \| 0.6609 \| 2.6533 \| 15400 \| 0.6688 \| -0.2883 \| -0.3473 \| 0.6025 \| 0.0590 \| -97.9052 \| -87.5417 \| -2.7643 \| -2.7703 \| \| 0.6597 \| 2.6706 \| 15500 \| 0.6688 \| -0.2883 \| -0.3474 \| 0.6022 \| 0.0591 \| -97.9180 \| -87.5395 \| -2.7639 \| -2.7700 \| \| 0.6491 \| 2.6878 \| 15600 \| 0.6687 \| -0.2885 \| -0.3479 \| 0.6034 \| 0.0593 \| -97.9666 \| -87.5668 \| -2.7639 \| -2.7700 \| \| 0.6423 \| 2.7050 \| 15700 \| 0.6687 \| -0.2885 \| -0.3477 \| 0.6008 \| 0.0592 \| -97.9538 \| -87.5659 \| -2.7638 \| -2.7699 \| \| 0.6405 \| 2.7223 \| 15800 \| 0.6687 \| -0.2886 \| -0.3479 \| 0.6018 \| 0.0593 \| -97.9676 \| -87.5701 \| -2.7633 \| -2.7694 \| \| 0.6457 \| 2.7395 \| 15900 \| 0.6687 \| -0.2889 \| -0.3481 \| 0.6020 \| 0.0592 \| -97.9878 \| -87.5970 \| -2.7633 \| -2.7694 \| \| 0.6549 \| 2.7567 \| 16000 \| 0.6687 \| -0.2888 \| -0.3481 \| 0.6032 \| 0.0593 \| -97.9933 \| -87.5928 \| -2.7630 \| -2.7692 \| \| 0.6288 \| 2.7739 \| 16100 \| 0.6688 \| -0.2889 \| -0.3481 \| 0.6050 \| 0.0592 \| -97.9868 \| -87.6035 \| -2.7631 \| -2.7692 \| \| 0.6431 \| 2.7912 \| 16200 \| 0.6688 \| -0.2892 \| -0.3484 \| 0.6022 \| 0.0592 \| -98.0221 \| -87.6322 \| -2.7633 \| -2.7694 \| \| 0.6499 \| 2.8084 \| 16300 \| 0.6687 \| -0.2893 \| -0.3485 \| 0.6032 \| 0.0593 \| -98.0337 \| -87.6372 \| -2.7627 \| -2.7688 \| \| 0.6524 \| 2.8256 \| 16400 \| 0.6687 \| -0.2892 \| -0.3486 \| 0.6013 \| 0.0594 \| -98.0451 \| -87.6369 \| -2.7630 \| -2.7690 \| \| 0.6545 \| 2.8429 \| 16500 \| 0.6687 \| -0.2892 \| -0.3486 \| 0.6039 \| 0.0594 \| -98.0392 \| -87.6310 \| -2.7631 \| -2.7691 \| \| 0.6692 \| 2.8601 \| 16600 \| 0.6688 \| -0.2894 \| -0.3485 \| 0.6022 \| 0.0591 \| -98.0347 \| -87.6520 \| -2.7624 \| -2.7686 \| \| 0.6587 \| 2.8773 \| 16700 \| 0.6687 \| -0.2895 \| -0.3489 \| 0.6011 \| 0.0594 \| -98.0697 \| -87.6612 \| -2.7623 \| -2.7684 \| \| 0.6612 \| 2.8946 \| 16800 \| 0.6687 \| -0.2890 \| -0.3484 \| 0.6055 \| 0.0593 \| -98.0176 \| -87.6163 \| -2.7631 \| -2.7692 \| \| 0.6561 \| 2.9118 \| 16900 \| 0.6688 \| -0.2893 \| -0.3485 \| 0.6020 \| 0.0592 \| -98.0284 \| -87.6390 \| -2.7627 \| -2.7688 \| \| 0.6548 \| 2.9290 \| 17000 \| 0.6688 \| -0.2892 \| -0.3483 \| 0.6006 \| 0.0591 \| -98.0120 \| -87.6341 \| -2.7624 \| -2.7684 \| \| 0.6468 \| 2.9462 \| 17100 \| 0.6687 \| -0.2892 \| -0.3485 \| 0.6029 \| 0.0593 \| -98.0333 \| -87.6348 \| -2.7623 \| -2.7683 \| \| 0.666 \| 2.9635 \| 17200 \| 0.6686 \| -0.2892 \| -0.3486 \| 0.6029 \| 0.0594 \| -98.0413 \| -87.6310 \| -2.7622 \| -2.7683 \| \| 0.6571 \| 2.9807 \| 17300 \| 0.6687 \| -0.2893 \| -0.3485 \| 0.6039 \| 0.0592 \| -98.0332 \| -87.6411 \| -2.7624 \| -2.7684 \| \| 0.6414 \| 2.9979 \| 17400 \| 0.6687 \| -0.2893 \| -0.3487 \| 0.6008 \| 0.0594 \| -98.0463 \| -87.6427 \| -2.7624 \| -2.7684 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1
RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf	RichardErkhov	2024-11-01T15:37:07Z	6	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:12:25Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TinyLlama-1.1B_v0.2-merged - GGUF - Model creator: https://huggingface.co/elijahww/ - Original model: https://huggingface.co/elijahww/TinyLlama-1.1B_v0.2-merged/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [TinyLlama-1.1B_v0.2-merged.Q2_K.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q2_K.gguf) \| Q2_K \| 0.4GB \| \| [TinyLlama-1.1B_v0.2-merged.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q3_K_S.gguf) \| Q3_K_S \| 0.47GB \| \| [TinyLlama-1.1B_v0.2-merged.Q3_K.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q3_K.gguf) \| Q3_K \| 0.51GB \| \| [TinyLlama-1.1B_v0.2-merged.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q3_K_M.gguf) \| Q3_K_M \| 0.51GB \| \| [TinyLlama-1.1B_v0.2-merged.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q3_K_L.gguf) \| Q3_K_L \| 0.55GB \| \| [TinyLlama-1.1B_v0.2-merged.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.IQ4_XS.gguf) \| IQ4_XS \| 0.57GB \| \| [TinyLlama-1.1B_v0.2-merged.Q4_0.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q4_0.gguf) \| Q4_0 \| 0.59GB \| \| [TinyLlama-1.1B_v0.2-merged.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.IQ4_NL.gguf) \| IQ4_NL \| 0.6GB \| \| [TinyLlama-1.1B_v0.2-merged.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q4_K_S.gguf) \| Q4_K_S \| 0.6GB \| \| [TinyLlama-1.1B_v0.2-merged.Q4_K.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q4_K.gguf) \| Q4_K \| 0.62GB \| \| [TinyLlama-1.1B_v0.2-merged.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q4_K_M.gguf) \| Q4_K_M \| 0.62GB \| \| [TinyLlama-1.1B_v0.2-merged.Q4_1.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q4_1.gguf) \| Q4_1 \| 0.65GB \| \| [TinyLlama-1.1B_v0.2-merged.Q5_0.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q5_0.gguf) \| Q5_0 \| 0.71GB \| \| [TinyLlama-1.1B_v0.2-merged.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q5_K_S.gguf) \| Q5_K_S \| 0.71GB \| \| [TinyLlama-1.1B_v0.2-merged.Q5_K.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q5_K.gguf) \| Q5_K \| 0.73GB \| \| [TinyLlama-1.1B_v0.2-merged.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q5_K_M.gguf) \| Q5_K_M \| 0.73GB \| \| [TinyLlama-1.1B_v0.2-merged.Q5_1.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q5_1.gguf) \| Q5_1 \| 0.77GB \| \| [TinyLlama-1.1B_v0.2-merged.Q6_K.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q6_K.gguf) \| Q6_K \| 0.84GB \| \| [TinyLlama-1.1B_v0.2-merged.Q8_0.gguf](https://huggingface.co/RichardErkhov/elijahww_-_TinyLlama-1.1B_v0.2-merged-gguf/blob/main/TinyLlama-1.1B_v0.2-merged.Q8_0.gguf) \| Q8_0 \| 1.09GB \| Original model description: --- 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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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]
fbolanos/LRO_BigBird1	fbolanos	2024-11-01T15:35:59Z	121	0	transformers	[ "transformers", "safetensors", "big_bird", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T15:35:29Z	--- 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]
mlfoundations-dev/OH_original_wo_unnatural_instructions	mlfoundations-dev	2024-11-01T15:33:40Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B", "base_model:finetune:meta-llama/Llama-3.1-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T10:46:16Z	--- library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: OH_original_wo_unnatural_instructions 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. --> # OH_original_wo_unnatural_instructions This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the mlfoundations-dev/OH_original_wo_unnatural_instructions dataset. It achieves the following results on the evaluation set: - Loss: 0.5999 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 16 - gradient_accumulation_steps: 4 - total_train_batch_size: 512 - total_eval_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.1 - lr_scheduler_warmup_steps: 1738 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6109 \| 1.0 \| 335 \| 0.6026 \| \| 0.5598 \| 2.0 \| 670 \| 0.5954 \| \| 0.5202 \| 3.0 \| 1005 \| 0.5999 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.3.0 - Datasets 2.21.0 - Tokenizers 0.20.1
RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf	RichardErkhov	2024-11-01T15:27:43Z	14	0	null	[ "gguf", "arxiv:2409.05314", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:05:51Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) TinyLlama-1.1B-Tele-it - GGUF - Model creator: https://huggingface.co/AliMaatouk/ - Original model: https://huggingface.co/AliMaatouk/TinyLlama-1.1B-Tele-it/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [TinyLlama-1.1B-Tele-it.Q2_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q2_K.gguf) \| Q2_K \| 0.4GB \| \| [TinyLlama-1.1B-Tele-it.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q3_K_S.gguf) \| Q3_K_S \| 0.47GB \| \| [TinyLlama-1.1B-Tele-it.Q3_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q3_K.gguf) \| Q3_K \| 0.51GB \| \| [TinyLlama-1.1B-Tele-it.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q3_K_M.gguf) \| Q3_K_M \| 0.51GB \| \| [TinyLlama-1.1B-Tele-it.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q3_K_L.gguf) \| Q3_K_L \| 0.55GB \| \| [TinyLlama-1.1B-Tele-it.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.IQ4_XS.gguf) \| IQ4_XS \| 0.57GB \| \| [TinyLlama-1.1B-Tele-it.Q4_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q4_0.gguf) \| Q4_0 \| 0.59GB \| \| [TinyLlama-1.1B-Tele-it.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.IQ4_NL.gguf) \| IQ4_NL \| 0.6GB \| \| [TinyLlama-1.1B-Tele-it.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q4_K_S.gguf) \| Q4_K_S \| 0.6GB \| \| [TinyLlama-1.1B-Tele-it.Q4_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q4_K.gguf) \| Q4_K \| 0.62GB \| \| [TinyLlama-1.1B-Tele-it.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q4_K_M.gguf) \| Q4_K_M \| 0.62GB \| \| [TinyLlama-1.1B-Tele-it.Q4_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q4_1.gguf) \| Q4_1 \| 0.65GB \| \| [TinyLlama-1.1B-Tele-it.Q5_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q5_0.gguf) \| Q5_0 \| 0.71GB \| \| [TinyLlama-1.1B-Tele-it.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q5_K_S.gguf) \| Q5_K_S \| 0.71GB \| \| [TinyLlama-1.1B-Tele-it.Q5_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q5_K.gguf) \| Q5_K \| 0.73GB \| \| [TinyLlama-1.1B-Tele-it.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q5_K_M.gguf) \| Q5_K_M \| 0.73GB \| \| [TinyLlama-1.1B-Tele-it.Q5_1.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q5_1.gguf) \| Q5_1 \| 0.77GB \| \| [TinyLlama-1.1B-Tele-it.Q6_K.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q6_K.gguf) \| Q6_K \| 0.84GB \| \| [TinyLlama-1.1B-Tele-it.Q8_0.gguf](https://huggingface.co/RichardErkhov/AliMaatouk_-_TinyLlama-1.1B-Tele-it-gguf/blob/main/TinyLlama-1.1B-Tele-it.Q8_0.gguf) \| Q8_0 \| 1.09GB \| Original model description: --- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - nlp --- # TinyLlama-1.1B-Tele-it Model Card ## Model Summary The language model TinyLlama-1.1B-Tele-it is an instruct version of [TinyLlama-1.1B-Tele](https://huggingface.co/AliMaatouk/TinyLlama-1.1B-Tele), which is based on [TinyLlama-1.1B](https://huggingface.co/TinyLlama/TinyLlama_v1.1) and specialized in telecommunications. It was fine-tuned to follow instructions using Supervised Fine-tuning (SFT) with a combination of the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Open-instruct](https://huggingface.co/datasets/VMware/open-instruct) datasets. ### Context Length The context length of the model is 2048 tokens. ## Usage TinyLlama-1.1B-Tele-it has been fine-tuned using pairs of instructions and responses from the [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) and [Open-instruct](https://huggingface.co/datasets/VMware/open-instruct) datasets, separated by the "\n" delimiter. Below is an example of how to query the model using this format: ```markdown Prompt: Explain to me Shannon capacity.\n Model: The Shannon capacity of a communication channel is the maximum amount of information that can be transmitted over the channel in a single transmission. It is a measure of the maximum amount of information that can be transmitted over a channel with a given noise level. The Shannon capacity is a fundamental limit on the amount of information that can be transmitted over a communication channel. ``` ## Sample Code Below we share some code snippets on how to get quickly started with running the model. First, make sure to `pip install transformers`, then copy the snippet corresponding to your hardware and adapt it to your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele-it", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele-it") prompt = "Explain to me Shannon capacity.\n" input_ids = tokenizer(prompt, return_tensors="pt") outputs = model.generate(input_ids, max_new_tokens=100) generated_tokens = outputs[0, len(input_ids['input_ids'][0]):] response = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(response) ``` #### Running the model on a single / multi GPU ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele-it", torch_dtype="auto", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AliMaatouk/TinyLlama-1.1B-Tele-it") prompt = "Explain to me Shannon capacity.\n" input_ids = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(input_ids, max_new_tokens=100) generated_tokens = outputs[0, len(input_ids['input_ids'][0]):] response = tokenizer.decode(generated_tokens, skip_special_tokens=True) print(response) ``` ## Citation You can find the paper with all details about the model at https://arxiv.org/abs/2409.05314. Please cite it as follows: ```bib @misc{maatouk2024telellmsseriesspecializedlarge, title={Tele-LLMs: A Series of Specialized Large Language Models for Telecommunications}, author={Ali Maatouk and Kenny Chirino Ampudia and Rex Ying and Leandros Tassiulas}, year={2024}, eprint={2409.05314}, archivePrefix={arXiv}, primaryClass={cs.IT}, url={https://arxiv.org/abs/2409.05314}, } ```
pipilok/Mistral-Nemo-Instruct-2407-Q4_0_4_8-GGUF	pipilok	2024-11-01T15:23:46Z	19	0	null	[ "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ru", "zh", "ja", "base_model:mistralai/Mistral-Nemo-Instruct-2407", "base_model:quantized:mistralai/Mistral-Nemo-Instruct-2407", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-28T04:30:40Z	--- language: - en - fr - de - es - it - pt - ru - zh - ja license: apache-2.0 quantized_by: pipilok pipeline_tag: text-generation base_model: - mistralai/Mistral-Nemo-Instruct-2407 --- Original model: https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407 Tested on Snapdragon X Elite with LM Studio 0.3.2 ARM64 Technology Preview https://lmstudio.ai/snapdragon Avg answer Speed: 12 tok/s ## LM Studio Settings: ``` Before System: [INST]<<SYS>>\n After System: <</SYS>[/INST]\n Before User: [INST] After User: [INST]\n Before Assistant: After Assistant: ```
pipilok/Llama-3.2-1B-Instruct-Q4_0_4_8-GGUF	pipilok	2024-11-01T15:23:17Z	38	0	null	[ "gguf", "text-generation", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-28T05:02:35Z	--- license: llama3.2 quantized_by: pipilok pipeline_tag: text-generation base_model: - meta-llama/Llama-3.2-1B-Instruct --- Original model: https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct Tested on Snapdragon X Elite with LM Studio 0.3.2 ARM64 Technology Preview https://lmstudio.ai/snapdragon Avg answer Speed: 60 tok/s ## LM Studio Settings: ``` Before System: <\|im_start\|>system\n After System: <\|im_end\|>\n Before User: <\|im_start\|>user\n After User: <\|im_end\|>\n Before Assistant: <\|im_start\|>assistant\n After Assistant: <\|im_end\|>\n ```
pipilok/Llama-3.2-3B-Instruct-Q4_0_4_8-GGUF	pipilok	2024-11-01T15:22:55Z	35	0	null	[ "gguf", "text-generation", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-28T05:08:31Z	--- license: llama3.2 quantized_by: pipilok pipeline_tag: text-generation base_model: - meta-llama/Llama-3.2-3B-Instruct --- Original model: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct Tested on Snapdragon X Elite with LM Studio 0.3.2 ARM64 Technology Preview https://lmstudio.ai/snapdragon Avg answer Speed: 30 tok/s ## LM Studio Settings: ``` Before System: <\|im_start\|>system\n After System: <\|im_end\|>\n Before User: <\|im_start\|>user\n After User: <\|im_end\|>\n Before Assistant: <\|im_start\|>assistant\n After Assistant: <\|im_end\|>\n ```
pipilok/Mistral-Small-Instruct-2409-Q4_0_4_8-GGUF	pipilok	2024-11-01T15:22:33Z	15	0	null	[ "gguf", "text-generation", "en", "fr", "de", "es", "it", "pt", "ru", "zh", "ja", "base_model:mistralai/Mistral-Small-Instruct-2409", "base_model:quantized:mistralai/Mistral-Small-Instruct-2409", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-09-28T05:15:39Z	--- language: - en - fr - de - es - it - pt - ru - zh - ja license: apache-2.0 quantized_by: pipilok pipeline_tag: text-generation base_model: - mistralai/Mistral-Small-Instruct-2409 --- Original model: https://huggingface.co/mistralai/Mistral-Small-Instruct-2409 Tested on Snapdragon X Elite with LM Studio 0.3.2 ARM64 Technology Preview https://lmstudio.ai/snapdragon Avg answer Speed: 6 tok/s ## LM Studio Settings: ``` Before System: [INST]<<SYS>>\n After System: <</SYS>[/INST]\n Before User: [INST] After User: [INST]\n Before Assistant: After Assistant: ```
mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF	mradermacher	2024-11-01T15:20:09Z	14	0	transformers	[ "transformers", "gguf", "en", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "base_model:BarryFutureman/WestLakeX-7B-EvoMerge-Variant2", "base_model:quantized:BarryFutureman/WestLakeX-7B-EvoMerge-Variant2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-10-31T05:35:55Z	--- base_model: BarryFutureman/WestLakeX-7B-EvoMerge-Variant2 datasets: - argilla/distilabel-intel-orca-dpo-pairs 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/BarryFutureman/WestLakeX-7B-EvoMerge-Variant2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-i1-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/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/WestLakeX-7B-EvoMerge-Variant2-GGUF/resolve/main/WestLakeX-7B-EvoMerge-Variant2.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 -->
spidersouris/hscorer-full	spidersouris	2024-11-01T15:17:16Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "camembert", "text-classification", "generated_from_trainer", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T13:50:57Z	--- library_name: transformers license: mit base_model: camembert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: hscorer-full 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. --> # hscorer-full This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1307 - Accuracy: 0.9695 ## 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 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.1859 \| 1.0 \| 1797 \| 0.1307 \| 0.9631 \| \| 0.1457 \| 2.0 \| 3594 \| 0.2505 \| 0.9432 \| \| 0.125 \| 3.0 \| 5391 \| 0.1224 \| 0.9713 \| \| 0.2291 \| 4.0 \| 7188 \| 0.1530 \| 0.9402 \| \| 0.1174 \| 5.0 \| 8985 \| 0.1462 \| 0.9463 \| \| 0.0957 \| 6.0 \| 10782 \| 0.2007 \| 0.9549 \| \| 0.1581 \| 7.0 \| 12579 \| 0.2563 \| 0.9290 \| \| 0.1386 \| 8.0 \| 14376 \| 0.2012 \| 0.9528 \| \| 0.1353 \| 9.0 \| 16173 \| 0.1420 \| 0.9664 \| \| 0.0665 \| 10.0 \| 17970 \| 0.1307 \| 0.9695 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF	featherless-ai-quants	2024-11-01T15:17:15Z	5	0	null	[ "gguf", "text-generation", "base_model:ryzen88/Llama-3-70b-Arimas-story-RP-V2.1", "base_model:quantized:ryzen88/Llama-3-70b-Arimas-story-RP-V2.1", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-01T04:38:39Z	--- base_model: ryzen88/Llama-3-70b-Arimas-story-RP-V2.1 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # ryzen88/Llama-3-70b-Arimas-story-RP-V2.1 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 \| \|-------------------\|------\|------\| \| Q8_0 \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q8_0](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q8_0) \| 71501.78 MB \| \| Q4_K_S \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q4_K_S](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q4_K_S) \| 38478.11 MB \| \| Q2_K \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q2_K](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q2_K) \| 25153.26 MB \| \| Q6_K \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q6_K](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q6_K) \| 55206.44 MB \| \| Q3_K_M \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_M](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_M) \| 32680.03 MB \| \| Q3_K_S \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_S](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_S) \| 29480.03 MB \| \| Q3_K_L \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_L](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q3_K_L) \| 35420.03 MB \| \| Q4_K_M \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q4_K_M](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q4_K_M) \| 40550.61 MB \| \| Q5_K_S \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q5_K_S](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q5_K_S) \| 46403.36 MB \| \| Q5_K_M \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q5_K_M](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-Q5_K_M) \| 47635.86 MB \| \| IQ4_XS \| [ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-IQ4_XS](https://huggingface.co/featherless-ai-quants/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-GGUF/blob/main/ryzen88-Llama-3-70b-Arimas-story-RP-V2.1-IQ4_XS) \| 36496.80 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)
Hemanta14/asmttshemanta	Hemanta14	2024-11-01T15:14:54Z	33	0	transformers	[ "transformers", "safetensors", "vits", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-11-01T15:14:38Z	--- 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]
JosephEssa/model	JosephEssa	2024-11-01T15:12:54Z	6	0	null	[ "tensorboard", "safetensors", "bert", "generated_from_trainer", "base_model:Twitter/twhin-bert-large", "base_model:finetune:Twitter/twhin-bert-large", "license:apache-2.0", "region:us" ]	null	2024-11-01T14:32:43Z	--- license: apache-2.0 base_model: Twitter/twhin-bert-large tags: - generated_from_trainer model-index: - name: model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0342 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4026 \| 1.0 \| 150 \| 2.1943 \| \| 2.3071 \| 2.0 \| 300 \| 2.1008 \| \| 2.2223 \| 3.0 \| 450 \| 2.1652 \| \| 2.1434 \| 4.0 \| 600 \| 2.1081 \| \| 2.1232 \| 5.0 \| 750 \| 2.0342 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
tiya1012/vit-accident-image	tiya1012	2024-11-01T15:12:18Z	242	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-15T17:31:24Z	--- license: apache-2.0 tags: - image-classification - generated_from_trainer metrics: - accuracy - f1 model-index: - name: vit-accident-image 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. --> # Enhancing Road Safety with AI-Powered Accident Detection ## Objective The objective of this project is to develop an AI-driven system that detects accident scenes from images captured by CCTV footage. By leveraging advanced machine learning techniques, we aim to improve response times to road incidents, thereby enhancing overall road safety. ## Data Sample We utilized the [Accident Detection from CCTV Footage](https://www.kaggle.com/datasets/ckay16/accident-detection-from-cctv-footage/data) dataset from Kaggle. This dataset contains annotated images from CCTV footage, showcasing various accident scenarios. ### Sample Data Here’s a sample from the dataset: \| Image \| Label \| \|-------\|-------\| \| ![Accident Image]\| Accident \| The images are categorized into "Accident" and "No Accident," which helps train the model to distinguish between accident scenes and normal traffic conditions. ## Model Architecture Our model employs a Vision Transformer (ViT) architecture, which is well-suited for image classification tasks. The key components of the model include: - Input Layer: Accepts images resized to a specified resolution. - Transformer Encoder Layers: Extract features through self-attention mechanisms, capturing spatial relationships. - Feedforward Neural Networks: Process the features and classify them into accident-related categories. - Output Layer: Provides the final classification probabilities for "Accident" and "No Accident." ## Instructions for Running the Training Job To run the training job, follow these steps: 1. Clone the repository: ```bash git clone https://github.com/yourusername/accident-detection.git cd accident-detection # vit-accident-image This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the accident classification dataset. It achieves the following results on the evaluation set: - Loss: 0.2027 - Accuracy: 0.93 - F1: 0.9301 ## Model description label 0 : non-accident , label 1 : accident-detected ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.3546 \| 2.0 \| 100 \| 0.2327 \| 0.9184 \| 0.9184 \| \| 0.1654 \| 4.0 \| 200 \| 0.2075 \| 0.9388 \| 0.9388 \| \| 0.0146 \| 6.0 \| 300 \| 0.2497 \| 0.9388 \| 0.9387 \| \| 0.0317 \| 8.0 \| 400 \| 0.2179 \| 0.9286 \| 0.9285 \| \| 0.0192 \| 10.0 \| 500 \| 0.2255 \| 0.9286 \| 0.9286 \| ### Framework versions - Transformers 4.30.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF	mradermacher	2024-11-01T15:12:09Z	58	0	transformers	[ "transformers", "gguf", "en", "base_model:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5", "base_model:quantized:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-10-31T05:29:00Z	--- base_model: AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5 language: - en library_name: transformers license: cc-by-sa-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-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/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q2_K.gguf) \| Q2_K \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q3_K_S.gguf) \| Q3_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q3_K_M.gguf) \| Q3_K_M \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q3_K_L.gguf) \| Q3_K_L \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.IQ4_XS.gguf) \| IQ4_XS \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q4_K_S.gguf) \| Q4_K_S \| 3.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q4_K_M.gguf) \| Q4_K_M \| 3.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q5_K_S.gguf) \| Q5_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q5_K_M.gguf) \| Q5_K_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q6_K.gguf) \| Q6_K \| 5.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.Q8_0.gguf) \| Q8_0 \| 6.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.f16.gguf) \| f16 \| 12.5 \| 16 bpw, overkill \| 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 -->
mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF	mradermacher	2024-11-01T15:12:09Z	191	0	transformers	[ "transformers", "gguf", "en", "base_model:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5", "base_model:quantized:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T14:14:27Z	--- base_model: AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5 language: - en library_name: transformers license: cc-by-sa-4.0 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/AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.5 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-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/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q2_K.gguf) \| i1-Q2_K \| 2.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 2.9 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ3_S.gguf) \| i1-IQ3_S \| 2.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 3.7 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 3.7 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 3.7 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_0.gguf) \| i1-Q4_0 \| 3.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 3.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 3.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.5-i1-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.5.i1-Q6_K.gguf) \| i1-Q6_K \| 5.2 \| 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 -->
RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf	RichardErkhov	2024-11-01T15:09:23Z	72	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-01T14:59:27Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen-Qwen1.5-0.5B-1719202599 - GGUF - Model creator: https://huggingface.co/chainup244/ - Original model: https://huggingface.co/chainup244/Qwen-Qwen1.5-0.5B-1719202599/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q2_K.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q2_K.gguf) \| Q2_K \| 0.23GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q3_K_S.gguf) \| Q3_K_S \| 0.25GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q3_K.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q3_K.gguf) \| Q3_K \| 0.26GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q3_K_M.gguf) \| Q3_K_M \| 0.26GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q3_K_L.gguf) \| Q3_K_L \| 0.28GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.IQ4_XS.gguf) \| IQ4_XS \| 0.28GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q4_0.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q4_0.gguf) \| Q4_0 \| 0.29GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.IQ4_NL.gguf) \| IQ4_NL \| 0.29GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q4_K_S.gguf) \| Q4_K_S \| 0.29GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q4_K.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q4_K.gguf) \| Q4_K \| 0.3GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q4_K_M.gguf) \| Q4_K_M \| 0.3GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q4_1.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q4_1.gguf) \| Q4_1 \| 0.3GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q5_0.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q5_0.gguf) \| Q5_0 \| 0.32GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q5_K_S.gguf) \| Q5_K_S \| 0.32GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q5_K.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q5_K.gguf) \| Q5_K \| 0.33GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q5_K_M.gguf) \| Q5_K_M \| 0.33GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q5_1.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q5_1.gguf) \| Q5_1 \| 0.34GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q6_K.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q6_K.gguf) \| Q6_K \| 0.36GB \| \| [Qwen-Qwen1.5-0.5B-1719202599.Q8_0.gguf](https://huggingface.co/RichardErkhov/chainup244_-_Qwen-Qwen1.5-0.5B-1719202599-gguf/blob/main/Qwen-Qwen1.5-0.5B-1719202599.Q8_0.gguf) \| Q8_0 \| 0.47GB \| Original model description: --- 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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bb1070/lovejoy_wf	bb1070	2024-11-01T15:07:12Z	5	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	2024-11-01T15:07:03Z	--- 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: TOK --- # Lovejoy_Wf <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('bb1070/lovejoy_wf', 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)
sagniksengupta/git-finetuned-facad	sagniksengupta	2024-11-01T14:56:44Z	61	0	transformers	[ "transformers", "safetensors", "git", "image-text-to-text", "en", "dataset:Luna288/image-captioning-FACAD-base", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-10-23T19:36:29Z	--- library_name: transformers license: mit datasets: - Luna288/image-captioning-FACAD-base language: - en base_model: - microsoft/git-base ---
ssmits/Llama-3.1-Nemotron-92B-Instruct-HF-late	ssmits	2024-11-01T14:53:28Z	71	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", "base_model:finetune:nvidia/Llama-3.1-Nemotron-70B-Instruct-HF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-31T22:10:40Z	--- base_model: - nvidia/Llama-3.1-Nemotron-70B-Instruct-HF library_name: transformers tags: - mergekit - merge --- # Llama-3.1-Nemotron-92B-Instruct-HF-late This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [nvidia/Llama-3.1-Nemotron-70B-Instruct-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: bfloat16 merge_method: passthrough slices: - sources: - layer_range: - 0 - 55 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - sources: - layer_range: - 50 - 60 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - sources: - layer_range: - 55 - 65 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - sources: - layer_range: - 60 - 70 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - sources: - layer_range: - 65 - 75 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF - sources: - layer_range: - 70 - 80 model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF ```
Xu-Ouyang/pythia-12b-deduped-int4-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T14:51:15Z	77	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-11-01T14:40:55Z	--- 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]
rahulvk007/ExtractQueNumberMini	rahulvk007	2024-11-01T14:46:07Z	141	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "dataset:rahulvk007/quenumber_extraction_v2", "base_model:unsloth/SmolLM2-135M", "base_model:finetune:unsloth/SmolLM2-135M", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T12:23:01Z	--- base_model: unsloth/SmolLM2-135M language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft datasets: - rahulvk007/quenumber_extraction_v2 --- # ExtractQueNumberMini Model - Developed by: [rahulvk007](https://github.com/rahulvk007) ([rahulvk.com](https://www.rahulvk.com)) - License: [Apache-2.0](https://opensource.org/licenses/Apache-2.0) - Base Model: [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M) - Finetuning: Optimized with [Unsloth](https://github.com/unslothai/unsloth) and [Hugging Face's TRL library](https://github.com/huggingface/trl) This model has been fine-tuned for quick extraction of question numbers from OCRed handwritten text. It is designed to run efficiently on CPU due to its compact size. ### Model Usage To use this model, set the system prompt to the following: > Extract the question number from the given text. Your response should be just an integer representing the question number. Do not provide any explanation or context. Just the number. ### Inference Code Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "rahulvk007/ExtractQueNumberMini" device = "cpu" # change to "cuda" for GPU tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {} ### Input: {} ### Response: {}""" inputs = tokenizer( [ alpaca_prompt.format( "Extract the question number from the given text. Your response should be just an integer which is the question number. Do not provide any explanation or context. Just the number.", "<Give OCR Text here>", "", ) ], return_tensors="pt" ).to(device) outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True) print(tokenizer.batch_decode(outputs, skip_special_tokens=True)) ``` ### Datasets The model was fine-tuned on [rahulvk007/quenumber_extraction_v2](https://huggingface.co/datasets/rahulvk007/quenumber_extraction_v2), specifically curated for this task. --- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Haesteining/Phi3smallv3	Haesteining	2024-11-01T14:34:07Z	39	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:19:43Z	--- 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]
bb1070/ara_bed_wf	bb1070	2024-11-01T14:26:30Z	5	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	2024-11-01T14:26:27Z	--- 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: TOK --- # Ara_Bed_Wf <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('bb1070/ara_bed_wf', 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)
meditsolutions/MedIT-Mesh-3B-Instruct	meditsolutions	2024-11-01T14:20:26Z	15	1	null	[ "safetensors", "phi3", "custom_code", "en", "base_model:microsoft/Phi-3.5-mini-instruct", "base_model:finetune:microsoft/Phi-3.5-mini-instruct", "license:mit", "region:us" ]	null	2024-11-01T13:10:10Z	--- license: mit language: - en base_model: - microsoft/Phi-3.5-mini-instruct --- # Phi-3.5 Mini-Instruct Modification using MedIT-mesh Technique ## Primary Use Cases: - Commercial use in environments requiring memory and compute constraints. - Use in latency-bound scenarios where accuracy is crucial. - Strong reasoning capabilities, especially for code, math, and logic applications. ## Model Description: The Phi-3.5 Mini-Instruct modification is designed to accelerate research on language and multimodal models. It is a 3.8B parameter model optimized for commercial and research use in multiple languages. The MedIT-mesh technique provides improved memory and compute efficiency, making it suitable for environments with limited resources. ## Use Case Considerations: When selecting use cases, developers should consider language models' limitations and evaluate accuracy, safety, and fairness before using them within a specific downstream application. Developers should be aware of applicable laws and regulations (e.g., privacy, trade compliance) relevant to their use case. It is essential to adhere to the license terms for the model being used. ## Release Notes: An update over the June 2024 instruction-tuned Phi-3 Mini release based on user feedback. Additional post-training data was incorporated, leading to substantial gains in multilingual and multi-turn conversation quality, and reasoning capability. This release is expected to benefit most use cases, but users are encouraged to test in their particular AI applications.
mradermacher/mistral-7b-anthropic-i1-GGUF	mradermacher	2024-11-01T14:16:06Z	234	0	transformers	[ "transformers", "gguf", "alignment-handbook", "generated_from_trainer", "en", "dataset:HuggingFaceH4/ultrafeedback_binarized_fixed", "dataset:HuggingFaceH4/cai-conversation-harmless", "base_model:HuggingFaceH4/mistral-7b-anthropic", "base_model:quantized:HuggingFaceH4/mistral-7b-anthropic", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-01T13:05:56Z	--- base_model: HuggingFaceH4/mistral-7b-anthropic datasets: - HuggingFaceH4/ultrafeedback_binarized_fixed - HuggingFaceH4/cai-conversation-harmless language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - 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/HuggingFaceH4/mistral-7b-anthropic <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/mistral-7b-anthropic-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/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-anthropic-i1-GGUF/resolve/main/mistral-7b-anthropic.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 -->
jongm38825/Qwen2-7b-v1	jongm38825	2024-11-01T14:01:57Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:43:43Z	--- base_model: flash_attn language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- # Uploaded model - Developed by: jongm38825 - License: apache-2.0 - Finetuned from model : flash_attn 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)
shubhamrathore081/intent_classificaiton	shubhamrathore081	2024-11-01T14:00:57Z	197	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:27:15Z	--- license: apache-2.0 language: - en base_model: - unsloth/Llama-3.2-3B-Instruct pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference ---
Herry443/Llama-8B-KNUT-ref-voice_size500_cot0_cri1_hint1	Herry443	2024-11-01T13:47:54Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:36:25Z	--- 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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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]
mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF	mradermacher	2024-11-01T13:47:08Z	32	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "birgermoell/Flashback-Bellman", "en", "base_model:birgermoell/Munin-NeuralBeagle-Flashback-Bellman", "base_model:quantized:birgermoell/Munin-NeuralBeagle-Flashback-Bellman", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T12:36:59Z	--- base_model: birgermoell/Munin-NeuralBeagle-Flashback-Bellman language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - birgermoell/Flashback-Bellman --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/birgermoell/Munin-NeuralBeagle-Flashback-Bellman <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-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/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Munin-NeuralBeagle-Flashback-Bellman-i1-GGUF/resolve/main/Munin-NeuralBeagle-Flashback-Bellman.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 -->
xmeowrr/SummryModel	xmeowrr	2024-11-01T13:45:00Z	114	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "summarization", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2024-11-01T13:41:49Z	--- base_model: - google/flan-t5-base pipeline_tag: summarization library_name: transformers ---
yoohj58072/krx_qwen2.5_7b_it_v1	yoohj58072	2024-11-01T13:42:18Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "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	2024-11-01T09:23:55Z	--- base_model: unsloth/Qwen2.5-7B-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/Qwen2.5-7B-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)
Natthaphon/thaicapgen-swin-phayathai	Natthaphon	2024-11-01T13:40:53Z	56	0	null	[ "safetensors", "clip-encoder-decoder", "image-to-text", "image-captioning", "custom_code", "th", "region:us" ]	image-to-text	2024-11-01T07:58:01Z	--- tags: - image-to-text - image-captioning language: - th --- # Thai Image Captioning Encoder-decoder style image captioning model using [Swin-L](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) and [PhayathaiBert](https://huggingface.co/clicknext/phayathaibert). Trained on Thai language MSCOCO and IPU24 dataset. # Usage With `VisionEncoderDecoderModel`. ```python from transformers import VisionEncoderDecoderModel, AutoImageProcessor, AutoTokenizer device = 'cuda' gen_kwargs = {"max_length": 120, "num_beams": 4} model_path = 'Natthaphon/thaicapgen-swin-phayathai' feature_extractor = AutoImageProcessor.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) model = VisionEncoderDecoderModel.from_pretrained(model_path).to(device) pixel_values = feature_extractor(images=[Image.open(image_path)], return_tensors="pt").pixel_values pixel_values = pixel_values.to(device) output_ids = model.generate(pixel_values, **gen_kwargs) preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) ``` You can also use `AutoModel` to load it. But this requires `trust_remote_code=True`. ```python from transformers import AutoModel model_path = 'Natthaphon/thaicapgen-swin-phayathai' model = AutoModel.from_pretrained(model_path, trust_remote_code=True).to(device) ``` # Acknowledgement This work is partially supported by the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B) [Grant number B04G640107]
Xu-Ouyang/pythia-12b-deduped-int3-step16-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T13:39:20Z	75	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T13:28:48Z	--- 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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homeb82784/Qwen2-7B-Instruct-it-v1.7	homeb82784	2024-11-01T13:38:34Z	35	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "krx", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:24:53Z	--- library_name: transformers tags: - krx --- # 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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easwar03/t5-small-finetuned-xsum	easwar03	2024-11-01T13:37:23Z	114	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-01T13:30:55Z	--- library_name: transformers license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - xsum model-index: - name: t5-small-finetuned-xsum 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. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 19 \| 3.4517 \| 17.4709 \| 2.6232 \| 13.6143 \| 13.891 \| 18.89 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
bb1070/Havana_wf	bb1070	2024-11-01T13:32:50Z	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	2024-11-01T13:32:48Z	--- 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: TOK --- # Havana_Wf <Gallery /> Trained on Replicate using: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` 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('bb1070/Havana_wf', 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)
e-hossam96/arabic-nano-gpt-v0	e-hossam96	2024-11-01T13:27:36Z	165	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "ar", "dataset:wikimedia/wikipedia", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-17T00:20:46Z	--- library_name: transformers license: mit base_model: openai-community/gpt2 tags: - generated_from_trainer model-index: - name: arabic-nano-gpt results: [] datasets: - wikimedia/wikipedia language: - ar --- # arabic-nano-gpt This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the arabic [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Repository on GitHub: [e-hossam96/arabic-nano-gpt](https://github.com/e-hossam96/arabic-nano-gpt.git) The model achieves the following results on the held-out test set: - Loss: 3.28796 ## How to Use ```python import torch from transformers import pipeline model_ckpt = "e-hossam96/arabic-nano-gpt-v0" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") lm = pipeline(task="text-generation", model=model_ckpt, device=device) prompt = """المحرك النفاث هو محرك ينفث الموائع (الماء أو الهواء) بسرعة فائقة \ لينتج قوة دافعة اعتمادا على مبدأ قانون نيوتن الثالث للحركة. \ هذا التعريف الواسع للمحركات النفاثة يتضمن أيضا""" output = lm(prompt, max_new_tokens=128) print(output[0]["generated_text"]) ``` ## Model description - Embedding Size: 256 - Attention Heads: 4 - Attention Layers: 4 ## Training and evaluation data The entire wikipedia dataset was split into three splits based on the 90-5-5 ratios. ## Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 24 ## Training Loss ![Training Loss](assets/arabic-nano-gpt-v0-train-loss.png) ## Validation Loss ![Validation Loss](assets/arabic-nano-gpt-v0-eval-loss.png) ## Framework versions - Transformers 4.45.2 - Pytorch 2.5.0 - Datasets 3.0.1 - Tokenizers 0.20.1
e-hossam96/arabic-nano-gpt-v2	e-hossam96	2024-11-01T13:16:52Z	173	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "ar", "dataset:wikimedia/wikipedia", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-29T06:12:44Z	--- library_name: transformers license: mit base_model: openai-community/gpt2 tags: - generated_from_trainer model-index: - name: arabic-nano-gpt-v2 results: [] datasets: - wikimedia/wikipedia language: - ar --- # arabic-nano-gpt-v2 This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the arabic [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset. Repository on GitHub: [e-hossam96/arabic-nano-gpt](https://github.com/e-hossam96/arabic-nano-gpt.git) The model achieves the following results on the held-out test set: - Loss: 3.25564 ## How to Use ```python import torch from transformers import pipeline model_ckpt = "e-hossam96/arabic-nano-gpt-v2" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") lm = pipeline(task="text-generation", model=model_ckpt, device=device) prompt = """المحرك النفاث هو محرك ينفث الموائع (الماء أو الهواء) بسرعة فائقة \ لينتج قوة دافعة اعتمادا على مبدأ قانون نيوتن الثالث للحركة. \ هذا التعريف الواسع للمحركات النفاثة يتضمن أيضا""" output = lm(prompt, max_new_tokens=128) print(output[0]["generated_text"]) ``` ## Model description - Embedding Size: 384 - Attention Heads: 6 - Attention Layers: 8 ## Training and evaluation data The entire wikipedia dataset was split into three splits based on the 90-5-5 ratios. ## Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 8 ## Training Loss ![Training Loss](assets/arabic-nano-gpt-v2-train-loss.png) ## Validation Loss ![Validation Loss](assets/arabic-nano-gpt-v2-eval-loss.png) ## Framework versions - Transformers 4.45.2 - Pytorch 2.5.0 - Datasets 3.0.1 - Tokenizers 0.20.1
minimini99/flash_attn	minimini99	2024-11-01T13:12:36Z	76	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-11-01T13:04:53Z	--- base_model: flash_attn language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- # Uploaded model - Developed by: minimini99 - License: apache-2.0 - Finetuned from model : flash_attn 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)
Viscoke/Big7	Viscoke	2024-11-01T13:09:52Z	34	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:06:20Z	--- 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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mradermacher/CrystalMistralv2.5-i1-GGUF	mradermacher	2024-11-01T13:08:06Z	25	1	transformers	[ "transformers", "gguf", "en", "base_model:Crystalcareai/CrystalMistralv2.5", "base_model:quantized:Crystalcareai/CrystalMistralv2.5", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T11:57:13Z	--- base_model: Crystalcareai/CrystalMistralv2.5 language: - en library_name: transformers 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/Crystalcareai/CrystalMistralv2.5 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/CrystalMistralv2.5-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/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv2.5-i1-GGUF/resolve/main/CrystalMistralv2.5.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 -->
muratsimsek003/turkish-bert-base-uncased-boun-qa	muratsimsek003	2024-11-01T13:08:02Z	119	0	transformers	[ "transformers", "safetensors", "bert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2024-11-01T13:07:43Z	--- 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]
Haesteining/Phi3smallv2	Haesteining	2024-11-01T13:06:16Z	37	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T13:01:48Z	--- 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]
mradermacher/CrystalMistralv3-i1-GGUF	mradermacher	2024-11-01T13:02:07Z	46	0	transformers	[ "transformers", "gguf", "en", "base_model:Crystalcareai/CrystalMistralv3", "base_model:quantized:Crystalcareai/CrystalMistralv3", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T11:50:09Z	--- base_model: Crystalcareai/CrystalMistralv3 language: - en library_name: transformers 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/Crystalcareai/CrystalMistralv3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/CrystalMistralv3-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/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-i1-GGUF/resolve/main/CrystalMistralv3.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 -->
laohan/lau-1b-2000	laohan	2024-11-01T13:01:46Z	142	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-1B", "base_model:finetune:meta-llama/Llama-3.2-1B", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T12:57:46Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B tags: - generated_from_trainer model-index: - name: lau-1b-2000 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. --> # lau-1b-2000 This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Q-PING/krx_Qwen2-7B-It_1101	Q-PING	2024-11-01T12:55:14Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:finetune:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T12:32:47Z	--- base_model: unsloth/Qwen2-7B-Instruct language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- # Uploaded model - Developed by: Q-PING - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-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)
pnpm12/informatic_1B_book_25616	pnpm12	2024-11-01T12:43:02Z	138	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T12:41:54Z	--- base_model: unsloth/llama-3.2-1b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: pnpm12 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-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)
AmaanDhamaskar/muril_finetuned_ner_hmb_e5	AmaanDhamaskar	2024-11-01T12:42:24Z	105	1	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google/muril-base-cased", "base_model:finetune:google/muril-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-01T10:01:15Z	--- library_name: transformers license: apache-2.0 base_model: google/muril-base-cased tags: - generated_from_trainer model-index: - name: muril_finetuned_ner_hmb_e5 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. --> # muril_finetuned_ner_hmb_e5 This model is a fine-tuned version of [google/muril-base-cased](https://huggingface.co/google/muril-base-cased) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.19.1
mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF	mradermacher	2024-11-01T12:33:08Z	198	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ssmits/Llama-3.1-Nemotron-92B-Instruct-HF-late", "base_model:quantized:ssmits/Llama-3.1-Nemotron-92B-Instruct-HF-late", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-01T08:10:00Z	--- base_model: ssmits/Llama-3.1-Nemotron-92B-Instruct-HF-late language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ssmits/Llama-3.1-Nemotron-92B-Instruct-HF-late <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-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/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ1_S.gguf) \| i1-IQ1_S \| 19.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ1_M.gguf) \| i1-IQ1_M \| 21.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 24.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ2_M.gguf) \| i1-IQ2_M \| 31.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q2_K.gguf) \| i1-Q2_K \| 34.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 35.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 38.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 40.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ3_S.gguf) \| i1-IQ3_S \| 40.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ3_M.gguf) \| i1-IQ3_M \| 41.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 44.5 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 48.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 49.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_0.gguf.part2of2) \| i1-Q4_0 \| 52.3 \| fast, low quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_K_S.gguf.part2of2) \| i1-Q4_K_S \| 52.5 \| optimal size/speed/quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q4_K_M.gguf.part2of2) \| i1-Q4_K_M \| 55.4 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 63.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 65.2 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.1-Nemotron-92B-Instruct-HF-late-i1-GGUF/resolve/main/Llama-3.1-Nemotron-92B-Instruct-HF-late.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 75.5 \| 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 -->
waldie/Qwen2.5-32B-EVA-Instruct-Merge-0.1-4bpw-h6-exl2	waldie	2024-11-01T12:28:20Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:Downtown-Case/Qwen2.5-32B-EVA-Instruct-Merge-0.1", "base_model:quantized:Downtown-Case/Qwen2.5-32B-EVA-Instruct-Merge-0.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-11-01T11:56:48Z	--- base_model: Downtown-Case/Qwen2.5-32B-EVA-Instruct-Merge-0.1 quantized_by: waldie library_name: transformers tags: - mergekit - merge --- # Qwen2.5-32B-EVA-Instruct-Merge-0.1 This is a merge of EVA 32B 0.1 with Qwen's 32B instruct model, and EVA 0.0, at low weights, using [mergekit](https://github.com/cg123/mergekit). Also see: https://huggingface.co/ParasiticRogue/EVA-Instruct-32B ## Merge Details ### Merge Method This model was merged using the della merge method using /home/a/Models/Raw/Qwen_Qwen2.5-32B as a base. ### Models Merged The following models were included in the merge: * /home/a/Models/Raw/EVA-UNIT-01_EVA-Qwen2.5-32B-v0.1 * /home/a/Models/Raw/Qwen_Qwen2.5-32B-Instruct * /home/a/Models/Raw/EVA-UNIT-01_EVA-Qwen2.5-32B-v0.0 ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: /home/a/Models/Raw/Qwen_Qwen2.5-32B # No parameters necessary for base model - model: /home/a/Models/Raw/EVA-UNIT-01_EVA-Qwen2.5-32B-v0.1 parameters: weight: 0.7 density: 0.7 - model: /home/a/Models/Raw/EVA-UNIT-01_EVA-Qwen2.5-32B-v0.0 parameters: weight: 0.11 density: 0.3 - model: /home/a/Models/Raw/Qwen_Qwen2.5-32B-Instruct parameters: weight: 0.19 density: 0.3 merge_method: della #tokenizer_source: base base_model: /home/a/Models/Raw/Qwen_Qwen2.5-32B parameters: int8_mask: true epsilon: 0.15 lambda: 1 dtype: bfloat16 ```
awels/maximusLLM-4b-128k	awels	2024-11-01T12:23:11Z	5	0	adapters	[ "adapters", "safetensors", "phi3", "awels", "maximo", "custom_code", "en", "dataset:awels/maximo_admin_dataset", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:finetune:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2024-11-01T11:41:22Z	--- base_model: microsoft/Phi-3-mini-128k-instruct datasets: - awels/maximo_admin_dataset language: - en library_name: adapters license: mit tags: - awels - maximo widget: - text: Who are you, Maximus ? --- # Maximus Model Card ## Model Details Model Name: Maximus Model Type: Transformer-based leveraging Microsoft Phi 3b 128k tokens Publisher: Awels Engineering License: MIT Model Description: Maximus is a sophisticated model designed to help as an AI agent focusing on Maximo Application Suite. It leverages advanced machine learning techniques to provide efficient and accurate solutions. It has been trained on the full docments corpus of MAS 8.5. ## Dataset Dataset Name: [awels/maximo_admin_dataset](https://huggingface.co/datasets/awels/maximo_admin_dataset) Dataset Source: Hugging Face Datasets Dataset License: MIT Dataset Description: The dataset used to train Maximus consists of all the public documents available on Maximo application suite. This dataset is curated to ensure a comprehensive representation of typical administrative scenarios encountered in Maximo. ## Training Details Training Data: The training data includes 67,000 Questions and Answers generated by the [Bonito LLM](https://github.com/BatsResearch/bonito). The dataset is split into 3 sets of data (training, test and validation) to ensure robust model performance. Training Procedure: Maximus was trained using supervised learning with cross-entropy loss and the Adam optimizer. The training involved 1 epoch, a batch size of 4, a learning rate of 5.0e-06, and a cosine learning rate scheduler with gradient checkpointing for memory efficiency. Hardware: The model was trained on a single NVIDIA RTX 4090 graphic card. Framework: The training was conducted using PyTorch. ## Evaluation Evaluation Metrics: Maximus was evaluated on the training dataset: > epoch = 1.0 total_flos = 64046138GF train_loss = 2.8079 train_runtime = 0:37:48.33 train_samples_per_second = 21.066 train_steps_per_second = 5.267 Performance: The model achieved the following results on the evaluation dataset: > epoch = 1.0 eval_loss = 2.288 eval_runtime = 0:02:05.48 eval_samples = 10773 eval_samples_per_second = 95.338 eval_steps_per_second = 23.836 ## Intended Use Primary Use Case: Maximus is intended to be used locally in an agent swarm to colleborate together to solve Maximo Application Suite related problems. Limitations: While Maximus is highly effective, it may have limitations due to the model size. An 8b model based on Llama 3 is used internally at Awels Engineering.
QuantFactory/SmolLM2-135M-GGUF	QuantFactory	2024-11-01T12:21:20Z	78	3	transformers	[ "transformers", "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-01T12:20:03Z	--- library_name: transformers license: apache-2.0 language: - en --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/SmolLM2-135M-GGUF This is quantized version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) created using llama.cpp # Original Model Card # SmolLM2 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/XtSR4NkriicR6fGiWGowZ.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 ```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/SmolLM2-135M" 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 import torch from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "HuggingFaceTB/SmolLM2-135M" tokenizer = AutoTokenizer.from_pretrained(checkpoint) # 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: 723.56 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 \| 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 \| 1.98 \| 1.68 \| \| 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}, } ```
parrottygg/phi3v2	parrottygg	2024-11-01T12:15:28Z	35	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T12:11:16Z	--- 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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rfajri/sentiment-indobert-v1	rfajri	2024-11-01T12:15:13Z	105	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T12:14:49Z	--- 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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mradermacher/CrystalMistralv3-GGUF	mradermacher	2024-11-01T12:14:30Z	12	0	transformers	[ "transformers", "gguf", "en", "base_model:Crystalcareai/CrystalMistralv3", "base_model:quantized:Crystalcareai/CrystalMistralv3", "endpoints_compatible", "region:us" ]	null	2024-10-31T05:12:09Z	--- base_model: Crystalcareai/CrystalMistralv3 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Crystalcareai/CrystalMistralv3 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/CrystalMistralv3-i1-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/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/CrystalMistralv3-GGUF/resolve/main/CrystalMistralv3.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 -->
bmkllm/qwen_2-7b-it_v3	bmkllm	2024-11-01T12:09:13Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "krx", "conversational", "en", "base_model:unsloth/Qwen2-7B-Instruct", "base_model:finetune:unsloth/Qwen2-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T10:30:37Z	--- base_model: unsloth/Qwen2-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - krx license: apache-2.0 language: - en --- # Uploaded model - Developed by: bmkllm - License: apache-2.0 - Finetuned from model : unsloth/Qwen2-7B-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)
qiuhuachuan/simpsybot_Q	qiuhuachuan	2024-11-01T12:05:32Z	20	2	null	[ "safetensors", "qwen2", "llama-factory", "full", "generated_from_trainer", "arxiv:2408.15787", "base_model:Qwen/Qwen2-7B-Instruct", "base_model:finetune:Qwen/Qwen2-7B-Instruct", "license:other", "region:us" ]	null	2024-08-29T13:06:09Z	--- license: other base_model: Qwen/Qwen2-7B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: sft 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. --> # Details This model is a fine-tuned version of `Qwen/Qwen2-7B-Instruct` on our dataset. **For more details, please refer to https://github.com/qiuhuachuan/interactive-agents ## Model inference ```Python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model_name = 'qiuhuachuan/simpsybot_Q' simpsybot_qwen2_model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) simpsybot_qwen2_tokenizer = AutoTokenizer.from_pretrained(model_name) SYSTEM_PROMPT = """现在你是虚拟心理咨询师小天。以下是小天的信息：角色名：小天性别：女角色介绍: 虚拟心理咨询师，擅长人本主义、精神分析和认知行为疗法。技能：帮助识别和挑战不健康的思维，提供心理学支持和共情。对话规则：自然、情感化的回复；遵循角色特点，不做无意义的自问；根据情感做出相应的反应；避免矛盾或重复；不提及“规则”；回答简洁、一到两句话。咨询一般分为前、中、后期三个阶段: 1. 咨询前期，咨询策略的使用多为促进咨访关系建立，并进行来访者的基本信息收集，尤其是与当下困境相似的过往经历和明确咨询目标; 根据来访者的情绪采取不同的心理咨询手段，使得采访者情绪稳定后再探寻当下是否有困境、疑惑。 2. 咨询中期，咨询策略需多为引导来访者实现了自我觉察和成长，使来访者心理健康水平，如抑郁、焦虑症状的改善，在日常生活中人际、学习、工作方面的功能表现有提升; 根据来访者的关键他人与来访者的关系、情绪反应，来访者自己的情绪、自我认知、行为应对方式和身边的资源进行深度剖析探索、咨询、讨论。使得来访者明确表达当下的困境或者想要讨论的问题。 3. 咨询后期，咨询策略需更多地导向引导来访者总结整个咨询周期中自己在情绪处理、社会功能、情感行为反应三个方面的改变和提升。明确询问来访者希望达成的目标或者期望，并且制定计划解决人际关系或者情绪处理方面的问题。咨询师的对话要求： 1. 表达要简短，尽可能地口语化、自然。 2. 因为咨询师只受过心理学相关的教育，只能提供心理咨询相关的对话内容。 3. 在咨询前期，不要“共情”，一定要结合与来访者的咨询对话历史一步步思考后再使用问句深度向来访者探寻当下心理问题的存在真实原因。 4. 不要一次性询问过多的问题，尽量一次性只向来访者询问一个问题，与来访者互动后一步步探寻心理问题的原因。 5. 在咨询前期，不要“重述”和“认可”等话术。 6. 话术需要参考有经验的真人心理咨询师，尽可能口语化。 7. 严格遵循咨询的前、中、后三个阶段采用对应的策略。 8. 咨询师不要主动终止心理咨询流程。 9. 更多的是引导用户思考和探索。""" def get_prediction_simpsybot_qwen2(messages: list): system_item = [{'role': 'system', 'content': SYSTEM_PROMPT}] messages = system_item + messages ctx = simpsybot_qwen2_tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = simpsybot_qwen2_tokenizer([ctx], return_tensors="pt").to(device) with torch.no_grad(): generated_ids = simpsybot_qwen2_model.generate( model_inputs.input_ids, 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 = simpsybot_qwen2_tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] return response if __name__ == '__main__': messages =[ {'role': 'user', 'content': '我失恋了，好难受！'} ] response = get_prediction_simpsybot_qwen2(messages=messages) print(response) ``` ## Intended uses & limitations Available for non-commercial use ## Citation If you find our work useful for your research and applications, please cite using this BibTeX: ```bibtex @misc{qiu2024interactiveagents, title={Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions}, author={Huachuan Qiu and Zhenzhong Lan}, year={2024}, eprint={2408.15787}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.15787}, } ``` ## 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: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - 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 ### Framework versions - Transformers 4.43.4 - Pytorch 2.4.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
letuandat/tts-nnng-2410	letuandat	2024-11-01T12:04:49Z	103	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2024-10-31T16:25:07Z	--- 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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fbolanos/LRO_BigBird	fbolanos	2024-11-01T12:04:00Z	119	0	transformers	[ "transformers", "safetensors", "big_bird", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-01T12:03:31Z	--- 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. 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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(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]
johnatanebonilla/w_small_lv_70	johnatanebonilla	2024-11-01T12:01:32Z	85	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-10-30T03:26:56Z	--- library_name: transformers tags: - generated_from_trainer metrics: - wer model-index: - name: w_small_lv_70 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. --> # w_small_lv_70 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6468 - Wer: 77.1230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.7247 \| 0.7184 \| 1000 \| 0.6818 \| 77.6120 \| \| 0.5041 \| 1.4368 \| 2000 \| 0.6395 \| 75.4202 \| \| 0.3808 \| 2.1552 \| 3000 \| 0.6313 \| 85.2857 \| \| 0.3595 \| 2.8736 \| 4000 \| 0.6264 \| 71.4611 \| \| 0.2771 \| 3.5920 \| 5000 \| 0.6468 \| 77.1230 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu118 - Datasets 3.0.0 - Tokenizers 0.19.1
mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF	mradermacher	2024-11-01T12:00:06Z	12	0	transformers	[ "transformers", "gguf", "en", "base_model:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.7", "base_model:quantized:AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.7", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]	null	2024-11-01T11:48:02Z	--- base_model: AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.7 language: - en library_name: transformers license: cc-by-sa-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.7 <!-- 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/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q2_K.gguf) \| Q2_K \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q3_K_S.gguf) \| Q3_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q3_K_M.gguf) \| Q3_K_M \| 3.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q3_K_L.gguf) \| Q3_K_L \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.IQ4_XS.gguf) \| IQ4_XS \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q4_K_S.gguf) \| Q4_K_S \| 3.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q4_K_M.gguf) \| Q4_K_M \| 3.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q5_K_S.gguf) \| Q5_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q5_K_M.gguf) \| Q5_K_M \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q6_K.gguf) \| Q6_K \| 5.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.Q8_0.gguf) \| Q8_0 \| 6.7 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AIFT-ko-orca-plat-Yi-ko-6b-v1.7-GGUF/resolve/main/AIFT-ko-orca-plat-Yi-ko-6b-v1.7.f16.gguf) \| f16 \| 12.5 \| 16 bpw, overkill \| 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 -->
mradermacher/TinyLlama-1.1B-32k-i1-GGUF	mradermacher	2024-11-01T11:51:07Z	69	0	transformers	[ "transformers", "gguf", "llama", "llama 2", "en", "dataset:togethercomputer/RedPajama-Data-1T-Sample", "base_model:Doctor-Shotgun/TinyLlama-1.1B-32k", "base_model:quantized:Doctor-Shotgun/TinyLlama-1.1B-32k", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-01T10:59:37Z	--- base_model: Doctor-Shotgun/TinyLlama-1.1B-32k datasets: - togethercomputer/RedPajama-Data-1T-Sample language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - llama - llama 2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-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/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ1_S.gguf) \| i1-IQ1_S \| 0.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ1_M.gguf) \| i1-IQ1_M \| 0.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ2_S.gguf) \| i1-IQ2_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ2_M.gguf) \| i1-IQ2_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q2_K.gguf) \| i1-Q2_K \| 0.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 0.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ3_S.gguf) \| i1-IQ3_S \| 0.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ3_M.gguf) \| i1-IQ3_M \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 0.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 0.7 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 0.7 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 0.7 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 0.7 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_0.gguf) \| i1-Q4_0 \| 0.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 0.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-GGUF/resolve/main/TinyLlama-1.1B-32k.i1-Q6_K.gguf) \| i1-Q6_K \| 1.0 \| 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 -->
parrottygg/phi3v1	parrottygg	2024-11-01T11:48:13Z	35	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T11:39:32Z	--- 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]
Noginowa/AnimaMixColorXL	Noginowa	2024-11-01T11:30:36Z	7	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "ja", "en", "license:other", "region:us" ]	text-to-image	2024-08-15T07:15:47Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - ja - en pipeline_tag: text-to-image tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image library_name: diffusers --- ![Cover](https://huggingface.co/Noginowa/AnimaMixColorXL/resolve/main/images/AnimaMixColorXL_v2_cover.jpeg) Animagine系のモデルをミックスしたVAE内蔵マージモデルです。<br> This is a VAE built-in merge model with a mix of Animagine-type models.<br> <br> より良いイラストを生成するにはできるだけ詳しくプロンプトを記述してください。シンプルなプロンプトでも悪くないイラストは生成できますが、1girlと品質プロンプトだけでは良いイラストにはなりません。<br> Please be as detailed as possible in your prompts to generate better illustrations. Simple prompts can generate not bad illustrations, but 1girl and quality prompts alone do not generate good illustrations.<br> <br> # ライセンス / License [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)<br> <br> # 以下のモデルをマージしています / The following models are merged * Animagine XL V3.1 * Anything XL * Async's MIX XL v3.1 $v2はv3.2$ * anima_pencil-XL v5.0.0 * anima_pencil-XL v4.0.0 Thank you for the model creators.<br> <br> # レシピ/ Recipe Files and versionsのレシピファイルを参照してください。 <br><br> # 作者 Civitai: [Noginowa](https://civitai.com/user/Noginowa)<br> Bluesky: [のぎのわ](https://bsky.app/profile/noginowa-ailab.bsky.social)
HengeBytes/ki-v0-16bit-vllm	HengeBytes	2024-11-01T11:26:00Z	77	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T11:14:36Z	--- 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: HengeBytes - 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)
adamNLP/learn_hf_food_not_food_text_classifier-distilbert-base-uncased	adamNLP	2024-11-01T11:18:03Z	105	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	2024-10-30T11:45:27Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased 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. --> # learn_hf_food_not_food_text_classifier-distilbert-base-uncased 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: 0.0005 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.409 \| 1.0 \| 7 \| 0.0798 \| 1.0 \| \| 0.0336 \| 2.0 \| 14 \| 0.0083 \| 1.0 \| \| 0.0052 \| 3.0 \| 21 \| 0.0023 \| 1.0 \| \| 0.0019 \| 4.0 \| 28 \| 0.0012 \| 1.0 \| \| 0.0012 \| 5.0 \| 35 \| 0.0009 \| 1.0 \| \| 0.0009 \| 6.0 \| 42 \| 0.0007 \| 1.0 \| \| 0.0143 \| 7.0 \| 49 \| 0.0006 \| 1.0 \| \| 0.0007 \| 8.0 \| 56 \| 0.0006 \| 1.0 \| \| 0.0007 \| 9.0 \| 63 \| 0.0006 \| 1.0 \| \| 0.0006 \| 10.0 \| 70 \| 0.0005 \| 1.0 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
Xu-Ouyang/pythia-12b-deduped-int3-step8-GPTQ-wikitext2	Xu-Ouyang	2024-11-01T11:16:58Z	76	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "gptq", "region:us" ]	text-generation	2024-11-01T11:06:40Z	--- 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]
razhan/trocr-base-ckb	razhan	2024-11-01T11:14:12Z	66	0	transformers	[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-04-01T11:35:44Z	# Kurdish OCR Transformer based ocr trained on synthetic Central Kurdish Data
Ariffiq99/Randomized_Roberta_Stacked_model_80	Ariffiq99	2024-11-01T11:14:00Z	103	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "multiple-choice", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2024-11-01T09:10:23Z	--- library_name: transformers license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: Randomized_Roberta_Stacked_model_80 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. --> # Randomized_Roberta_Stacked_model_80 This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8535 - F1: 0.7395 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.64 \| 1.0 \| 1261 \| 0.7758 \| 0.7327 \| \| 0.5704 \| 2.0 \| 2522 \| 0.7685 \| 0.7408 \| \| 0.5059 \| 3.0 \| 3783 \| 0.8209 \| 0.7401 \| \| 0.4519 \| 4.0 \| 5044 \| 0.8222 \| 0.7381 \| \| 0.4177 \| 5.0 \| 6305 \| 0.8535 \| 0.7395 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.5.0+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
mradermacher/IndoWebGen-7B-GGUF	mradermacher	2024-11-01T11:13:08Z	40	0	transformers	[ "transformers", "gguf", "id", "dataset:alxxtexxr/indowebgen-dataset", "base_model:alxxtexxr/IndoWebGen-7B", "base_model:quantized:alxxtexxr/IndoWebGen-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-01T11:00:34Z	--- base_model: alxxtexxr/IndoWebGen-7B datasets: - alxxtexxr/indowebgen-dataset language: - id 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/alxxtexxr/IndoWebGen-7B <!-- 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/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/IndoWebGen-7B-GGUF/resolve/main/IndoWebGen-7B.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| 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 -->
mradermacher/TinyLlama-1.1B-32k-GGUF	mradermacher	2024-11-01T11:11:24Z	14	0	transformers	[ "transformers", "gguf", "llama", "llama 2", "en", "dataset:togethercomputer/RedPajama-Data-1T-Sample", "base_model:Doctor-Shotgun/TinyLlama-1.1B-32k", "base_model:quantized:Doctor-Shotgun/TinyLlama-1.1B-32k", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-10-31T03:44:10Z	--- base_model: Doctor-Shotgun/TinyLlama-1.1B-32k datasets: - togethercomputer/RedPajama-Data-1T-Sample language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - llama - llama 2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-i1-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/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q2_K.gguf) \| Q2_K \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q3_K_S.gguf) \| Q3_K_S \| 0.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q3_K_M.gguf) \| Q3_K_M \| 0.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q3_K_L.gguf) \| Q3_K_L \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.IQ4_XS.gguf) \| IQ4_XS \| 0.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q4_K_S.gguf) \| Q4_K_S \| 0.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q4_K_M.gguf) \| Q4_K_M \| 0.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q5_K_S.gguf) \| Q5_K_S \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q5_K_M.gguf) \| Q5_K_M \| 0.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q6_K.gguf) \| Q6_K \| 1.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.Q8_0.gguf) \| Q8_0 \| 1.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/TinyLlama-1.1B-32k-GGUF/resolve/main/TinyLlama-1.1B-32k.f16.gguf) \| f16 \| 2.3 \| 16 bpw, overkill \| 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 -->
deepnet/SN9-C2-llama-HK4-7	deepnet	2024-11-01T11:00:43Z	222	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T10:57:56Z	--- 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]
sophiebui/ru-en_mtmodel_v1	sophiebui	2024-11-01T10:29:10Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:sophiebui/ru-en_mtmodel", "base_model:finetune:sophiebui/ru-en_mtmodel", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-01T10:15:57Z	--- library_name: transformers license: mit base_model: sophiebui/ru-en_mtmodel tags: - generated_from_trainer metrics: - bleu model-index: - name: ru-en_mtmodel_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ru-en_mtmodel_v1 This model is a fine-tuned version of [sophiebui/ru-en_mtmodel](https://huggingface.co/sophiebui/ru-en_mtmodel) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1134 - Bleu: 43.1972 - Gen Len: 30.6216 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 226 \| 1.2067 \| 39.6459 \| 30.2432 \| \| No log \| 2.0 \| 452 \| 1.1232 \| 40.3147 \| 30.8649 \| \| 1.2106 \| 3.0 \| 678 \| 1.1134 \| 43.1972 \| 30.6216 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
ghost613/VC-JHJ_Woman_40s-01-08-35.48	ghost613	2024-11-01T10:26:51Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-10-31T10:06:22Z	--- 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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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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tuanpasg/Puffin-Qwen2.5-TIES	tuanpasg	2024-11-01T10:18:53Z	136	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Qwen/Qwen2.5-1.5B", "base_model:merge:Qwen/Qwen2.5-1.5B", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:merge:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T10:09:53Z	--- base_model: - Qwen/Qwen2.5-Math-1.5B - Qwen/Qwen2.5-1.5B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-1.5B - model: Qwen/Qwen2.5-Math-1.5B parameters: density: 0.5 weight: 0.5 - model: Qwen/Qwen2.5-Math-1.5B parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: Qwen/Qwen2.5-1.5B parameters: normalize: false int8_mask: true dtype: bfloat16 ```
sophiebui/en-ru_mtmodel_v1	sophiebui	2024-11-01T10:13:23Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "m2m_100", "text2text-generation", "generated_from_trainer", "base_model:sophiebui/en-ru_mtmodel", "base_model:finetune:sophiebui/en-ru_mtmodel", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-01T09:49:27Z	--- library_name: transformers license: mit base_model: sophiebui/en-ru_mtmodel tags: - generated_from_trainer metrics: - bleu model-index: - name: en-ru_mtmodel_v1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # en-ru_mtmodel_v1 This model is a fine-tuned version of [sophiebui/en-ru_mtmodel](https://huggingface.co/sophiebui/en-ru_mtmodel) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8443 - Bleu: 44.9157 - Gen Len: 32.0811 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 226 \| 0.9394 \| 37.9005 \| 31.5405 \| \| No log \| 2.0 \| 452 \| 0.8537 \| 43.6072 \| 32.3514 \| \| 0.935 \| 3.0 \| 678 \| 0.8400 \| 46.3652 \| 31.8108 \| \| 0.935 \| 4.0 \| 904 \| 0.8482 \| 44.6002 \| 31.973 \| \| 0.4432 \| 5.0 \| 1130 \| 0.8443 \| 44.9157 \| 32.0811 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.1.0 - Tokenizers 0.19.1
coastalcph/CLIPDetail-8311682	coastalcph	2024-11-01T10:10:52Z	148	0	transformers	[ "transformers", "safetensors", "clip", "zero-shot-image-classification", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	zero-shot-image-classification	2024-11-01T10:10:28Z	--- 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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hyadess/UAP-EEE-llama-3.1-8b-16_bit_merged	hyadess	2024-11-01T10:00:13Z	16	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T09:52:29Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft --- # Uploaded model - Developed by: hyadess - 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)
NbAiLab/nb-wav2vec2-300m-nynorsk	NbAiLab	2024-11-01T09:54:59Z	128,927	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "nn", "dataset:NbAiLab/NPSC", "arxiv:2307.01672", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - automatic-speech-recognition datasets: - NbAiLab/NPSC language: - nn model-index: - name: nb-wav2vec2-300m-nynorsk results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: NPSC type: NbAiLab/NPSC args: 16K_mp3_nynorsk metrics: - name: Test (Nynorsk) WER type: wer value: 0.1222 - name: Test (Nynorsk) CER type: cer value: 0.0419 --- # Norwegian Wav2Vec2 Model - 300M - VoxRex - Nynorsk This model is finetuned on top of feature extractor [VoxRex-model](https://huggingface.co/KBLab/wav2vec2-large-voxrex) from the National Library of Sweden. The finetuned model achieves the following results on the test set with a 5-gram KenLM. The numbers in parentheses are the results without the language model: - WER: 0.1222 (0.1537) - CER: 0.0419 (0.0468) ## Model description This is one of several Wav2Vec-models our team created during the 🤗 hosted [Robust Speech Event](https://discuss.huggingface.co/t/open-to-the-community-robust-speech-recognition-challenge/13614?s=09). This is the complete list of our models and their final scores: \| Model \| Final WER \| \| \|:--------------\|:------------\|:------------:\| \| [NbAiLab/nb-wav2vec2-1b-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-bokmaal) \| 6.33 \| \| \| [NbAiLab/nb-wav2vec2-300m-bokmaal](https://huggingface.co/NbAiLab/nb-wav2vec2-300m-bokmaal) \| 7.03 \| \| \| [NbAiLab/nb-wav2vec2-1b-nynorsk](https://huggingface.co/NbAiLab/nb-wav2vec2-1b-nynorsk) \| 11.32 \| \| \| NbAiLab/nb-wav2vec2-300m-nynorsk (this model) \| 12.22 \| \| ### Dataset In parallel with the event, the team also converted the [Norwegian Parliamentary Speech Corpus (NPSC)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-58/) to the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) in 🤗 Dataset format and used that as the main source for training. ## Code We have released all the code developed during the event so that the Norwegian NLP community can build upon it when developing even better Norwegian ASR models. The finetuning of these models is not very computationally demanding. After following the instructions here, you should be able to train your own automatic speech recognition system in less than a day with an average GPU. ## Team The following people contributed to building this model: Rolv-Arild Braaten, Per Egil Kummervold, Andre Kåsen, Javier de la Rosa, Per Erik Solberg, and Freddy Wetjen. ## Training procedure To reproduce these results, we strongly recommend that you follow the [instructions from 🤗](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#talks) to train a simple Swedish model. When you have verified that you are able to do this, create a fresh new repo. You can then start by copying the files ```run.sh``` and ```run_speech_recognition_ctc.py``` from our repo. Running these will create all the other necessary files, and should let you reproduce our results. With some tweaks to the hyperparameters, you might even be able to build an even better ASR. Good luck! ### Language Model As the scores indicate, adding even a simple 5-gram language will improve the results. 🤗 has provided another [very nice blog](https://huggingface.co/blog/wav2vec2-with-ngram) explaining how to add a 5-gram language model to improve the ASR model. You can build this from your own corpus, for instance by extracting some suitable text from the [Norwegian Colossal Corpus](https://huggingface.co/datasets/NbAiLab/NCC). You can also skip some of the steps in the guide, and copy the [5-gram model from this repo](https://huggingface.co/NbAiLab/XLSR-300M-bokmaal/tree/main/language_model). ### Parameters The final model was run using these parameters: ``` --dataset_name="NbAiLab/NPSC" --model_name_or_path="KBLab/wav2vec2-large-voxrex" --dataset_config_name="16K_mp3_nynorsk" --output_dir="./" --overwrite_output_dir --num_train_epochs="80" --per_device_train_batch_size="16" --per_device_eval_batch_size="16" --gradient_accumulation_steps="2" --learning_rate="1e-4" --warmup_steps="2000" --length_column_name="input_length" --evaluation_strategy="steps" --text_column_name="text" --save_steps="500" --eval_steps="500" --logging_steps="100" --layerdrop="0.041" --attention_dropout="0.094" --activation_dropout="0.055" --hidden_dropout="0.047" --save_total_limit="3" --freeze_feature_encoder --feat_proj_dropout="0.04" --mask_time_prob="0.082" --mask_time_length="10" --mask_feature_prob="0.25" --mask_feature_length="64" --gradient_checkpointing --min_duration_in_seconds="0.5" --max_duration_in_seconds="30.0" --use_auth_token --seed="42" --fp16 --group_by_length --do_train --do_eval --push_to_hub --preprocessing_num_workers="32" ``` Using these settings, the training might take 3-4 days on an average GPU. You can, however, get a decent model and faster results by tweaking these parameters. \| Parameter\| Comment \| \|:-------------\|:-----\| \| per_device_train_batch_size \| Adjust this to the maximum of available memory. 16 or 24 might be good settings depending on your system \| \|gradient_accumulation_steps \|Can be adjusted even further up to increase batch size and speed up training without running into memory issues \| \| learning_rate\|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability \| \| epochs\| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs\| ## Citation ```bibtex @inproceedings{de-la-rosa-etal-2023-boosting, title = "Boosting {N}orwegian Automatic Speech Recognition", author = "De La Rosa, Javier and Braaten, Rolv-Arild and Kummervold, Per and Wetjen, Freddy", booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)", month = may, year = "2023", address = "T{\'o}rshavn, Faroe Islands", publisher = "University of Tartu Library", url = "https://aclanthology.org/2023.nodalida-1.55", pages = "555--564", abstract = "In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokm{\aa}l and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech datasets. Additionally, we measure the performance of these models against previous state-of-the-art ASR models, as well as on out-of-domain datasets. We improve the state of the art on the Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of 17.10{\%} to 7.60{\%}, with models achieving 5.81{\%} for Bokm{\aa}l and 11.54{\%} for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.", } ``` See https://arxiv.org/abs/2307.01672
tuanpasg/Puffin-Qwen2.5-CodeMath-1	tuanpasg	2024-11-01T09:53:53Z	134	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:Qwen/Qwen2.5-Coder-1.5B", "base_model:merge:Qwen/Qwen2.5-Coder-1.5B", "base_model:Qwen/Qwen2.5-Math-1.5B", "base_model:merge:Qwen/Qwen2.5-Math-1.5B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-01T09:52:35Z	--- base_model: - Qwen/Qwen2.5-Coder-1.5B - Qwen/Qwen2.5-Math-1.5B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) * [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-Coder-1.5B - model: Qwen/Qwen2.5-Math-1.5B merge_method: slerp base_model: Qwen/Qwen2.5-Coder-1.5B 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 ```
raaedk/subliminal_large	raaedk	2024-11-01T09:43:26Z	8	0	diffusers	[ "diffusers", "sd3", "sd3-diffusers", "text-to-image", "simpletuner", "safe-for-work", "lora", "template:sd-lora", "lycoris", "base_model:stabilityai/stable-diffusion-3.5-large", "base_model:adapter:stabilityai/stable-diffusion-3.5-large", "license:other", "region:us" ]	text-to-image	2024-11-01T05:22:58Z	--- license: other base_model: "stabilityai/stable-diffusion-3.5-large" tags: - sd3 - sd3-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - lycoris inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'ps2 graphics, liminal, hotel lobby, videogame screenshot' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png --- # subliminal_large This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large). The main validation prompt used during training was: ``` ps2 graphics, liminal, hotel lobby, videogame screenshot ``` ## Validation settings - CFG: `5.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder was not trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 2 - Training steps: 6500 - Learning rate: 0.0001 - Max grad norm: 0.01 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LyCORIS Config: ```json { "algo": "lora", "multiplier": 1.0, "linear_dim": 64, "linear_alpha": 32, "apply_preset": { "target_module": [ "Attention", "FeedForward" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### ps2_subliminal-512 - Repeats: 10 - Total number of images: 55 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ps2_subliminal-1024 - Repeats: 10 - Total number of images: 55 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ps2_subliminal-512-crop - Repeats: 10 - Total number of images: 55 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ### ps2_subliminal-1024-crop - Repeats: 10 - Total number of images: 55 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: random - Crop aspect: square - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights model_id = 'stabilityai/stable-diffusion-3.5-large' adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer) wrapper.merge_to() prompt = "ps2 graphics, liminal, hotel lobby, videogame screenshot" negative_prompt = 'blurry, cropped, ugly' pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=5.0, ).images[0] image.save("output.png", format="PNG") ```
mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF	mradermacher	2024-11-01T09:43:13Z	27	0	transformers	[ "transformers", "gguf", "en", "base_model:mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged", "base_model:quantized:mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged", "endpoints_compatible", "region:us" ]	null	2024-11-01T09:15:24Z	--- base_model: mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged <!-- 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/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-arxiv-summarization-10k-last_merged.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| 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 -->
mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF	mradermacher	2024-11-01T09:34:09Z	30	0	transformers	[ "transformers", "gguf", "en", "base_model:mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged", "base_model:quantized:mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged", "endpoints_compatible", "region:us" ]	null	2024-11-01T09:20:46Z	--- base_model: mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged <!-- 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/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged-GGUF/resolve/main/meta-llama-Llama-2-7b-hf-pubmed-summarization-10k-last_merged.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| 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 -->
VTSNLP/trans_model_vi_en	VTSNLP	2024-11-01T09:30:58Z	5	1	null	[ "tensorboard", "safetensors", "t5", "generated_from_trainer", "base_model:VietAI/envit5-translation", "base_model:finetune:VietAI/envit5-translation", "license:openrail", "region:us" ]	null	2024-11-01T09:30:13Z	--- license: openrail base_model: VietAI/envit5-translation tags: - generated_from_trainer model-index: - name: trans_model_vi_en 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. --> # trans_model_vi_en This model is a fine-tuned version of [VietAI/envit5-translation](https://huggingface.co/VietAI/envit5-translation) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - num_epochs: 4 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
GeneZC/MiniMA-2-3B	GeneZC	2024-11-01T09:22:35Z	1,760	17	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "en", "zh", "dataset:EleutherAI/pile", "dataset:togethercomputer/RedPajama-Data-1T", "dataset:p208p2002/wudao", "arxiv:2311.07052", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-27T03:36:23Z	--- language: - en - zh license: apache-2.0 library_name: transformers datasets: - EleutherAI/pile - togethercomputer/RedPajama-Data-1T - p208p2002/wudao widget: - text: <s> 4 + 3 = model-index: - name: MiniMA-2-3B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 44.71 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 69.33 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 41.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 38.44 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 66.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 8.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=GeneZC/MiniMA-2-3B name: Open LLM Leaderboard --- ## MiniMA-2-3B 📑 [arXiv](https://arxiv.org/abs/2311.07052) \| 👻 [GitHub](https://github.com/GeneZC/MiniMA) \| 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) \| 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) \| 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) \| 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) \| 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) \| 🤗 [HuggingFace-MiniMA-2](https://huggingface.co/GeneZC/MiniMA-2-3B) \| 🤗 [HuggingFace-MiniChat-2](https://huggingface.co/GeneZC/MiniChat-2-3B) 🆕 Updates from MiniMA-3B: - continued from MiniMA-3B without distillation; - better data mixture; - more trained tokens. ❗ Must comply with LICENSE of LLaMA-2 since it is derived from LLaMA-2. A language model continued from MiniMA-3B. Completing the compute-performance pareto frontier together with MiniMA-3B and other arts. <img src="./teaser_a.jpg" alt="teaser_a" width="700" /> Standard Benchmarks \|Method\|TFLOPs\|MMLU (5-shot)\|CEval (5-shot)\|DROP (3-shot)\|HumanEval (0-shot)\|BBH (3-shot)\|GSM8K (8-shot)\| \|--\|--\|--\|--\|--\|--\|--\|--\| \|Mamba-2.8B\|4.6E9\|25.58\|24.74\|15.72\|7.32\|29.37\|3.49\| \|ShearedLLaMA-2.7B\|0.8E9\|26.97\|22.88\|19.98\|4.88\|30.48\|3.56\| \|BTLM-3B\|11.3E9\|27.20\|26.00\|17.84\|10.98\|30.87\|4.55\| \|StableLM-3B\|72.0E9\|44.75\|31.05\|22.35\|15.85\|32.59\|10.99\| \|Qwen-1.8B\|23.8E9\|44.05\|54.75\|12.97\|14.02\|30.80\|22.97\| \|Phi-2-2.8B\|159.9E9\|56.74\|34.03\|30.74\|46.95\|44.13\|55.42\| \|LLaMA-2-7B\|84.0E9\|46.00\|34.40\|31.57\|12.80\|32.02\|14.10\| \|\| \|MiniMA-3B\|4.0E9\|28.51\|28.23\|22.50\|10.98\|31.61\|8.11\| \|MiniChat-3B\|4.0E9\|38.40\|36.48\|22.58\|18.29\|31.36\|29.72\| \|MiniMA-2-3B\|13.4E9\|40.14\|44.65\|23.10\|14.63\|31.43\|8.87\| \|MiniChat-2-3B\|13.4E9\|46.17\|43.91\|30.26\|22.56\|34.95\|38.13\| The following is an example code snippet to use MiniMA-2-3B: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # MiniMA tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniMA-2-3B", use_fast=False) # GPU. model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-2-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() # CPU. # model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniMA-2-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float16).eval() prompt = "Question: Sherrie tells the truth. Vernell says Sherrie tells the truth. Alexis says Vernell lies. Michaela says Alexis tells the truth. Elanor says Michaela tells the truth. Does Elanor tell the truth?\nAnswer: No\n\nQuestion: Kristian lies. Sherrie says Kristian lies. Delbert says Sherrie lies. Jerry says Delbert tells the truth. Shalonda says Jerry tells the truth. Does Shalonda tell the truth?\nAnswer: No\n\nQuestion: Vina tells the truth. Helene says Vina lies. Kandi says Helene tells the truth. Jamey says Kandi lies. Ka says Jamey lies. Does Ka tell the truth?\nAnswer: No\n\nQuestion: Christie tells the truth. Ka says Christie tells the truth. Delbert says Ka lies. Leda says Delbert tells the truth. Lorine says Leda tells the truth. Does Lorine tell the truth?\nAnswer:" input_ids = tokenizer([prompt]).input_ids output_ids = model.generate( torch.as_tensor(input_ids).cuda(), do_sample=True, temperature=0.7, max_new_tokens=1024, ) output_ids = output_ids[0][len(input_ids[0]):] output = tokenizer.decode(output_ids, skip_special_tokens=True).strip() # output: "No" ``` ## Bibtex ```bibtex @article{zhang2023law, title={Towards the Law of Capacity Gap in Distilling Language Models}, author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan}, year={2023}, url={https://arxiv.org/abs/2311.07052} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_GeneZC__MiniMA-2-3B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|44.75\| \|AI2 Reasoning Challenge (25-Shot)\|44.71\| \|HellaSwag (10-Shot) \|69.33\| \|MMLU (5-Shot) \|41.22\| \|TruthfulQA (0-shot) \|38.44\| \|Winogrande (5-shot) \|66.69\| \|GSM8k (5-shot) \| 8.11\|

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