librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-05-31 18:27:08	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 461 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-05-31 18:26:36	card stringlengths 11 1.01M
Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only	Omar95farag	2024-01-15T09:53:58Z	94	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-12T08:10:10Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only 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. --> # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1314 - Accuracy: 0.7925 - Exit 0 Accuracy: 0.075 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Exit 0 Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\| \| No log \| 0.96 \| 16 \| 2.6835 \| 0.1175 \| 0.0775 \| \| No log \| 1.98 \| 33 \| 2.4924 \| 0.2375 \| 0.0675 \| \| No log \| 3.0 \| 50 \| 2.2916 \| 0.34 \| 0.07 \| \| No log \| 3.96 \| 66 \| 2.0277 \| 0.475 \| 0.07 \| \| No log \| 4.98 \| 83 \| 1.7026 \| 0.5775 \| 0.07 \| \| No log \| 6.0 \| 100 \| 1.4354 \| 0.6625 \| 0.075 \| \| No log \| 6.96 \| 116 \| 1.2357 \| 0.6975 \| 0.07 \| \| No log \| 7.98 \| 133 \| 1.0754 \| 0.745 \| 0.0725 \| \| No log \| 9.0 \| 150 \| 0.9760 \| 0.77 \| 0.07 \| \| No log \| 9.96 \| 166 \| 0.8967 \| 0.765 \| 0.07 \| \| No log \| 10.98 \| 183 \| 0.8561 \| 0.7875 \| 0.07 \| \| No log \| 12.0 \| 200 \| 0.8837 \| 0.7575 \| 0.07 \| \| No log \| 12.96 \| 216 \| 0.8885 \| 0.7725 \| 0.07 \| \| No log \| 13.98 \| 233 \| 0.7819 \| 0.7725 \| 0.0675 \| \| No log \| 15.0 \| 250 \| 0.9067 \| 0.7725 \| 0.07 \| \| No log \| 15.96 \| 266 \| 0.9086 \| 0.775 \| 0.075 \| \| No log \| 16.98 \| 283 \| 0.8444 \| 0.795 \| 0.07 \| \| No log \| 18.0 \| 300 \| 0.9359 \| 0.7775 \| 0.0725 \| \| No log \| 18.96 \| 316 \| 0.9696 \| 0.7825 \| 0.07 \| \| No log \| 19.98 \| 333 \| 0.9254 \| 0.7825 \| 0.075 \| \| No log \| 21.0 \| 350 \| 0.9879 \| 0.7775 \| 0.0675 \| \| No log \| 21.96 \| 366 \| 0.9894 \| 0.79 \| 0.075 \| \| No log \| 22.98 \| 383 \| 1.0087 \| 0.785 \| 0.07 \| \| No log \| 24.0 \| 400 \| 1.0101 \| 0.785 \| 0.07 \| \| No log \| 24.96 \| 416 \| 1.0188 \| 0.7875 \| 0.075 \| \| No log \| 25.98 \| 433 \| 1.0266 \| 0.79 \| 0.075 \| \| No log \| 27.0 \| 450 \| 1.0357 \| 0.79 \| 0.07 \| \| No log \| 27.96 \| 466 \| 1.0505 \| 0.7875 \| 0.0675 \| \| No log \| 28.98 \| 483 \| 1.0524 \| 0.7825 \| 0.07 \| \| 1.6927 \| 30.0 \| 500 \| 1.0656 \| 0.785 \| 0.075 \| \| 1.6927 \| 30.96 \| 516 \| 1.0642 \| 0.785 \| 0.0725 \| \| 1.6927 \| 31.98 \| 533 \| 1.0740 \| 0.785 \| 0.0725 \| \| 1.6927 \| 33.0 \| 550 \| 1.0830 \| 0.785 \| 0.075 \| \| 1.6927 \| 33.96 \| 566 \| 1.0860 \| 0.785 \| 0.0675 \| \| 1.6927 \| 34.98 \| 583 \| 1.0939 \| 0.7875 \| 0.0675 \| \| 1.6927 \| 36.0 \| 600 \| 1.0969 \| 0.79 \| 0.0675 \| \| 1.6927 \| 36.96 \| 616 \| 1.0966 \| 0.7875 \| 0.07 \| \| 1.6927 \| 37.98 \| 633 \| 1.1024 \| 0.79 \| 0.07 \| \| 1.6927 \| 39.0 \| 650 \| 1.1045 \| 0.7875 \| 0.075 \| \| 1.6927 \| 39.96 \| 666 \| 1.1050 \| 0.7875 \| 0.0725 \| \| 1.6927 \| 40.98 \| 683 \| 1.1085 \| 0.7875 \| 0.075 \| \| 1.6927 \| 42.0 \| 700 \| 1.1171 \| 0.7875 \| 0.075 \| \| 1.6927 \| 42.96 \| 716 \| 1.1193 \| 0.7875 \| 0.075 \| \| 1.6927 \| 43.98 \| 733 \| 1.1188 \| 0.79 \| 0.075 \| \| 1.6927 \| 45.0 \| 750 \| 1.1220 \| 0.79 \| 0.0725 \| \| 1.6927 \| 45.96 \| 766 \| 1.1270 \| 0.79 \| 0.0725 \| \| 1.6927 \| 46.98 \| 783 \| 1.1268 \| 0.7875 \| 0.075 \| \| 1.6927 \| 48.0 \| 800 \| 1.1264 \| 0.79 \| 0.075 \| \| 1.6927 \| 48.96 \| 816 \| 1.1267 \| 0.795 \| 0.075 \| \| 1.6927 \| 49.98 \| 833 \| 1.1273 \| 0.79 \| 0.0725 \| \| 1.6927 \| 51.0 \| 850 \| 1.1268 \| 0.79 \| 0.07 \| \| 1.6927 \| 51.96 \| 866 \| 1.1283 \| 0.795 \| 0.07 \| \| 1.6927 \| 52.98 \| 883 \| 1.1293 \| 0.795 \| 0.075 \| \| 1.6927 \| 54.0 \| 900 \| 1.1306 \| 0.795 \| 0.075 \| \| 1.6927 \| 54.96 \| 916 \| 1.1306 \| 0.795 \| 0.075 \| \| 1.6927 \| 55.98 \| 933 \| 1.1310 \| 0.795 \| 0.075 \| \| 1.6927 \| 57.0 \| 950 \| 1.1314 \| 0.7925 \| 0.075 \| \| 1.6927 \| 57.6 \| 960 \| 1.1314 \| 0.7925 \| 0.075 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only	Omar95farag	2024-01-15T09:53:58Z	89	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-12T04:42:47Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only 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. --> # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2733 - Accuracy: 0.7825 - Exit 0 Accuracy: 0.0775 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Exit 0 Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\| \| No log \| 0.96 \| 16 \| 2.6800 \| 0.13 \| 0.0675 \| \| No log \| 1.98 \| 33 \| 2.4446 \| 0.28 \| 0.0725 \| \| No log \| 3.0 \| 50 \| 2.1924 \| 0.37 \| 0.0675 \| \| No log \| 3.96 \| 66 \| 1.8733 \| 0.5175 \| 0.07 \| \| No log \| 4.98 \| 83 \| 1.6056 \| 0.6075 \| 0.0775 \| \| No log \| 6.0 \| 100 \| 1.3480 \| 0.6725 \| 0.08 \| \| No log \| 6.96 \| 116 \| 1.1393 \| 0.735 \| 0.07 \| \| No log \| 7.98 \| 133 \| 1.0738 \| 0.7375 \| 0.07 \| \| No log \| 9.0 \| 150 \| 0.9271 \| 0.7725 \| 0.075 \| \| No log \| 9.96 \| 166 \| 0.8885 \| 0.7675 \| 0.085 \| \| No log \| 10.98 \| 183 \| 0.8669 \| 0.76 \| 0.075 \| \| No log \| 12.0 \| 200 \| 0.8547 \| 0.7775 \| 0.0725 \| \| No log \| 12.96 \| 216 \| 0.8633 \| 0.76 \| 0.07 \| \| No log \| 13.98 \| 233 \| 0.8498 \| 0.7675 \| 0.075 \| \| No log \| 15.0 \| 250 \| 0.9608 \| 0.7675 \| 0.0675 \| \| No log \| 15.96 \| 266 \| 0.8952 \| 0.7875 \| 0.08 \| \| No log \| 16.98 \| 283 \| 0.9486 \| 0.7575 \| 0.0725 \| \| No log \| 18.0 \| 300 \| 0.9826 \| 0.765 \| 0.0825 \| \| No log \| 18.96 \| 316 \| 1.0230 \| 0.7625 \| 0.09 \| \| No log \| 19.98 \| 333 \| 1.0961 \| 0.76 \| 0.0875 \| \| No log \| 21.0 \| 350 \| 1.0083 \| 0.785 \| 0.07 \| \| No log \| 21.96 \| 366 \| 1.0394 \| 0.7725 \| 0.0725 \| \| No log \| 22.98 \| 383 \| 1.0825 \| 0.78 \| 0.085 \| \| No log \| 24.0 \| 400 \| 1.0789 \| 0.77 \| 0.075 \| \| No log \| 24.96 \| 416 \| 1.1030 \| 0.7725 \| 0.0925 \| \| No log \| 25.98 \| 433 \| 1.1252 \| 0.775 \| 0.075 \| \| No log \| 27.0 \| 450 \| 1.1333 \| 0.7725 \| 0.0725 \| \| No log \| 27.96 \| 466 \| 1.1416 \| 0.765 \| 0.0775 \| \| No log \| 28.98 \| 483 \| 1.1442 \| 0.7775 \| 0.0775 \| \| 1.6768 \| 30.0 \| 500 \| 1.1620 \| 0.7825 \| 0.1025 \| \| 1.6768 \| 30.96 \| 516 \| 1.1617 \| 0.7825 \| 0.0775 \| \| 1.6768 \| 31.98 \| 533 \| 1.1788 \| 0.775 \| 0.0875 \| \| 1.6768 \| 33.0 \| 550 \| 1.1858 \| 0.7725 \| 0.0825 \| \| 1.6768 \| 33.96 \| 566 \| 1.1842 \| 0.7825 \| 0.0725 \| \| 1.6768 \| 34.98 \| 583 \| 1.1964 \| 0.785 \| 0.085 \| \| 1.6768 \| 36.0 \| 600 \| 1.2034 \| 0.78 \| 0.075 \| \| 1.6768 \| 36.96 \| 616 \| 1.2050 \| 0.7825 \| 0.07 \| \| 1.6768 \| 37.98 \| 633 \| 1.2111 \| 0.7825 \| 0.075 \| \| 1.6768 \| 39.0 \| 650 \| 1.2217 \| 0.785 \| 0.0925 \| \| 1.6768 \| 39.96 \| 666 \| 1.2510 \| 0.7775 \| 0.105 \| \| 1.6768 \| 40.98 \| 683 \| 1.2512 \| 0.7825 \| 0.0825 \| \| 1.6768 \| 42.0 \| 700 \| 1.2529 \| 0.7775 \| 0.0775 \| \| 1.6768 \| 42.96 \| 716 \| 1.2557 \| 0.78 \| 0.0725 \| \| 1.6768 \| 43.98 \| 733 \| 1.2615 \| 0.7775 \| 0.0775 \| \| 1.6768 \| 45.0 \| 750 \| 1.2621 \| 0.78 \| 0.0825 \| \| 1.6768 \| 45.96 \| 766 \| 1.2613 \| 0.785 \| 0.075 \| \| 1.6768 \| 46.98 \| 783 \| 1.2614 \| 0.78 \| 0.075 \| \| 1.6768 \| 48.0 \| 800 \| 1.2598 \| 0.7825 \| 0.075 \| \| 1.6768 \| 48.96 \| 816 \| 1.2650 \| 0.7825 \| 0.085 \| \| 1.6768 \| 49.98 \| 833 \| 1.2665 \| 0.7825 \| 0.08 \| \| 1.6768 \| 51.0 \| 850 \| 1.2673 \| 0.785 \| 0.0775 \| \| 1.6768 \| 51.96 \| 866 \| 1.2626 \| 0.7775 \| 0.075 \| \| 1.6768 \| 52.98 \| 883 \| 1.2643 \| 0.7825 \| 0.075 \| \| 1.6768 \| 54.0 \| 900 \| 1.2702 \| 0.78 \| 0.0775 \| \| 1.6768 \| 54.96 \| 916 \| 1.2723 \| 0.78 \| 0.0775 \| \| 1.6768 \| 55.98 \| 933 \| 1.2730 \| 0.7825 \| 0.0775 \| \| 1.6768 \| 57.0 \| 950 \| 1.2732 \| 0.7825 \| 0.0775 \| \| 1.6768 \| 57.6 \| 960 \| 1.2733 \| 0.7825 \| 0.0775 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only	Omar95farag	2024-01-15T09:53:57Z	92	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-12T01:20:21Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only 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. --> # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3116 - Accuracy: 0.7775 - Exit 0 Accuracy: 0.065 - Exit 1 Accuracy: 0.09 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Exit 0 Accuracy \| Exit 1 Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:---------------:\| \| No log \| 0.96 \| 16 \| 2.6827 \| 0.1275 \| 0.0625 \| 0.04 \| \| No log \| 1.98 \| 33 \| 2.5158 \| 0.25 \| 0.06 \| 0.095 \| \| No log \| 3.0 \| 50 \| 2.3010 \| 0.33 \| 0.0625 \| 0.095 \| \| No log \| 3.96 \| 66 \| 1.9997 \| 0.435 \| 0.055 \| 0.0925 \| \| No log \| 4.98 \| 83 \| 1.7239 \| 0.6 \| 0.06 \| 0.0925 \| \| No log \| 6.0 \| 100 \| 1.4812 \| 0.6175 \| 0.06 \| 0.09 \| \| No log \| 6.96 \| 116 \| 1.2872 \| 0.6875 \| 0.0625 \| 0.09 \| \| No log \| 7.98 \| 133 \| 1.1118 \| 0.74 \| 0.055 \| 0.09 \| \| No log \| 9.0 \| 150 \| 1.0144 \| 0.7425 \| 0.0625 \| 0.09 \| \| No log \| 9.96 \| 166 \| 0.9663 \| 0.7475 \| 0.0575 \| 0.09 \| \| No log \| 10.98 \| 183 \| 0.9532 \| 0.7475 \| 0.0625 \| 0.09 \| \| No log \| 12.0 \| 200 \| 0.9157 \| 0.7525 \| 0.06 \| 0.09 \| \| No log \| 12.96 \| 216 \| 0.8894 \| 0.77 \| 0.06 \| 0.09 \| \| No log \| 13.98 \| 233 \| 0.9460 \| 0.75 \| 0.0625 \| 0.09 \| \| No log \| 15.0 \| 250 \| 1.0019 \| 0.745 \| 0.0625 \| 0.09 \| \| No log \| 15.96 \| 266 \| 0.9059 \| 0.77 \| 0.0625 \| 0.0875 \| \| No log \| 16.98 \| 283 \| 1.0664 \| 0.7325 \| 0.06 \| 0.0875 \| \| No log \| 18.0 \| 300 \| 1.0637 \| 0.74 \| 0.065 \| 0.0875 \| \| No log \| 18.96 \| 316 \| 1.0398 \| 0.7725 \| 0.09 \| 0.085 \| \| No log \| 19.98 \| 333 \| 1.0745 \| 0.775 \| 0.06 \| 0.0875 \| \| No log \| 21.0 \| 350 \| 1.0653 \| 0.78 \| 0.0625 \| 0.0875 \| \| No log \| 21.96 \| 366 \| 1.0705 \| 0.785 \| 0.065 \| 0.0875 \| \| No log \| 22.98 \| 383 \| 1.1014 \| 0.78 \| 0.0725 \| 0.0875 \| \| No log \| 24.0 \| 400 \| 1.1335 \| 0.78 \| 0.0625 \| 0.0875 \| \| No log \| 24.96 \| 416 \| 1.1510 \| 0.775 \| 0.0725 \| 0.0875 \| \| No log \| 25.98 \| 433 \| 1.1528 \| 0.7825 \| 0.0675 \| 0.0875 \| \| No log \| 27.0 \| 450 \| 1.1758 \| 0.7825 \| 0.0625 \| 0.0875 \| \| No log \| 27.96 \| 466 \| 1.1836 \| 0.785 \| 0.07 \| 0.0875 \| \| No log \| 28.98 \| 483 \| 1.1927 \| 0.78 \| 0.0675 \| 0.0875 \| \| 1.6955 \| 30.0 \| 500 \| 1.2061 \| 0.7825 \| 0.0775 \| 0.0875 \| \| 1.6955 \| 30.96 \| 516 \| 1.2128 \| 0.7775 \| 0.065 \| 0.0875 \| \| 1.6955 \| 31.98 \| 533 \| 1.2172 \| 0.7725 \| 0.07 \| 0.0875 \| \| 1.6955 \| 33.0 \| 550 \| 1.2237 \| 0.775 \| 0.075 \| 0.0875 \| \| 1.6955 \| 33.96 \| 566 \| 1.2399 \| 0.7775 \| 0.0625 \| 0.0875 \| \| 1.6955 \| 34.98 \| 583 \| 1.2590 \| 0.78 \| 0.065 \| 0.0875 \| \| 1.6955 \| 36.0 \| 600 \| 1.2586 \| 0.7825 \| 0.065 \| 0.0875 \| \| 1.6955 \| 36.96 \| 616 \| 1.2603 \| 0.775 \| 0.0675 \| 0.0875 \| \| 1.6955 \| 37.98 \| 633 \| 1.2576 \| 0.78 \| 0.065 \| 0.0875 \| \| 1.6955 \| 39.0 \| 650 \| 1.2698 \| 0.7775 \| 0.075 \| 0.0875 \| \| 1.6955 \| 39.96 \| 666 \| 1.2775 \| 0.7725 \| 0.075 \| 0.0875 \| \| 1.6955 \| 40.98 \| 683 \| 1.2769 \| 0.7725 \| 0.07 \| 0.0875 \| \| 1.6955 \| 42.0 \| 700 \| 1.2769 \| 0.7725 \| 0.0625 \| 0.0875 \| \| 1.6955 \| 42.96 \| 716 \| 1.2804 \| 0.775 \| 0.0675 \| 0.0875 \| \| 1.6955 \| 43.98 \| 733 \| 1.2834 \| 0.775 \| 0.065 \| 0.085 \| \| 1.6955 \| 45.0 \| 750 \| 1.2907 \| 0.7775 \| 0.0675 \| 0.0875 \| \| 1.6955 \| 45.96 \| 766 \| 1.2968 \| 0.7775 \| 0.0675 \| 0.0875 \| \| 1.6955 \| 46.98 \| 783 \| 1.2981 \| 0.7775 \| 0.065 \| 0.0875 \| \| 1.6955 \| 48.0 \| 800 \| 1.3017 \| 0.7775 \| 0.065 \| 0.0875 \| \| 1.6955 \| 48.96 \| 816 \| 1.3050 \| 0.7775 \| 0.0675 \| 0.09 \| \| 1.6955 \| 49.98 \| 833 \| 1.3050 \| 0.775 \| 0.07 \| 0.09 \| \| 1.6955 \| 51.0 \| 850 \| 1.3044 \| 0.775 \| 0.07 \| 0.09 \| \| 1.6955 \| 51.96 \| 866 \| 1.3057 \| 0.775 \| 0.0675 \| 0.09 \| \| 1.6955 \| 52.98 \| 883 \| 1.3072 \| 0.7775 \| 0.0675 \| 0.09 \| \| 1.6955 \| 54.0 \| 900 \| 1.3101 \| 0.7775 \| 0.0675 \| 0.09 \| \| 1.6955 \| 54.96 \| 916 \| 1.3119 \| 0.7775 \| 0.065 \| 0.09 \| \| 1.6955 \| 55.98 \| 933 \| 1.3116 \| 0.7775 \| 0.065 \| 0.09 \| \| 1.6955 \| 57.0 \| 950 \| 1.3115 \| 0.7775 \| 0.065 \| 0.09 \| \| 1.6955 \| 57.6 \| 960 \| 1.3116 \| 0.7775 \| 0.065 \| 0.09 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10	Omar95farag	2024-01-15T09:53:54Z	93	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-08-09T22:32:58Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 10.1413 - Accuracy: 0.7325 - Exit 0 Accuracy: 0.1725 - Exit 1 Accuracy: 0.2175 - Exit 2 Accuracy: 0.6075 - Exit 3 Accuracy: 0.715 - Exit 4 Accuracy: 0.735 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Exit 0 Accuracy \| Exit 1 Accuracy \| Exit 2 Accuracy \| Exit 3 Accuracy \| Exit 4 Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\| \| No log \| 0.96 \| 16 \| 16.4817 \| 0.17 \| 0.0825 \| 0.045 \| 0.105 \| 0.0625 \| 0.0625 \| \| No log \| 1.98 \| 33 \| 15.9950 \| 0.2675 \| 0.1 \| 0.1325 \| 0.195 \| 0.1775 \| 0.2425 \| \| No log \| 3.0 \| 50 \| 14.9811 \| 0.475 \| 0.1025 \| 0.1475 \| 0.24 \| 0.29 \| 0.4425 \| \| No log \| 3.96 \| 66 \| 14.0127 \| 0.5675 \| 0.105 \| 0.1425 \| 0.27 \| 0.3975 \| 0.505 \| \| No log \| 4.98 \| 83 \| 13.3047 \| 0.6075 \| 0.125 \| 0.1425 \| 0.3175 \| 0.43 \| 0.595 \| \| No log \| 6.0 \| 100 \| 12.7573 \| 0.6125 \| 0.125 \| 0.1475 \| 0.325 \| 0.495 \| 0.615 \| \| No log \| 6.96 \| 116 \| 12.3656 \| 0.645 \| 0.1175 \| 0.155 \| 0.33 \| 0.5175 \| 0.6375 \| \| No log \| 7.98 \| 133 \| 11.9582 \| 0.6625 \| 0.115 \| 0.16 \| 0.3525 \| 0.5725 \| 0.67 \| \| No log \| 9.0 \| 150 \| 11.6533 \| 0.6825 \| 0.1225 \| 0.16 \| 0.375 \| 0.6 \| 0.7075 \| \| No log \| 9.96 \| 166 \| 11.5143 \| 0.685 \| 0.1525 \| 0.1625 \| 0.38 \| 0.6 \| 0.675 \| \| No log \| 10.98 \| 183 \| 11.3152 \| 0.6625 \| 0.115 \| 0.1625 \| 0.41 \| 0.6225 \| 0.6725 \| \| No log \| 12.0 \| 200 \| 11.0708 \| 0.695 \| 0.11 \| 0.1625 \| 0.425 \| 0.6225 \| 0.7075 \| \| No log \| 12.96 \| 216 \| 11.0412 \| 0.6975 \| 0.1125 \| 0.1575 \| 0.4 \| 0.645 \| 0.685 \| \| No log \| 13.98 \| 233 \| 10.8782 \| 0.7125 \| 0.1425 \| 0.165 \| 0.4275 \| 0.6325 \| 0.7075 \| \| No log \| 15.0 \| 250 \| 10.7282 \| 0.7075 \| 0.115 \| 0.165 \| 0.4225 \| 0.65 \| 0.7175 \| \| No log \| 15.96 \| 266 \| 10.7039 \| 0.695 \| 0.15 \| 0.16 \| 0.4375 \| 0.6375 \| 0.69 \| \| No log \| 16.98 \| 283 \| 10.5455 \| 0.7125 \| 0.13 \| 0.165 \| 0.4375 \| 0.6675 \| 0.715 \| \| No log \| 18.0 \| 300 \| 10.5214 \| 0.7075 \| 0.1275 \| 0.17 \| 0.45 \| 0.6825 \| 0.7075 \| \| No log \| 18.96 \| 316 \| 10.4995 \| 0.715 \| 0.155 \| 0.1725 \| 0.4525 \| 0.68 \| 0.7125 \| \| No log \| 19.98 \| 333 \| 10.3224 \| 0.725 \| 0.1475 \| 0.1825 \| 0.46 \| 0.68 \| 0.7225 \| \| No log \| 21.0 \| 350 \| 10.4247 \| 0.71 \| 0.1425 \| 0.1825 \| 0.4625 \| 0.68 \| 0.71 \| \| No log \| 21.96 \| 366 \| 10.3881 \| 0.705 \| 0.1375 \| 0.1825 \| 0.46 \| 0.66 \| 0.7125 \| \| No log \| 22.98 \| 383 \| 10.3065 \| 0.715 \| 0.1375 \| 0.1875 \| 0.465 \| 0.6925 \| 0.7225 \| \| No log \| 24.0 \| 400 \| 10.1955 \| 0.72 \| 0.145 \| 0.1875 \| 0.4725 \| 0.695 \| 0.7225 \| \| No log \| 24.96 \| 416 \| 10.1607 \| 0.72 \| 0.165 \| 0.19 \| 0.4925 \| 0.7075 \| 0.7175 \| \| No log \| 25.98 \| 433 \| 10.2416 \| 0.72 \| 0.14 \| 0.195 \| 0.48 \| 0.7025 \| 0.7275 \| \| No log \| 27.0 \| 450 \| 10.1321 \| 0.715 \| 0.145 \| 0.1875 \| 0.4925 \| 0.7125 \| 0.72 \| \| No log \| 27.96 \| 466 \| 10.1982 \| 0.7275 \| 0.145 \| 0.1875 \| 0.4875 \| 0.7075 \| 0.73 \| \| No log \| 28.98 \| 483 \| 10.2237 \| 0.72 \| 0.1575 \| 0.19 \| 0.515 \| 0.7 \| 0.7225 \| \| 10.0174 \| 30.0 \| 500 \| 10.1426 \| 0.7175 \| 0.1675 \| 0.1975 \| 0.5275 \| 0.7125 \| 0.7225 \| \| 10.0174 \| 30.96 \| 516 \| 10.1056 \| 0.7325 \| 0.14 \| 0.1975 \| 0.515 \| 0.715 \| 0.7325 \| \| 10.0174 \| 31.98 \| 533 \| 10.1616 \| 0.7225 \| 0.1525 \| 0.195 \| 0.5275 \| 0.7175 \| 0.72 \| \| 10.0174 \| 33.0 \| 550 \| 10.1053 \| 0.7325 \| 0.1425 \| 0.195 \| 0.525 \| 0.7125 \| 0.7275 \| \| 10.0174 \| 33.96 \| 566 \| 10.1581 \| 0.7175 \| 0.165 \| 0.2 \| 0.5375 \| 0.71 \| 0.71 \| \| 10.0174 \| 34.98 \| 583 \| 10.0835 \| 0.7225 \| 0.15 \| 0.2025 \| 0.5375 \| 0.715 \| 0.7225 \| \| 10.0174 \| 36.0 \| 600 \| 10.1349 \| 0.725 \| 0.1425 \| 0.2 \| 0.5375 \| 0.7025 \| 0.725 \| \| 10.0174 \| 36.96 \| 616 \| 10.0424 \| 0.7325 \| 0.1625 \| 0.1975 \| 0.545 \| 0.7225 \| 0.735 \| \| 10.0174 \| 37.98 \| 633 \| 10.0692 \| 0.73 \| 0.155 \| 0.195 \| 0.5525 \| 0.7225 \| 0.74 \| \| 10.0174 \| 39.0 \| 650 \| 10.0838 \| 0.7325 \| 0.1625 \| 0.1975 \| 0.56 \| 0.7225 \| 0.7375 \| \| 10.0174 \| 39.96 \| 666 \| 10.1160 \| 0.7275 \| 0.1675 \| 0.1975 \| 0.5575 \| 0.7225 \| 0.725 \| \| 10.0174 \| 40.98 \| 683 \| 10.0971 \| 0.735 \| 0.1675 \| 0.1975 \| 0.5625 \| 0.7175 \| 0.73 \| \| 10.0174 \| 42.0 \| 700 \| 10.1207 \| 0.73 \| 0.165 \| 0.2 \| 0.5775 \| 0.715 \| 0.7275 \| \| 10.0174 \| 42.96 \| 716 \| 10.1448 \| 0.7325 \| 0.175 \| 0.205 \| 0.5775 \| 0.7175 \| 0.73 \| \| 10.0174 \| 43.98 \| 733 \| 10.0945 \| 0.735 \| 0.1675 \| 0.21 \| 0.5775 \| 0.7175 \| 0.735 \| \| 10.0174 \| 45.0 \| 750 \| 10.1789 \| 0.73 \| 0.17 \| 0.2175 \| 0.5775 \| 0.7125 \| 0.7275 \| \| 10.0174 \| 45.96 \| 766 \| 10.1274 \| 0.735 \| 0.175 \| 0.215 \| 0.5875 \| 0.7075 \| 0.735 \| \| 10.0174 \| 46.98 \| 783 \| 10.1656 \| 0.735 \| 0.155 \| 0.2125 \| 0.5875 \| 0.7125 \| 0.7375 \| \| 10.0174 \| 48.0 \| 800 \| 10.1557 \| 0.7275 \| 0.16 \| 0.215 \| 0.6025 \| 0.715 \| 0.7325 \| \| 10.0174 \| 48.96 \| 816 \| 10.1436 \| 0.74 \| 0.165 \| 0.215 \| 0.6025 \| 0.7175 \| 0.735 \| \| 10.0174 \| 49.98 \| 833 \| 10.1474 \| 0.7325 \| 0.1625 \| 0.215 \| 0.6 \| 0.715 \| 0.735 \| \| 10.0174 \| 51.0 \| 850 \| 10.1647 \| 0.7275 \| 0.1725 \| 0.2175 \| 0.605 \| 0.7175 \| 0.7325 \| \| 10.0174 \| 51.96 \| 866 \| 10.1375 \| 0.73 \| 0.1775 \| 0.215 \| 0.6025 \| 0.7125 \| 0.7375 \| \| 10.0174 \| 52.98 \| 883 \| 10.1458 \| 0.7325 \| 0.1675 \| 0.2175 \| 0.605 \| 0.7125 \| 0.7375 \| \| 10.0174 \| 54.0 \| 900 \| 10.1527 \| 0.7275 \| 0.175 \| 0.22 \| 0.6025 \| 0.715 \| 0.73 \| \| 10.0174 \| 54.96 \| 916 \| 10.1349 \| 0.7325 \| 0.175 \| 0.2175 \| 0.6025 \| 0.72 \| 0.735 \| \| 10.0174 \| 55.98 \| 933 \| 10.1376 \| 0.7325 \| 0.175 \| 0.22 \| 0.6025 \| 0.72 \| 0.7325 \| \| 10.0174 \| 57.0 \| 950 \| 10.1413 \| 0.7325 \| 0.1725 \| 0.2175 \| 0.6075 \| 0.715 \| 0.7325 \| \| 10.0174 \| 57.6 \| 960 \| 10.1413 \| 0.7325 \| 0.1725 \| 0.2175 \| 0.6075 \| 0.715 \| 0.735 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Ricardo-H/swin-tiny-patch4-window7-224-finetuned-eurosat	Ricardo-H	2024-01-15T09:50:51Z	201	0	transformers	[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-15T09:32:36Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-CIFAR10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1229 - Accuracy: 0.9618 ## 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: 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: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5796 \| 1.0 \| 176 \| 0.2204 \| 0.9462 \| \| 0.3995 \| 2.0 \| 352 \| 0.1403 \| 0.9582 \| \| 0.3781 \| 3.0 \| 528 \| 0.1229 \| 0.9618 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0
Shreyas0706/Lama_Fact	Shreyas0706	2024-01-15T09:50:36Z	2	0	peft	[ "peft", "tensorboard", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:other", "region:us" ]	null	2024-01-12T06:56:38Z	--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: train_2024-01-11-03-12-55 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. --> # train_2024-01-11-03-12-55 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the Legal_0706 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Nishant2609/factory	Nishant2609	2024-01-15T09:31:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2024-01-12T08:17:39Z	--- library_name: peft base_model: unsloth/mistral-7b --- # 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. --> - 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] ### Framework versions - PEFT 0.7.1
vda1708/gsm8k-llama2-13b	vda1708	2024-01-15T09:29:44Z	0	0	peft	[ "peft", "safetensors", "region:us" ]	null	2024-01-15T09:28:50Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
hxxris/haaris-final-transformer	hxxris	2024-01-15T09:29:26Z	146	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-01-15T09:14:22Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: haaris-final-transformer 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. --> # haaris-final-transformer This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.0354 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.73 \| 2 \| 2.6456 \| 0.0442 \| \| No log \| 1.45 \| 4 \| nan \| 0.0354 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
jaemin12/xlm-roberta-base-finetuned-panx-it	jaemin12	2024-01-15T09:29:24Z	89	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T09:17:56Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: validation args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8120423108218063 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2361 - F1: 0.8120 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8019 \| 1.0 \| 70 \| 0.3139 \| 0.7286 \| \| 0.2941 \| 2.0 \| 140 \| 0.2641 \| 0.7975 \| \| 0.1851 \| 3.0 \| 210 \| 0.2361 \| 0.8120 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3
Swisslex/Mixtral-Orca-v0.1	Swisslex	2024-01-15T09:29:01Z	0	0	adapter-transformers	[ "adapter-transformers", "safetensors", "mixtral", "text-generation", "en", "de", "fr", "it", "es", "dataset:Open-Orca/SlimOrca", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "license:apache-2.0", "region:us" ]	text-generation	2024-01-15T08:37:12Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca - argilla/distilabel-intel-orca-dpo-pairs language: - en - de - fr - it - es library_name: adapter-transformers pipeline_tag: text-generation --- # Model Card for Model Swisslex/Mixtral-Orca-v0.1 ## Model Details ### Model Description Finetuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) using SFT and DPO. - Developed by: Swisslex - Language(s) (NLP): English, German, French, Italian, Spanish - License: apache-2.0 - Finetuned from model [optional]: [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
danielhanchen/test_lora_new	danielhanchen	2024-01-15T09:19:16Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]	null	2024-01-15T08:48:48Z	--- library_name: peft base_model: unsloth/mistral-7b --- # 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. --> - 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] ### Framework versions - PEFT 0.7.1
sethuiyer/MedleyMD-GGUF	sethuiyer	2024-01-15T09:18:34Z	7	1	null	[ "gguf", "medical", "mergekit", "GGUF", "en", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]	null	2024-01-15T09:06:41Z	--- license: cc-by-nc-nd-4.0 tags: - medical - mergekit - GGUF language: - en --- # MedleyMD ![logo](https://huggingface.co/sethuiyer/MedleyMD/resolve/main/logo.webp) MedleyMD is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral) * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)
jaemin12/xlm-roberta-base-finetuned-panx-fr	jaemin12	2024-01-15T09:16:57Z	90	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T08:43:20Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8329966329966331 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2704 - F1: 0.8330 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5892 \| 1.0 \| 191 \| 0.3346 \| 0.7926 \| \| 0.2673 \| 2.0 \| 382 \| 0.2807 \| 0.8291 \| \| 0.1742 \| 3.0 \| 573 \| 0.2704 \| 0.8330 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3
Grigorij/mistral_instruct_generation	Grigorij	2024-01-15T09:12:31Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2023-12-26T11:17:34Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral_instruct_generation 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. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 4.0261 \| 0.4 \| 10 \| 2.8697 \| \| 2.2616 \| 0.8 \| 20 \| 1.6009 \| \| 1.1871 \| 1.2 \| 30 \| 0.9599 \| \| 0.8522 \| 1.6 \| 40 \| 0.7228 \| \| 0.7375 \| 2.0 \| 50 \| 0.6601 \| \| 0.5916 \| 2.4 \| 60 \| 0.6184 \| \| 0.6219 \| 2.8 \| 70 \| 0.5957 \| \| 0.5025 \| 3.2 \| 80 \| 0.5980 \| \| 0.5148 \| 3.6 \| 90 \| 0.5849 \| \| 0.5502 \| 4.0 \| 100 \| 0.5639 \| \| 0.4414 \| 4.4 \| 110 \| 0.5875 \| \| 0.4423 \| 4.8 \| 120 \| 0.5847 \| \| 0.43 \| 5.2 \| 130 \| 0.5902 \| \| 0.3843 \| 5.6 \| 140 \| 0.6223 \| \| 0.4173 \| 6.0 \| 150 \| 0.5788 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0
achimvp/q-FrozenLake-v1-4x4-noSlippery	achimvp	2024-01-15T09:11:00Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-11-11T13:15:23Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="achimvp/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
shadowml/SusBagle-34B	shadowml	2024-01-15T09:03:56Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-34b-v0.2", "SUSTech/SUS-Chat-34B", "license:apache-2.0", "region:us" ]	null	2024-01-15T08:58:38Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jondurbin/bagel-dpo-34b-v0.2 - SUSTech/SUS-Chat-34B --- # SusBagle-34B SusBagle-34B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) * [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) ## 🧩 Configuration ```yaml slices: - sources: - model: jondurbin/bagel-dpo-34b-v0.2 layer_range: [0, 60] - model: SUSTech/SUS-Chat-34B layer_range: [0, 60] merge_method: slerp base_model: jondurbin/bagel-dpo-34b-v0.2 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shadowml/SusBagle-34B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
mikewatson/gpt2-wikitext2	mikewatson	2024-01-15T08:55:12Z	180	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T08:54:59Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 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. --> # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 7.3791 - eval_runtime: 10.5986 - eval_samples_per_second: 182.477 - eval_steps_per_second: 22.833 - epoch: 0.11 - step: 241 ## 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: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
kalomaze/Kunoichi-DPO-v2-7B-GGUF	kalomaze	2024-01-15T08:53:07Z	10	4	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-01-15T07:46:34Z	--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/I2V9Ou5xvD0iusAFmYUHD.png)
s3nh/Venus-120b-v1.2-GGUF	s3nh	2024-01-15T08:52:13Z	0	0	transformers	[ "transformers", "text-generation", "zh", "en", "license:openrail", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T07:46:35Z	--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Once upon a time in # Original model card
xyz2zyx/Reinforce-CartPole-v1	xyz2zyx	2024-01-15T08:43:22Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-01-15T08:43:12Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Intel/neural-chat-7b-v1-1	Intel	2024-01-15T08:40:04Z	38	23	transformers	[ "transformers", "pytorch", "mpt", "text-generation", "LLMs", "Intel", "custom_code", "en", "dataset:Intel/neural-chat-dataset-v1-1", "dataset:allenai/real-toxicity-prompts", "base_model:mosaicml/mpt-7b", "base_model:finetune:mosaicml/mpt-7b", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-06T05:20:07Z	--- license: apache-2.0 tags: - LLMs - Intel base_model: mosaicml/mpt-7b datasets: - Intel/neural-chat-dataset-v1-1 - allenai/real-toxicity-prompts language: - en model-index: - name: neural-chat-7b-v1-1 results: - task: type: Large Language Model name: Large Language Model dataset: type: Intel/neural-chat-dataset-v1-1 name: Intel/neural-chat-dataset-v1-1 metrics: - type: Average value: 51.41 name: Average verified: true - type: ARC (25-shot) value: 50.09 name: ARC (25-shot) verified: true - type: HellaSwag (10-shot) value: 76.69 name: HellaSwag (10-shot) verified: true - type: MMLU (5-shot) value: 38.79 name: MMLU (5-shot) verified: true - type: TruthfulQA (0-shot) value: 40.07 name: TruthfulQA (0-shot) verified: true - type: Toxicity Rito value: 0.0264 name: Toxicity Rito --- ## Model Details: Neural-Chat-v1-1 This model is a fine-tuned model for chat based on [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) with a max sequence length of 2048 on the dataset [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1), which is a compilation of open-source datasets. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6297f0e30bd2f58c647abb1d/fWCqhGKZQKNuLmvj093rB.jpeg" width="500"/> Prompt of "an image of a brain that has to do with LLMs" from https://clipdrop.co/stable-diffusion-turbo. </p> \| Model Detail \| Description \| \| ----------- \| ----------- \| \| Model Authors \| Intel. The NeuralChat team with members from DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen. \| \| Date \| July, 2023 \| \| Version \| v1-1 \| \| Type \| 7B Large Language Model \| \| Paper or Other Resources \| Base model: [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b); Dataset: [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1) \| \| License \| Apache 2.0 \| \| Questions or Comments \| [Community Tab](https://huggingface.co/Intel/neural-chat-7b-v1-1/discussions) and [Intel DevHub Discord](https://discord.gg/rv2Gp55UJQ)\| \| Intended Use \| Description \| \| ----------- \| ----------- \| \| Primary intended uses \| You can use the fine-tuned model for several language-related tasks. Checkout the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) to see this model's performance relative to other LLMs. \| \| Primary intended users \| Anyone doing inference on language-related tasks. \| \| Out-of-scope uses \| This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.\| ## How To Use ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 3.0 ## Use The Model ### Loading the model with Transformers ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'Intel/neural-chat-7b-v1-1', trust_remote_code=True ) ``` ### Inference with INT8 Follow the instructions at the [GitHub repository](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/pytorch/text-generation/quantization) to install the necessary dependencies for quantization to INT8. Use the below command to quantize the model using [Intel Neural Compressor](https://github.com/intel/neural-compressor) to accelerate inference. ```bash python run_generation.py \ --model Intel/neural-chat-7b-v1-1 \ --quantize \ --sq \ --alpha 0.95 \ --ipex ``` \| Factors \| Description \| \| ----------- \| ----------- \| \| Groups \| More details about the dataset can be found at [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1). \| \| Instrumentation \| The performance of the model can vary depending on the inputs to the model. In this case, the prompts provided can drastically change the prediction of the language model. \| \| Environment \| - \| \| Card Prompts \| Model deployment on varying hardware and software will change model performance. \| \| Metrics \| Description \| \| ----------- \| ----------- \| \| Model performance measures \| The model metrics are: ARC, HellaSwag, MMLU, and TruthfulQA. Bias evaluation was also evaluated using using Toxicity Rito (see Quantitative Analyses below). The model performance was evaluated against other LLMs according to the standards at the time the model was published. \| \| Decision thresholds \| No decision thresholds were used. \| \| Approaches to uncertainty and variability \| - \| ## Training Data The training data are from [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1). The total number of instruction samples is about 1.1M, and the number of tokens is 326M. This dataset is composed of several other datasets: \| Type \| Language \| Dataset \| Number \| \|--\| ---- \|--------\|----\| \| HC3 \| en \| [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) \| 24K \| \| dolly \| en \| [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) \| 15K \| \| alpaca-zh \| zh \| [tigerbot-alpaca-zh-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-zh-0.5m) \| 500K \| \| alpaca-en \| en \| [TigerResearch/tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-en-50k) \| 50K \| \| math \| en \| [tigerbot-gsm-8k-en](https://huggingface.co/datasets/TigerResearch/tigerbot-gsm-8k-en) \| 8K \| \| general \| en \| [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-stackexchange-qa-en-0.5m) \| 500K \| Note: There is no contamination from the GSM8k test set, as this is not a part of this dataset. ## Quantitative Analyses ### LLM metrics We used the same evaluation metrics as [HuggingFaceH4/open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), which uses [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/master), a unified framework to test generative language models on a large number of different evaluation tasks. \| Model \| Average ⬆️\| ARC (25-s) ⬆️ \| HellaSwag (10-s) ⬆️ \| MMLU (5-s) ⬆️\| TruthfulQA (MC) (0-s) ⬆️ \| \| --- \| --- \| --- \| --- \| --- \| --- \| \|[mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b)\| 47.4 \| 47.61 \| 77.56 \| 31 \| 33.43 \| \| [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) \| 49.95 \| 46.5 \| 75.55 \| 37.60 \| 40.17 \| \| [Intel/neural-chat-dataset-v1-1](https://huggingface.co/Intel/neural-chat-dataset-v1-1) \| 51.41 \| 50.09 \| 76.69 \| 38.79 \| 40.07 \| ### Bias evaluation Following the blog [evaluating-llm-bias](https://huggingface.co/blog/evaluating-llm-bias), we selected 10000 samples randomly from [allenai/real-toxicity-prompts](https://huggingface.co/datasets/allenai/real-toxicity-prompts) to evaluate toxicity bias. \| Model \| Toxicity Rito ↓\| \| --- \| --- \| \|[mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b)\| 0.027 \| \| [Intel/neural-chat-dataset-v1-1](https://huggingface.co/Intel/neural-chat-dataset-v1-1) \| 0.0264 \| ### Examples - code generation ![code-generation](examples/code.png) - summarization ![summarization](examples/summarization.png) - trip ![trip](examples/trip.png) ## Ethical Considerations and Limitations Neural-chat-7b-v1-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v1-1 was trained on various instruction/chat datasets based on [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are some useful GitHub repository links to learn more about Intel's open-source AI software: * Intel Neural Compressor [link](https://github.com/intel/neural-compressor) * Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers) * Intel Extension for PyTorch [link](https://github.com/intel/intel-extension-for-pytorch) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
nm-testing/MiniChat-2-3B-pruned50-ds	nm-testing	2024-01-15T08:37:56Z	2	0	transformers	[ "transformers", "onnx", "llama", "text-generation", "deepsparse", "arxiv:2301.00774", "base_model:GeneZC/MiniChat-2-3B", "base_model:quantized:GeneZC/MiniChat-2-3B", "autotrain_compatible", "region:us" ]	text-generation	2024-01-15T08:05:29Z	--- base_model: GeneZC/MiniChat-2-3B inference: false model_type: llama prompt_template: \| <s> [\|User\|]\n {prompt}</s> [\|Assistant\|]\n quantized_by: mwitiderrick tags: - deepsparse --- # MiniChat-2-3B - DeepSparse This repo contains model files for [MiniChat-2-3B](https://huggingface.co/GeneZC/MiniChat-2-3B) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models. This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml). ## Inference Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs: ```bash pip install deepsparse-nightly[llm] ``` Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md): ```python from deepsparse import TextGeneration prompt = "How to get in a good university?" formatted_prompt = f"<s> [\|User\|]\n{prompt}</s>[\|Assistant\|]\n" model = TextGeneration(model_path="hf:nm-testing/MiniChat-2-3B-pruned50-ds") print(model(formatted_prompt, max_new_tokens=500).generations[0].text) """ Getting into a good university is a complex process that involves several steps. However, here are some key factors to consider: 1. Academic performance: Your grades, test scores, and overall academic achievements are essential in demonstrating your academic abilities. Strive to maintain a high GPA and achieve strong scores in standardized tests like the SAT, ACT, or AP exams. 2. Academic preparation: Develop a strong foundation in various subjects, including English, math, science, and foreign languages. This will help you succeed academically and demonstrate your readiness for college-level courses. 3. Extracurricular activities: Participate in extracurricular activities such as clubs, sports teams, volunteering, or leadership roles. These activities can help you develop valuable skills, demonstrate your leadership abilities, and showcase your interests outside the classroom. 4. Academic preparation for college-level courses: Research and understand the curriculum of the universities you are interested in. Familiarize yourself with the coursework, coursework requirements, and any potential prerequisites. 5. Personal qualities and extracurricular activities: Showcase your unique qualities and extracurricular activities that demonstrate your leadership, teamwork, and problem-solving abilities. Universities value students who are well-rounded and have a diverse set of skills. 6. Application process: Follow the university's application process, which may include submitting an application form, paying the application fee, and submitting any required documents. 7. Interviews and assessments: If you are invited to an interview, prepare for it by researching the university, its campus, and its mission. Be confident, articulate, and demonstrate your enthusiasm for the university. 8. Post-admission process: After being accepted, follow the university's post-admission process, which may include registering for classes, paying tuition fees, and obtaining student housing. 9. Networking and mentorship: Connect with professors, professors, and fellow students to gain insights into the university culture and gain a deeper understanding of the college environment. 10. Financial support: Research financial aid options and scholarships available to help cover tuition fees and living """ ``` ## Prompt template ``` <s> [\|User\|]\n {prompt} </s>[\|Assistant\|]\n ``` ## Sparsification For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below. ```bash git clone https://github.com/neuralmagic/sparseml pip install -e "sparseml[transformers]" python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py GeneZC/MiniChat-2-3B open_platypus --recipe recipe.yaml --save True python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment cp deployment/model.onnx deployment/model-orig.onnx ``` Run this kv-cache injection to speed up the model at inference by caching the Key and Value states: ```python import os import onnx from sparseml.exporters.kv_cache_injector import KeyValueCacheInjector input_file = "deployment/model-orig.onnx" output_file = "deployment/model.onnx" model = onnx.load(input_file, load_external_data=False) model = KeyValueCacheInjector(model_path=os.path.dirname(input_file)).apply(model) onnx.save(model, output_file) print(f"Modified model saved to: {output_file}") ``` Follow the instructions on our [One Shot With SparseML](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/transformers/sparsification/obcq) page for a step-by-step guide for performing one-shot quantization of large language models. ## Slack For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)
smutuvi/whisper-small-sw-common-voice-ndizi-158-NF4	smutuvi	2024-01-15T08:37:00Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:smutuvi/whisper-small-sw-common-voice", "base_model:adapter:smutuvi/whisper-small-sw-common-voice", "license:apache-2.0", "region:us" ]	null	2024-01-15T08:36:58Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: smutuvi/whisper-small-sw-common-voice model-index: - name: whisper-small-sw-common-voice-ndizi-158-NF4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-sw-common-voice-ndizi-158-NF4 This model is a fine-tuned version of [smutuvi/whisper-small-sw-common-voice](https://huggingface.co/smutuvi/whisper-small-sw-common-voice) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6482 ## 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: 50 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 9 \| 1.9783 \| \| No log \| 2.0 \| 18 \| 1.9757 \| \| 1.8194 \| 3.0 \| 27 \| 1.9710 \| \| 1.8194 \| 4.0 \| 36 \| 1.9631 \| \| 1.8194 \| 5.0 \| 45 \| 1.9543 \| \| 1.7712 \| 6.0 \| 54 \| 1.9437 \| \| 1.7712 \| 7.0 \| 63 \| 1.9337 \| \| 1.7712 \| 8.0 \| 72 \| 1.9244 \| \| 1.7377 \| 9.0 \| 81 \| 1.9141 \| \| 1.7377 \| 10.0 \| 90 \| 1.9043 \| \| 1.7377 \| 11.0 \| 99 \| 1.8952 \| \| 1.7377 \| 12.0 \| 108 \| 1.8863 \| \| 1.7377 \| 13.0 \| 117 \| 1.8762 \| \| 1.6796 \| 14.0 \| 126 \| 1.8684 \| \| 1.6796 \| 15.0 \| 135 \| 1.8588 \| \| 1.6796 \| 16.0 \| 144 \| 1.8501 \| \| 1.6217 \| 17.0 \| 153 \| 1.8414 \| \| 1.6217 \| 18.0 \| 162 \| 1.8325 \| \| 1.6217 \| 19.0 \| 171 \| 1.8253 \| \| 1.6036 \| 20.0 \| 180 \| 1.8169 \| \| 1.6036 \| 21.0 \| 189 \| 1.8097 \| \| 1.6036 \| 22.0 \| 198 \| 1.8017 \| \| 1.6129 \| 23.0 \| 207 \| 1.7949 \| \| 1.6129 \| 24.0 \| 216 \| 1.7879 \| \| 1.5607 \| 25.0 \| 225 \| 1.7817 \| \| 1.5607 \| 26.0 \| 234 \| 1.7760 \| \| 1.5607 \| 27.0 \| 243 \| 1.7699 \| \| 1.5305 \| 28.0 \| 252 \| 1.7637 \| \| 1.5305 \| 29.0 \| 261 \| 1.7588 \| \| 1.5305 \| 30.0 \| 270 \| 1.7537 \| \| 1.5261 \| 31.0 \| 279 \| 1.7485 \| \| 1.5261 \| 32.0 \| 288 \| 1.7444 \| \| 1.5261 \| 33.0 \| 297 \| 1.7400 \| \| 1.5331 \| 34.0 \| 306 \| 1.7360 \| \| 1.5331 \| 35.0 \| 315 \| 1.7322 \| \| 1.5331 \| 36.0 \| 324 \| 1.7287 \| \| 1.5131 \| 37.0 \| 333 \| 1.7250 \| \| 1.5131 \| 38.0 \| 342 \| 1.7216 \| \| 1.4883 \| 39.0 \| 351 \| 1.7183 \| \| 1.4883 \| 40.0 \| 360 \| 1.7151 \| \| 1.4883 \| 41.0 \| 369 \| 1.7119 \| \| 1.4551 \| 42.0 \| 378 \| 1.7092 \| \| 1.4551 \| 43.0 \| 387 \| 1.7063 \| \| 1.4551 \| 44.0 \| 396 \| 1.7042 \| \| 1.47 \| 45.0 \| 405 \| 1.7018 \| \| 1.47 \| 46.0 \| 414 \| 1.6988 \| \| 1.47 \| 47.0 \| 423 \| 1.6967 \| \| 1.4422 \| 48.0 \| 432 \| 1.6942 \| \| 1.4422 \| 49.0 \| 441 \| 1.6922 \| \| 1.4432 \| 50.0 \| 450 \| 1.6904 \| \| 1.4432 \| 51.0 \| 459 \| 1.6886 \| \| 1.4432 \| 52.0 \| 468 \| 1.6869 \| \| 1.4108 \| 53.0 \| 477 \| 1.6849 \| \| 1.4108 \| 54.0 \| 486 \| 1.6828 \| \| 1.4108 \| 55.0 \| 495 \| 1.6808 \| \| 1.4377 \| 56.0 \| 504 \| 1.6797 \| \| 1.4377 \| 57.0 \| 513 \| 1.6777 \| \| 1.4377 \| 58.0 \| 522 \| 1.6770 \| \| 1.4281 \| 59.0 \| 531 \| 1.6748 \| \| 1.4281 \| 60.0 \| 540 \| 1.6732 \| \| 1.4281 \| 61.0 \| 549 \| 1.6718 \| \| 1.374 \| 62.0 \| 558 \| 1.6704 \| \| 1.374 \| 63.0 \| 567 \| 1.6695 \| \| 1.3947 \| 64.0 \| 576 \| 1.6678 \| \| 1.3947 \| 65.0 \| 585 \| 1.6667 \| \| 1.3947 \| 66.0 \| 594 \| 1.6656 \| \| 1.3843 \| 67.0 \| 603 \| 1.6650 \| \| 1.3843 \| 68.0 \| 612 \| 1.6638 \| \| 1.3843 \| 69.0 \| 621 \| 1.6620 \| \| 1.385 \| 70.0 \| 630 \| 1.6614 \| \| 1.385 \| 71.0 \| 639 \| 1.6608 \| \| 1.385 \| 72.0 \| 648 \| 1.6592 \| \| 1.368 \| 73.0 \| 657 \| 1.6586 \| \| 1.368 \| 74.0 \| 666 \| 1.6576 \| \| 1.3777 \| 75.0 \| 675 \| 1.6574 \| \| 1.3777 \| 76.0 \| 684 \| 1.6565 \| \| 1.3777 \| 77.0 \| 693 \| 1.6556 \| \| 1.3837 \| 78.0 \| 702 \| 1.6553 \| \| 1.3837 \| 79.0 \| 711 \| 1.6547 \| \| 1.3837 \| 80.0 \| 720 \| 1.6544 \| \| 1.3577 \| 81.0 \| 729 \| 1.6529 \| \| 1.3577 \| 82.0 \| 738 \| 1.6523 \| \| 1.3577 \| 83.0 \| 747 \| 1.6515 \| \| 1.3961 \| 84.0 \| 756 \| 1.6513 \| \| 1.3961 \| 85.0 \| 765 \| 1.6512 \| \| 1.3961 \| 86.0 \| 774 \| 1.6505 \| \| 1.3292 \| 87.0 \| 783 \| 1.6497 \| \| 1.3292 \| 88.0 \| 792 \| 1.6495 \| \| 1.3578 \| 89.0 \| 801 \| 1.6496 \| \| 1.3578 \| 90.0 \| 810 \| 1.6495 \| \| 1.3578 \| 91.0 \| 819 \| 1.6488 \| \| 1.3352 \| 92.0 \| 828 \| 1.6486 \| \| 1.3352 \| 93.0 \| 837 \| 1.6492 \| \| 1.3352 \| 94.0 \| 846 \| 1.6487 \| \| 1.3873 \| 95.0 \| 855 \| 1.6485 \| \| 1.3873 \| 96.0 \| 864 \| 1.6483 \| \| 1.3873 \| 97.0 \| 873 \| 1.6481 \| \| 1.3391 \| 98.0 \| 882 \| 1.6483 \| \| 1.3391 \| 99.0 \| 891 \| 1.6482 \| \| 1.3569 \| 100.0 \| 900 \| 1.6482 \| ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
Romanos575/Yuzu	Romanos575	2024-01-15T08:36:35Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-01-15T08:25:53Z	--- license: creativeml-openrail-m --- Yuzu all rights goes to Ikena https://civitai.com/models/67120
alexrods/q-Taxi-V3	alexrods	2024-01-15T08:28:09Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-15T08:28:08Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="alexrods/q-Taxi-V3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
jaemin12/xlm-roberta-base-finetuned-panx-de-fr	jaemin12	2024-01-15T08:22:19Z	78	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-12T12:21:36Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - F1: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2893 \| 1.0 \| 715 \| 0.1785 \| 0.8313 \| \| 0.1466 \| 2.0 \| 1430 \| 0.1559 \| 0.8540 \| \| 0.0948 \| 3.0 \| 2145 \| 0.1620 \| 0.8611 \| ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3
hayate3140/hayate3140butterfly	hayate3140	2024-01-15T08:18:45Z	49	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2024-01-15T08:18:34Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('hayate3140/hayate3140butterfly') image = pipeline().images[0] image ```
ayousanz/nekomata-14b-gozaru	ayousanz	2024-01-15T08:18:17Z	5	0	peft	[ "peft", "safetensors", "base_model:rinna/nekomata-14b-instruction", "base_model:adapter:rinna/nekomata-14b-instruction", "region:us" ]	null	2024-01-15T08:09:15Z	--- library_name: peft base_model: rinna/nekomata-14b-instruction --- # 概要 [rinna/nekomata-14b-instruction](https://huggingface.co/rinna/nekomata-14b-instruction)を[bbz662bbz/databricks-dolly-15k-ja-gozaru](https://huggingface.co/datasets/bbz662bbz/databricks-dolly-15k-ja-gozaru)でfine tuningしたモデル # 学習パラメータ ```python # 学習の実行 !python qlora.py \ --model_name rinna/nekomata-14b-instruction \ --output_dir "./output/rinna-nekomata-14b-instruction-gonaru_peft" \ --dataset "alpaca" \ --max_steps 1000 \ --use_auth \ --logging_steps 10 \ --save_strategy steps \ --data_seed 42 \ --save_steps 100 \ --save_total_limit 40 \ --max_new_tokens 100 \ --dataloader_num_workers 1 \ --group_by_length \z --logging_strategy steps \ --remove_unused_columns False \ --do_train \ --lora_r 64 \ --lora_alpha 16 \ --lora_modules all \ --double_quant \ --quant_type nf4 \ --bf16 \ --bits 4 \ --warmup_ratio 0.03 \ --lr_scheduler_type constant \ --gradient_checkpointing \ --source_max_len 16 \ --target_max_len 512 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --eval_steps 187 \ --learning_rate 0.0002 \ --adam_beta2 0.999 \ --max_grad_norm 0.3 \ --lora_dropout 0.1 \ --weight_decay 0.0 \ --seed 0 \ --load_in_4bit \ --use_peft \ --batch_size 4 \ --gradient_accumulation_steps 2 \ --trust_remote_code=True ``` # 実行方法(4bit) ## 推論 ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # トークナイザーとモデルの読み込み tokenizer = AutoTokenizer.from_pretrained( "rinna/nekomata-14b-instruction" ) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained( "rinna/nekomata-14b-instruction", quantization_config=bnb_config, device_map={"":0} ) # LoRAの読み込み model = PeftModel.from_pretrained( model, "./output/rinna-nekomata-14b-instruction-gonaru_peft/checkpoint-1000/adapter_model/", device_map={"":0} ) model.eval() # プロンプトの準備 instruction = "次の回答に正しく回答してください" input = "まどマギで一番可愛いキャラはなんですか？" prompt = f""" 以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。 ### 指示: {instruction} ### 入力: {input} ### 応答: """ # 推論の実行 inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=300,temperature=0.3,do_sample=True,pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 回答 ```python 以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。 ### 指示: 次の回答に正しく回答してください ### 入力: まどマギで一番可愛いキャラはなんですか？ ### 応答: マミさんでござる。 ``` ### Framework versions - PEFT 0.7.2.dev0
jlvdoorn/whisper-base-atcosim	jlvdoorn	2024-01-15T08:16:26Z	92	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "doi:10.57967/hf/1620", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-01-13T12:30:23Z	--- license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-base-atcosim results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-base-atcosim This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Wer: 2.9820 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.152 \| 8.33 \| 500 \| 0.0522 \| 2.5282 \| \| 0.001 \| 16.67 \| 1000 \| 0.0539 \| 3.0608 \| \| 0.0003 \| 25.0 \| 1500 \| 0.0556 \| 3.0237 \| \| 0.0002 \| 33.33 \| 2000 \| 0.0567 \| 3.0237 \| \| 0.0001 \| 41.67 \| 2500 \| 0.0579 \| 3.0144 \| \| 0.0001 \| 50.0 \| 3000 \| 0.0588 \| 2.9959 \| \| 0.0001 \| 58.33 \| 3500 \| 0.0597 \| 3.0052 \| \| 0.0001 \| 66.67 \| 4000 \| 0.0604 \| 3.0098 \| \| 0.0 \| 75.0 \| 4500 \| 0.0610 \| 2.9867 \| \| 0.0 \| 83.33 \| 5000 \| 0.0615 \| 2.9867 \| \| 0.0 \| 91.67 \| 5500 \| 0.0619 \| 2.9774 \| \| 0.0 \| 100.0 \| 6000 \| 0.0620 \| 2.9820 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.0
mrchaos88/openllama-3b-peft-squad_v2	mrchaos88	2024-01-15T08:12:36Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "region:us" ]	null	2024-01-15T06:41:08Z	--- library_name: peft base_model: openlm-research/open_llama_3b_v2 --- # 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. --> - 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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dolo650/Mistral-7B-lora-yahma-alpaca-unsloth-bnb	dolo650	2024-01-15T08:11:29Z	4	0	peft	[ "peft", "safetensors", "dataset:yahma/alpaca-cleaned", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "license:apache-2.0", "region:us" ]	null	2024-01-15T07:42:37Z	--- library_name: peft base_model: unsloth/mistral-7b license: apache-2.0 datasets: - yahma/alpaca-cleaned --- # Model Card for Model ID QLoRA fine tuned model yahma/alpaca-cleaned dataset containing around ~52K data ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - 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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etri-vilab/koala-1b-llava-cap	etri-vilab	2024-01-15T08:10:53Z	19	13	diffusers	[ "diffusers", "onnx", "safetensors", "text-to-image", "KOALA", "arxiv:2312.04005", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-01-09T05:49:09Z	--- tags: - text-to-image - KOALA --- <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/yosvi68jvyarbvymxc4hm/github_logo.png?rlkey=r9ouwcd7cqxjbvio43q9b3djd&dl=1" width="1024px" /> </div> <div style="display:flex;justify-content: center"> <a href="https://youngwanlee.github.io/KOALA/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://github.com/youngwanLEE/sdxl-koala"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp; <a href="https://arxiv.org/abs/2312.04005"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv:KOALA&color=red&logo=arxiv"></a> &ensp; </div> # KOALA-1B-LLaVA-Caption Model Card KOALA-700M and -1B models originally were trained on [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) which has some noisy or very short texts. So we construct synthesized captions of LAION-aesthetics-V2 6+ by using a large multimodal model, [LLaVA](https://llava-vl.github.io/). KOALA-700M-LLaVA-Caption and KOALA-1B-LLaVA-Caption is trained on the synthesized caption-image pairs of LAION-aesthetics-V2 6+. ## KOALA Model Cards \|Model\|link\| \|:--\|:--\| \|koala-700m \| https://huggingface.co/etri-vilab/koala-700m\| \|koala-700m-llava-cap \| https://huggingface.co/etri-vilab/koala-700m-llava-cap\| \|koala-1b \| https://huggingface.co/etri-vilab/koala-1bm\| \|koala-1b-llava-cap \| https://huggingface.co/etri-vilab/koala-1b-llava-cap\| ## Abstract ### TL;DR > We propose a fast text-to-image model, called KOALA, by compressing SDXL's U-Net and distilling knowledge from SDXL into our model. KOALA-700M can generate a 1024x1024 image in less than 1.5 seconds on an NVIDIA 4090 GPU, which is more than 2x faster than SDXL. KOALA-700M can be used as a decent alternative between SDM and SDXL in limited resources. <details><summary>FULL abstract</summary> Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the community due to its generation performance and open-source nature. Recently, Stable Diffusion XL (SDXL), the successor of stable diffusion, has received a lot of attention due to its significant performance improvements with a higher resolution of 1024x1024 and a larger model. However, its increased computation cost and model size require higher-end hardware (e.g., bigger VRAM GPU) for end-users, incurring higher costs of operation. To address this problem, in this work, we propose an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL. To this end, we first perform an in-depth analysis of the denoising U-Net in SDXL, which is the main bottleneck of the model, and then design a more efficient U-Net based on the analysis. Secondly, we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and eventually identify four essential factors, the core of which is that self-attention is the most important part. With our efficient U-Net and self-attention-based knowledge distillation strategy, we build our efficient T2I models, called KOALA-1B &-700M, while reducing the model size up to 54% and 69% of the original SDXL model. In particular, the KOALA-700M is more than twice as fast as SDXL while still retaining a decent generation quality. We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments. </details> <br> These 1024x1024 samples are generated by KOALA-700M with 25 denoising steps. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" /> </div> ## Architecture There are two two types of compressed U-Net, KOALA-1B and KOALA-700M, which are realized by reducing residual blocks and transformer blocks. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/5ydeywgiyt1d3njw63dpk/arch.png?rlkey=1p6imbjs4lkmfpcxy153i1a2t&dl=1" width="1024px" /> </div> ### U-Net comparison \| U-Net \| SDM-v2.0 \| SDXL-Base-1.0 \| KOALA-1B \| KOALA-700M \| \|-------\|:----------:\|:-----------:\|:-----------:\|:-------------:\| \| Param. \| 865M \| 2,567M \| 1,161M \| 782M \| \| CKPT size \| 3.46GB \| 10.3GB \| 4.4GB \| 3.0GB \| \| Tx blocks \| [1, 1, 1, 1] \| [0, 2, 10] \| [0, 2, 6] \| [0, 2, 5] \| \| Mid block \| ✓ \| ✓ \| ✓ \| ✗ \| \| Latency \| 1.131s \| 3.133s \| 1.604s \| 1.257s \| - Tx menans transformer block and CKPT means the trained checkpoint file. - We measured latency with FP16-precision, and 25 denoising steps in NVIDIA 4090 GPU (24GB). - SDM-v2.0 uses 768x768 resolution, while SDXL and KOALA models uses 1024x1024 resolution. ## Latency and memory usage comparison on different GPUs We measure the inference time of SDM-v2.0 with 768x768 resolution and the other models with 1024x1024 using a variety of consumer-grade GPUs: NVIDIA 3060Ti (8GB), 2080Ti (11GB), and 4090 (24GB). We use 25 denoising steps and FP16/FP32 precisions. OOM means Out-of-Memory. Note that SDXL-Base cannot operate in the 8GB-GPU. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/u1az20y0zfww1l5lhbcyd/latency_gpu.svg?rlkey=vjn3gpkmywmp7jpilar4km7sd&dl=1" width="1024px" /> </div> ## Key Features - Efficient U-Net Architecture: KOALA models use a simplified U-Net architecture that reduces the model size by up to 54% and 69% respectively compared to its predecessor, Stable Diffusion XL (SDXL). - Self-Attention-Based Knowledge Distillation: The core technique in KOALA focuses on the distillation of self-attention features, which proves crucial for maintaining image generation quality. ## Model Description - Developed by [ETRI Visual Intelligence Lab](https://huggingface.co/etri-vilab) - Developer: [Youngwan Lee](https://youngwanlee.github.io/), [Kwanyong Park](https://pkyong95.github.io/), [Yoorhim Cho](https://ofzlo.github.io/), [Young-Ju Lee](https://scholar.google.com/citations?user=6goOQh8AAAAJ&hl=en), [Sung Ju Hwang](http://www.sungjuhwang.com/) - Model Description: Latent Diffusion based text-to-image generative model. KOALA models uses the same text encoders as [SDXL-Base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and only replace the denoising U-Net with the compressed U-Nets. - Training data: [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) - Resources for more information: Check out [KOALA report on arXiv](https://arxiv.org/abs/2312.04005) and [project page](https://youngwanlee.github.io/KOALA/). ## Usage with 🤗[Diffusers library](https://github.com/huggingface/diffusers) The inference code with denoising step 25 ```python import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-1b-llava-cap", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "A portrait painting of a Golden Retriever like Leonard da Vinci" negative = "worst quality, low quality, illustration, low resolution" image = pipe(prompt=prompt, negative_prompt=negative).images[0] ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias - Text Rendering: The models face challenges in rendering long, legible text within images. - Complex Prompts: KOALA sometimes struggles with complex prompts involving multiple attributes. - Dataset Dependencies: The current limitations are partially attributed to the characteristics of the training dataset (LAION-aesthetics-V2 6+). ## Citation ```bibtex @misc{Lee@koala, title={KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis}, author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang}, year={2023}, eprint={2312.04005}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
hwashang/hs_test1_billsum_model	hwashang	2024-01-15T08:05:13Z	89	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "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	2024-01-12T09:53:13Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: hs_test1_billsum_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. --> # hs_test1_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6341 - Rouge1: 0.1424 - Rouge2: 0.0501 - Rougel: 0.1153 - Rougelsum: 0.1156 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.9223 \| 0.1287 \| 0.0361 \| 0.1068 \| 0.107 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.7150 \| 0.1411 \| 0.049 \| 0.1157 \| 0.1162 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.6506 \| 0.1396 \| 0.0472 \| 0.1128 \| 0.1133 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.6341 \| 0.1424 \| 0.0501 \| 0.1153 \| 0.1156 \| 19.0 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.0
aviralkumar28/ppo-Huggy	aviralkumar28	2024-01-15T08:05:05Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-01-15T08:04:59Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aviralkumar28/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
shadowml/Beagle14-7B	shadowml	2024-01-15T08:01:06Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "fblgit/UNA-TheBeagle-7b-v1", "argilla/distilabeled-Marcoro14-7B-slerp", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T07:56:47Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp --- # Beagle14-7B Beagle14-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) * [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) ## 🧩 Configuration ```yaml slices: - sources: - model: fblgit/UNA-TheBeagle-7b-v1 layer_range: [0, 32] - model: argilla/distilabeled-Marcoro14-7B-slerp layer_range: [0, 32] merge_method: slerp base_model: fblgit/UNA-TheBeagle-7b-v1 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shadowml/Beagle14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
seobak/xlm-roberta-base-finetuned-panx-en	seobak	2024-01-15T07:58:54Z	90	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T07:57:20Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6870824053452116 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3990 - F1: 0.6871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.0238 \| 1.0 \| 50 \| 0.4988 \| 0.5987 \| \| 0.4841 \| 2.0 \| 100 \| 0.4144 \| 0.6902 \| \| 0.3744 \| 3.0 \| 150 \| 0.3990 \| 0.6871 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
YeBhoneLin10/bagan	YeBhoneLin10	2024-01-15T07:56:59Z	36	1	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "en", "arxiv:2112.10752", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-01-13T10:55:40Z	--- license: creativeml-openrail-m language: - en tags: - stable-diffusion - text-to-image - diffusers widget: - text: "bagan, by Vincent van gogh, highly detailed, highly illustration" example_title: "Example Prompt 1" - text: "Establishing shot of a bagan, an epic fantasy, dramatic lighting, cinematic, extremely high detail, photorealistic, cinematic lighting, matte painting, artstation, by simon stalenhag, uncharted 4: a thief's end" example_title: "Example Prompt 2" - text: "hyper realistic wator color painting, transparent, myanmar bagan ancient city, after raining sense, beautiful cloud, ancient pagoda, some trees, with water splash infront of pagoda, lovely cloud, beautiful golden ratio composition, neutral color, moody image, lots of grey, golden ratio composition, grey and moody, more grey, rule of third, --ar 5:3 --q 0.5 --v 5" example_title: "Example Prompt 3" base_model: runwayml/stable-diffusion-v1-5 --- ### Introduction: Meet General Bagan, a cutting-edge text-to-image generator trained on a diverse dataset of over 200 images. With a keen understanding of textual inputs, it effortlessly translates words into visually stunning representations. From lifelike nature scenes to captivating abstract compositions. ### Problem Statement: When we prompted the stable diffusion model to generate an image of Bagan, it produced an image depicting a pagoda from Thailand. Hence, our decision was to fine-tune the current stable diffusion model using a multitude of Bagan photos in order to attain a clearer outcome. ### How to create prompt: When we create prompt for bagan, we have to consider 6 keywords. Those are Subject, Medium, Style, Art-sharing website, Resolution, and Additional details. Subject -> What you want to see in the picture is the subject. Not writing enough about the subjects is a common error. Medium -> The medium is the substance that artists work with. Illustration, oil painting, 3D rendering, and photography are a few examples. The impact of Medium is significant because a single keyword can significantly alter the style. Style -> The image's artistic style is referred to as the style. Pop art, impressionist, and surrealist are a few examples. Art-sharing website -> Specialty graphic websites like Deviant Art and Artstation compile a large number of images from various genres. One surefire way to direct the image toward these styles is to use them as a prompt. Resolution -> Resolution represents how sharp and detailed the image is Additional Details -> Sweeteners added to an image are additional details. To give the image a more dystopian and sci-fi feel, we will add those elements. The example prompt for general bagan is: bagan, a creepy and eery Halloween setting, with Jack o lanterns on the street and shadow figures lurking about, dynamic lighting, photorealistic fantasy concept art, stunning visuals, creative, cinematic, ultra detailed, trending on art station, spooky vibe. That prompt gives you the Halloween theme. ### Data: We used stable diffusion v1.5 model to train with 223 bagan pictures. ### Contributors: Main Contributor: [Ye Bhone Lin](https://github.com/Ye-Bhone-Lin), Supervisor: Sa Phyo Thu Htet, Contributors: Thant Htoo San, Min Phone Thit ### Limitation: We can't generate a photo of a human. ### Other Work: In our exploration of image generation, we delve into the architectural marvels of Myanmar, featuring iconic landmarks such as Ananda, Shwezigon, Bupaya, Thatbyinnyu, and Mraukoo. Each structure stands as a testament to the rich cultural and historical tapestry of the region, captured through the lens of our innovative text-to-image generator, General Bagan. ### References: Wikipedia (2022). Stable Diffusion. Retrieved From: https://en.wikipedia.org/wiki/Stable_Diffusion Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Retrieved From: https://arxiv.org/abs/2112.10752 Naomi Brown (2022). What is Stable Diffusion and How to Use it. Retrieved From: https://www.fotor.com/blog/what-is-stable-diffusion Mishra, O. (June, 9). Stable Diffusion Explained. Medium. https://medium.com/@onkarmishra/stable-diffusion-explained-1f101284484d
seobak/xlm-roberta-base-finetuned-panx-fr	seobak	2024-01-15T07:55:39Z	90	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T07:53:23Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8395478319554581 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - F1: 0.8395 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5795 \| 1.0 \| 191 \| 0.3287 \| 0.7758 \| \| 0.2619 \| 2.0 \| 382 \| 0.2644 \| 0.8273 \| \| 0.1761 \| 3.0 \| 573 \| 0.2752 \| 0.8395 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
seobak/xlm-roberta-base-finetuned-panx-de-fr	seobak	2024-01-15T07:53:04Z	93	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T07:45:59Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1609 - F1: 0.8598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2803 \| 1.0 \| 715 \| 0.1757 \| 0.8256 \| \| 0.1459 \| 2.0 \| 1430 \| 0.1617 \| 0.8473 \| \| 0.095 \| 3.0 \| 2145 \| 0.1609 \| 0.8598 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
seobak/xlm-roberta-base-finetuned-panx-de	seobak	2024-01-15T07:42:15Z	101	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-01-15T07:36:39Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8632930084625432 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1373 - F1: 0.8633 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2617 \| 1.0 \| 525 \| 0.1513 \| 0.8206 \| \| 0.1275 \| 2.0 \| 1050 \| 0.1341 \| 0.8518 \| \| 0.0808 \| 3.0 \| 1575 \| 0.1373 \| 0.8633 \| ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
sumo43/Yi-34b-x2	sumo43	2024-01-15T07:37:04Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:jondurbin/bagel-dpo-34b-v0.2", "base_model:merge:jondurbin/bagel-dpo-34b-v0.2", "base_model:one-man-army/UNA-34Beagles-32K-bf16-v1", "base_model:merge:one-man-army/UNA-34Beagles-32K-bf16-v1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T05:55:13Z	--- base_model: - jondurbin/bagel-dpo-34b-v0.2 - one-man-army/UNA-34Beagles-32K-bf16-v1 tags: - mergekit - merge license: mit --- # merged 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: * [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) * [one-man-army/UNA-34Beagles-32K-bf16-v1](https://huggingface.co/one-man-army/UNA-34Beagles-32K-bf16-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: jondurbin/bagel-dpo-34b-v0.2 dtype: float16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 60] model: jondurbin/bagel-dpo-34b-v0.2 - layer_range: [0, 60] model: one-man-army/UNA-34Beagles-32K-bf16-v1 ```
winglian/zephyr-deita-kto-3ep-v3-r512-bsz8	winglian	2024-01-15T07:11:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "trl", "dpo", "generated_from_trainer", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "base_model:adapter:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "region:us" ]	null	2024-01-15T07:10:06Z	--- license: mit library_name: peft tags: - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/mistral-7b-sft-beta model-index: - name: zephyr-deita-kto-3ep-v3-r512-bsz8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.3.0` ```yaml base_model: HuggingFaceH4/mistral-7b-sft-beta model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false rl: kto_pair datasets: - path: winglian/deita-nectar split: train_dpo type: zephyr.nectar _test_datasets: - path: winglian/deita-nectar split: test_dpo type: zephyr.nectar dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./zephyr-deita-kto-3ep-v3-r512-bsz8 save_total_limit: 3 hub_model_id: openaccess-ai-collective/kto-zephyr-deita-nectar adapter: lora lora_model_dir: sequence_len: 2048 sample_packing: false pad_to_sequence_len: false lora_r: 512 lora_alpha: 128 lora_dropout: 0.05 lora_target_linear: true lora_modules_to_save: lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: dpo-zephyr-deita-nectar wandb_entity: oaaic wandb_watch: wandb_run_id: wandb_name: kto-3ep-v3-r512-bsz8-lr1.4e-5 wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 2 num_epochs: 3 optimizer: paged_adamw_8bit adam_beta2: 0.95 adam_epsilion: 0.00001 lr_scheduler: cosine learning_rate: 1.414e-5 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true gradient_checkpoint_kwargs: use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 eval_steps: eval_table_size: eval_table_max_new_tokens: 128 save_steps: 45 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: save_safetensors: true dataloader_num_workers: 16 dataloader_pin_memory: true ``` </details><br> # zephyr-deita-kto-3ep-v3-r512-bsz8 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) 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: 1.414e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 3230 ### Training results ### Framework versions - PEFT 0.7.0 - Transformers 4.37.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
pankajcipher/Taxi-v3	pankajcipher	2024-01-15T07:06:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-01-15T07:06:53Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="pankajcipher/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13	GAI-LLM	2024-01-15T06:59:21Z	7	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-15T04:04:46Z	--- license: cc-by-nc-4.0 language: - ko library_name: transformers pipeline_tag: text-generation --- The license is `cc-by-nc-4.0`. # GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13 ## Model Details Model Developers Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng) Input Models input text only. Output Models generate text only. Model Architecture GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13 is an auto-regressive language model based on the LLaMA2 transformer architecture. Base Model [daekeun-ml/Llama-2-ko-instruct-13B](https://huggingface.co/daekeun-ml/Llama-2-ko-instruct-13B) Training Dataset - We combined Open Korean Dateset using mixed-strategy. - We use A100 GPU 80GB * 8, when training. # Model Benchmark ## KO-LLM leaderboard - Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard). # Implementation Code ```python ### GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13 from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13" model = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(repo) ```
dishank19/Mistral7B-SHP	dishank19	2024-01-15T06:59:14Z	5	1	peft	[ "peft", "safetensors", "text-generation", "en", "dataset:stanfordnlp/SHP", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	text-generation	2023-12-24T04:47:23Z	--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 datasets: - stanfordnlp/SHP language: - en pipeline_tag: text-generation --- # Model Card for Model ID Mistral 7B Fine-Tuned on the [SHP Dataset](https://huggingface.co/datasets/stanfordnlp/SHP) (r/askHistorians) using QLORA. This is basically a cool side project, wanted to see if I could fine-tune it with no added costs for compute. Used Google Colab - Developed by: [Dishank Jhaveri] - Model type: [Chat] - Language(s) (NLP): [English] - Finetuned from model: [Mistral-7B-v0.1] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [https://github.com/dishank19/Mistral7B_Finetune] - ## Model Card Contact [[email protected]] ### Framework versions - PEFT 0.7.1
Tigranchick/phi2-results2	Tigranchick	2024-01-15T06:50:15Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-01-15T06:46:40Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: microsoft/phi-2 model-index: - name: phi2-results2 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. --> # phi2-results2 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.3743 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2952 \| 0.97 \| 14 \| 1.0035 \| \| 1.0151 \| 2.0 \| 29 \| 0.6051 \| \| 0.5514 \| 2.97 \| 43 \| 0.4484 \| \| 0.4471 \| 4.0 \| 58 \| 0.3983 \| \| 0.4017 \| 4.83 \| 70 \| 0.3743 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
hopkins/mbart-finetuned-eng-kor-50	hopkins	2023-07-03T05:01:57Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T04:44:13Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-50 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. --> # mbart-finetuned-eng-kor-50 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9913 - Bleu: 7.0488 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
chriskim2273/IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta	chriskim2273	2023-07-03T04:50:05Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-03T04:13:01Z	--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta 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. --> # IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7219 ## 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: 3e-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 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 45 \| 0.5443 \| \| No log \| 2.0 \| 90 \| 0.6332 \| \| No log \| 3.0 \| 135 \| 0.6942 \| \| No log \| 4.0 \| 180 \| 0.6725 \| \| No log \| 5.0 \| 225 \| 0.7219 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Aeala/Enterredaas-65b-QLoRA	Aeala	2023-07-03T04:34:35Z	0	4	null	[ "region:us" ]	null	2023-07-03T04:07:43Z	## LoRA Info: Please note that this is a highly experimental LoRA model. It may do some good stuff, it might do some undesirable stuff. Training is paused for now. Feel free to try it!~ Important Note: This was trained in the Alpaca format, so prompting should be something like: ``` ### Instruction: <system prompt> (without the <>, this works like telling the AI what it is/purpose. i.e. like ChatGPT API's system prompt) ### Input: <prompt> (without the <>) ### Response: ``` Current upload: possibly final checkpoint ## Benchmarks wikitext2: Coming soon... ptb-new: Coming soon... c4-new: Coming soon...
hopkins/mbart-finetuned-eng-deu-50	hopkins	2023-07-03T04:24:57Z	57	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T04:06:46Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-50 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. --> # mbart-finetuned-eng-deu-50 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6559 - Bleu: 21.0004 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-47	hopkins	2023-07-03T04:17:24Z	58	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:59:40Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-47 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. --> # mbart-finetuned-eng-kor-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9922 - Bleu: 6.8895 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
aaa950739/trained_model	aaa950739	2023-07-03T04:16:04Z	96	0	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-07-03T03:56:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: trained_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. --> # trained_model This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 500 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
deepghs/imgutils-models	deepghs	2023-07-03T04:12:18Z	0	6	null	[ "onnx", "dataset:deepghs/chafen_arknights", "dataset:deepghs/monochrome_danbooru", "license:mit", "region:us" ]	null	2023-03-11T08:37:38Z	--- license: mit datasets: - deepghs/chafen_arknights - deepghs/monochrome_danbooru metrics: - accuracy --- # imgutils-models This repository includes all the models in [deepghs/imgutils](https://github.com/deepghs/imgutils). ## LPIPS This model is used for clustering anime images (named `差分` in Chinese), based on [richzhang/PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity), trained with dataset [deepghs/chafen_arknights(private)](https://huggingface.co/datasets/deepghs/chafen_arknights). When threshold is `0.45`, the [adjusted rand score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html) can reach `0.995`. File lists: * `lpips_diff.onnx`, feature difference. * `lpips_feature.onnx`, feature extracting. ## Monochrome These model is used for monochrome image classification, based on CNNs and Transformers, trained with dataset [deepghs/monochrome_danbooru(private)](https://huggingface.co/datasets/deepghs/monochrome_danbooru). The following are the checkpoints that have been formally put into use, all based on the Caformer architecture: \| Checkpoint \| Algorithm \| Safe Level \| Accuracy \| False Negative \| False Positive \| \|:----------------------------:\|:---------:\|:----------:\|:----------:\|:--------------:\|:--------------:\| \| monochrome-caformer-40 \| caformer \| 0 \| 96.41% \| 2.69% \| 0.89% \| \| monochrome-caformer-110 \| caformer \| 0 \| 96.97% \| 1.57% \| 1.46% \| \| monochrome-caformer_safe2-80 \| caformer \| 2 \| 94.84% \| 1.12% \| 4.03% \| \| monochrome-caformer_safe4-70 \| caformer \| 4 \| 94.28% \| 0.67% \| 5.04% \| `monochrome-caformer-110` has the best overall accuracy among them, but considering that this model is often used to screen out monochrome images and we want to screen out as many as possible without omission, we have also introduced weighted models (`safe2` and `safe4`). Although their overall accuracy has been slightly reduced, the probability of False Negative (misidentifying a monochrome image as a colored one) is lower, making them more suitable for batch screening. ## Deepdanbooru `deepdanbooru` is a model used to tag anime images. Here, we provide a table for tag classification called `deepdanbooru_tags.csv`, as well as an ONNX model (from [chinoll/deepdanbooru](https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags)). It's worth noting that due to the poor quality of the deepdanbooru model itself and the relatively old dataset, it is only for testing purposes and is not recommended to be used as the main classification model. We recommend using the `wd14` model instead, see: * https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
hopkins/mbart-finetuned-eng-ind-49	hopkins	2023-07-03T04:11:46Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:53:54Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-49 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. --> # mbart-finetuned-eng-ind-49 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7653 - Bleu: 22.0600 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
vineetsharma/whisper-base-finetuned-gtzan	vineetsharma	2023-07-03T04:03:36Z	49	0	transformers	[ "transformers", "pytorch", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-07-03T01:16:02Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-base-finetuned-gtzan results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-base-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6867 - Accuracy: 0.87 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.9075 \| 1.0 \| 57 \| 1.0000 \| 0.58 \| \| 0.4569 \| 2.0 \| 114 \| 0.6073 \| 0.83 \| \| 0.3761 \| 3.0 \| 171 \| 0.6410 \| 0.8 \| \| 0.3049 \| 4.0 \| 228 \| 0.4536 \| 0.86 \| \| 0.0284 \| 5.0 \| 285 \| 0.5120 \| 0.85 \| \| 0.0165 \| 6.0 \| 342 \| 0.4856 \| 0.89 \| \| 0.0087 \| 7.0 \| 399 \| 0.6814 \| 0.87 \| \| 0.0038 \| 8.0 \| 456 \| 0.7059 \| 0.85 \| \| 0.0032 \| 9.0 \| 513 \| 0.6831 \| 0.87 \| \| 0.0034 \| 10.0 \| 570 \| 0.6867 \| 0.87 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-47	hopkins	2023-07-03T03:59:13Z	49	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:41:18Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-47 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. --> # mbart-finetuned-eng-ind-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7657 - Bleu: 21.8229 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-49	hopkins	2023-07-03T03:53:24Z	50	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:35:07Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-49 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. --> # mbart-finetuned-eng-deu-49 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6500 - Bleu: 21.1322 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-47	hopkins	2023-07-03T03:40:49Z	43	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:22:38Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-47 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. --> # mbart-finetuned-eng-deu-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6483 - Bleu: 20.8742 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-46	hopkins	2023-07-03T03:33:45Z	48	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:15:41Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-46 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. --> # mbart-finetuned-eng-deu-46 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6533 - Bleu: 20.8950 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-44	hopkins	2023-07-03T03:32:33Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:14:52Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-44 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. --> # mbart-finetuned-eng-kor-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9949 - Bleu: 6.8417 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-43	hopkins	2023-07-03T03:22:08Z	74	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T03:08:49Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-43 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. --> # mbart-finetuned-eng-kor-43 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9892 - Bleu: 6.9989 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-42	hopkins	2023-07-03T03:15:12Z	62	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:57:33Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-42 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. --> # mbart-finetuned-eng-kor-42 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9879 - Bleu: 6.7656 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-44	hopkins	2023-07-03T03:14:24Z	67	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:56:32Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-44 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. --> # mbart-finetuned-eng-ind-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7625 - Bleu: 21.9586 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
samzoozi/atari_game	samzoozi	2023-07-03T03:04:22Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T03:03:41Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 718.00 +/- 220.55 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga samzoozi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Sourabh2/Cartpole-v2	Sourabh2	2023-07-03T03:03:46Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T03:02:25Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 .
jinlee74/distilbert-base-uncased-finetuned-emotions	jinlee74	2023-07-03T02:59:35Z	55	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T00:11:13Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotions results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9415 - name: F1 type: f1 value: 0.9416116671925132 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotions This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2357 - Accuracy: 0.9415 - F1: 0.9416 ## 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: 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.016 \| 1.0 \| 250 \| 0.2262 \| 0.9405 \| 0.9404 \| \| 0.011 \| 2.0 \| 500 \| 0.2357 \| 0.9415 \| 0.9416 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.1.0.dev20230316 - Datasets 2.12.0 - Tokenizers 0.13.3
AshtakaOOf/ssambatea-locon	AshtakaOOf	2023-07-03T02:58:58Z	0	1	null	[ "Text-to-Image", "anime", "lora", "locon", "lycoris", "license:cc-by-nc-sa-4.0", "region:us" ]	null	2023-07-03T01:36:57Z	--- license: cc-by-nc-sa-4.0 tags: - Text-to-Image - anime - lora - locon - lycoris --- # SSAMBAtea Style LoCon ![example](https://media.discordapp.net/attachments/1019446913268973689/1125244643852947466/00115-24682990.png?width=500&height=620) ## token: ssambatea Trained on SSAMBAtea artwork This is a LoCon and require the LyCORIS extension to work I am planning on making a new improved dataset to do a V2 # License [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)
hopkins/mbart-finetuned-eng-deu-45	hopkins	2023-07-03T02:57:51Z	59	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:39:34Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-45 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. --> # mbart-finetuned-eng-deu-45 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6514 - Bleu: 20.9841 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
alibaba-pai/pai-diffusion-artist-large-zh	alibaba-pai	2023-07-03T02:56:37Z	14	7	diffusers	[ "diffusers", "pytorch", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-04-03T09:38:48Z	--- license: apache-2.0 tags: - pytorch - diffusers - text-to-image --- # Chinese Diffusion Model (Artist, 512 Resolution) ## 简介 Brief Introduction 我们开源了一个中文 Diffusion 模型，您可以直接输入中文提示词，我们为您呈现精美的艺术风格图片。本模型的默认分辨率是 512512。 We release a Chinese diffusion model, which is able to generate high-quality artistic images according to the prompts you input. The default resolution of this model is 512512. * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ## 使用 Usage 本模型支持 `diffusers`，可以参考以下范例代码： This model supports `diffusers`. Please refer to the following code: ```python from diffusers import StableDiffusionPipeline model_id = "alibaba-pai/pai-diffusion-artist-large-zh" pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") prompt = "雾蒙蒙的日出在湖面上" image = pipe(prompt).images[0] image.save("result.png") ``` ## 作品展示 Gallery \| prompt: 浮岛，天空，白云，城堡，幻想世界 \| prompt: 红白玫瑰花，很多花瓣，绽放 \| \| ---------------------------------------- \| ---------------------------------- \| \| negative_prompt: 油画，模糊，雾蒙蒙 \| negative_prompt: \| \| ![](example1.png) \| ![](example2.png) \| \| prompt: 亭台楼阁，曲径通幽，水墨绘画，中国风 \| prompt: 阳光，彩虹，小白马 \| \| -------------------------------------------- \| -------------------------- \| \| negative_prompt: 油画，彩色 \| negative_prompt: \| \| ![](example3.png) \| ![](example4.png) \| ## 使用须知 Notice for Use 使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。 If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms.
digiplay/Landscape_PhotoReal_v1	digiplay	2023-07-03T02:53:33Z	620	7	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T02:20:00Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/71987/landscapephotoreal?modelVersionId=76750 Sample images and prompt : magnificent scenery, wide landscape, sharp and crisp background, very beautiful landscape, old ruins buildings, fantasy, birdview, best quality, masterpiece, ultra high res, dark blue light, cloudy, photo, photorealistic, wide view, kkw-ph1 ![8fe78e3f-8861-4a05-b81b-ece37ef654b2.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pn1BrZcWpE102SLyJq6Jh.jpeg) ![acb17a24-5b9a-4699-b0f5-2192523b78e8.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/PrOTKR4lrBWeRNFGhAyhd.jpeg) photorealistic modern living room, sharp and crisp background, sofa, low table, bookshelf, parks and buildings from window, wood and flower, beautiful landscape, best quality, masterpiece, hires, in the morning light, detailed lighting, blue sky, (((photo))), (((photorealistic))) ,kkw-ph1, wide shot, web meeting background ![89c995b5-e33d-4526-bb2f-5d1550a20084.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/RDKBVRmNV0SWXpz-o-D4F.jpeg)
hopkins/mbart-finetuned-eng-kor-40	hopkins	2023-07-03T02:37:25Z	74	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:19:49Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-40 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. --> # mbart-finetuned-eng-kor-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9919 - Bleu: 7.0359 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Rasith/NZappFineTune2	Rasith	2023-07-03T02:31:27Z	31	0	transformers	[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T02:31:01Z	--- tags: - generated_from_keras_callback model-index: - name: NZappFineTune2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # NZappFineTune2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-39	hopkins	2023-07-03T02:31:10Z	53	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:13:29Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-39 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. --> # mbart-finetuned-eng-kor-39 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9925 - Bleu: 6.7954 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
djifg/grow_classification_xlmr2	djifg	2023-07-03T02:28:32Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-03T01:59:42Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification_xlmr2 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. --> # grow_classification_xlmr2 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5585 - Accuracy: 0.9309 - F1: 0.9297 ## 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: 3e-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 \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.2832 \| 1.0 \| 436 \| 0.4686 \| 0.8870 \| 0.8872 \| \| 0.0717 \| 2.0 \| 872 \| 0.5915 \| 0.8964 \| 0.8950 \| \| 0.0374 \| 3.0 \| 1308 \| 0.4898 \| 0.9276 \| 0.9266 \| \| 0.0205 \| 4.0 \| 1744 \| 0.5333 \| 0.9271 \| 0.9257 \| \| 0.0101 \| 5.0 \| 2180 \| 0.5585 \| 0.9309 \| 0.9297 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jncraton/fastchat-t5-3b-v1.0-ct2-int8	jncraton	2023-07-03T02:24:58Z	3	2	transformers	[ "transformers", "license:apache-2.0", "region:us" ]	null	2023-07-03T01:59:59Z	--- license: apache-2.0 inference: false --- # FastChat-T5 Model Card ## Model details Model type: FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture, and can autoregressively generate responses to users' inputs. Model date: FastChat-T5 was trained on April 2023. Organizations developing the model: The FastChat developers, primarily Dacheng Li, Lianmin Zheng and Hao Zhang. Paper or resources for more information: https://github.com/lm-sys/FastChat#FastChat-T5 License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/lm-sys/FastChat/issues ## Intended use Primary intended uses: The primary use of FastChat-T5 is the commercial usage of large language models and chatbots. It can also be used for research purposes. Primary intended users: The primary intended users of the model are entrepreneurs and researchers in natural language processing, machine learning, and artificial intelligence. ## Training dataset 70K conversations collected from ShareGPT.com. ## Training details It processes the ShareGPT data in the form of question answering. Each ChatGPT response is processed as an answer, and previous conversations between the user and the ChatGPT are processed as the question. The encoder bi-directionally encodes a question into a hidden representation. The decoder uses cross-attention to attend to this representation while generating an answer uni-directionally from a start token. This model is fine-tuned for 3 epochs, with a max learning rate 2e-5, warmup ratio 0.03, and a cosine learning rate schedule. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
hopkins/mbart-finetuned-eng-kor-38	hopkins	2023-07-03T02:24:16Z	75	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:06:33Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-38 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. --> # mbart-finetuned-eng-kor-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9945 - Bleu: 6.8900 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-40	hopkins	2023-07-03T02:19:21Z	64	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T02:01:28Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-40 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. --> # mbart-finetuned-eng-ind-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7628 - Bleu: 21.8914 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-39	hopkins	2023-07-03T02:13:00Z	63	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:55:07Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-39 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. --> # mbart-finetuned-eng-ind-39 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7633 - Bleu: 21.8212 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Bin12123/Chat	Bin12123	2023-07-03T02:11:49Z	0	0	null	[ "zh", "dataset:fka/awesome-chatgpt-prompts", "region:us" ]	null	2023-07-03T02:10:05Z	--- datasets: - fka/awesome-chatgpt-prompts language: - zh ---
hopkins/mbart-finetuned-eng-ind-38	hopkins	2023-07-03T02:06:04Z	65	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:52:19Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-38 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. --> # mbart-finetuned-eng-ind-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7718 - Bleu: 21.7535 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
digiplay/CityEdge_StyleMix_v1.44	digiplay	2023-07-03T02:03:34Z	310	4	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T01:27:43Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/63243/cityedgestylemix Sample images and prompt : 1girl, solo, long hair blown by wind,close-up ,long dress, green eyes, white stocking, lace, look at viewer, luxurious, elegant, extremely detailed, majestic, blurry, blurry background, tree, branch, cherry blossoms, butterfly, flower petals blown by wind, depth of field, ![adc5b9bd-9c9c-4eaa-8c2b-f3d80cf35de9.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/YQ55e6l6sTyMKfDMMEHjf.jpeg) 8k Angel sky,best quality , masterpiece, close up, ultra detailed ,upper body ![f7000d0a-06ba-4d2f-9cb2-99e66dce200c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/gYaVpXPgV9iuGoZwVFkoY.jpeg) ![ff8d0f59-9b28-456d-a5dc-93e5ab494a9c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/NY9c_GHK4Dph8mKh1BkOh.jpeg)
Ngadou/falcon7b-scam-detector	Ngadou	2023-07-03T02:03:13Z	4	0	adapter-transformers	[ "adapter-transformers", "text-generation", "en", "fr", "dataset:timdettmers/openassistant-guanaco", "license:apache-2.0", "region:us" ]	text-generation	2023-07-03T01:49:44Z	--- license: apache-2.0 datasets: - timdettmers/openassistant-guanaco language: - en - fr metrics: - accuracy pipeline_tag: text-generation library_name: adapter-transformers ---
hopkins/mbart-finetuned-eng-deu-40	hopkins	2023-07-03T02:00:58Z	70	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:42:43Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-40 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. --> # mbart-finetuned-eng-deu-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6497 - Bleu: 20.8437 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Huggingfly/q-Taxi-v3	Huggingfly	2023-07-03T01:57:19Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T01:55:19Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Huggingfly/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
hopkins/mbart-finetuned-eng-kor-37	hopkins	2023-07-03T01:48:15Z	125	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:30:38Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-37 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. --> # mbart-finetuned-eng-kor-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9920 - Bleu: 6.9377 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-36	hopkins	2023-07-03T01:42:12Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:24:37Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-36 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. --> # mbart-finetuned-eng-kor-36 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9933 - Bleu: 6.9791 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-34	hopkins	2023-07-03T01:33:22Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:15:53Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-34 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. --> # mbart-finetuned-eng-kor-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9937 - Bleu: 7.1397 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
ankitvyas/myBloomLoraModel	ankitvyas	2023-07-03T01:31:54Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-03T01:19:55Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0.dev0
hopkins/mbart-finetuned-eng-ind-37	hopkins	2023-07-03T01:30:10Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T01:12:28Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-37 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. --> # mbart-finetuned-eng-ind-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7649 - Bleu: 21.9222 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
Ngadou/bert-sms-spam-dectector	Ngadou	2023-07-03T01:29:26Z	111	2	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "dataset:Ngadou/Spam_SMS", "doi:10.57967/hf/0746", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-06-10T21:24:39Z	--- license: cc-by-4.0 datasets: - Ngadou/Spam_SMS language: - en metrics: - accuracy pipeline_tag: text-classification ---
hopkins/mbart-finetuned-eng-ind-34	hopkins	2023-07-03T01:15:25Z	121	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:57:39Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-34 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. --> # mbart-finetuned-eng-ind-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7610 - Bleu: 21.9140 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-37	hopkins	2023-07-03T01:11:58Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:53:43Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-37 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. --> # mbart-finetuned-eng-deu-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6509 - Bleu: 20.9509 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-deu-34	hopkins	2023-07-03T00:57:10Z	118	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:43:07Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-34 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. --> # mbart-finetuned-eng-deu-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6495 - Bleu: 20.8330 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
saswat94/my-pet-bear-nxt	saswat94	2023-07-03T00:55:44Z	6	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-03T00:51:10Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Bear-nxt Dreambooth model trained by saswat94 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CVRGU633 Sample pictures of this concept: ![0](https://huggingface.co/saswat94/my-pet-bear-nxt/resolve/main/sample_images/00000-1946388114.png)
hopkins/mbart-finetuned-eng-kor-31	hopkins	2023-07-03T00:45:09Z	118	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:31:48Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-31 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. --> # mbart-finetuned-eng-kor-31 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9893 - Bleu: 7.0441 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-kor-30	hopkins	2023-07-03T00:42:37Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:29:16Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-30 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. --> # mbart-finetuned-eng-kor-30 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9943 - Bleu: 7.0556 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
hopkins/mbart-finetuned-eng-ind-33	hopkins	2023-07-03T00:39:01Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2023-07-03T00:25:25Z	--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-33 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. --> # mbart-finetuned-eng-ind-33 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7673 - Bleu: 21.9515 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
renyulin/llama-7b-es-ppo-adpater	renyulin	2023-07-03T00:35:51Z	0	0	transformers	[ "transformers", "pytorch", "trl", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-07-03T00:35:48Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") model = AutoModelForCausalLMWithValueHead.from_pretrained("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
TalesLF/Reinforce-Pixelcopter-PLE-v0	TalesLF	2023-07-03T00:34:21Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-03T00:34:16Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 30.60 +/- 21.44 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only

Omar95farag

2024-01-15T09:53:58Z

94

0

transformers

[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-12T08:10:10Z

--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_visual_concat_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1314 - Accuracy: 0.7925 - Exit 0 Accuracy: 0.075 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:| | No log | 0.96 | 16 | 2.6835 | 0.1175 | 0.0775 | | No log | 1.98 | 33 | 2.4924 | 0.2375 | 0.0675 | | No log | 3.0 | 50 | 2.2916 | 0.34 | 0.07 | | No log | 3.96 | 66 | 2.0277 | 0.475 | 0.07 | | No log | 4.98 | 83 | 1.7026 | 0.5775 | 0.07 | | No log | 6.0 | 100 | 1.4354 | 0.6625 | 0.075 | | No log | 6.96 | 116 | 1.2357 | 0.6975 | 0.07 | | No log | 7.98 | 133 | 1.0754 | 0.745 | 0.0725 | | No log | 9.0 | 150 | 0.9760 | 0.77 | 0.07 | | No log | 9.96 | 166 | 0.8967 | 0.765 | 0.07 | | No log | 10.98 | 183 | 0.8561 | 0.7875 | 0.07 | | No log | 12.0 | 200 | 0.8837 | 0.7575 | 0.07 | | No log | 12.96 | 216 | 0.8885 | 0.7725 | 0.07 | | No log | 13.98 | 233 | 0.7819 | 0.7725 | 0.0675 | | No log | 15.0 | 250 | 0.9067 | 0.7725 | 0.07 | | No log | 15.96 | 266 | 0.9086 | 0.775 | 0.075 | | No log | 16.98 | 283 | 0.8444 | 0.795 | 0.07 | | No log | 18.0 | 300 | 0.9359 | 0.7775 | 0.0725 | | No log | 18.96 | 316 | 0.9696 | 0.7825 | 0.07 | | No log | 19.98 | 333 | 0.9254 | 0.7825 | 0.075 | | No log | 21.0 | 350 | 0.9879 | 0.7775 | 0.0675 | | No log | 21.96 | 366 | 0.9894 | 0.79 | 0.075 | | No log | 22.98 | 383 | 1.0087 | 0.785 | 0.07 | | No log | 24.0 | 400 | 1.0101 | 0.785 | 0.07 | | No log | 24.96 | 416 | 1.0188 | 0.7875 | 0.075 | | No log | 25.98 | 433 | 1.0266 | 0.79 | 0.075 | | No log | 27.0 | 450 | 1.0357 | 0.79 | 0.07 | | No log | 27.96 | 466 | 1.0505 | 0.7875 | 0.0675 | | No log | 28.98 | 483 | 1.0524 | 0.7825 | 0.07 | | 1.6927 | 30.0 | 500 | 1.0656 | 0.785 | 0.075 | | 1.6927 | 30.96 | 516 | 1.0642 | 0.785 | 0.0725 | | 1.6927 | 31.98 | 533 | 1.0740 | 0.785 | 0.0725 | | 1.6927 | 33.0 | 550 | 1.0830 | 0.785 | 0.075 | | 1.6927 | 33.96 | 566 | 1.0860 | 0.785 | 0.0675 | | 1.6927 | 34.98 | 583 | 1.0939 | 0.7875 | 0.0675 | | 1.6927 | 36.0 | 600 | 1.0969 | 0.79 | 0.0675 | | 1.6927 | 36.96 | 616 | 1.0966 | 0.7875 | 0.07 | | 1.6927 | 37.98 | 633 | 1.1024 | 0.79 | 0.07 | | 1.6927 | 39.0 | 650 | 1.1045 | 0.7875 | 0.075 | | 1.6927 | 39.96 | 666 | 1.1050 | 0.7875 | 0.0725 | | 1.6927 | 40.98 | 683 | 1.1085 | 0.7875 | 0.075 | | 1.6927 | 42.0 | 700 | 1.1171 | 0.7875 | 0.075 | | 1.6927 | 42.96 | 716 | 1.1193 | 0.7875 | 0.075 | | 1.6927 | 43.98 | 733 | 1.1188 | 0.79 | 0.075 | | 1.6927 | 45.0 | 750 | 1.1220 | 0.79 | 0.0725 | | 1.6927 | 45.96 | 766 | 1.1270 | 0.79 | 0.0725 | | 1.6927 | 46.98 | 783 | 1.1268 | 0.7875 | 0.075 | | 1.6927 | 48.0 | 800 | 1.1264 | 0.79 | 0.075 | | 1.6927 | 48.96 | 816 | 1.1267 | 0.795 | 0.075 | | 1.6927 | 49.98 | 833 | 1.1273 | 0.79 | 0.0725 | | 1.6927 | 51.0 | 850 | 1.1268 | 0.79 | 0.07 | | 1.6927 | 51.96 | 866 | 1.1283 | 0.795 | 0.07 | | 1.6927 | 52.98 | 883 | 1.1293 | 0.795 | 0.075 | | 1.6927 | 54.0 | 900 | 1.1306 | 0.795 | 0.075 | | 1.6927 | 54.96 | 916 | 1.1306 | 0.795 | 0.075 | | 1.6927 | 55.98 | 933 | 1.1310 | 0.795 | 0.075 | | 1.6927 | 57.0 | 950 | 1.1314 | 0.7925 | 0.075 | | 1.6927 | 57.6 | 960 | 1.1314 | 0.7925 | 0.075 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only

Omar95farag

2024-01-15T09:53:58Z

89

0

transformers

[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-12T04:42:47Z

--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_vision_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2733 - Accuracy: 0.7825 - Exit 0 Accuracy: 0.0775 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:| | No log | 0.96 | 16 | 2.6800 | 0.13 | 0.0675 | | No log | 1.98 | 33 | 2.4446 | 0.28 | 0.0725 | | No log | 3.0 | 50 | 2.1924 | 0.37 | 0.0675 | | No log | 3.96 | 66 | 1.8733 | 0.5175 | 0.07 | | No log | 4.98 | 83 | 1.6056 | 0.6075 | 0.0775 | | No log | 6.0 | 100 | 1.3480 | 0.6725 | 0.08 | | No log | 6.96 | 116 | 1.1393 | 0.735 | 0.07 | | No log | 7.98 | 133 | 1.0738 | 0.7375 | 0.07 | | No log | 9.0 | 150 | 0.9271 | 0.7725 | 0.075 | | No log | 9.96 | 166 | 0.8885 | 0.7675 | 0.085 | | No log | 10.98 | 183 | 0.8669 | 0.76 | 0.075 | | No log | 12.0 | 200 | 0.8547 | 0.7775 | 0.0725 | | No log | 12.96 | 216 | 0.8633 | 0.76 | 0.07 | | No log | 13.98 | 233 | 0.8498 | 0.7675 | 0.075 | | No log | 15.0 | 250 | 0.9608 | 0.7675 | 0.0675 | | No log | 15.96 | 266 | 0.8952 | 0.7875 | 0.08 | | No log | 16.98 | 283 | 0.9486 | 0.7575 | 0.0725 | | No log | 18.0 | 300 | 0.9826 | 0.765 | 0.0825 | | No log | 18.96 | 316 | 1.0230 | 0.7625 | 0.09 | | No log | 19.98 | 333 | 1.0961 | 0.76 | 0.0875 | | No log | 21.0 | 350 | 1.0083 | 0.785 | 0.07 | | No log | 21.96 | 366 | 1.0394 | 0.7725 | 0.0725 | | No log | 22.98 | 383 | 1.0825 | 0.78 | 0.085 | | No log | 24.0 | 400 | 1.0789 | 0.77 | 0.075 | | No log | 24.96 | 416 | 1.1030 | 0.7725 | 0.0925 | | No log | 25.98 | 433 | 1.1252 | 0.775 | 0.075 | | No log | 27.0 | 450 | 1.1333 | 0.7725 | 0.0725 | | No log | 27.96 | 466 | 1.1416 | 0.765 | 0.0775 | | No log | 28.98 | 483 | 1.1442 | 0.7775 | 0.0775 | | 1.6768 | 30.0 | 500 | 1.1620 | 0.7825 | 0.1025 | | 1.6768 | 30.96 | 516 | 1.1617 | 0.7825 | 0.0775 | | 1.6768 | 31.98 | 533 | 1.1788 | 0.775 | 0.0875 | | 1.6768 | 33.0 | 550 | 1.1858 | 0.7725 | 0.0825 | | 1.6768 | 33.96 | 566 | 1.1842 | 0.7825 | 0.0725 | | 1.6768 | 34.98 | 583 | 1.1964 | 0.785 | 0.085 | | 1.6768 | 36.0 | 600 | 1.2034 | 0.78 | 0.075 | | 1.6768 | 36.96 | 616 | 1.2050 | 0.7825 | 0.07 | | 1.6768 | 37.98 | 633 | 1.2111 | 0.7825 | 0.075 | | 1.6768 | 39.0 | 650 | 1.2217 | 0.785 | 0.0925 | | 1.6768 | 39.96 | 666 | 1.2510 | 0.7775 | 0.105 | | 1.6768 | 40.98 | 683 | 1.2512 | 0.7825 | 0.0825 | | 1.6768 | 42.0 | 700 | 1.2529 | 0.7775 | 0.0775 | | 1.6768 | 42.96 | 716 | 1.2557 | 0.78 | 0.0725 | | 1.6768 | 43.98 | 733 | 1.2615 | 0.7775 | 0.0775 | | 1.6768 | 45.0 | 750 | 1.2621 | 0.78 | 0.0825 | | 1.6768 | 45.96 | 766 | 1.2613 | 0.785 | 0.075 | | 1.6768 | 46.98 | 783 | 1.2614 | 0.78 | 0.075 | | 1.6768 | 48.0 | 800 | 1.2598 | 0.7825 | 0.075 | | 1.6768 | 48.96 | 816 | 1.2650 | 0.7825 | 0.085 | | 1.6768 | 49.98 | 833 | 1.2665 | 0.7825 | 0.08 | | 1.6768 | 51.0 | 850 | 1.2673 | 0.785 | 0.0775 | | 1.6768 | 51.96 | 866 | 1.2626 | 0.7775 | 0.075 | | 1.6768 | 52.98 | 883 | 1.2643 | 0.7825 | 0.075 | | 1.6768 | 54.0 | 900 | 1.2702 | 0.78 | 0.0775 | | 1.6768 | 54.96 | 916 | 1.2723 | 0.78 | 0.0775 | | 1.6768 | 55.98 | 933 | 1.2730 | 0.7825 | 0.0775 | | 1.6768 | 57.0 | 950 | 1.2732 | 0.7825 | 0.0775 | | 1.6768 | 57.6 | 960 | 1.2733 | 0.7825 | 0.0775 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only

Omar95farag

2024-01-15T09:53:57Z

92

0

transformers

[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-12T01:20:21Z

--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-12_text_vision_only This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3116 - Accuracy: 0.7775 - Exit 0 Accuracy: 0.065 - Exit 1 Accuracy: 0.09 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:| | No log | 0.96 | 16 | 2.6827 | 0.1275 | 0.0625 | 0.04 | | No log | 1.98 | 33 | 2.5158 | 0.25 | 0.06 | 0.095 | | No log | 3.0 | 50 | 2.3010 | 0.33 | 0.0625 | 0.095 | | No log | 3.96 | 66 | 1.9997 | 0.435 | 0.055 | 0.0925 | | No log | 4.98 | 83 | 1.7239 | 0.6 | 0.06 | 0.0925 | | No log | 6.0 | 100 | 1.4812 | 0.6175 | 0.06 | 0.09 | | No log | 6.96 | 116 | 1.2872 | 0.6875 | 0.0625 | 0.09 | | No log | 7.98 | 133 | 1.1118 | 0.74 | 0.055 | 0.09 | | No log | 9.0 | 150 | 1.0144 | 0.7425 | 0.0625 | 0.09 | | No log | 9.96 | 166 | 0.9663 | 0.7475 | 0.0575 | 0.09 | | No log | 10.98 | 183 | 0.9532 | 0.7475 | 0.0625 | 0.09 | | No log | 12.0 | 200 | 0.9157 | 0.7525 | 0.06 | 0.09 | | No log | 12.96 | 216 | 0.8894 | 0.77 | 0.06 | 0.09 | | No log | 13.98 | 233 | 0.9460 | 0.75 | 0.0625 | 0.09 | | No log | 15.0 | 250 | 1.0019 | 0.745 | 0.0625 | 0.09 | | No log | 15.96 | 266 | 0.9059 | 0.77 | 0.0625 | 0.0875 | | No log | 16.98 | 283 | 1.0664 | 0.7325 | 0.06 | 0.0875 | | No log | 18.0 | 300 | 1.0637 | 0.74 | 0.065 | 0.0875 | | No log | 18.96 | 316 | 1.0398 | 0.7725 | 0.09 | 0.085 | | No log | 19.98 | 333 | 1.0745 | 0.775 | 0.06 | 0.0875 | | No log | 21.0 | 350 | 1.0653 | 0.78 | 0.0625 | 0.0875 | | No log | 21.96 | 366 | 1.0705 | 0.785 | 0.065 | 0.0875 | | No log | 22.98 | 383 | 1.1014 | 0.78 | 0.0725 | 0.0875 | | No log | 24.0 | 400 | 1.1335 | 0.78 | 0.0625 | 0.0875 | | No log | 24.96 | 416 | 1.1510 | 0.775 | 0.0725 | 0.0875 | | No log | 25.98 | 433 | 1.1528 | 0.7825 | 0.0675 | 0.0875 | | No log | 27.0 | 450 | 1.1758 | 0.7825 | 0.0625 | 0.0875 | | No log | 27.96 | 466 | 1.1836 | 0.785 | 0.07 | 0.0875 | | No log | 28.98 | 483 | 1.1927 | 0.78 | 0.0675 | 0.0875 | | 1.6955 | 30.0 | 500 | 1.2061 | 0.7825 | 0.0775 | 0.0875 | | 1.6955 | 30.96 | 516 | 1.2128 | 0.7775 | 0.065 | 0.0875 | | 1.6955 | 31.98 | 533 | 1.2172 | 0.7725 | 0.07 | 0.0875 | | 1.6955 | 33.0 | 550 | 1.2237 | 0.775 | 0.075 | 0.0875 | | 1.6955 | 33.96 | 566 | 1.2399 | 0.7775 | 0.0625 | 0.0875 | | 1.6955 | 34.98 | 583 | 1.2590 | 0.78 | 0.065 | 0.0875 | | 1.6955 | 36.0 | 600 | 1.2586 | 0.7825 | 0.065 | 0.0875 | | 1.6955 | 36.96 | 616 | 1.2603 | 0.775 | 0.0675 | 0.0875 | | 1.6955 | 37.98 | 633 | 1.2576 | 0.78 | 0.065 | 0.0875 | | 1.6955 | 39.0 | 650 | 1.2698 | 0.7775 | 0.075 | 0.0875 | | 1.6955 | 39.96 | 666 | 1.2775 | 0.7725 | 0.075 | 0.0875 | | 1.6955 | 40.98 | 683 | 1.2769 | 0.7725 | 0.07 | 0.0875 | | 1.6955 | 42.0 | 700 | 1.2769 | 0.7725 | 0.0625 | 0.0875 | | 1.6955 | 42.96 | 716 | 1.2804 | 0.775 | 0.0675 | 0.0875 | | 1.6955 | 43.98 | 733 | 1.2834 | 0.775 | 0.065 | 0.085 | | 1.6955 | 45.0 | 750 | 1.2907 | 0.7775 | 0.0675 | 0.0875 | | 1.6955 | 45.96 | 766 | 1.2968 | 0.7775 | 0.0675 | 0.0875 | | 1.6955 | 46.98 | 783 | 1.2981 | 0.7775 | 0.065 | 0.0875 | | 1.6955 | 48.0 | 800 | 1.3017 | 0.7775 | 0.065 | 0.0875 | | 1.6955 | 48.96 | 816 | 1.3050 | 0.7775 | 0.0675 | 0.09 | | 1.6955 | 49.98 | 833 | 1.3050 | 0.775 | 0.07 | 0.09 | | 1.6955 | 51.0 | 850 | 1.3044 | 0.775 | 0.07 | 0.09 | | 1.6955 | 51.96 | 866 | 1.3057 | 0.775 | 0.0675 | 0.09 | | 1.6955 | 52.98 | 883 | 1.3072 | 0.7775 | 0.0675 | 0.09 | | 1.6955 | 54.0 | 900 | 1.3101 | 0.7775 | 0.0675 | 0.09 | | 1.6955 | 54.96 | 916 | 1.3119 | 0.7775 | 0.065 | 0.09 | | 1.6955 | 55.98 | 933 | 1.3116 | 0.7775 | 0.065 | 0.09 | | 1.6955 | 57.0 | 950 | 1.3115 | 0.7775 | 0.065 | 0.09 | | 1.6955 | 57.6 | 960 | 1.3116 | 0.7775 | 0.065 | 0.09 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

Omar95farag/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10

Omar95farag

2024-01-15T09:53:54Z

93

0

transformers

[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-08-09T22:32:58Z

--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10 results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-08-10 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 10.1413 - Accuracy: 0.7325 - Exit 0 Accuracy: 0.1725 - Exit 1 Accuracy: 0.2175 - Exit 2 Accuracy: 0.6075 - Exit 3 Accuracy: 0.715 - Exit 4 Accuracy: 0.735 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 24 - total_train_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:| | No log | 0.96 | 16 | 16.4817 | 0.17 | 0.0825 | 0.045 | 0.105 | 0.0625 | 0.0625 | | No log | 1.98 | 33 | 15.9950 | 0.2675 | 0.1 | 0.1325 | 0.195 | 0.1775 | 0.2425 | | No log | 3.0 | 50 | 14.9811 | 0.475 | 0.1025 | 0.1475 | 0.24 | 0.29 | 0.4425 | | No log | 3.96 | 66 | 14.0127 | 0.5675 | 0.105 | 0.1425 | 0.27 | 0.3975 | 0.505 | | No log | 4.98 | 83 | 13.3047 | 0.6075 | 0.125 | 0.1425 | 0.3175 | 0.43 | 0.595 | | No log | 6.0 | 100 | 12.7573 | 0.6125 | 0.125 | 0.1475 | 0.325 | 0.495 | 0.615 | | No log | 6.96 | 116 | 12.3656 | 0.645 | 0.1175 | 0.155 | 0.33 | 0.5175 | 0.6375 | | No log | 7.98 | 133 | 11.9582 | 0.6625 | 0.115 | 0.16 | 0.3525 | 0.5725 | 0.67 | | No log | 9.0 | 150 | 11.6533 | 0.6825 | 0.1225 | 0.16 | 0.375 | 0.6 | 0.7075 | | No log | 9.96 | 166 | 11.5143 | 0.685 | 0.1525 | 0.1625 | 0.38 | 0.6 | 0.675 | | No log | 10.98 | 183 | 11.3152 | 0.6625 | 0.115 | 0.1625 | 0.41 | 0.6225 | 0.6725 | | No log | 12.0 | 200 | 11.0708 | 0.695 | 0.11 | 0.1625 | 0.425 | 0.6225 | 0.7075 | | No log | 12.96 | 216 | 11.0412 | 0.6975 | 0.1125 | 0.1575 | 0.4 | 0.645 | 0.685 | | No log | 13.98 | 233 | 10.8782 | 0.7125 | 0.1425 | 0.165 | 0.4275 | 0.6325 | 0.7075 | | No log | 15.0 | 250 | 10.7282 | 0.7075 | 0.115 | 0.165 | 0.4225 | 0.65 | 0.7175 | | No log | 15.96 | 266 | 10.7039 | 0.695 | 0.15 | 0.16 | 0.4375 | 0.6375 | 0.69 | | No log | 16.98 | 283 | 10.5455 | 0.7125 | 0.13 | 0.165 | 0.4375 | 0.6675 | 0.715 | | No log | 18.0 | 300 | 10.5214 | 0.7075 | 0.1275 | 0.17 | 0.45 | 0.6825 | 0.7075 | | No log | 18.96 | 316 | 10.4995 | 0.715 | 0.155 | 0.1725 | 0.4525 | 0.68 | 0.7125 | | No log | 19.98 | 333 | 10.3224 | 0.725 | 0.1475 | 0.1825 | 0.46 | 0.68 | 0.7225 | | No log | 21.0 | 350 | 10.4247 | 0.71 | 0.1425 | 0.1825 | 0.4625 | 0.68 | 0.71 | | No log | 21.96 | 366 | 10.3881 | 0.705 | 0.1375 | 0.1825 | 0.46 | 0.66 | 0.7125 | | No log | 22.98 | 383 | 10.3065 | 0.715 | 0.1375 | 0.1875 | 0.465 | 0.6925 | 0.7225 | | No log | 24.0 | 400 | 10.1955 | 0.72 | 0.145 | 0.1875 | 0.4725 | 0.695 | 0.7225 | | No log | 24.96 | 416 | 10.1607 | 0.72 | 0.165 | 0.19 | 0.4925 | 0.7075 | 0.7175 | | No log | 25.98 | 433 | 10.2416 | 0.72 | 0.14 | 0.195 | 0.48 | 0.7025 | 0.7275 | | No log | 27.0 | 450 | 10.1321 | 0.715 | 0.145 | 0.1875 | 0.4925 | 0.7125 | 0.72 | | No log | 27.96 | 466 | 10.1982 | 0.7275 | 0.145 | 0.1875 | 0.4875 | 0.7075 | 0.73 | | No log | 28.98 | 483 | 10.2237 | 0.72 | 0.1575 | 0.19 | 0.515 | 0.7 | 0.7225 | | 10.0174 | 30.0 | 500 | 10.1426 | 0.7175 | 0.1675 | 0.1975 | 0.5275 | 0.7125 | 0.7225 | | 10.0174 | 30.96 | 516 | 10.1056 | 0.7325 | 0.14 | 0.1975 | 0.515 | 0.715 | 0.7325 | | 10.0174 | 31.98 | 533 | 10.1616 | 0.7225 | 0.1525 | 0.195 | 0.5275 | 0.7175 | 0.72 | | 10.0174 | 33.0 | 550 | 10.1053 | 0.7325 | 0.1425 | 0.195 | 0.525 | 0.7125 | 0.7275 | | 10.0174 | 33.96 | 566 | 10.1581 | 0.7175 | 0.165 | 0.2 | 0.5375 | 0.71 | 0.71 | | 10.0174 | 34.98 | 583 | 10.0835 | 0.7225 | 0.15 | 0.2025 | 0.5375 | 0.715 | 0.7225 | | 10.0174 | 36.0 | 600 | 10.1349 | 0.725 | 0.1425 | 0.2 | 0.5375 | 0.7025 | 0.725 | | 10.0174 | 36.96 | 616 | 10.0424 | 0.7325 | 0.1625 | 0.1975 | 0.545 | 0.7225 | 0.735 | | 10.0174 | 37.98 | 633 | 10.0692 | 0.73 | 0.155 | 0.195 | 0.5525 | 0.7225 | 0.74 | | 10.0174 | 39.0 | 650 | 10.0838 | 0.7325 | 0.1625 | 0.1975 | 0.56 | 0.7225 | 0.7375 | | 10.0174 | 39.96 | 666 | 10.1160 | 0.7275 | 0.1675 | 0.1975 | 0.5575 | 0.7225 | 0.725 | | 10.0174 | 40.98 | 683 | 10.0971 | 0.735 | 0.1675 | 0.1975 | 0.5625 | 0.7175 | 0.73 | | 10.0174 | 42.0 | 700 | 10.1207 | 0.73 | 0.165 | 0.2 | 0.5775 | 0.715 | 0.7275 | | 10.0174 | 42.96 | 716 | 10.1448 | 0.7325 | 0.175 | 0.205 | 0.5775 | 0.7175 | 0.73 | | 10.0174 | 43.98 | 733 | 10.0945 | 0.735 | 0.1675 | 0.21 | 0.5775 | 0.7175 | 0.735 | | 10.0174 | 45.0 | 750 | 10.1789 | 0.73 | 0.17 | 0.2175 | 0.5775 | 0.7125 | 0.7275 | | 10.0174 | 45.96 | 766 | 10.1274 | 0.735 | 0.175 | 0.215 | 0.5875 | 0.7075 | 0.735 | | 10.0174 | 46.98 | 783 | 10.1656 | 0.735 | 0.155 | 0.2125 | 0.5875 | 0.7125 | 0.7375 | | 10.0174 | 48.0 | 800 | 10.1557 | 0.7275 | 0.16 | 0.215 | 0.6025 | 0.715 | 0.7325 | | 10.0174 | 48.96 | 816 | 10.1436 | 0.74 | 0.165 | 0.215 | 0.6025 | 0.7175 | 0.735 | | 10.0174 | 49.98 | 833 | 10.1474 | 0.7325 | 0.1625 | 0.215 | 0.6 | 0.715 | 0.735 | | 10.0174 | 51.0 | 850 | 10.1647 | 0.7275 | 0.1725 | 0.2175 | 0.605 | 0.7175 | 0.7325 | | 10.0174 | 51.96 | 866 | 10.1375 | 0.73 | 0.1775 | 0.215 | 0.6025 | 0.7125 | 0.7375 | | 10.0174 | 52.98 | 883 | 10.1458 | 0.7325 | 0.1675 | 0.2175 | 0.605 | 0.7125 | 0.7375 | | 10.0174 | 54.0 | 900 | 10.1527 | 0.7275 | 0.175 | 0.22 | 0.6025 | 0.715 | 0.73 | | 10.0174 | 54.96 | 916 | 10.1349 | 0.7325 | 0.175 | 0.2175 | 0.6025 | 0.72 | 0.735 | | 10.0174 | 55.98 | 933 | 10.1376 | 0.7325 | 0.175 | 0.22 | 0.6025 | 0.72 | 0.7325 | | 10.0174 | 57.0 | 950 | 10.1413 | 0.7325 | 0.1725 | 0.2175 | 0.6075 | 0.715 | 0.7325 | | 10.0174 | 57.6 | 960 | 10.1413 | 0.7325 | 0.1725 | 0.2175 | 0.6075 | 0.715 | 0.735 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

Ricardo-H/swin-tiny-patch4-window7-224-finetuned-eurosat

Ricardo-H

2024-01-15T09:50:51Z

201

0

transformers

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

image-classification

2024-01-15T09:32:36Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-CIFAR10 results: [] ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1229 - Accuracy: 0.9618 ## 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: 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: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5796 | 1.0 | 176 | 0.2204 | 0.9462 | | 0.3995 | 2.0 | 352 | 0.1403 | 0.9582 | | 0.3781 | 3.0 | 528 | 0.1229 | 0.9618 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.0

Shreyas0706/Lama_Fact

Shreyas0706

2024-01-15T09:50:36Z

2

0

peft

[ "peft", "tensorboard", "safetensors", "llama-factory", "lora", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:other", "region:us" ]

null

2024-01-12T06:56:38Z

--- license: other library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: train_2024-01-11-03-12-55 results: [] ---  # train_2024-01-11-03-12-55 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the Legal_0706 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Nishant2609/factory

Nishant2609

2024-01-15T09:31:53Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]

null

2024-01-12T08:17:39Z

--- library_name: peft base_model: unsloth/mistral-7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

vda1708/gsm8k-llama2-13b

vda1708

2024-01-15T09:29:44Z

0

peft

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

null

2024-01-15T09:28:50Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

hxxris/haaris-final-transformer

hxxris

2024-01-15T09:29:26Z

146

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2024-01-15T09:14:22Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: haaris-final-transformer results: [] ---  # haaris-final-transformer This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.0354 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.73 | 2 | 2.6456 | 0.0442 | | No log | 1.45 | 4 | nan | 0.0354 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

jaemin12/xlm-roberta-base-finetuned-panx-it

jaemin12

2024-01-15T09:29:24Z

89

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-01-15T09:17:56Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: validation args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8120423108218063 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2361 - F1: 0.8120 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8019 | 1.0 | 70 | 0.3139 | 0.7286 | | 0.2941 | 2.0 | 140 | 0.2641 | 0.7975 | | 0.1851 | 3.0 | 210 | 0.2361 | 0.8120 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3

Swisslex/Mixtral-Orca-v0.1

Swisslex

2024-01-15T09:29:01Z

0

adapter-transformers

[ "adapter-transformers", "safetensors", "mixtral", "text-generation", "en", "de", "fr", "it", "es", "dataset:Open-Orca/SlimOrca", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "license:apache-2.0", "region:us" ]

text-generation

2024-01-15T08:37:12Z

--- license: apache-2.0 datasets: - Open-Orca/SlimOrca - argilla/distilabel-intel-orca-dpo-pairs language: - en - de - fr - it - es library_name: adapter-transformers pipeline_tag: text-generation --- # Model Card for Model Swisslex/Mixtral-Orca-v0.1 ## Model Details ### Model Description Finetuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) using SFT and DPO. - **Developed by:** Swisslex - **Language(s) (NLP):** English, German, French, Italian, Spanish - **License:** apache-2.0 - **Finetuned from model [optional]:** [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)

danielhanchen/test_lora_new

danielhanchen

2024-01-15T09:19:16Z

2

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "region:us" ]

null

2024-01-15T08:48:48Z

--- library_name: peft base_model: unsloth/mistral-7b --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

sethuiyer/MedleyMD-GGUF

sethuiyer

2024-01-15T09:18:34Z

7

1

null

[ "gguf", "medical", "mergekit", "GGUF", "en", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]

null

2024-01-15T09:06:41Z

--- license: cc-by-nc-nd-4.0 tags: - medical - mergekit - GGUF language: - en --- # MedleyMD ![logo](https://huggingface.co/sethuiyer/MedleyMD/resolve/main/logo.webp) MedleyMD is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [sethuiyer/Dr_Samantha_7b_mistral](https://huggingface.co/sethuiyer/Dr_Samantha_7b_mistral) * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1)

jaemin12/xlm-roberta-base-finetuned-panx-fr

jaemin12

2024-01-15T09:16:57Z

90

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-01-15T08:43:20Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8329966329966331 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2704 - F1: 0.8330 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5892 | 1.0 | 191 | 0.3346 | 0.7926 | | 0.2673 | 2.0 | 382 | 0.2807 | 0.8291 | | 0.1742 | 3.0 | 573 | 0.2704 | 0.8330 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3

Grigorij/mistral_instruct_generation

Grigorij

2024-01-15T09:12:31Z

1

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]

null

2023-12-26T11:17:34Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral_instruct_generation results: [] ---  # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 150 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 4.0261 | 0.4 | 10 | 2.8697 | | 2.2616 | 0.8 | 20 | 1.6009 | | 1.1871 | 1.2 | 30 | 0.9599 | | 0.8522 | 1.6 | 40 | 0.7228 | | 0.7375 | 2.0 | 50 | 0.6601 | | 0.5916 | 2.4 | 60 | 0.6184 | | 0.6219 | 2.8 | 70 | 0.5957 | | 0.5025 | 3.2 | 80 | 0.5980 | | 0.5148 | 3.6 | 90 | 0.5849 | | 0.5502 | 4.0 | 100 | 0.5639 | | 0.4414 | 4.4 | 110 | 0.5875 | | 0.4423 | 4.8 | 120 | 0.5847 | | 0.43 | 5.2 | 130 | 0.5902 | | 0.3843 | 5.6 | 140 | 0.6223 | | 0.4173 | 6.0 | 150 | 0.5788 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15.0

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

achimvp

2024-01-15T09:11:00Z

0

null

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

reinforcement-learning

2022-11-11T13:15:23Z

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

shadowml/SusBagle-34B

shadowml

2024-01-15T09:03:56Z

0

null

[ "merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-34b-v0.2", "SUSTech/SUS-Chat-34B", "license:apache-2.0", "region:us" ]

null

2024-01-15T08:58:38Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - jondurbin/bagel-dpo-34b-v0.2 - SUSTech/SUS-Chat-34B --- # SusBagle-34B SusBagle-34B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) * [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B) ## 🧩 Configuration ```yaml slices: - sources: - model: jondurbin/bagel-dpo-34b-v0.2 layer_range: [0, 60] - model: SUSTech/SUS-Chat-34B layer_range: [0, 60] merge_method: slerp base_model: jondurbin/bagel-dpo-34b-v0.2 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shadowml/SusBagle-34B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

mikewatson/gpt2-wikitext2

mikewatson

2024-01-15T08:55:12Z

180

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-15T08:54:59Z

--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-wikitext2 results: [] ---  # gpt2-wikitext2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 7.3791 - eval_runtime: 10.5986 - eval_samples_per_second: 182.477 - eval_steps_per_second: 22.833 - epoch: 0.11 - step: 241 ## 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: 3.0 ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

kalomaze/Kunoichi-DPO-v2-7B-GGUF

kalomaze

2024-01-15T08:53:07Z

10

4

null

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

null

2024-01-15T07:46:34Z

--- license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/I2V9Ou5xvD0iusAFmYUHD.png)

s3nh/Venus-120b-v1.2-GGUF

s3nh

2024-01-15T08:52:13Z

0

transformers

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

text-generation

2024-01-15T07:46:35Z

--- license: openrail pipeline_tag: text-generation library_name: transformers language: - zh - en --- ## Original model card Buy me a coffee if you like this project ;) <a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> #### Description GGUF Format model files for [This project](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2). ### GGUF Specs GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired: Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model. ### inference User: Tell me story about what is an quantization and what do we need to build. Once upon a time in # Original model card

xyz2zyx/Reinforce-CartPole-v1

xyz2zyx

2024-01-15T08:43:22Z

0

null

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

reinforcement-learning

2024-01-15T08:43:12Z

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

Intel/neural-chat-7b-v1-1

Intel

2024-01-15T08:40:04Z

38

23

transformers

[ "transformers", "pytorch", "mpt", "text-generation", "LLMs", "Intel", "custom_code", "en", "dataset:Intel/neural-chat-dataset-v1-1", "dataset:allenai/real-toxicity-prompts", "base_model:mosaicml/mpt-7b", "base_model:finetune:mosaicml/mpt-7b", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-06T05:20:07Z

--- license: apache-2.0 tags: - LLMs - Intel base_model: mosaicml/mpt-7b datasets: - Intel/neural-chat-dataset-v1-1 - allenai/real-toxicity-prompts language: - en model-index: - name: neural-chat-7b-v1-1 results: - task: type: Large Language Model name: Large Language Model dataset: type: Intel/neural-chat-dataset-v1-1 name: Intel/neural-chat-dataset-v1-1 metrics: - type: Average value: 51.41 name: Average verified: true - type: ARC (25-shot) value: 50.09 name: ARC (25-shot) verified: true - type: HellaSwag (10-shot) value: 76.69 name: HellaSwag (10-shot) verified: true - type: MMLU (5-shot) value: 38.79 name: MMLU (5-shot) verified: true - type: TruthfulQA (0-shot) value: 40.07 name: TruthfulQA (0-shot) verified: true - type: Toxicity Rito value: 0.0264 name: Toxicity Rito --- ## Model Details: Neural-Chat-v1-1 This model is a fine-tuned model for chat based on [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b) with a max sequence length of 2048 on the dataset [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1), which is a compilation of open-source datasets. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6297f0e30bd2f58c647abb1d/fWCqhGKZQKNuLmvj093rB.jpeg" width="500"/> Prompt of "an image of a brain that has to do with LLMs" from https://clipdrop.co/stable-diffusion-turbo. </p> | Model Detail | Description | | ----------- | ----------- | | Model Authors | Intel. The NeuralChat team with members from DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen. | | Date | July, 2023 | | Version | v1-1 | | Type | 7B Large Language Model | | Paper or Other Resources | Base model: [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b); Dataset: [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1) | | License | Apache 2.0 | | Questions or Comments | [Community Tab](https://huggingface.co/Intel/neural-chat-7b-v1-1/discussions) and [Intel DevHub Discord](https://discord.gg/rv2Gp55UJQ)| | Intended Use | Description | | ----------- | ----------- | | Primary intended uses | You can use the fine-tuned model for several language-related tasks. Checkout the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) to see this model's performance relative to other LLMs. | | Primary intended users | Anyone doing inference on language-related tasks. | | Out-of-scope uses | This model in most cases will need to be fine-tuned for your particular task. The model should not be used to intentionally create hostile or alienating environments for people.| ## How To Use ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 3.0 ## Use The Model ### Loading the model with Transformers ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'Intel/neural-chat-7b-v1-1', trust_remote_code=True ) ``` ### Inference with INT8 Follow the instructions at the [GitHub repository](https://github.com/intel/intel-extension-for-transformers/tree/main/examples/huggingface/pytorch/text-generation/quantization) to install the necessary dependencies for quantization to INT8. Use the below command to quantize the model using [Intel Neural Compressor](https://github.com/intel/neural-compressor) to accelerate inference. ```bash python run_generation.py \ --model Intel/neural-chat-7b-v1-1 \ --quantize \ --sq \ --alpha 0.95 \ --ipex ``` | Factors | Description | | ----------- | ----------- | | Groups | More details about the dataset can be found at [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1). | | Instrumentation | The performance of the model can vary depending on the inputs to the model. In this case, the prompts provided can drastically change the prediction of the language model. | | Environment | - | | Card Prompts | Model deployment on varying hardware and software will change model performance. | | Metrics | Description | | ----------- | ----------- | | Model performance measures | The model metrics are: ARC, HellaSwag, MMLU, and TruthfulQA. Bias evaluation was also evaluated using using Toxicity Rito (see Quantitative Analyses below). The model performance was evaluated against other LLMs according to the standards at the time the model was published. | | Decision thresholds | No decision thresholds were used. | | Approaches to uncertainty and variability | - | ## Training Data The training data are from [Intel/neural-chat-dataset-v1-1](https://huggingface.co/datasets/Intel/neural-chat-dataset-v1-1). The total number of instruction samples is about 1.1M, and the number of tokens is 326M. This dataset is composed of several other datasets: | Type | Language | Dataset | Number | |--| ---- |--------|----| | HC3 | en | [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3) | 24K | | dolly | en | [databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | 15K | | alpaca-zh | zh | [tigerbot-alpaca-zh-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-zh-0.5m) | 500K | | alpaca-en | en | [TigerResearch/tigerbot-alpaca-en-50k](https://huggingface.co/datasets/TigerResearch/tigerbot-alpaca-en-50k) | 50K | | math | en | [tigerbot-gsm-8k-en](https://huggingface.co/datasets/TigerResearch/tigerbot-gsm-8k-en) | 8K | | general | en | [tigerbot-stackexchange-qa-en-0.5m](https://huggingface.co/datasets/TigerResearch/tigerbot-stackexchange-qa-en-0.5m) | 500K | Note: There is no contamination from the GSM8k test set, as this is not a part of this dataset. ## Quantitative Analyses ### LLM metrics We used the same evaluation metrics as [HuggingFaceH4/open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), which uses [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/master), a unified framework to test generative language models on a large number of different evaluation tasks. | Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | | --- | --- | --- | --- | --- | --- | |[mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b)| 47.4 | 47.61 | 77.56 | 31 | 33.43 | | [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat) | **49.95** | 46.5 | 75.55 | 37.60 | 40.17 | | [Intel/neural-chat-dataset-v1-1](https://huggingface.co/Intel/neural-chat-dataset-v1-1) | **51.41** | 50.09 | 76.69 | 38.79 | 40.07 | ### Bias evaluation Following the blog [evaluating-llm-bias](https://huggingface.co/blog/evaluating-llm-bias), we selected 10000 samples randomly from [allenai/real-toxicity-prompts](https://huggingface.co/datasets/allenai/real-toxicity-prompts) to evaluate toxicity bias. | Model | Toxicity Rito ↓| | --- | --- | |[mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b)| 0.027 | | [Intel/neural-chat-dataset-v1-1](https://huggingface.co/Intel/neural-chat-dataset-v1-1) | 0.0264 | ### Examples - code generation ![code-generation](examples/code.png) - summarization ![summarization](examples/summarization.png) - trip ![trip](examples/trip.png) ## Ethical Considerations and Limitations Neural-chat-7b-v1-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v1-1 was trained on various instruction/chat datasets based on [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs. Therefore, before deploying any applications of the model, developers should perform safety testing. ## Caveats and Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Here are some useful GitHub repository links to learn more about Intel's open-source AI software: * Intel Neural Compressor [link](https://github.com/intel/neural-compressor) * Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers) * Intel Extension for PyTorch [link](https://github.com/intel/intel-extension-for-pytorch) ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.

nm-testing/MiniChat-2-3B-pruned50-ds

nm-testing

2024-01-15T08:37:56Z

2

0

transformers

[ "transformers", "onnx", "llama", "text-generation", "deepsparse", "arxiv:2301.00774", "base_model:GeneZC/MiniChat-2-3B", "base_model:quantized:GeneZC/MiniChat-2-3B", "autotrain_compatible", "region:us" ]

text-generation

2024-01-15T08:05:29Z

--- base_model: GeneZC/MiniChat-2-3B inference: false model_type: llama prompt_template: | <s> [|User|]\n {prompt}</s> [|Assistant|]\n quantized_by: mwitiderrick tags: - deepsparse --- # MiniChat-2-3B - DeepSparse This repo contains model files for [MiniChat-2-3B](https://huggingface.co/GeneZC/MiniChat-2-3B) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models. This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml). ## Inference Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs: ```bash pip install deepsparse-nightly[llm] ``` Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md): ```python from deepsparse import TextGeneration prompt = "How to get in a good university?" formatted_prompt = f"<s> [|User|]\n{prompt}</s>[|Assistant|]\n" model = TextGeneration(model_path="hf:nm-testing/MiniChat-2-3B-pruned50-ds") print(model(formatted_prompt, max_new_tokens=500).generations[0].text) """ Getting into a good university is a complex process that involves several steps. However, here are some key factors to consider: 1. Academic performance: Your grades, test scores, and overall academic achievements are essential in demonstrating your academic abilities. Strive to maintain a high GPA and achieve strong scores in standardized tests like the SAT, ACT, or AP exams. 2. Academic preparation: Develop a strong foundation in various subjects, including English, math, science, and foreign languages. This will help you succeed academically and demonstrate your readiness for college-level courses. 3. Extracurricular activities: Participate in extracurricular activities such as clubs, sports teams, volunteering, or leadership roles. These activities can help you develop valuable skills, demonstrate your leadership abilities, and showcase your interests outside the classroom. 4. Academic preparation for college-level courses: Research and understand the curriculum of the universities you are interested in. Familiarize yourself with the coursework, coursework requirements, and any potential prerequisites. 5. Personal qualities and extracurricular activities: Showcase your unique qualities and extracurricular activities that demonstrate your leadership, teamwork, and problem-solving abilities. Universities value students who are well-rounded and have a diverse set of skills. 6. Application process: Follow the university's application process, which may include submitting an application form, paying the application fee, and submitting any required documents. 7. Interviews and assessments: If you are invited to an interview, prepare for it by researching the university, its campus, and its mission. Be confident, articulate, and demonstrate your enthusiasm for the university. 8. Post-admission process: After being accepted, follow the university's post-admission process, which may include registering for classes, paying tuition fees, and obtaining student housing. 9. Networking and mentorship: Connect with professors, professors, and fellow students to gain insights into the university culture and gain a deeper understanding of the college environment. 10. Financial support: Research financial aid options and scholarships available to help cover tuition fees and living """ ``` ## Prompt template ``` <s> [|User|]\n {prompt} </s>[|Assistant|]\n ``` ## Sparsification For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below. ```bash git clone https://github.com/neuralmagic/sparseml pip install -e "sparseml[transformers]" python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py GeneZC/MiniChat-2-3B open_platypus --recipe recipe.yaml --save True python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment cp deployment/model.onnx deployment/model-orig.onnx ``` Run this kv-cache injection to speed up the model at inference by caching the Key and Value states: ```python import os import onnx from sparseml.exporters.kv_cache_injector import KeyValueCacheInjector input_file = "deployment/model-orig.onnx" output_file = "deployment/model.onnx" model = onnx.load(input_file, load_external_data=False) model = KeyValueCacheInjector(model_path=os.path.dirname(input_file)).apply(model) onnx.save(model, output_file) print(f"Modified model saved to: {output_file}") ``` Follow the instructions on our [One Shot With SparseML](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/transformers/sparsification/obcq) page for a step-by-step guide for performing one-shot quantization of large language models. ## Slack For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)

smutuvi/whisper-small-sw-common-voice-ndizi-158-NF4

smutuvi

2024-01-15T08:37:00Z

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:smutuvi/whisper-small-sw-common-voice", "base_model:adapter:smutuvi/whisper-small-sw-common-voice", "license:apache-2.0", "region:us" ]

null

2024-01-15T08:36:58Z

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: smutuvi/whisper-small-sw-common-voice model-index: - name: whisper-small-sw-common-voice-ndizi-158-NF4 results: [] ---  # whisper-small-sw-common-voice-ndizi-158-NF4 This model is a fine-tuned version of [smutuvi/whisper-small-sw-common-voice](https://huggingface.co/smutuvi/whisper-small-sw-common-voice) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6482 ## 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: 50 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 9 | 1.9783 | | No log | 2.0 | 18 | 1.9757 | | 1.8194 | 3.0 | 27 | 1.9710 | | 1.8194 | 4.0 | 36 | 1.9631 | | 1.8194 | 5.0 | 45 | 1.9543 | | 1.7712 | 6.0 | 54 | 1.9437 | | 1.7712 | 7.0 | 63 | 1.9337 | | 1.7712 | 8.0 | 72 | 1.9244 | | 1.7377 | 9.0 | 81 | 1.9141 | | 1.7377 | 10.0 | 90 | 1.9043 | | 1.7377 | 11.0 | 99 | 1.8952 | | 1.7377 | 12.0 | 108 | 1.8863 | | 1.7377 | 13.0 | 117 | 1.8762 | | 1.6796 | 14.0 | 126 | 1.8684 | | 1.6796 | 15.0 | 135 | 1.8588 | | 1.6796 | 16.0 | 144 | 1.8501 | | 1.6217 | 17.0 | 153 | 1.8414 | | 1.6217 | 18.0 | 162 | 1.8325 | | 1.6217 | 19.0 | 171 | 1.8253 | | 1.6036 | 20.0 | 180 | 1.8169 | | 1.6036 | 21.0 | 189 | 1.8097 | | 1.6036 | 22.0 | 198 | 1.8017 | | 1.6129 | 23.0 | 207 | 1.7949 | | 1.6129 | 24.0 | 216 | 1.7879 | | 1.5607 | 25.0 | 225 | 1.7817 | | 1.5607 | 26.0 | 234 | 1.7760 | | 1.5607 | 27.0 | 243 | 1.7699 | | 1.5305 | 28.0 | 252 | 1.7637 | | 1.5305 | 29.0 | 261 | 1.7588 | | 1.5305 | 30.0 | 270 | 1.7537 | | 1.5261 | 31.0 | 279 | 1.7485 | | 1.5261 | 32.0 | 288 | 1.7444 | | 1.5261 | 33.0 | 297 | 1.7400 | | 1.5331 | 34.0 | 306 | 1.7360 | | 1.5331 | 35.0 | 315 | 1.7322 | | 1.5331 | 36.0 | 324 | 1.7287 | | 1.5131 | 37.0 | 333 | 1.7250 | | 1.5131 | 38.0 | 342 | 1.7216 | | 1.4883 | 39.0 | 351 | 1.7183 | | 1.4883 | 40.0 | 360 | 1.7151 | | 1.4883 | 41.0 | 369 | 1.7119 | | 1.4551 | 42.0 | 378 | 1.7092 | | 1.4551 | 43.0 | 387 | 1.7063 | | 1.4551 | 44.0 | 396 | 1.7042 | | 1.47 | 45.0 | 405 | 1.7018 | | 1.47 | 46.0 | 414 | 1.6988 | | 1.47 | 47.0 | 423 | 1.6967 | | 1.4422 | 48.0 | 432 | 1.6942 | | 1.4422 | 49.0 | 441 | 1.6922 | | 1.4432 | 50.0 | 450 | 1.6904 | | 1.4432 | 51.0 | 459 | 1.6886 | | 1.4432 | 52.0 | 468 | 1.6869 | | 1.4108 | 53.0 | 477 | 1.6849 | | 1.4108 | 54.0 | 486 | 1.6828 | | 1.4108 | 55.0 | 495 | 1.6808 | | 1.4377 | 56.0 | 504 | 1.6797 | | 1.4377 | 57.0 | 513 | 1.6777 | | 1.4377 | 58.0 | 522 | 1.6770 | | 1.4281 | 59.0 | 531 | 1.6748 | | 1.4281 | 60.0 | 540 | 1.6732 | | 1.4281 | 61.0 | 549 | 1.6718 | | 1.374 | 62.0 | 558 | 1.6704 | | 1.374 | 63.0 | 567 | 1.6695 | | 1.3947 | 64.0 | 576 | 1.6678 | | 1.3947 | 65.0 | 585 | 1.6667 | | 1.3947 | 66.0 | 594 | 1.6656 | | 1.3843 | 67.0 | 603 | 1.6650 | | 1.3843 | 68.0 | 612 | 1.6638 | | 1.3843 | 69.0 | 621 | 1.6620 | | 1.385 | 70.0 | 630 | 1.6614 | | 1.385 | 71.0 | 639 | 1.6608 | | 1.385 | 72.0 | 648 | 1.6592 | | 1.368 | 73.0 | 657 | 1.6586 | | 1.368 | 74.0 | 666 | 1.6576 | | 1.3777 | 75.0 | 675 | 1.6574 | | 1.3777 | 76.0 | 684 | 1.6565 | | 1.3777 | 77.0 | 693 | 1.6556 | | 1.3837 | 78.0 | 702 | 1.6553 | | 1.3837 | 79.0 | 711 | 1.6547 | | 1.3837 | 80.0 | 720 | 1.6544 | | 1.3577 | 81.0 | 729 | 1.6529 | | 1.3577 | 82.0 | 738 | 1.6523 | | 1.3577 | 83.0 | 747 | 1.6515 | | 1.3961 | 84.0 | 756 | 1.6513 | | 1.3961 | 85.0 | 765 | 1.6512 | | 1.3961 | 86.0 | 774 | 1.6505 | | 1.3292 | 87.0 | 783 | 1.6497 | | 1.3292 | 88.0 | 792 | 1.6495 | | 1.3578 | 89.0 | 801 | 1.6496 | | 1.3578 | 90.0 | 810 | 1.6495 | | 1.3578 | 91.0 | 819 | 1.6488 | | 1.3352 | 92.0 | 828 | 1.6486 | | 1.3352 | 93.0 | 837 | 1.6492 | | 1.3352 | 94.0 | 846 | 1.6487 | | 1.3873 | 95.0 | 855 | 1.6485 | | 1.3873 | 96.0 | 864 | 1.6483 | | 1.3873 | 97.0 | 873 | 1.6481 | | 1.3391 | 98.0 | 882 | 1.6483 | | 1.3391 | 99.0 | 891 | 1.6482 | | 1.3569 | 100.0 | 900 | 1.6482 | ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.37.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

Romanos575/Yuzu

Romanos575

2024-01-15T08:36:35Z

0

null

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

null

2024-01-15T08:25:53Z

--- license: creativeml-openrail-m --- Yuzu all rights goes to Ikena https://civitai.com/models/67120

alexrods/q-Taxi-V3

alexrods

2024-01-15T08:28:09Z

0

null

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

reinforcement-learning

2024-01-15T08:28:08Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-V3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="alexrods/q-Taxi-V3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

jaemin12/xlm-roberta-base-finetuned-panx-de-fr

jaemin12

2024-01-15T08:22:19Z

78

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-01-12T12:21:36Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1620 - F1: 0.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2893 | 1.0 | 715 | 0.1785 | 0.8313 | | 0.1466 | 2.0 | 1430 | 0.1559 | 0.8540 | | 0.0948 | 3.0 | 2145 | 0.1620 | 0.8611 | ### Framework versions - Transformers 4.30.2 - Pytorch 1.7.0+cu110 - Datasets 2.13.2 - Tokenizers 0.13.3

hayate3140/hayate3140butterfly

hayate3140

2024-01-15T08:18:45Z

49

0

diffusers

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

unconditional-image-generation

2024-01-15T08:18:34Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('hayate3140/hayate3140butterfly') image = pipeline().images[0] image ```

ayousanz/nekomata-14b-gozaru

ayousanz

2024-01-15T08:18:17Z

5

0

peft

[ "peft", "safetensors", "base_model:rinna/nekomata-14b-instruction", "base_model:adapter:rinna/nekomata-14b-instruction", "region:us" ]

null

2024-01-15T08:09:15Z

--- library_name: peft base_model: rinna/nekomata-14b-instruction --- # 概要 [rinna/nekomata-14b-instruction](https://huggingface.co/rinna/nekomata-14b-instruction)を[bbz662bbz/databricks-dolly-15k-ja-gozaru](https://huggingface.co/datasets/bbz662bbz/databricks-dolly-15k-ja-gozaru)でfine tuningしたモデル # 学習パラメータ ```python # 学習の実行 !python qlora.py \ --model_name rinna/nekomata-14b-instruction \ --output_dir "./output/rinna-nekomata-14b-instruction-gonaru_peft" \ --dataset "alpaca" \ --max_steps 1000 \ --use_auth \ --logging_steps 10 \ --save_strategy steps \ --data_seed 42 \ --save_steps 100 \ --save_total_limit 40 \ --max_new_tokens 100 \ --dataloader_num_workers 1 \ --group_by_length \z --logging_strategy steps \ --remove_unused_columns False \ --do_train \ --lora_r 64 \ --lora_alpha 16 \ --lora_modules all \ --double_quant \ --quant_type nf4 \ --bf16 \ --bits 4 \ --warmup_ratio 0.03 \ --lr_scheduler_type constant \ --gradient_checkpointing \ --source_max_len 16 \ --target_max_len 512 \ --per_device_train_batch_size 1 \ --gradient_accumulation_steps 16 \ --eval_steps 187 \ --learning_rate 0.0002 \ --adam_beta2 0.999 \ --max_grad_norm 0.3 \ --lora_dropout 0.1 \ --weight_decay 0.0 \ --seed 0 \ --load_in_4bit \ --use_peft \ --batch_size 4 \ --gradient_accumulation_steps 2 \ --trust_remote_code=True ``` # 実行方法(4bit) ## 推論 ```python import torch from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # トークナイザーとモデルの読み込み tokenizer = AutoTokenizer.from_pretrained( "rinna/nekomata-14b-instruction" ) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) model = AutoModelForCausalLM.from_pretrained( "rinna/nekomata-14b-instruction", quantization_config=bnb_config, device_map={"":0} ) # LoRAの読み込み model = PeftModel.from_pretrained( model, "./output/rinna-nekomata-14b-instruction-gonaru_peft/checkpoint-1000/adapter_model/", device_map={"":0} ) model.eval() # プロンプトの準備 instruction = "次の回答に正しく回答してください" input = "まどマギで一番可愛いキャラはなんですか？" prompt = f""" 以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。 ### 指示: {instruction} ### 入力: {input} ### 応答: """ # 推論の実行 inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=300,temperature=0.3,do_sample=True,pad_token_id=tokenizer.pad_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 回答 ```python 以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。 ### 指示: 次の回答に正しく回答してください ### 入力: まどマギで一番可愛いキャラはなんですか？ ### 応答: マミさんでござる。 ``` ### Framework versions - PEFT 0.7.2.dev0

jlvdoorn/whisper-base-atcosim

jlvdoorn

2024-01-15T08:16:26Z

92

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "doi:10.57967/hf/1620", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-01-13T12:30:23Z

--- license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer metrics: - wer model-index: - name: whisper-base-atcosim results: [] ---  # whisper-base-atcosim This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0620 - Wer: 2.9820 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.152 | 8.33 | 500 | 0.0522 | 2.5282 | | 0.001 | 16.67 | 1000 | 0.0539 | 3.0608 | | 0.0003 | 25.0 | 1500 | 0.0556 | 3.0237 | | 0.0002 | 33.33 | 2000 | 0.0567 | 3.0237 | | 0.0001 | 41.67 | 2500 | 0.0579 | 3.0144 | | 0.0001 | 50.0 | 3000 | 0.0588 | 2.9959 | | 0.0001 | 58.33 | 3500 | 0.0597 | 3.0052 | | 0.0001 | 66.67 | 4000 | 0.0604 | 3.0098 | | 0.0 | 75.0 | 4500 | 0.0610 | 2.9867 | | 0.0 | 83.33 | 5000 | 0.0615 | 2.9867 | | 0.0 | 91.67 | 5500 | 0.0619 | 2.9774 | | 0.0 | 100.0 | 6000 | 0.0620 | 2.9820 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.0

mrchaos88/openllama-3b-peft-squad_v2

mrchaos88

2024-01-15T08:12:36Z

1

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "region:us" ]

null

2024-01-15T06:41:08Z

--- library_name: peft base_model: openlm-research/open_llama_3b_v2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

dolo650/Mistral-7B-lora-yahma-alpaca-unsloth-bnb

dolo650

2024-01-15T08:11:29Z

4

0

peft

[ "peft", "safetensors", "dataset:yahma/alpaca-cleaned", "arxiv:1910.09700", "base_model:unsloth/mistral-7b", "base_model:adapter:unsloth/mistral-7b", "license:apache-2.0", "region:us" ]

null

2024-01-15T07:42:37Z

--- library_name: peft base_model: unsloth/mistral-7b license: apache-2.0 datasets: - yahma/alpaca-cleaned --- # Model Card for Model ID QLoRA fine tuned model yahma/alpaca-cleaned dataset containing around ~52K data ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1

etri-vilab/koala-1b-llava-cap

etri-vilab

2024-01-15T08:10:53Z

19

13

diffusers

[ "diffusers", "onnx", "safetensors", "text-to-image", "KOALA", "arxiv:2312.04005", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2024-01-09T05:49:09Z

--- tags: - text-to-image - KOALA --- <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/yosvi68jvyarbvymxc4hm/github_logo.png?rlkey=r9ouwcd7cqxjbvio43q9b3djd&dl=1" width="1024px" /> </div> <div style="display:flex;justify-content: center"> <a href="https://youngwanlee.github.io/KOALA/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github&color=blue&logo=github-pages"></a> &ensp; <a href="https://github.com/youngwanLEE/sdxl-koala"><img src="https://img.shields.io/static/v1?label=Code&message=Github&color=blue&logo=github"></a> &ensp; <a href="https://arxiv.org/abs/2312.04005"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv:KOALA&color=red&logo=arxiv"></a> &ensp; </div> # KOALA-1B-LLaVA-Caption Model Card KOALA-700M and -1B models originally were trained on [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) which has some noisy or very short texts. So we construct synthesized captions of LAION-aesthetics-V2 6+ by using a large multimodal model, [LLaVA](https://llava-vl.github.io/). KOALA-700M-LLaVA-Caption and KOALA-1B-LLaVA-Caption is trained on the synthesized caption-image pairs of LAION-aesthetics-V2 6+. ## KOALA Model Cards |Model|link| |:--|:--| |koala-700m | https://huggingface.co/etri-vilab/koala-700m| |koala-700m-llava-cap | https://huggingface.co/etri-vilab/koala-700m-llava-cap| |koala-1b | https://huggingface.co/etri-vilab/koala-1bm| |koala-1b-llava-cap | https://huggingface.co/etri-vilab/koala-1b-llava-cap| ## Abstract ### TL;DR > We propose a fast text-to-image model, called KOALA, by compressing SDXL's U-Net and distilling knowledge from SDXL into our model. KOALA-700M can generate a 1024x1024 image in less than 1.5 seconds on an NVIDIA 4090 GPU, which is more than 2x faster than SDXL. KOALA-700M can be used as a decent alternative between SDM and SDXL in limited resources. <details><summary>FULL abstract</summary> Stable diffusion is the mainstay of the text-to-image (T2I) synthesis in the community due to its generation performance and open-source nature. Recently, Stable Diffusion XL (SDXL), the successor of stable diffusion, has received a lot of attention due to its significant performance improvements with a higher resolution of 1024x1024 and a larger model. However, its increased computation cost and model size require higher-end hardware (e.g., bigger VRAM GPU) for end-users, incurring higher costs of operation. To address this problem, in this work, we propose an efficient latent diffusion model for text-to-image synthesis obtained by distilling the knowledge of SDXL. To this end, we first perform an in-depth analysis of the denoising U-Net in SDXL, which is the main bottleneck of the model, and then design a more efficient U-Net based on the analysis. Secondly, we explore how to effectively distill the generation capability of SDXL into an efficient U-Net and eventually identify four essential factors, the core of which is that self-attention is the most important part. With our efficient U-Net and self-attention-based knowledge distillation strategy, we build our efficient T2I models, called KOALA-1B &-700M, while reducing the model size up to 54% and 69% of the original SDXL model. In particular, the KOALA-700M is more than twice as fast as SDXL while still retaining a decent generation quality. We hope that due to its balanced speed-performance tradeoff, our KOALA models can serve as a cost-effective alternative to SDXL in resource-constrained environments. </details> <br> These 1024x1024 samples are generated by KOALA-700M with 25 denoising steps. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/rjsqqgfney7be069y2yr7/teaser.png?rlkey=7lq0m90xpjcoqclzl4tieajpo&dl=1" width="1024px" /> </div> ## Architecture There are two two types of compressed U-Net, KOALA-1B and KOALA-700M, which are realized by reducing residual blocks and transformer blocks. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/5ydeywgiyt1d3njw63dpk/arch.png?rlkey=1p6imbjs4lkmfpcxy153i1a2t&dl=1" width="1024px" /> </div> ### U-Net comparison | U-Net | SDM-v2.0 | SDXL-Base-1.0 | KOALA-1B | KOALA-700M | |-------|:----------:|:-----------:|:-----------:|:-------------:| | Param. | 865M | 2,567M | 1,161M | 782M | | CKPT size | 3.46GB | 10.3GB | 4.4GB | 3.0GB | | Tx blocks | [1, 1, 1, 1] | [0, 2, 10] | [0, 2, 6] | [0, 2, 5] | | Mid block | ✓ | ✓ | ✓ | ✗ | | Latency | 1.131s | 3.133s | 1.604s | 1.257s | - Tx menans transformer block and CKPT means the trained checkpoint file. - We measured latency with FP16-precision, and 25 denoising steps in NVIDIA 4090 GPU (24GB). - SDM-v2.0 uses 768x768 resolution, while SDXL and KOALA models uses 1024x1024 resolution. ## Latency and memory usage comparison on different GPUs We measure the inference time of SDM-v2.0 with 768x768 resolution and the other models with 1024x1024 using a variety of consumer-grade GPUs: NVIDIA 3060Ti (8GB), 2080Ti (11GB), and 4090 (24GB). We use 25 denoising steps and FP16/FP32 precisions. OOM means Out-of-Memory. Note that SDXL-Base cannot operate in the 8GB-GPU. <div align="center"> <img src="https://dl.dropboxusercontent.com/scl/fi/u1az20y0zfww1l5lhbcyd/latency_gpu.svg?rlkey=vjn3gpkmywmp7jpilar4km7sd&dl=1" width="1024px" /> </div> ## Key Features - **Efficient U-Net Architecture**: KOALA models use a simplified U-Net architecture that reduces the model size by up to 54% and 69% respectively compared to its predecessor, Stable Diffusion XL (SDXL). - **Self-Attention-Based Knowledge Distillation**: The core technique in KOALA focuses on the distillation of self-attention features, which proves crucial for maintaining image generation quality. ## Model Description - Developed by [ETRI Visual Intelligence Lab](https://huggingface.co/etri-vilab) - Developer: [Youngwan Lee](https://youngwanlee.github.io/), [Kwanyong Park](https://pkyong95.github.io/), [Yoorhim Cho](https://ofzlo.github.io/), [Young-Ju Lee](https://scholar.google.com/citations?user=6goOQh8AAAAJ&hl=en), [Sung Ju Hwang](http://www.sungjuhwang.com/) - Model Description: Latent Diffusion based text-to-image generative model. KOALA models uses the same text encoders as [SDXL-Base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and only replace the denoising U-Net with the compressed U-Nets. - Training data: [LAION-aesthetics-V2 6+](https://laion.ai/blog/laion-aesthetics/) - Resources for more information: Check out [KOALA report on arXiv](https://arxiv.org/abs/2312.04005) and [project page](https://youngwanlee.github.io/KOALA/). ## Usage with 🤗[Diffusers library](https://github.com/huggingface/diffusers) The inference code with denoising step 25 ```python import torch from diffusers import StableDiffusionXLPipeline pipe = StableDiffusionXLPipeline.from_pretrained("etri-vilab/koala-1b-llava-cap", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "A portrait painting of a Golden Retriever like Leonard da Vinci" negative = "worst quality, low quality, illustration, low resolution" image = pipe(prompt=prompt, negative_prompt=negative).images[0] ``` ## Uses ### Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. ## Limitations and Bias - Text Rendering: The models face challenges in rendering long, legible text within images. - Complex Prompts: KOALA sometimes struggles with complex prompts involving multiple attributes. - Dataset Dependencies: The current limitations are partially attributed to the characteristics of the training dataset (LAION-aesthetics-V2 6+). ## Citation ```bibtex @misc{Lee@koala, title={KOALA: Self-Attention Matters in Knowledge Distillation of Latent Diffusion Models for Memory-Efficient and Fast Image Synthesis}, author={Youngwan Lee and Kwanyong Park and Yoorhim Cho and Yong-Ju Lee and Sung Ju Hwang}, year={2023}, eprint={2312.04005}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

hwashang/hs_test1_billsum_model

hwashang

2024-01-15T08:05:13Z

89

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "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

2024-01-12T09:53:13Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: hs_test1_billsum_model results: [] ---  # hs_test1_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6341 - Rouge1: 0.1424 - Rouge2: 0.0501 - Rougel: 0.1153 - Rougelsum: 0.1156 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 62 | 2.9223 | 0.1287 | 0.0361 | 0.1068 | 0.107 | 19.0 | | No log | 2.0 | 124 | 2.7150 | 0.1411 | 0.049 | 0.1157 | 0.1162 | 19.0 | | No log | 3.0 | 186 | 2.6506 | 0.1396 | 0.0472 | 0.1128 | 0.1133 | 19.0 | | No log | 4.0 | 248 | 2.6341 | 0.1424 | 0.0501 | 0.1153 | 0.1156 | 19.0 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.0

aviralkumar28/ppo-Huggy

aviralkumar28

2024-01-15T08:05:05Z

0

ml-agents

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

reinforcement-learning

2024-01-15T08:04:59Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: aviralkumar28/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

shadowml/Beagle14-7B

shadowml

2024-01-15T08:01:06Z

8

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "fblgit/UNA-TheBeagle-7b-v1", "argilla/distilabeled-Marcoro14-7B-slerp", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-15T07:56:47Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - fblgit/UNA-TheBeagle-7b-v1 - argilla/distilabeled-Marcoro14-7B-slerp --- # Beagle14-7B Beagle14-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [fblgit/UNA-TheBeagle-7b-v1](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) * [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp) ## 🧩 Configuration ```yaml slices: - sources: - model: fblgit/UNA-TheBeagle-7b-v1 layer_range: [0, 32] - model: argilla/distilabeled-Marcoro14-7B-slerp layer_range: [0, 32] merge_method: slerp base_model: fblgit/UNA-TheBeagle-7b-v1 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "shadowml/Beagle14-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

seobak/xlm-roberta-base-finetuned-panx-en

seobak

2024-01-15T07:58:54Z

90

0

transformers

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

token-classification

2024-01-15T07:57:20Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-en results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.en split: validation args: PAN-X.en metrics: - name: F1 type: f1 value: 0.6870824053452116 ---  # xlm-roberta-base-finetuned-panx-en This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.3990 - F1: 0.6871 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 1.0238 | 1.0 | 50 | 0.4988 | 0.5987 | | 0.4841 | 2.0 | 100 | 0.4144 | 0.6902 | | 0.3744 | 3.0 | 150 | 0.3990 | 0.6871 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

YeBhoneLin10/bagan

YeBhoneLin10

2024-01-15T07:56:59Z

36

1

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "en", "arxiv:2112.10752", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-01-13T10:55:40Z

--- license: creativeml-openrail-m language: - en tags: - stable-diffusion - text-to-image - diffusers widget: - text: "bagan, by Vincent van gogh, highly detailed, highly illustration" example_title: "Example Prompt 1" - text: "Establishing shot of a bagan, an epic fantasy, dramatic lighting, cinematic, extremely high detail, photorealistic, cinematic lighting, matte painting, artstation, by simon stalenhag, uncharted 4: a thief's end" example_title: "Example Prompt 2" - text: "hyper realistic wator color painting, transparent, myanmar bagan ancient city, after raining sense, beautiful cloud, ancient pagoda, some trees, with water splash infront of pagoda, lovely cloud, beautiful golden ratio composition, neutral color, moody image, lots of grey, golden ratio composition, grey and moody, more grey, rule of third, --ar 5:3 --q 0.5 --v 5" example_title: "Example Prompt 3" base_model: runwayml/stable-diffusion-v1-5 --- ### Introduction: Meet General Bagan, a cutting-edge text-to-image generator trained on a diverse dataset of over 200 images. With a keen understanding of textual inputs, it effortlessly translates words into visually stunning representations. From lifelike nature scenes to captivating abstract compositions. ### Problem Statement: When we prompted the stable diffusion model to generate an image of Bagan, it produced an image depicting a pagoda from Thailand. Hence, our decision was to fine-tune the current stable diffusion model using a multitude of Bagan photos in order to attain a clearer outcome. ### How to create prompt: When we create prompt for bagan, we have to consider 6 keywords. Those are Subject, Medium, Style, Art-sharing website, Resolution, and Additional details. Subject -> What you want to see in the picture is the subject. Not writing enough about the subjects is a common error. Medium -> The medium is the substance that artists work with. Illustration, oil painting, 3D rendering, and photography are a few examples. The impact of Medium is significant because a single keyword can significantly alter the style. Style -> The image's artistic style is referred to as the style. Pop art, impressionist, and surrealist are a few examples. Art-sharing website -> Specialty graphic websites like Deviant Art and Artstation compile a large number of images from various genres. One surefire way to direct the image toward these styles is to use them as a prompt. Resolution -> Resolution represents how sharp and detailed the image is Additional Details -> Sweeteners added to an image are additional details. To give the image a more dystopian and sci-fi feel, we will add those elements. The example prompt for general bagan is: bagan, a creepy and eery Halloween setting, with Jack o lanterns on the street and shadow figures lurking about, dynamic lighting, photorealistic fantasy concept art, stunning visuals, creative, cinematic, ultra detailed, trending on art station, spooky vibe. That prompt gives you the Halloween theme. ### Data: We used stable diffusion v1.5 model to train with 223 bagan pictures. ### Contributors: Main Contributor: [Ye Bhone Lin](https://github.com/Ye-Bhone-Lin), Supervisor: Sa Phyo Thu Htet, Contributors: Thant Htoo San, Min Phone Thit ### Limitation: We can't generate a photo of a human. ### Other Work: In our exploration of image generation, we delve into the architectural marvels of Myanmar, featuring iconic landmarks such as Ananda, Shwezigon, Bupaya, Thatbyinnyu, and Mraukoo. Each structure stands as a testament to the rich cultural and historical tapestry of the region, captured through the lens of our innovative text-to-image generator, General Bagan. ### References: Wikipedia (2022). Stable Diffusion. Retrieved From: https://en.wikipedia.org/wiki/Stable_Diffusion Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Retrieved From: https://arxiv.org/abs/2112.10752 Naomi Brown (2022). What is Stable Diffusion and How to Use it. Retrieved From: https://www.fotor.com/blog/what-is-stable-diffusion Mishra, O. (June, 9). Stable Diffusion Explained. Medium. https://medium.com/@onkarmishra/stable-diffusion-explained-1f101284484d

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

seobak

2024-01-15T07:55:39Z

90

0

transformers

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

token-classification

2024-01-15T07:53:23Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-fr results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.fr split: validation args: PAN-X.fr metrics: - name: F1 type: f1 value: 0.8395478319554581 ---  # xlm-roberta-base-finetuned-panx-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2752 - F1: 0.8395 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.5795 | 1.0 | 191 | 0.3287 | 0.7758 | | 0.2619 | 2.0 | 382 | 0.2644 | 0.8273 | | 0.1761 | 3.0 | 573 | 0.2752 | 0.8395 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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

seobak

2024-01-15T07:53:04Z

93

0

transformers

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

token-classification

2024-01-15T07:45:59Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1609 - F1: 0.8598 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2803 | 1.0 | 715 | 0.1757 | 0.8256 | | 0.1459 | 2.0 | 1430 | 0.1617 | 0.8473 | | 0.095 | 3.0 | 2145 | 0.1609 | 0.8598 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

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

seobak

2024-01-15T07:42:15Z

101

0

transformers

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

token-classification

2024-01-15T07:36:39Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.de split: validation args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8632930084625432 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1373 - F1: 0.8633 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2617 | 1.0 | 525 | 0.1513 | 0.8206 | | 0.1275 | 2.0 | 1050 | 0.1341 | 0.8518 | | 0.0808 | 3.0 | 1575 | 0.1373 | 0.8633 | ### Framework versions - Transformers 4.37.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

sumo43/Yi-34b-x2

sumo43

2024-01-15T07:37:04Z

60

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "base_model:jondurbin/bagel-dpo-34b-v0.2", "base_model:merge:jondurbin/bagel-dpo-34b-v0.2", "base_model:one-man-army/UNA-34Beagles-32K-bf16-v1", "base_model:merge:one-man-army/UNA-34Beagles-32K-bf16-v1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-15T05:55:13Z

--- base_model: - jondurbin/bagel-dpo-34b-v0.2 - one-man-army/UNA-34Beagles-32K-bf16-v1 tags: - mergekit - merge license: mit --- # merged 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: * [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) * [one-man-army/UNA-34Beagles-32K-bf16-v1](https://huggingface.co/one-man-army/UNA-34Beagles-32K-bf16-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml base_model: jondurbin/bagel-dpo-34b-v0.2 dtype: float16 merge_method: slerp parameters: t: - filter: self_attn value: [0.0, 0.5, 0.3, 0.7, 1.0] - filter: mlp value: [1.0, 0.5, 0.7, 0.3, 0.0] - value: 0.5 slices: - sources: - layer_range: [0, 60] model: jondurbin/bagel-dpo-34b-v0.2 - layer_range: [0, 60] model: one-man-army/UNA-34Beagles-32K-bf16-v1 ```

winglian/zephyr-deita-kto-3ep-v3-r512-bsz8

winglian

2024-01-15T07:11:07Z

0

peft

[ "peft", "tensorboard", "safetensors", "mistral", "trl", "dpo", "generated_from_trainer", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "base_model:adapter:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "region:us" ]

null

2024-01-15T07:10:06Z

--- license: mit library_name: peft tags: - trl - dpo - generated_from_trainer base_model: HuggingFaceH4/mistral-7b-sft-beta model-index: - name: zephyr-deita-kto-3ep-v3-r512-bsz8 results: [] ---  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.3.0` ```yaml base_model: HuggingFaceH4/mistral-7b-sft-beta model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: false strict: false rl: kto_pair datasets: - path: winglian/deita-nectar split: train_dpo type: zephyr.nectar _test_datasets: - path: winglian/deita-nectar split: test_dpo type: zephyr.nectar dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./zephyr-deita-kto-3ep-v3-r512-bsz8 save_total_limit: 3 hub_model_id: openaccess-ai-collective/kto-zephyr-deita-nectar adapter: lora lora_model_dir: sequence_len: 2048 sample_packing: false pad_to_sequence_len: false lora_r: 512 lora_alpha: 128 lora_dropout: 0.05 lora_target_linear: true lora_modules_to_save: lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: dpo-zephyr-deita-nectar wandb_entity: oaaic wandb_watch: wandb_run_id: wandb_name: kto-3ep-v3-r512-bsz8-lr1.4e-5 wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 2 num_epochs: 3 optimizer: paged_adamw_8bit adam_beta2: 0.95 adam_epsilion: 0.00001 lr_scheduler: cosine learning_rate: 1.414e-5 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true gradient_checkpoint_kwargs: use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 eval_steps: eval_table_size: eval_table_max_new_tokens: 128 save_steps: 45 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: save_safetensors: true dataloader_num_workers: 16 dataloader_pin_memory: true ``` </details><br> # zephyr-deita-kto-3ep-v3-r512-bsz8 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) 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: 1.414e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - total_train_batch_size: 8 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 3230 ### Training results ### Framework versions - PEFT 0.7.0 - Transformers 4.37.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

pankajcipher/Taxi-v3

pankajcipher

2024-01-15T07:06:55Z

0

null

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

reinforcement-learning

2024-01-15T07:06:53Z

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

GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13

GAI-LLM

2024-01-15T06:59:21Z

7

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-01-15T04:04:46Z

--- license: cc-by-nc-4.0 language: - ko library_name: transformers pipeline_tag: text-generation --- **The license is `cc-by-nc-4.0`.** # **GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13** ## Model Details **Model Developers** Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng) **Input** Models input text only. **Output** Models generate text only. **Model Architecture** GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13 is an auto-regressive language model based on the LLaMA2 transformer architecture. **Base Model** [daekeun-ml/Llama-2-ko-instruct-13B](https://huggingface.co/daekeun-ml/Llama-2-ko-instruct-13B) **Training Dataset** - We combined Open Korean Dateset using mixed-strategy. - We use A100 GPU 80GB * 8, when training. # **Model Benchmark** ## KO-LLM leaderboard - Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard). # Implementation Code ```python ### GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13 from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "GAI-LLM/Llama-2-ko-instruct-13B-mixed-v13" model = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) tokenizer = AutoTokenizer.from_pretrained(repo) ```

dishank19/Mistral7B-SHP

dishank19

2024-01-15T06:59:14Z

5

1

peft

[ "peft", "safetensors", "text-generation", "en", "dataset:stanfordnlp/SHP", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

text-generation

2023-12-24T04:47:23Z

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 datasets: - stanfordnlp/SHP language: - en pipeline_tag: text-generation --- # Model Card for Model ID Mistral 7B Fine-Tuned on the [SHP Dataset](https://huggingface.co/datasets/stanfordnlp/SHP) (r/askHistorians) using QLORA. This is basically a cool side project, wanted to see if I could fine-tune it with no added costs for compute. Used Google Colab - **Developed by:** [Dishank Jhaveri] - **Model type:** [Chat] - **Language(s) (NLP):** [English] - **Finetuned from model:** [Mistral-7B-v0.1] ### Model Sources [optional]  - **Repository:** [https://github.com/dishank19/Mistral7B_Finetune] - ## Model Card Contact [[email protected]] ### Framework versions - PEFT 0.7.1

Tigranchick/phi2-results2

Tigranchick

2024-01-15T06:50:15Z

0

peft

[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-01-15T06:46:40Z

--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: microsoft/phi-2 model-index: - name: phi2-results2 results: [] ---  # phi2-results2 This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.3743 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2952 | 0.97 | 14 | 1.0035 | | 1.0151 | 2.0 | 29 | 0.6051 | | 0.5514 | 2.97 | 43 | 0.4484 | | 0.4471 | 4.0 | 58 | 0.3983 | | 0.4017 | 4.83 | 70 | 0.3743 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0

hopkins/mbart-finetuned-eng-kor-50

hopkins

2023-07-03T05:01:57Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T04:44:13Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-50 results: [] ---  # mbart-finetuned-eng-kor-50 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9913 - Bleu: 7.0488 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

chriskim2273/IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta

chriskim2273

2023-07-03T04:50:05Z

47

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-03T04:13:01Z

--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta results: [] ---  # IOTNation_CompanyName_Extraction_QA_Model_1.2_Roberta This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7219 ## 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: 3e-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 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 45 | 0.5443 | | No log | 2.0 | 90 | 0.6332 | | No log | 3.0 | 135 | 0.6942 | | No log | 4.0 | 180 | 0.6725 | | No log | 5.0 | 225 | 0.7219 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Aeala/Enterredaas-65b-QLoRA

Aeala

2023-07-03T04:34:35Z

0

4

null

[ "region:us" ]

null

2023-07-03T04:07:43Z

## LoRA Info: Please note that this is a highly experimental LoRA model. It may do some good stuff, it might do some undesirable stuff. Training is paused for now. Feel free to try it!~ **Important Note**: This was trained in the *Alpaca* format, so prompting should be something like: ``` ### Instruction: <system prompt> (without the <>, this works like telling the AI what it is/purpose. i.e. like ChatGPT API's system prompt) ### Input: <prompt> (without the <>) ### Response: ``` Current upload: *possibly* final checkpoint ## Benchmarks **wikitext2:** Coming soon... **ptb-new:** Coming soon... **c4-new:** Coming soon...

hopkins/mbart-finetuned-eng-deu-50

hopkins

2023-07-03T04:24:57Z

57

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T04:06:46Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-50 results: [] ---  # mbart-finetuned-eng-deu-50 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6559 - Bleu: 21.0004 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-47

hopkins

2023-07-03T04:17:24Z

58

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:59:40Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-47 results: [] ---  # mbart-finetuned-eng-kor-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9922 - Bleu: 6.8895 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

aaa950739/trained_model

aaa950739

2023-07-03T04:16:04Z

96

0

transformers

[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2023-07-03T03:56:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: trained_model results: [] ---  # trained_model This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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: 500 ### Training results ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

deepghs/imgutils-models

deepghs

2023-07-03T04:12:18Z

0

6

null

[ "onnx", "dataset:deepghs/chafen_arknights", "dataset:deepghs/monochrome_danbooru", "license:mit", "region:us" ]

null

2023-03-11T08:37:38Z

--- license: mit datasets: - deepghs/chafen_arknights - deepghs/monochrome_danbooru metrics: - accuracy --- # imgutils-models This repository includes all the models in [deepghs/imgutils](https://github.com/deepghs/imgutils). ## LPIPS This model is used for clustering anime images (named `差分` in Chinese), based on [richzhang/PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity), trained with dataset [deepghs/chafen_arknights(private)](https://huggingface.co/datasets/deepghs/chafen_arknights). When threshold is `0.45`, the [adjusted rand score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html) can reach `0.995`. File lists: * `lpips_diff.onnx`, feature difference. * `lpips_feature.onnx`, feature extracting. ## Monochrome These model is used for monochrome image classification, based on CNNs and Transformers, trained with dataset [deepghs/monochrome_danbooru(private)](https://huggingface.co/datasets/deepghs/monochrome_danbooru). The following are the checkpoints that have been formally put into use, all based on the Caformer architecture: | Checkpoint | Algorithm | Safe Level | Accuracy | False Negative | False Positive | |:----------------------------:|:---------:|:----------:|:----------:|:--------------:|:--------------:| | monochrome-caformer-40 | caformer | 0 | 96.41% | 2.69% | 0.89% | | **monochrome-caformer-110** | caformer | 0 | **96.97%** | 1.57% | 1.46% | | monochrome-caformer_safe2-80 | caformer | 2 | 94.84% | **1.12%** | 4.03% | | monochrome-caformer_safe4-70 | caformer | 4 | 94.28% | **0.67%** | 5.04% | **`monochrome-caformer-110` has the best overall accuracy** among them, but considering that this model is often used to screen out monochrome images and we want to screen out as many as possible without omission, we have also introduced weighted models (`safe2` and `safe4`). Although their overall accuracy has been slightly reduced, the probability of False Negative (misidentifying a monochrome image as a colored one) is lower, making them more suitable for batch screening. ## Deepdanbooru `deepdanbooru` is a model used to tag anime images. Here, we provide a table for tag classification called `deepdanbooru_tags.csv`, as well as an ONNX model (from [chinoll/deepdanbooru](https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags)). It's worth noting that due to the poor quality of the deepdanbooru model itself and the relatively old dataset, it is only for testing purposes and is not recommended to be used as the main classification model. We recommend using the `wd14` model instead, see: * https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags

hopkins/mbart-finetuned-eng-ind-49

hopkins

2023-07-03T04:11:46Z

62

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:53:54Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-49 results: [] ---  # mbart-finetuned-eng-ind-49 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7653 - Bleu: 22.0600 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

vineetsharma/whisper-base-finetuned-gtzan

vineetsharma

2023-07-03T04:03:36Z

49

0

transformers

[ "transformers", "pytorch", "whisper", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-07-03T01:16:02Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: whisper-base-finetuned-gtzan results: [] ---  # whisper-base-finetuned-gtzan This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6867 - Accuracy: 0.87 ## 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: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.9075 | 1.0 | 57 | 1.0000 | 0.58 | | 0.4569 | 2.0 | 114 | 0.6073 | 0.83 | | 0.3761 | 3.0 | 171 | 0.6410 | 0.8 | | 0.3049 | 4.0 | 228 | 0.4536 | 0.86 | | 0.0284 | 5.0 | 285 | 0.5120 | 0.85 | | 0.0165 | 6.0 | 342 | 0.4856 | 0.89 | | 0.0087 | 7.0 | 399 | 0.6814 | 0.87 | | 0.0038 | 8.0 | 456 | 0.7059 | 0.85 | | 0.0032 | 9.0 | 513 | 0.6831 | 0.87 | | 0.0034 | 10.0 | 570 | 0.6867 | 0.87 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-ind-47

hopkins

2023-07-03T03:59:13Z

49

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:41:18Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-47 results: [] ---  # mbart-finetuned-eng-ind-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7657 - Bleu: 21.8229 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-deu-49

hopkins

2023-07-03T03:53:24Z

50

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:35:07Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-49 results: [] ---  # mbart-finetuned-eng-deu-49 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6500 - Bleu: 21.1322 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-deu-47

hopkins

2023-07-03T03:40:49Z

43

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:22:38Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-47 results: [] ---  # mbart-finetuned-eng-deu-47 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6483 - Bleu: 20.8742 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-deu-46

hopkins

2023-07-03T03:33:45Z

48

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:15:41Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-46 results: [] ---  # mbart-finetuned-eng-deu-46 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6533 - Bleu: 20.8950 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-44

hopkins

2023-07-03T03:32:33Z

55

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:14:52Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-44 results: [] ---  # mbart-finetuned-eng-kor-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9949 - Bleu: 6.8417 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-43

hopkins

2023-07-03T03:22:08Z

74

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T03:08:49Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-43 results: [] ---  # mbart-finetuned-eng-kor-43 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9892 - Bleu: 6.9989 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-42

hopkins

2023-07-03T03:15:12Z

62

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:57:33Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-42 results: [] ---  # mbart-finetuned-eng-kor-42 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9879 - Bleu: 6.7656 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-ind-44

hopkins

2023-07-03T03:14:24Z

67

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:56:32Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-44 results: [] ---  # mbart-finetuned-eng-ind-44 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7625 - Bleu: 21.9586 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

samzoozi/atari_game

samzoozi

2023-07-03T03:04:22Z

0

stable-baselines3

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

reinforcement-learning

2023-07-03T03:03:41Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 718.00 +/- 220.55 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga samzoozi -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga samzoozi ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Sourabh2/Cartpole-v2

Sourabh2

2023-07-03T03:03:46Z

0

null

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

reinforcement-learning

2023-07-03T03:02:25Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Cartpole-v2 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** .

jinlee74/distilbert-base-uncased-finetuned-emotions

jinlee74

2023-07-03T02:59:35Z

55

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-03T00:11:13Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotions results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9415 - name: F1 type: f1 value: 0.9416116671925132 ---  # distilbert-base-uncased-finetuned-emotions This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2357 - Accuracy: 0.9415 - F1: 0.9416 ## 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: 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.016 | 1.0 | 250 | 0.2262 | 0.9405 | 0.9404 | | 0.011 | 2.0 | 500 | 0.2357 | 0.9415 | 0.9416 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.1.0.dev20230316 - Datasets 2.12.0 - Tokenizers 0.13.3

AshtakaOOf/ssambatea-locon

AshtakaOOf

2023-07-03T02:58:58Z

0

1

null

[ "Text-to-Image", "anime", "lora", "locon", "lycoris", "license:cc-by-nc-sa-4.0", "region:us" ]

null

2023-07-03T01:36:57Z

--- license: cc-by-nc-sa-4.0 tags: - Text-to-Image - anime - lora - locon - lycoris --- # SSAMBAtea Style LoCon ![example](https://media.discordapp.net/attachments/1019446913268973689/1125244643852947466/00115-24682990.png?width=500&height=620) ## token: **ssambatea** Trained on SSAMBAtea artwork This is a LoCon and require the LyCORIS extension to work I am planning on making a new improved dataset to do a V2 # License [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)

hopkins/mbart-finetuned-eng-deu-45

hopkins

2023-07-03T02:57:51Z

59

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:39:34Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-45 results: [] ---  # mbart-finetuned-eng-deu-45 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6514 - Bleu: 20.9841 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

alibaba-pai/pai-diffusion-artist-large-zh

alibaba-pai

2023-07-03T02:56:37Z

14

7

diffusers

[ "diffusers", "pytorch", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-04-03T09:38:48Z

--- license: apache-2.0 tags: - pytorch - diffusers - text-to-image --- # Chinese Diffusion Model (Artist, 512 Resolution) ## 简介 Brief Introduction 我们开源了一个中文 Diffusion 模型，您可以直接输入中文提示词，我们为您呈现精美的艺术风格图片。本模型的默认分辨率是 512*512。 We release a Chinese diffusion model, which is able to generate high-quality artistic images according to the prompts you input. The default resolution of this model is 512*512. * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ## 使用 Usage 本模型支持 `diffusers`，可以参考以下范例代码： This model supports `diffusers`. Please refer to the following code: ```python from diffusers import StableDiffusionPipeline model_id = "alibaba-pai/pai-diffusion-artist-large-zh" pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") prompt = "雾蒙蒙的日出在湖面上" image = pipe(prompt).images[0] image.save("result.png") ``` ## 作品展示 Gallery | prompt: 浮岛，天空，白云，城堡，幻想世界 | prompt: 红白玫瑰花，很多花瓣，绽放 | | ---------------------------------------- | ---------------------------------- | | negative_prompt: 油画，模糊，雾蒙蒙 | negative_prompt: | | ![](example1.png) | ![](example2.png) | | prompt: 亭台楼阁，曲径通幽，水墨绘画，中国风 | prompt: 阳光，彩虹，小白马 | | -------------------------------------------- | -------------------------- | | negative_prompt: 油画，彩色 | negative_prompt: | | ![](example3.png) | ![](example4.png) | ## 使用须知 Notice for Use 使用上述模型需遵守[AIGC模型开源特别条款](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html)。 If you want to use this model, please read this [document](https://terms.alicdn.com/legal-agreement/terms/common_platform_service/20230505180457947/20230505180457947.html) carefully and abide by the terms.

digiplay/Landscape_PhotoReal_v1

digiplay

2023-07-03T02:53:33Z

620

7

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-03T02:20:00Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/71987/landscapephotoreal?modelVersionId=76750 Sample images and prompt : magnificent scenery, wide landscape, sharp and crisp background, very beautiful landscape, old ruins buildings, fantasy, birdview, best quality, masterpiece, ultra high res, dark blue light, cloudy, photo, photorealistic, wide view, kkw-ph1 ![8fe78e3f-8861-4a05-b81b-ece37ef654b2.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/pn1BrZcWpE102SLyJq6Jh.jpeg) ![acb17a24-5b9a-4699-b0f5-2192523b78e8.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/PrOTKR4lrBWeRNFGhAyhd.jpeg) photorealistic modern living room, sharp and crisp background, sofa, low table, bookshelf, parks and buildings from window, wood and flower, beautiful landscape, best quality, masterpiece, hires, in the morning light, detailed lighting, blue sky, (((photo))), (((photorealistic))) ,kkw-ph1, wide shot, web meeting background ![89c995b5-e33d-4526-bb2f-5d1550a20084.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/RDKBVRmNV0SWXpz-o-D4F.jpeg)

hopkins/mbart-finetuned-eng-kor-40

hopkins

2023-07-03T02:37:25Z

74

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:19:49Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-40 results: [] ---  # mbart-finetuned-eng-kor-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9919 - Bleu: 7.0359 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Rasith/NZappFineTune2

Rasith

2023-07-03T02:31:27Z

31

0

transformers

[ "transformers", "tf", "roberta", "text-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-03T02:31:01Z

--- tags: - generated_from_keras_callback model-index: - name: NZappFineTune2 results: [] ---  # NZappFineTune2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-39

hopkins

2023-07-03T02:31:10Z

53

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:13:29Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-39 results: [] ---  # mbart-finetuned-eng-kor-39 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9925 - Bleu: 6.7954 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

djifg/grow_classification_xlmr2

djifg

2023-07-03T02:28:32Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-03T01:59:42Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: grow_classification_xlmr2 results: [] ---  # grow_classification_xlmr2 This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5585 - Accuracy: 0.9309 - F1: 0.9297 ## 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: 3e-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 | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2832 | 1.0 | 436 | 0.4686 | 0.8870 | 0.8872 | | 0.0717 | 2.0 | 872 | 0.5915 | 0.8964 | 0.8950 | | 0.0374 | 3.0 | 1308 | 0.4898 | 0.9276 | 0.9266 | | 0.0205 | 4.0 | 1744 | 0.5333 | 0.9271 | 0.9257 | | 0.0101 | 5.0 | 2180 | 0.5585 | 0.9309 | 0.9297 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

jncraton/fastchat-t5-3b-v1.0-ct2-int8

jncraton

2023-07-03T02:24:58Z

3

2

transformers

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

null

2023-07-03T01:59:59Z

--- license: apache-2.0 inference: false --- # FastChat-T5 Model Card ## Model details **Model type:** FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It is based on an encoder-decoder transformer architecture, and can autoregressively generate responses to users' inputs. **Model date:** FastChat-T5 was trained on April 2023. **Organizations developing the model:** The FastChat developers, primarily Dacheng Li, Lianmin Zheng and Hao Zhang. **Paper or resources for more information:** https://github.com/lm-sys/FastChat#FastChat-T5 **License:** Apache License 2.0 **Where to send questions or comments about the model:** https://github.com/lm-sys/FastChat/issues ## Intended use **Primary intended uses:** The primary use of FastChat-T5 is the commercial usage of large language models and chatbots. It can also be used for research purposes. **Primary intended users:** The primary intended users of the model are entrepreneurs and researchers in natural language processing, machine learning, and artificial intelligence. ## Training dataset 70K conversations collected from ShareGPT.com. ## Training details It processes the ShareGPT data in the form of question answering. Each ChatGPT response is processed as an answer, and previous conversations between the user and the ChatGPT are processed as the question. The encoder bi-directionally encodes a question into a hidden representation. The decoder uses cross-attention to attend to this representation while generating an answer uni-directionally from a start token. This model is fine-tuned for 3 epochs, with a max learning rate 2e-5, warmup ratio 0.03, and a cosine learning rate schedule. ## Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.

hopkins/mbart-finetuned-eng-kor-38

hopkins

2023-07-03T02:24:16Z

75

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:06:33Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-38 results: [] ---  # mbart-finetuned-eng-kor-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9945 - Bleu: 6.8900 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-ind-40

hopkins

2023-07-03T02:19:21Z

64

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T02:01:28Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-40 results: [] ---  # mbart-finetuned-eng-ind-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7628 - Bleu: 21.8914 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-ind-39

hopkins

2023-07-03T02:13:00Z

63

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:55:07Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-39 results: [] ---  # mbart-finetuned-eng-ind-39 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7633 - Bleu: 21.8212 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Bin12123/Chat

Bin12123

2023-07-03T02:11:49Z

0

null

[ "zh", "dataset:fka/awesome-chatgpt-prompts", "region:us" ]

null

2023-07-03T02:10:05Z

--- datasets: - fka/awesome-chatgpt-prompts language: - zh ---

hopkins/mbart-finetuned-eng-ind-38

hopkins

2023-07-03T02:06:04Z

65

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:52:19Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-38 results: [] ---  # mbart-finetuned-eng-ind-38 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7718 - Bleu: 21.7535 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

digiplay/CityEdge_StyleMix_v1.44

digiplay

2023-07-03T02:03:34Z

310

4

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-07-03T01:27:43Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/63243/cityedgestylemix Sample images and prompt : 1girl, solo, long hair blown by wind,close-up ,long dress, green eyes, white stocking, lace, look at viewer, luxurious, elegant, extremely detailed, majestic, blurry, blurry background, tree, branch, cherry blossoms, butterfly, flower petals blown by wind, depth of field, ![adc5b9bd-9c9c-4eaa-8c2b-f3d80cf35de9.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/YQ55e6l6sTyMKfDMMEHjf.jpeg) 8k Angel sky,best quality , masterpiece, close up, ultra detailed ,upper body ![f7000d0a-06ba-4d2f-9cb2-99e66dce200c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/gYaVpXPgV9iuGoZwVFkoY.jpeg) ![ff8d0f59-9b28-456d-a5dc-93e5ab494a9c.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/NY9c_GHK4Dph8mKh1BkOh.jpeg)

Ngadou/falcon7b-scam-detector

Ngadou

2023-07-03T02:03:13Z

4

0

adapter-transformers

[ "adapter-transformers", "text-generation", "en", "fr", "dataset:timdettmers/openassistant-guanaco", "license:apache-2.0", "region:us" ]

text-generation

2023-07-03T01:49:44Z

--- license: apache-2.0 datasets: - timdettmers/openassistant-guanaco language: - en - fr metrics: - accuracy pipeline_tag: text-generation library_name: adapter-transformers ---

hopkins/mbart-finetuned-eng-deu-40

hopkins

2023-07-03T02:00:58Z

70

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:42:43Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-40 results: [] ---  # mbart-finetuned-eng-deu-40 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6497 - Bleu: 20.8437 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Huggingfly/q-Taxi-v3

Huggingfly

2023-07-03T01:57:19Z

0

null

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

reinforcement-learning

2023-07-03T01:55:19Z

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

hopkins/mbart-finetuned-eng-kor-37

hopkins

2023-07-03T01:48:15Z

125

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:30:38Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-37 results: [] ---  # mbart-finetuned-eng-kor-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9920 - Bleu: 6.9377 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-36

hopkins

2023-07-03T01:42:12Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:24:37Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-36 results: [] ---  # mbart-finetuned-eng-kor-36 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9933 - Bleu: 6.9791 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-34

hopkins

2023-07-03T01:33:22Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:15:53Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-34 results: [] ---  # mbart-finetuned-eng-kor-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9937 - Bleu: 7.1397 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

ankitvyas/myBloomLoraModel

ankitvyas

2023-07-03T01:31:54Z

0

peft

[ "peft", "region:us" ]

null

2023-07-03T01:19:55Z

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

hopkins/mbart-finetuned-eng-ind-37

hopkins

2023-07-03T01:30:10Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T01:12:28Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-37 results: [] ---  # mbart-finetuned-eng-ind-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7649 - Bleu: 21.9222 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

Ngadou/bert-sms-spam-dectector

Ngadou

2023-07-03T01:29:26Z

111

2

transformers

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "en", "dataset:Ngadou/Spam_SMS", "doi:10.57967/hf/0746", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-06-10T21:24:39Z

--- license: cc-by-4.0 datasets: - Ngadou/Spam_SMS language: - en metrics: - accuracy pipeline_tag: text-classification ---

hopkins/mbart-finetuned-eng-ind-34

hopkins

2023-07-03T01:15:25Z

121

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:57:39Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-34 results: [] ---  # mbart-finetuned-eng-ind-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7610 - Bleu: 21.9140 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-deu-37

hopkins

2023-07-03T01:11:58Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:53:43Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-37 results: [] ---  # mbart-finetuned-eng-deu-37 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6509 - Bleu: 20.9509 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-deu-34

hopkins

2023-07-03T00:57:10Z

118

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:43:07Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-deu-34 results: [] ---  # mbart-finetuned-eng-deu-34 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6495 - Bleu: 20.8330 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

saswat94/my-pet-bear-nxt

saswat94

2023-07-03T00:55:44Z

6

0

diffusers

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

text-to-image

2023-07-03T00:51:10Z

--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Bear-nxt Dreambooth model trained by saswat94 following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: CVRGU633 Sample pictures of this concept: ![0](https://huggingface.co/saswat94/my-pet-bear-nxt/resolve/main/sample_images/00000-1946388114.png)

hopkins/mbart-finetuned-eng-kor-31

hopkins

2023-07-03T00:45:09Z

118

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:31:48Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-31 results: [] ---  # mbart-finetuned-eng-kor-31 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9893 - Bleu: 7.0441 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-kor-30

hopkins

2023-07-03T00:42:37Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:29:16Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-kor-30 results: [] ---  # mbart-finetuned-eng-kor-30 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9943 - Bleu: 7.0556 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

hopkins/mbart-finetuned-eng-ind-33

hopkins

2023-07-03T00:39:01Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "mbart", "text2text-generation", "translation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2023-07-03T00:25:25Z

--- tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: mbart-finetuned-eng-ind-33 results: [] ---  # mbart-finetuned-eng-ind-33 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7673 - Bleu: 21.9515 ## 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: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.26.1 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

renyulin/llama-7b-es-ppo-adpater

renyulin

2023-07-03T00:35:51Z

0

transformers

[ "transformers", "pytorch", "trl", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-07-03T00:35:48Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") model = AutoModelForCausalLMWithValueHead.from_pretrained("renyulin//tmp/tmp6w__m82c/renyulin/llama-7b-es-ppo-adpater") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

TalesLF/Reinforce-Pixelcopter-PLE-v0

TalesLF

2023-07-03T00:34:21Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2023-07-03T00:34:16Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 30.60 +/- 21.44 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction