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huggingface-models-embeddings

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train · 241k rows

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null	null	transformers	<!-- 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. --> # SMIDS_3x_beit_large_RMSProp_lr00001_fold5 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0664 - Accuracy: 0.9133 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.172 \| 1.0 \| 450 \| 0.2655 \| 0.92 \| \| 0.1992 \| 2.0 \| 900 \| 0.2820 \| 0.915 \| \| 0.06 \| 3.0 \| 1350 \| 0.4639 \| 0.9117 \| \| 0.004 \| 4.0 \| 1800 \| 0.6294 \| 0.905 \| \| 0.0643 \| 5.0 \| 2250 \| 0.5938 \| 0.9067 \| \| 0.0508 \| 6.0 \| 2700 \| 0.7271 \| 0.9083 \| \| 0.0003 \| 7.0 \| 3150 \| 0.7950 \| 0.9017 \| \| 0.0354 \| 8.0 \| 3600 \| 0.7475 \| 0.905 \| \| 0.0038 \| 9.0 \| 4050 \| 0.7063 \| 0.91 \| \| 0.0625 \| 10.0 \| 4500 \| 1.0875 \| 0.885 \| \| 0.0288 \| 11.0 \| 4950 \| 0.7952 \| 0.9033 \| \| 0.0324 \| 12.0 \| 5400 \| 0.9845 \| 0.8983 \| \| 0.0152 \| 13.0 \| 5850 \| 0.8154 \| 0.9067 \| \| 0.0 \| 14.0 \| 6300 \| 0.7819 \| 0.9183 \| \| 0.0391 \| 15.0 \| 6750 \| 0.9429 \| 0.9067 \| \| 0.025 \| 16.0 \| 7200 \| 0.8302 \| 0.91 \| \| 0.0219 \| 17.0 \| 7650 \| 0.9499 \| 0.89 \| \| 0.0001 \| 18.0 \| 8100 \| 0.8533 \| 0.915 \| \| 0.0025 \| 19.0 \| 8550 \| 0.9293 \| 0.9133 \| \| 0.0 \| 20.0 \| 9000 \| 0.8810 \| 0.915 \| \| 0.0044 \| 21.0 \| 9450 \| 0.9572 \| 0.91 \| \| 0.0 \| 22.0 \| 9900 \| 0.9130 \| 0.9033 \| \| 0.0 \| 23.0 \| 10350 \| 0.9646 \| 0.915 \| \| 0.0 \| 24.0 \| 10800 \| 1.1208 \| 0.9033 \| \| 0.0 \| 25.0 \| 11250 \| 1.0487 \| 0.9067 \| \| 0.0 \| 26.0 \| 11700 \| 0.9981 \| 0.9067 \| \| 0.0 \| 27.0 \| 12150 \| 0.9795 \| 0.9167 \| \| 0.0 \| 28.0 \| 12600 \| 1.1751 \| 0.9117 \| \| 0.0 \| 29.0 \| 13050 \| 1.1591 \| 0.915 \| \| 0.0 \| 30.0 \| 13500 \| 1.2457 \| 0.9017 \| \| 0.0 \| 31.0 \| 13950 \| 1.0844 \| 0.91 \| \| 0.0 \| 32.0 \| 14400 \| 0.9787 \| 0.9233 \| \| 0.0 \| 33.0 \| 14850 \| 1.1946 \| 0.9083 \| \| 0.0 \| 34.0 \| 15300 \| 1.0155 \| 0.9233 \| \| 0.0 \| 35.0 \| 15750 \| 1.0275 \| 0.9233 \| \| 0.0 \| 36.0 \| 16200 \| 1.0705 \| 0.92 \| \| 0.0 \| 37.0 \| 16650 \| 1.1187 \| 0.915 \| \| 0.0 \| 38.0 \| 17100 \| 1.1296 \| 0.9083 \| \| 0.0092 \| 39.0 \| 17550 \| 1.1147 \| 0.915 \| \| 0.0 \| 40.0 \| 18000 \| 1.0541 \| 0.9183 \| \| 0.0 \| 41.0 \| 18450 \| 0.9861 \| 0.9217 \| \| 0.0 \| 42.0 \| 18900 \| 0.9950 \| 0.9167 \| \| 0.0 \| 43.0 \| 19350 \| 1.0470 \| 0.9183 \| \| 0.0 \| 44.0 \| 19800 \| 1.0409 \| 0.9183 \| \| 0.0 \| 45.0 \| 20250 \| 1.0609 \| 0.9183 \| \| 0.0 \| 46.0 \| 20700 \| 1.0465 \| 0.915 \| \| 0.0 \| 47.0 \| 21150 \| 1.0602 \| 0.9117 \| \| 0.0 \| 48.0 \| 21600 \| 1.0669 \| 0.9133 \| \| 0.0 \| 49.0 \| 22050 \| 1.0663 \| 0.9117 \| \| 0.0 \| 50.0 \| 22500 \| 1.0664 \| 0.9133 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9133333333333333, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_RMSProp_lr00001_fold5	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T02:21:53+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_RMSProp\_lr00001\_fold5 =============================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.0664 * Accuracy: 0.9133 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 116, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1292150914669037, 0.17132072150707245, -0.002415567170828581, 0.13183215260505676, 0.11657863855361938, 0.020753253251314163, 0.1335890144109726, 0.16620413959026337, -0.08238927274942398, 0.04929587244987488, 0.13697229325771332, 0.1357421576976776, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_RTSplit0208_12 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0134 - Wer: 0.1848 - Cer: 0.0800 ## 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: 5.7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.3332 \| 1.0 \| 120 \| 3.3301 \| 0.9236 \| 0.9767 \| \| 1.2965 \| 2.0 \| 240 \| 1.0130 \| 0.8225 \| 0.5628 \| \| 0.7799 \| 3.0 \| 360 \| 0.6071 \| 0.7291 \| 0.3890 \| \| 0.5541 \| 4.0 \| 480 \| 0.4468 \| 0.6364 \| 0.2997 \| \| 0.4316 \| 5.0 \| 600 \| 0.2639 \| 0.4166 \| 0.1565 \| \| 0.3012 \| 6.0 \| 720 \| 0.1418 \| 0.3079 \| 0.1106 \| \| 0.2648 \| 7.0 \| 840 \| 0.0718 \| 0.2336 \| 0.0809 \| \| 0.1725 \| 8.0 \| 960 \| 0.0348 \| 0.1993 \| 0.0924 \| \| 0.1531 \| 9.0 \| 1080 \| 0.0204 \| 0.1922 \| 0.0891 \| \| 0.0855 \| 10.0 \| 1200 \| 0.0134 \| 0.1848 \| 0.0800 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_12", "results": []}]}	automatic-speech-recognition	tndklab/wav2vec_RTSplit0208_12	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-08T02:27:17+00:00	[]	[]	TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us	wav2vec\_RTSplit0208\_12 ======================== This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0134 * Wer: 0.1848 * Cer: 0.0800 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: 5.7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ 80, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ -0.1433546096086502, 0.15157152712345123, -0.0008700366015546024, 0.094798743724823, 0.11746999621391296, -0.0017081478144973516, 0.16368862986564636, 0.12826868891716003, -0.04727891460061073, 0.10547343641519547, 0.12081724405288696, 0.07541237026453018, 0.05786728858947754, 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null	null	peft	<!-- 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. --> # koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO This model is a fine-tuned version of [ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256](https://huggingface.co/ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 8 - total_train_batch_size: 48 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### 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	{"library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256", "model-index": [{"name": "koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO", "results": []}]}	null	ENERGY-DRINK-LOVE/lora_translate_conversation_dedup_SOLAR_merge2_SFT_DPO_256	[ "peft", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256", "region:us" ]	2024-02-08T02:31:15+00:00	[]	[]	TAGS #peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 #region-us	# koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO This model is a fine-tuned version of ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 8 - total_train_batch_size: 48 - total_eval_batch_size: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### 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	[ "# koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ "TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 #region-us \n", "# koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ 62, 87, 6, 12, 8, 3, 160, 4, 39 ]	[ "passage: TAGS\n#peft #safetensors #trl #dpo #generated_from_trainer #base_model-ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 #region-us \n# koen_script_dpo_short_translate_conversation_dedup_SOLAR_merge2_SFT_256__SFT-DPO\n\nThis model is a fine-tuned version of ENERGY-DRINK-LOVE/translate_conversation_dedup_SOLAR_merge2_SFT_256 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 6\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 48\n- total_eval_batch_size: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 1### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.36.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.0" ]	[ -0.07359986752271652, 0.1180027648806572, -0.00453681917861104, 0.04750702530145645, 0.10449349135160446, 0.01964132860302925, 0.09952007234096527, 0.14184121787548065, -0.026857374235987663, 0.1671094000339508, 0.05837205424904823, -0.010340462438762188, 0.11255236715078354, 0.15193992853164673, 0.041989997029304504, -0.23005323112010956, 0.029667288064956665, -0.050175830721855164, 0.005245968699455261, 0.06732956320047379, 0.09657172113656998, -0.09134986996650696, 0.067580945789814, -0.023779911920428276, -0.06304305791854858, -0.011732361279428005, -0.08491536229848862, -0.05280108377337456, 0.06859420984983444, 0.028966395184397697, 0.034818388521671295, 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null	null	keras	## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>	{"library_name": "keras"}	null	MahtaFetrat/ner_base	[ "keras", "region:us" ]	2024-02-08T02:37:27+00:00	[]	[]	TAGS #keras #region-us	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: Model Plot ---------- View Model Plot !Model Image	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]	[ "TAGS\n#keras #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]	[ 9, 28 ]	[ "passage: TAGS\n#keras #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image" ]	[ -0.016794877126812935, 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null	null	null	# 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="mathreader/q-Taxi-v3-v2-large", 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"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3-v2-large", "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}]}]}]}	reinforcement-learning	mathreader/q-Taxi-v3-v2-large	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-08T02:37:48+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 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null	null	transformers	<h1>orca-platypus - instruct 모델 v1.7</h1> <b><학습 데이터 구축></b> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 - 기존에 1.2에서 성능 저하를 보인 일부 mmlu 데이터를 제거하였습니다. <br> ###학습 데이터 파일은 비공개입니다. <br> <b><학습></b> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.	{"license": "cc-by-sa-4.0"}	text-generation	AIFT/AIFT-ko-orca-plat-Yi-ko-6b-v1.7	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T02:41:38+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<h1>orca-platypus - instruct 모델 v1.7</h1> <b><학습 데이터 구축></b> kyujinpy 님이 공개하신 KOR-OpenOrca-Platypus 데이터를 일부 삭제(샘플링) 및 정제 작업 진행하여 활용. 그 이후 해당 데이터들을 보며 관련 태스크를 추출하였고 이를 기반으로 해당 태스크에 맞춰서 NLP 관련 오픈소스 데이터를 활용하여 학습데이터를 자체적으로 역사, 과학, 수학, 기계독해, 리뷰 분석 문제를 gpt를 통해서 구축하였고, aihub 일반상식 및 기계독해 데이터를 활용하여 추가로 학습 데이터를 구축(형태소 관련, 기계독해 관련 및 요약) 각종 블로그에서 역사 및 상식 퀴즈를 사람이 직접 학습데이터 형태로 변경 AI2AI Challenge 데이터 형태를 보고 gpt를 통해 초등 수준의 과학 수학 문제 유형을 제작 500문제 영어 번역 데이터 영한/한영 데이터 학습 데이터로 활용 진행 총 데이터 4만개 정도 사용하였습니다. <br> <br> + TruthfulQA 관련 문제 추가를 진행하였습니다.(속설 관련 참거짓 문제) + 기계독해 관련 학습 데이터를 ChatGPT를 통해서 답변을 얻어 학습 + 문법관련 학습 데이터 - 기존에 1.2에서 성능 저하를 보인 일부 mmlu 데이터를 제거하였습니다. <br> ###학습 데이터 파일은 비공개입니다. <br> <b><학습></b> 학습은 LoRA를 사용하여 A100 40G *2에서 학습을 진행하였습니다.	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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	text-generation	zheng438/distilgpt2-disease-syptom	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T02:42:40+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05622259899973869, 0.16002345085144043, -0.004987028427422047, 0.023115945979952812, 0.0962471067905426, 0.011845538392663002, 0.06785304099321365, 0.11496778577566147, -0.020396295934915543, 0.11142492294311523, 0.03292480856180191, 0.0972127765417099, 0.11474913358688354, 0.16215258836746216, 0.004439093638211489, -0.23455148935317993, 0.04782992601394653, -0.12695099413394928, -0.033447545021772385, 0.11785799264907837, 0.14491069316864014, -0.10402194410562515, 0.07766910642385483, -0.030544815585017204, -0.009361269883811474, -0.03290390968322754, -0.06365230679512024, -0.05152205005288124, 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null	null	peft	# 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 0.8.2	{"library_name": "peft", "base_model": "Trelis/Llama-2-7b-chat-hf-sharded-bf16"}	null	tnscorcoran/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters	[ "peft", "arxiv:1910.09700", "base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16", "region:us" ]	2024-02-08T02:48:43+00:00	[ "1910.09700" ]	[]	TAGS #peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 43, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-Trelis/Llama-2-7b-chat-hf-sharded-bf16 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.1181381344795227, 0.19727149605751038, -0.0028356341645121574, 0.029223458841443062, 0.07779452949762344, 0.015494933351874352, 0.05737197771668434, 0.1357649564743042, 0.036463662981987, 0.11323360353708267, 0.06885109841823578, 0.12088657170534134, 0.11562664806842804, 0.2212080955505371, 0.004279926419258118, -0.1664223074913025, 0.01706680655479431, -0.06928151845932007, 0.014364630915224552, 0.12016356736421585, 0.14479218423366547, -0.09526396542787552, 0.08089473843574524, -0.018188372254371643, -0.004930650815367699, -0.024937300011515617, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "252.33 +/- 40.48", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	ulichovick/RDL_ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-08T02:51:28+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	transformers	## Description This is ai-forever/FRED-T5-1.7B model trained on Question-Answering, Question-Generation and Answer-Aware Question Generation tasks on russian dataset (hivaze/ru-AAQG-QA-QG) ### Prompts ```python AAQG_PROMPT = "Сгенерируй вопрос по тексту, используя известный ответ. Текст: '{context}'. Ответ: '{answer}'." QG_PROMPT = "Сгенерируй вопрос по тексту. Текст: '{context}'." QA_PROMPT = "Сгенерируй ответ на вопрос по тексту. Текст: '{context}'. Вопрос: '{question}'." ``` ### Examples and code ```python from transformers import AutoTokenizer, T5ForConditionalGeneration from functools import partial saved_checkpoint = 'hivaze/AAQG-QA-QG-FRED-T5-1.7B' tokenizer = AutoTokenizer.from_pretrained(saved_checkpoint) model = T5ForConditionalGeneration.from_pretrained(saved_checkpoint).cuda() def generate_text(prompt, tokenizer, model, n=1, temperature=0.8, num_beams=3): encoded_input = tokenizer.encode_plus(prompt, return_tensors='pt') encoded_input = {k: v.to(model.device) for k, v in encoded_input.items()} resulted_tokens = model.generate(**encoded_input, max_new_tokens=64, do_sample=True, num_beams=num_beams, num_return_sequences=n, temperature=temperature, top_p=0.9, top_k=50) resulted_texts = tokenizer.batch_decode(resulted_tokens, skip_special_tokens=True) return resulted_texts generate_text = partial(generate_text, tokenizer=tokenizer, model=model) test_context = "Путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова" ``` #### AAQG ```python generate_text(AAQG_PROMPT.format( context=test_context, answer='на паралёте' ), n=1) ``` > "На чем путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета?" ```python generate_text(AAQG_PROMPT.format( context=test_context, answer='рекорд высоты полета' ), n=1) ``` > "Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?" #### QA ```python generate_text(QA_PROMPT.format( context=test_context, question='Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?' ), n=1) ``` > "Мировой рекорд высоты полета на паралёте" #### QG ```python generate_text(QG_PROMPT.format(context=test_context), n=1) ``` > "Кто установил мировой рекорд высоты полета на паралёте?" ## Metrics \| Step \| Training Loss \| Validation Loss \| Sbleu \| Chr F \| Rouge1 \| Rouge2 \| Rougel \| \|---\|---\|---\|---\|---\|---\|---\|---\| \| 500 \| 1.020500 \| 1.059296 \| 41.556000 \| 66.391100 \| 0.104200 \| 0.033700 \| 0.104200 \| \| 1000 \| 1.050200 \| 0.998357 \| 43.035900 \| 66.376800 \| 0.105100 \| 0.034100 \| 0.105200 \| \| 1500 \| 0.994000 \| 0.966051 \| 43.692200 \| 66.597600 \| 0.106300 \| 0.034400 \| 0.106400 \| \| 2000 \| 0.947800 \| 0.953637 \| 44.012400 \| 66.711100 \| 0.106600 \| 0.034900 \| 0.106800 \| \| 2500 \| 0.978200 \| 0.944621 \| 44.027900 \| 66.657400 \| 0.106500 \| 0.034600 \| 0.106500 \| ## Authors - Sergei Bratchikov (https://t.me/nlpwanderer)	{"language": ["ru"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["hivaze/ru-AAQG-QA-QG"], "pipeline_tag": "text2text-generation"}	text2text-generation	hivaze/AAQG-QA-QG-FRED-T5-1.7B	[ "transformers", "safetensors", "t5", "text2text-generation", "ru", "dataset:hivaze/ru-AAQG-QA-QG", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T02:57:28+00:00	[]	[ "ru" ]	TAGS #transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Description ----------- This is ai-forever/FRED-T5-1.7B model trained on Question-Answering, Question-Generation and Answer-Aware Question Generation tasks on russian dataset (hivaze/ru-AAQG-QA-QG) ### Prompts ### Examples and code #### AAQG > > "На чем путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета?" > > > > > "Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?" > > > #### QA > > "Мировой рекорд высоты полета на паралёте" > > > #### QG > > "Кто установил мировой рекорд высоты полета на паралёте?" > > > Metrics ------- Authors ------- * Sergei Bratchikov (https://t.me/nlpwanderer)	[ "### Prompts", "### Examples and code", "#### AAQG\n\n\n\n> \n> \"На чем путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n>", "#### QA\n\n\n\n> \n> \"Мировой рекорд высоты полета на паралёте\"\n> \n> \n>", "#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Prompts", "### Examples and code", "#### AAQG\n\n\n\n> \n> \"На чем путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n>", "#### QA\n\n\n\n> \n> \"Мировой рекорд высоты полета на паралёте\"\n> \n> \n>", "#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ 77, 5, 6, 61, 22, 52 ]	[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Prompts### Examples and code#### AAQG\n\n\n\n> \n> \"На чем путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n>#### QA\n\n\n\n> \n> \"Мировой рекорд высоты полета на паралёте\"\n> \n> \n>#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ -0.0012153401039540768, 0.08325638622045517, -0.008729732595384121, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "248.89 +/- 29.23", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	neozhang2003/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-08T03:01:30+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	null	<img src=https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001/resolve/main/anemoi.png> # QuartetAnemoi-70B-t0.0001 A sequential merge using a custom algorithm (NearSwap) of: - [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) - [Sao10K/WinterGoddess-1.4x-70B-L2](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2) - [Aurora-Nights-70B-v1.0](https://huggingface.co/sophosympatheia/Aurora-Nights-70B-v1.0) - [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) <br/> In our testing, this model seems like a storyteller, as might be expected, but the changes from this merge are extremely soft. We were impressed that, unlike most models, at the end of a story it did not often use cliches such as "In the end", "And so", "beacon of hope", etc. <br/> This repo has the GGUF quants. Full weights are available at: [alchemonaut/QuartetAnemoi-70B-t0.0001](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001) <br/> <br/> # NearSwap Algorithm NearSwap retains most of the weights of the base model (Miqu), but when a weight is similar between the two, it is interpolated to the secondary model value. A parameter t specifies the sameness threshold. When the distance between two values is below t, the weight from the secondary model is used. This version of the model uses t = 0.0001. At this t, about 0.8% of weights are fully switched to the secondary model during each pass. Model quality rapidly degrades above t = 0.0025: - t = 0.0001 (~0.8% full swap): This model - t = 0.0003 (~2% full swap) - t = 0.001 (~10% full swap): [BoreanGale-70B](https://huggingface.co/alchemonaut/BoreanGale-70B) - t = 0.0025 (~18% full swap): Generates one paragraph okay, but then reverts to garbage - t = 0.005 (~35% full swap): Garbage; semi-related word lists - t = 0.01 (~55% full swap): Garbage; pseudorandom tokens output For QuartetAnemoi-70B-t0.0001, the three secondary models were each merged sequentially with t = 0.0001. NearSwap implementation: ``` t: Union[float, np.ndarray], v0: Union[np.ndarray, torch.Tensor], v1: Union[np.ndarray, torch.Tensor], ... lweight = numpy.absolute(v0-v1) lweight = t / lweight lweight = numpy.nan_to_num(lweight, nan=1.0, posinf=1.0, neginf=1.0) numpy.clip(lweight, a_min=0.0, a_max=1.0, out=lweight) res = lerp(lweight,v0,v1) ``` <br/> <br/> # License and Use Since the ultimate origin of Miqu is at this time unknown beyond speculation, this model is for noncommercial research use only. <br/> <br/> # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_alchemonaut__QuartetAnemoi-70B-t0.0001) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|76.86\| \|AI2 Reasoning Challenge (25-Shot)\|73.38\| \|HellaSwag (10-Shot) \|88.9\| \|MMLU (5-Shot) \|75.42\| \|TruthfulQA (0-shot) \|69.53\| \|Winogrande (5-shot) \|85.32\| \|GSM8k (5-shot) \|68.61\|	{"license": "other", "tags": ["merge"], "model-index": [{"name": "QuartetAnemoi-70B-t0.0001", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.38, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 88.9, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 75.42, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 69.53}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 85.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 68.61, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=alchemonaut/QuartetAnemoi-70B-t0.0001", "name": "Open LLM Leaderboard"}}]}]}	null	alchemonaut/QuartetAnemoi-70B-t0.0001-GGUF	[ "gguf", "merge", "license:other", "model-index", "region:us" ]	2024-02-08T03:03:29+00:00	[]	[]	TAGS #gguf #merge #license-other #model-index #region-us	<img src=URL QuartetAnemoi-70B-t0.0001 ========================= A sequential merge using a custom algorithm (NearSwap) of: * 152334H/miqu-1-70b-sf * Sao10K/WinterGoddess-1.4x-70B-L2 * Aurora-Nights-70B-v1.0 * Xwin-LM-70B-V0.1 In our testing, this model seems like a storyteller, as might be expected, but the changes from this merge are extremely soft. We were impressed that, unlike most models, at the end of a story it did not often use cliches such as "In the end", "And so", "beacon of hope", etc. This repo has the GGUF quants. Full weights are available at: alchemonaut/QuartetAnemoi-70B-t0.0001 NearSwap Algorithm ================== NearSwap retains most of the weights of the base model (Miqu), but when a weight is similar between the two, it is interpolated to the secondary model value. A parameter t specifies the sameness threshold. When the distance between two values is below t, the weight from the secondary model is used. This version of the model uses t = 0.0001. At this t, about 0.8% of weights are fully switched to the secondary model during each pass. Model quality rapidly degrades above t = 0.0025: * t = 0.0001 (~0.8% full swap): This model * t = 0.0003 (~2% full swap) * t = 0.001 (~10% full swap): BoreanGale-70B * t = 0.0025 (~18% full swap): Generates one paragraph okay, but then reverts to garbage * t = 0.005 (~35% full swap): Garbage; semi-related word lists * t = 0.01 (~55% full swap): Garbage; pseudorandom tokens output For QuartetAnemoi-70B-t0.0001, the three secondary models were each merged sequentially with t = 0.0001. NearSwap implementation: License and Use =============== Since the ultimate origin of Miqu is at this time unknown beyond speculation, this model is for noncommercial research use only. Open LLM Leaderboard Evaluation Results ======================================= Detailed results can be found here	[]	[ "TAGS\n#gguf #merge #license-other #model-index #region-us \n" ]	[ 21 ]	[ "passage: TAGS\n#gguf #merge #license-other #model-index #region-us \n" ]	[ -0.033744730055332184, 0.062039565294981, -0.005369937513023615, -0.033388372510671616, 0.05190665274858475, 0.07593902945518494, 0.1788526028394699, 0.05813658609986305, 0.27102652192115784, -0.07036369293928146, 0.05012398585677147, 0.07539182901382446, 0.004803454969078302, 0.17504942417144775, -0.027579886838793755, -0.1915922611951828, 0.09313752502202988, -0.014372341334819794, -0.08303762227296829, 0.01685304567217827, 0.08581485599279404, -0.040000706911087036, 0.04665631800889969, 0.0034858006983995438, -0.11021286249160767, 0.04654785990715027, -0.016734924167394638, -0.029835576191544533, 0.08054140210151672, 0.10533258318901062, 0.021704263985157013, 0.03988125920295715, -0.05440034717321396, 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null	null	transformers	<!-- 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. --> # sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k 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 openwebtext dataset. It achieves the following results on the evaluation set: - Loss: 0.7590 ## 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: 8 - eval_batch_size: 16 - seed: 0 - distributed_type: multi-GPU - num_devices: 6 - gradient_accumulation_steps: 2 - total_train_batch_size: 96 - total_eval_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 5000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2712 \| 0.05 \| 50 \| 1.2374 \| \| 1.0533 \| 0.1 \| 100 \| 1.0529 \| \| 0.9603 \| 0.15 \| 150 \| 0.9668 \| \| 0.9102 \| 0.19 \| 200 \| 0.9145 \| \| 0.8754 \| 0.24 \| 250 \| 0.8775 \| \| 0.8514 \| 0.29 \| 300 \| 0.8503 \| \| 0.8417 \| 0.34 \| 350 \| 0.8298 \| \| 0.8217 \| 0.39 \| 400 \| 0.8146 \| \| 0.8019 \| 0.44 \| 450 \| 0.8026 \| \| 0.7902 \| 0.48 \| 500 \| 0.7914 \| \| 0.7856 \| 0.53 \| 550 \| 0.7819 \| \| 0.7599 \| 0.58 \| 600 \| 0.7734 \| \| 0.7646 \| 0.63 \| 650 \| 0.7689 \| \| 0.7542 \| 0.68 \| 700 \| 0.7635 \| \| 0.7529 \| 0.73 \| 750 \| 0.7581 \| \| 0.7594 \| 0.78 \| 800 \| 0.7533 \| \| 0.7489 \| 0.82 \| 850 \| 0.7493 \| \| 0.7494 \| 0.87 \| 900 \| 0.7452 \| \| 0.7441 \| 0.92 \| 950 \| 0.7472 \| \| 0.7467 \| 0.97 \| 1000 \| 0.7442 \| \| 0.728 \| 1.02 \| 1050 \| 0.7413 \| \| 0.7263 \| 1.07 \| 1100 \| 0.7384 \| \| 0.7206 \| 1.11 \| 1150 \| 0.7362 \| \| 0.7223 \| 1.16 \| 1200 \| 0.7343 \| \| 0.7362 \| 1.21 \| 1250 \| 0.7421 \| \| 0.7374 \| 1.26 \| 1300 \| 0.7401 \| \| 0.7284 \| 1.31 \| 1350 \| 0.7378 \| \| 0.7309 \| 1.36 \| 1400 \| 0.7356 \| \| 0.724 \| 1.41 \| 1450 \| 0.7339 \| \| 0.72 \| 1.45 \| 1500 \| 0.7317 \| \| 0.73 \| 1.5 \| 1550 \| 0.7509 \| \| 0.7464 \| 1.55 \| 1600 \| 0.7489 \| \| 0.742 \| 1.6 \| 1650 \| 0.7461 \| \| 0.7378 \| 1.65 \| 1700 \| 0.7447 \| \| 0.7328 \| 1.7 \| 1750 \| 0.7433 \| \| 0.7433 \| 1.75 \| 1800 \| 0.7411 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["openwebtext"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k", "results": []}]}	text-generation	thrunlab/sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "dataset:openwebtext", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T03:03:48+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	sparse\_sparse\_80\_percent\_pretraining\_warmup\_20K\_steps\_5k ================================================================ This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the openwebtext dataset. It achieves the following results on the evaluation set: * Loss: 0.7590 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: 8 * eval\_batch\_size: 16 * seed: 0 * distributed\_type: multi-GPU * num\_devices: 6 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 96 * total\_eval\_batch\_size: 96 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 5000 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 88, 159, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #dataset-openwebtext #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 0\n* distributed\\_type: multi-GPU\n* num\\_devices: 6\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 96\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 5000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.11613860726356506, 0.16285601258277893, -0.004015443380922079, 0.08496980369091034, 0.11045904457569122, 0.04193746671080589, 0.13951893150806427, 0.15293191373348236, -0.0465170256793499, 0.12261621654033661, 0.11853302270174026, 0.05479864403605461, 0.06425037980079651, 0.19747602939605713, -0.03552094101905823, -0.23224647343158722, 0.03845938295125961, -0.04181455820798874, -0.10101693868637085, 0.10659915953874588, 0.08191104233264923, -0.10471220314502716, 0.070194311439991, -0.033692169934511185, -0.10063648223876953, -0.04701340198516846, -0.030814234167337418, -0.03729020431637764, 0.09593965113162994, 0.035501543432474136, 0.07318910211324692, 0.01762923039495945, 0.07793665677309036, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	DeepNoteAi/Auto_whisper_large3_converted_to_hub	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T03:07:04+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. 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null	null	transformers	实验性Base模型，基于Qwen并在~5B数量级的中日领域内数据上继续预训练。此模型无法直接用于各项任务，需微调后才能适配相关领域下游任务，因此只提供给确认会进行微调的开发者。如您有微调需求可联系邮箱，并附上Huggingface用户名与要微调的下游任务定义（如续写，roleplay等）。使用本Base模型微调的模型：https://huggingface.co/SakuraLLM/Sakura-1B8-Qwen2beta-v0.9/ 项目地址：https://github.com/SakuraLLM/Sakura-13B-Galgame	{"license": "gpl-3.0"}	text-generation	SakuraLLM/Sakura-1B8-Base-v0.9.0	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "license:gpl-3.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T03:13:30+00:00	[]	[]	TAGS #transformers #safetensors #qwen2 #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us	实验性Base模型，基于Qwen并在~5B数量级的中日领域内数据上继续预训练。此模型无法直接用于各项任务，需微调后才能适配相关领域下游任务，因此只提供给确认会进行微调的开发者。如您有微调需求可联系邮箱，并附上Huggingface用户名与要微调的下游任务定义（如续写，roleplay等）。使用本Base模型微调的模型：URL 项目地址：URL	[]	[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 51 ]	[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.03477787598967552, 0.075380839407444, -0.004925156943500042, 0.025683850049972534, 0.07712317258119583, -0.0252146627753973, 0.1696764975786209, 0.10786139219999313, 0.030668020248413086, -0.06683797389268875, 0.16204047203063965, 0.1682228446006775, -0.021553101018071175, 0.09187246114015579, -0.10045748949050903, -0.1744309663772583, 0.1029253676533699, -0.0032071059104055166, 0.0076133920811116695, 0.08735236525535583, 0.11980511993169785, -0.048379652202129364, 0.07232208549976349, -0.02114902436733246, -0.11894071847200394, 0.004727814346551895, 0.06479175388813019, -0.12040004134178162, 0.10823138058185577, 0.07677730917930603, 0.06349777430295944, 0.1327531337738037, -0.0055161649361252785, -0.24388115108013153, 0.02913178689777851, -0.04176304116845131, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_RTSplit0208_13 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0628 - Wer: 0.2624 - Cer: 0.1998 ## 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: 5.7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 3.6438 \| 1.0 \| 120 \| 3.6089 \| 0.9891 \| 0.9900 \| \| 1.3643 \| 2.0 \| 240 \| 1.1285 \| 0.9916 \| 0.6248 \| \| 0.7667 \| 3.0 \| 360 \| 0.6526 \| 0.8201 \| 0.5589 \| \| 0.6173 \| 4.0 \| 480 \| 0.5368 \| 0.7853 \| 0.5041 \| \| 0.5062 \| 5.0 \| 600 \| 0.3535 \| 0.5504 \| 0.3226 \| \| 0.3561 \| 6.0 \| 720 \| 0.2035 \| 0.4105 \| 0.2472 \| \| 0.2857 \| 7.0 \| 840 \| 0.1240 \| 0.3396 \| 0.2130 \| \| 0.2119 \| 8.0 \| 960 \| 0.0628 \| 0.2624 \| 0.1998 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_13", "results": []}]}	automatic-speech-recognition	tndklab/wav2vec_RTSplit0208_13	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-08T03:15:30+00:00	[]	[]	TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us	wav2vec\_RTSplit0208\_13 ======================== This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0628 * Wer: 0.2624 * Cer: 0.1998 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: 5.7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 8 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ 80, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.7e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ -0.14328604936599731, 0.15207324922084808, -0.0008769851992838085, 0.0955214723944664, 0.11775391548871994, -0.001090139034204185, 0.1634601652622223, 0.12874279916286469, -0.047303516417741776, 0.10555363446474075, 0.12039066851139069, 0.07425211369991302, 0.057400524616241455, 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null	null	null	# 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	{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "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}]}]}]}	reinforcement-learning	flipchip167/cartpole_v1	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-08T03:22:54+00:00	[]	[]	TAGS #CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# 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: URL	[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 39, 54 ]	[ "passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.007526164408773184, -0.12498430907726288, -0.0013541718944907188, 0.09601131081581116, 0.11848696321249008, -0.04186001420021057, 0.11405468732118607, 0.05624859035015106, 0.09539441019296646, 0.04239490255713463, 0.13636724650859833, 0.06906966865062714, -0.004102868959307671, 0.12412862479686737, 0.09840741008520126, -0.26058563590049744, 0.07420794665813446, -0.04403980076313019, -0.009944677352905273, 0.10139261186122894, 0.07836852967739105, -0.08325441926717758, 0.051592715084552765, 0.00009572553972247988, -0.044259943068027496, 0.0321260429918766, 0.013628939166665077, -0.053157225251197815, 0.1606452465057373, -0.07313758134841919, 0.10494591295719147, -0.03843724727630615, 0.14574295282363892, -0.1126825287938118, 0.04758213832974434, 0.05111503228545189, -0.04548581689596176, 0.03848232328891754, -0.12538743019104004, -0.06033875793218613, 0.026815801858901978, -0.015865681692957878, 0.12249194830656052, 0.03647647053003311, -0.1777559220790863, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	cvzion/mistral-dqg-v5	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T03:24:38+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	## Model Details Original LlamaGuard 7b model can be found [here](https://huggingface.co/meta-llama/LlamaGuard-7b) Llama-Guard is a 7B parameter [Llama 2](https://arxiv.org/abs/2307.09288)-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example: ![](Llama-Guard_example.png) These are exl2 4.0bpw quantized weights. Original 7B model performs on binary classification of 2k toxic chat test examples Precision: 0.9, Recall: 0.277, F1 Score: 0.424 4.0bpw performs Precision: 0.92, Recall: 0.246, F1 Score: 0.389	{}	text-generation	ek826/LlamaGuard-7b-4.0bpw-exl2	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2307.09288", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T03:28:16+00:00	[ "2307.09288" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## Model Details Original LlamaGuard 7b model can be found here Llama-Guard is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example: ![](Llama-Guard_example.png) These are exl2 4.0bpw quantized weights. Original 7B model performs on binary classification of 2k toxic chat test examples Precision: 0.9, Recall: 0.277, F1 Score: 0.424 4.0bpw performs Precision: 0.92, Recall: 0.246, F1 Score: 0.389	[ "## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.0bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.0bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389" ]	[ 59, 216 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.0bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389" ]	[ -0.10862310975790024, 0.041736334562301636, -0.0041666715405881405, 0.05714704468846321, 0.06548796594142914, -0.020450318232178688, 0.11182621121406555, 0.12684519588947296, 0.12853817641735077, 0.03781633824110031, 0.02785337157547474, 0.09968878328800201, -0.03600148856639862, 0.16830162703990936, -0.08663906157016754, -0.047292325645685196, 0.05976998805999756, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	DeepNoteAi/PEFT_whisper_large3_converted_to_hub	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T03:36:20+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "235.87 +/- 66.61", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	pjpkvarma/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-08T03:36:44+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	diffusers	# LoRA text2image fine-tuning - nightdude/kanji-lora-conv These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "inference": true}	text-to-image	nightdude/kanji-lora-conv	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	2024-02-08T03:37:14+00:00	[]	[]	TAGS #diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us	# LoRA text2image fine-tuning - nightdude/kanji-lora-conv These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3	[ "# LoRA text2image fine-tuning - nightdude/kanji-lora-conv\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ "TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - nightdude/kanji-lora-conv\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ 66, 96 ]	[ "passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - nightdude/kanji-lora-conv\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the nightdude/sakana-kanji dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ -0.07309036701917648, -0.10849705338478088, -0.0006745658465661108, 0.02773876301944256, 0.06960398703813553, 0.02498733252286911, 0.22034196555614471, 0.11219657957553864, -0.1002342477440834, 0.057412199676036835, 0.03533234819769859, 0.014032432809472084, 0.051060549914836884, 0.12019810080528259, 0.016562750563025475, -0.2670476734638214, 0.004817552398890257, -0.014130756258964539, -0.06333204358816147, 0.06623762845993042, 0.10112468153238297, -0.04872913286089897, 0.1185750737786293, -0.011047790758311749, -0.14544598758220673, -0.019371397793293, -0.05586329475045204, -0.11476694792509079, 0.06368616968393326, 0.039010725915431976, -0.03315568342804909, 0.09775890409946442, 0.06697428971529007, -0.12099248915910721, 0.026027260348200798, -0.029028955847024918, -0.023190557956695557, 0.05618492513895035, -0.03439606726169586, 0.04257689043879509, 0.1247008815407753, -0.03520897030830383, 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null	null	transformers	<!-- 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. --> # SMIDS_3x_beit_large_SGD_lr00001_fold5 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9487 - Accuracy: 0.5733 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.7369 \| 1.0 \| 450 \| 1.6475 \| 0.3017 \| \| 1.6101 \| 2.0 \| 900 \| 1.5834 \| 0.2967 \| \| 1.4663 \| 3.0 \| 1350 \| 1.5248 \| 0.2933 \| \| 1.5046 \| 4.0 \| 1800 \| 1.4711 \| 0.3 \| \| 1.4858 \| 5.0 \| 2250 \| 1.4220 \| 0.2967 \| \| 1.4147 \| 6.0 \| 2700 \| 1.3771 \| 0.3033 \| \| 1.2313 \| 7.0 \| 3150 \| 1.3357 \| 0.3133 \| \| 1.3086 \| 8.0 \| 3600 \| 1.2981 \| 0.3217 \| \| 1.2266 \| 9.0 \| 4050 \| 1.2639 \| 0.3383 \| \| 1.3231 \| 10.0 \| 4500 \| 1.2331 \| 0.365 \| \| 1.2737 \| 11.0 \| 4950 \| 1.2053 \| 0.37 \| \| 1.2907 \| 12.0 \| 5400 \| 1.1803 \| 0.3817 \| \| 1.3059 \| 13.0 \| 5850 \| 1.1579 \| 0.4067 \| \| 1.184 \| 14.0 \| 6300 \| 1.1380 \| 0.4167 \| \| 1.1806 \| 15.0 \| 6750 \| 1.1204 \| 0.44 \| \| 1.1721 \| 16.0 \| 7200 \| 1.1045 \| 0.4567 \| \| 1.1033 \| 17.0 \| 7650 \| 1.0902 \| 0.465 \| \| 1.0663 \| 18.0 \| 8100 \| 1.0773 \| 0.4733 \| \| 1.1022 \| 19.0 \| 8550 \| 1.0655 \| 0.4867 \| \| 1.1228 \| 20.0 \| 9000 \| 1.0549 \| 0.4933 \| \| 1.1337 \| 21.0 \| 9450 \| 1.0451 \| 0.5017 \| \| 1.0755 \| 22.0 \| 9900 \| 1.0362 \| 0.5117 \| \| 1.0718 \| 23.0 \| 10350 \| 1.0279 \| 0.5117 \| \| 1.0451 \| 24.0 \| 10800 \| 1.0205 \| 0.515 \| \| 1.0523 \| 25.0 \| 11250 \| 1.0137 \| 0.5183 \| \| 1.0036 \| 26.0 \| 11700 \| 1.0074 \| 0.5267 \| \| 1.0151 \| 27.0 \| 12150 \| 1.0015 \| 0.54 \| \| 1.0147 \| 28.0 \| 12600 \| 0.9962 \| 0.545 \| \| 1.0152 \| 29.0 \| 13050 \| 0.9912 \| 0.545 \| \| 1.0103 \| 30.0 \| 13500 \| 0.9866 \| 0.55 \| \| 1.0142 \| 31.0 \| 13950 \| 0.9823 \| 0.5467 \| \| 1.0003 \| 32.0 \| 14400 \| 0.9783 \| 0.5483 \| \| 0.9717 \| 33.0 \| 14850 \| 0.9747 \| 0.55 \| \| 0.9879 \| 34.0 \| 15300 \| 0.9714 \| 0.555 \| \| 0.9664 \| 35.0 \| 15750 \| 0.9684 \| 0.5567 \| \| 0.9855 \| 36.0 \| 16200 \| 0.9656 \| 0.5617 \| \| 1.0112 \| 37.0 \| 16650 \| 0.9630 \| 0.565 \| \| 0.9801 \| 38.0 \| 17100 \| 0.9607 \| 0.5667 \| \| 0.9487 \| 39.0 \| 17550 \| 0.9586 \| 0.5683 \| \| 1.0493 \| 40.0 \| 18000 \| 0.9568 \| 0.5683 \| \| 1.0398 \| 41.0 \| 18450 \| 0.9551 \| 0.57 \| \| 0.946 \| 42.0 \| 18900 \| 0.9537 \| 0.5683 \| \| 0.9705 \| 43.0 \| 19350 \| 0.9524 \| 0.5683 \| \| 1.0162 \| 44.0 \| 19800 \| 0.9513 \| 0.5683 \| \| 0.9905 \| 45.0 \| 20250 \| 0.9505 \| 0.57 \| \| 0.8843 \| 46.0 \| 20700 \| 0.9498 \| 0.5717 \| \| 1.0022 \| 47.0 \| 21150 \| 0.9493 \| 0.5717 \| \| 0.9336 \| 48.0 \| 21600 \| 0.9489 \| 0.5733 \| \| 1.0057 \| 49.0 \| 22050 \| 0.9487 \| 0.5733 \| \| 1.0572 \| 50.0 \| 22500 \| 0.9487 \| 0.5733 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_SGD_lr00001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5733333333333334, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_SGD_lr00001_fold5	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T03:37:38+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_SGD\_lr00001\_fold5 =========================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.9487 * Accuracy: 0.5733 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 116, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1292150914669037, 0.17132072150707245, -0.002415567170828581, 0.13183215260505676, 0.11657863855361938, 0.020753253251314163, 0.1335890144109726, 0.16620413959026337, -0.08238927274942398, 0.04929587244987488, 0.13697229325771332, 0.1357421576976776, 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null	null	transformers	<!-- 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. --> # SMIDS_3x_beit_large_SGD_lr0001_fold5 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3619 - Accuracy: 0.8533 ## 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: 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 1.2379 \| 1.0 \| 450 \| 1.1845 \| 0.3783 \| \| 0.9624 \| 2.0 \| 900 \| 0.9758 \| 0.5567 \| \| 0.8449 \| 3.0 \| 1350 \| 0.8745 \| 0.6183 \| \| 0.8095 \| 4.0 \| 1800 \| 0.7990 \| 0.6533 \| \| 0.8042 \| 5.0 \| 2250 \| 0.7374 \| 0.7017 \| \| 0.736 \| 6.0 \| 2700 \| 0.6877 \| 0.7233 \| \| 0.6222 \| 7.0 \| 3150 \| 0.6456 \| 0.74 \| \| 0.6424 \| 8.0 \| 3600 \| 0.6095 \| 0.755 \| \| 0.5733 \| 9.0 \| 4050 \| 0.5803 \| 0.7667 \| \| 0.5784 \| 10.0 \| 4500 \| 0.5559 \| 0.7817 \| \| 0.6239 \| 11.0 \| 4950 \| 0.5348 \| 0.7867 \| \| 0.5192 \| 12.0 \| 5400 \| 0.5163 \| 0.8017 \| \| 0.5597 \| 13.0 \| 5850 \| 0.5006 \| 0.8083 \| \| 0.4883 \| 14.0 \| 6300 \| 0.4864 \| 0.81 \| \| 0.5124 \| 15.0 \| 6750 \| 0.4742 \| 0.8117 \| \| 0.5307 \| 16.0 \| 7200 \| 0.4630 \| 0.82 \| \| 0.4455 \| 17.0 \| 7650 \| 0.4536 \| 0.8267 \| \| 0.4617 \| 18.0 \| 8100 \| 0.4447 \| 0.8267 \| \| 0.4615 \| 19.0 \| 8550 \| 0.4365 \| 0.83 \| \| 0.4351 \| 20.0 \| 9000 \| 0.4292 \| 0.835 \| \| 0.4799 \| 21.0 \| 9450 \| 0.4227 \| 0.83 \| \| 0.4016 \| 22.0 \| 9900 \| 0.4167 \| 0.8367 \| \| 0.4316 \| 23.0 \| 10350 \| 0.4113 \| 0.8383 \| \| 0.4429 \| 24.0 \| 10800 \| 0.4065 \| 0.84 \| \| 0.3885 \| 25.0 \| 11250 \| 0.4023 \| 0.84 \| \| 0.4073 \| 26.0 \| 11700 \| 0.3983 \| 0.84 \| \| 0.3383 \| 27.0 \| 12150 \| 0.3942 \| 0.8417 \| \| 0.3586 \| 28.0 \| 12600 \| 0.3911 \| 0.84 \| \| 0.385 \| 29.0 \| 13050 \| 0.3874 \| 0.8433 \| \| 0.405 \| 30.0 \| 13500 \| 0.3847 \| 0.8433 \| \| 0.3896 \| 31.0 \| 13950 \| 0.3823 \| 0.845 \| \| 0.3962 \| 32.0 \| 14400 \| 0.3796 \| 0.8483 \| \| 0.3283 \| 33.0 \| 14850 \| 0.3776 \| 0.8483 \| \| 0.4099 \| 34.0 \| 15300 \| 0.3755 \| 0.8483 \| \| 0.3408 \| 35.0 \| 15750 \| 0.3736 \| 0.8483 \| \| 0.3493 \| 36.0 \| 16200 \| 0.3719 \| 0.8483 \| \| 0.3897 \| 37.0 \| 16650 \| 0.3706 \| 0.85 \| \| 0.2925 \| 38.0 \| 17100 \| 0.3694 \| 0.85 \| \| 0.4387 \| 39.0 \| 17550 \| 0.3681 \| 0.8533 \| \| 0.4503 \| 40.0 \| 18000 \| 0.3668 \| 0.8533 \| \| 0.3623 \| 41.0 \| 18450 \| 0.3658 \| 0.8533 \| \| 0.379 \| 42.0 \| 18900 \| 0.3650 \| 0.8533 \| \| 0.3317 \| 43.0 \| 19350 \| 0.3642 \| 0.8533 \| \| 0.3508 \| 44.0 \| 19800 \| 0.3636 \| 0.8533 \| \| 0.3712 \| 45.0 \| 20250 \| 0.3632 \| 0.8533 \| \| 0.294 \| 46.0 \| 20700 \| 0.3627 \| 0.8533 \| \| 0.3722 \| 47.0 \| 21150 \| 0.3623 \| 0.8533 \| \| 0.3585 \| 48.0 \| 21600 \| 0.3621 \| 0.8533 \| \| 0.3989 \| 49.0 \| 22050 \| 0.3620 \| 0.8533 \| \| 0.3917 \| 50.0 \| 22500 \| 0.3619 \| 0.8533 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_SGD_lr0001_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8533333333333334, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_SGD_lr0001_fold5	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T03:37:46+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_SGD\_lr0001\_fold5 ========================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.3619 * Accuracy: 0.8533 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: 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: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 115, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.12968555092811584, 0.17251011729240417, -0.0023243443574756384, 0.1362919956445694, 0.1120586097240448, 0.015268749557435513, 0.14003369212150574, 0.16890837252140045, -0.08239254355430603, 0.046998485922813416, 0.14023225009441376, 0.13628867268562317, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> RegLLM is LLM model for regulatory compliance. It has been domain adapted by unsupervised pretraining and instruction finetuned for regulatory compliance. This release focuses on Indian Banking rules and regulations. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [dataeaze systems pvt ltd](https://www.dataeaze.io/) - Funded by: [dataeaze systems pvt ltd](https://www.dataeaze.io/) - Shared by: [dataeaze systems pvt ltd](https://www.dataeaze.io/) - Model type: PhiForCausalLM - Language(s) (NLP): English - License: [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) Model is made available under non-commercial use for research purposes only. For commercial usage please connect at [email protected] - Finetuned from model: [miscrosoft-phi-2](https://huggingface.co/microsoft/phi-2) ## 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. --> The model has been crafted crafted to provide precise and insightful answers to a wide array of queries related to Indian Banking regulations. ### Downstream Use <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> This model can be used as core component in RegTech application ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> Model has been fine tuned on a specific task of answering questions related to Indian regulatory compliance. Any use beyond this is not guaranteed to be accurate. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> - Bias: Trained for English language only (as of now). - Risk: Guardrails are reliant on the base models Microsoft Phi-2. Finetuning could impact this behaviour. - Limitations: Intended to be a small model optimised for Indian regulations (as of now). ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> * This model is supposed to be used as an assistive AI technology. Kindly consult and verify with the source documents for decision making. * This model should be used with grounding on a set of regulatory documents. ## How to Get Started with the Model ``` from transformers import pipeline import torch pipe = pipeline("text-generation", model="dataeaze/dataeaze-RegLLM-microsoft_phi_2-dzcompli", torch_dtype=torch.bfloat16, device_map="auto") pipe.tokenizer.pad_token = pipe.tokenizer.eos_token result = pipe(f"What are the skills that a CCO should have?", max_new_tokens=256, do_sample=True, temperature=0.1, top_k=50, top_p=0.95)[0]['generated_text'] print(result) ``` ## Sample Output ### Question What are the skills that a CCO should have? ### RegLLM respose ``` Instruct: What are the skills that a CCO should have? Output: The skills that a CCO should have include leadership, communication, and a strong understanding of compliance. They should also be able to work effectively with other departments and have a good track record of compliance. ``` ### GPT-4 response <table> <tr style="border-spacing: 5px;"> <td> <img src="chatgpt_cco_1.png" alt="gpt-4-respnse" width="750" /> </td> </tr> <tr> <td> <img src="chatgpt_cco_2.png" alt="gpt-4-respnse" width="750" /> </td> </tr> </table> ### Reference For evalating truthfulness / hallucination of this response, refer to RBI notification [RBI/2022-23/24 Ref.No.DoS.CO.PPG./SEC.01/11.01.005/2022-23](https://rbidocs.rbi.org.in/rdocs/Notification/PDFs/NT244C25EB0BBB1E4F91AEB101D425EA639A.PDF) (page 8) Screenshot below <img src="CCO_Skills.png" alt="rbi-gold-answer" width="1000"/> As you can see, RegLLM has identified CCO has identified Chief Compliance Officer, while GPT-4 (Copilot) identifies CCO has Chief Commercial Officer. Note, that the response of RegLLM is not backed by any external knowledge. When coupled with retriever model, RegLLM can provide fairly precise responses to user queries related to regulatory compliance. Keep watching this space for more updates on the model and evaluations. ## Model Card Authors * Niranjan Kakade * Atharva Inamdar * Tony Tom * Nayan Chheda * Sourabh Daptardar ## Model Card Contact "dataeaze systems" <[email protected]>	{"language": ["en"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "tags": ["finance", "legal"], "pipeline_tag": "text-generation"}	text-generation	dataeaze/dataeaze-RegLLM-microsoft_phi_2-dzcompli	[ "transformers", "safetensors", "phi", "text-generation", "finance", "legal", "custom_code", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T03:40:06+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #phi #text-generation #finance #legal #custom_code #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us	Model Card for Model ID ======================= RegLLM is LLM model for regulatory compliance. It has been domain adapted by unsupervised pretraining and instruction finetuned for regulatory compliance. This release focuses on Indian Banking rules and regulations. Model Details ------------- ### Model Description * Developed by: dataeaze systems pvt ltd * Funded by: dataeaze systems pvt ltd * Shared by: dataeaze systems pvt ltd * Model type: PhiForCausalLM * Language(s) (NLP): English * License: cc-by-nc-sa-4.0 Model is made available under non-commercial use for research purposes only. For commercial usage please connect at contactus@URL * Finetuned from model: miscrosoft-phi-2 Uses ---- ### Direct Use The model has been crafted crafted to provide precise and insightful answers to a wide array of queries related to Indian Banking regulations. ### Downstream Use This model can be used as core component in RegTech application ### Out-of-Scope Use Model has been fine tuned on a specific task of answering questions related to Indian regulatory compliance. Any use beyond this is not guaranteed to be accurate. Bias, Risks, and Limitations ---------------------------- * Bias: Trained for English language only (as of now). * Risk: Guardrails are reliant on the base models Microsoft Phi-2. Finetuning could impact this behaviour. * Limitations: Intended to be a small model optimised for Indian regulations (as of now). ### Recommendations * This model is supposed to be used as an assistive AI technology. Kindly consult and verify with the source documents for decision making. * This model should be used with grounding on a set of regulatory documents. How to Get Started with the Model --------------------------------- Sample Output ------------- ### Question What are the skills that a CCO should have? ### RegLLM respose ### GPT-4 response ### Reference For evalating truthfulness / hallucination of this response, refer to RBI notification RBI/2022-23/24 Ref.No.DoS.CO.PPG./SEC.01/11.01.005/2022-23 (page 8) Screenshot below ![rbi-gold-answer](CCO_Skills.png) As you can see, RegLLM has identified CCO has identified Chief Compliance Officer, while GPT-4 (Copilot) identifies CCO has Chief Commercial Officer. Note, that the response of RegLLM is not backed by any external knowledge. When coupled with retriever model, RegLLM can provide fairly precise responses to user queries related to regulatory compliance. Keep watching this space for more updates on the model and evaluations. Model Card Authors ------------------ * Niranjan Kakade * Atharva Inamdar * Tony Tom * Nayan Chheda * Sourabh Daptardar Model Card Contact ------------------ "dataeaze systems" [contactus@URL](mailto:contactus@URL)	[ "### Model Description\n\n\n* Developed by: dataeaze systems pvt ltd\n* Funded by: dataeaze systems pvt ltd\n* Shared by: dataeaze systems pvt ltd\n* Model type: PhiForCausalLM\n* Language(s) (NLP): English\n* License: cc-by-nc-sa-4.0 Model is made available under non-commercial use for research purposes only. For commercial usage please connect at contactus@URL\n* Finetuned from model: miscrosoft-phi-2\n\n\nUses\n----", "### Direct Use\n\n\nThe model has been crafted crafted to provide precise and insightful answers to a wide array of queries related to Indian Banking regulations.", "### Downstream Use\n\n\nThis model can be used as core component in RegTech application", "### Out-of-Scope Use\n\n\nModel has been fine tuned on a specific task of answering questions related to Indian regulatory compliance.\nAny use beyond this is not guaranteed to be accurate.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n* Bias: Trained for English language only (as of now).\n* Risk: Guardrails are reliant on the base models Microsoft Phi-2. Finetuning could impact this behaviour.\n* Limitations: Intended to be a small model optimised for Indian regulations (as of now).", "### Recommendations\n\n\n* This model is supposed to be used as an assistive AI technology. Kindly consult and verify with the source documents for decision making.\n* This model should be used with grounding on a set of regulatory documents.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nSample Output\n-------------", "### Question\n\n\nWhat are the skills that a CCO should have?", "### RegLLM respose", "### GPT-4 response", "### Reference\n\n\nFor evalating truthfulness / hallucination of this response, refer to RBI notification\nRBI/2022-23/24\nRef.No.DoS.CO.PPG./SEC.01/11.01.005/2022-23 (page 8)\n\n\nScreenshot below\n\n\n![rbi-gold-answer](CCO_Skills.png)\nAs you can see, RegLLM has identified CCO has identified Chief Compliance Officer, while GPT-4 (Copilot) identifies CCO has Chief Commercial Officer.\nNote, that the response of RegLLM is not backed by any external knowledge.\nWhen coupled with retriever model, RegLLM can provide fairly precise responses to user queries related to regulatory compliance.\n\n\nKeep watching this space for more updates on the model and evaluations.\n\n\nModel Card Authors\n------------------\n\n\n* Niranjan Kakade\n* Atharva Inamdar\n* Tony Tom\n* Nayan Chheda\n* Sourabh Daptardar\n\n\nModel Card Contact\n------------------\n\n\n\"dataeaze systems\" [contactus@URL](mailto:contactus@URL)" ]	[ "TAGS\n#transformers #safetensors #phi #text-generation #finance #legal #custom_code #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Model Description\n\n\n* Developed by: dataeaze systems pvt ltd\n* Funded by: dataeaze systems pvt ltd\n* Shared by: dataeaze systems pvt ltd\n* Model type: PhiForCausalLM\n* Language(s) (NLP): English\n* License: cc-by-nc-sa-4.0 Model is made available under non-commercial use for research purposes only. For commercial usage please connect at contactus@URL\n* Finetuned from model: miscrosoft-phi-2\n\n\nUses\n----", "### Direct Use\n\n\nThe model has been crafted crafted to provide precise and insightful answers to a wide array of queries related to Indian Banking regulations.", "### Downstream Use\n\n\nThis model can be used as core component in RegTech application", "### Out-of-Scope Use\n\n\nModel has been fine tuned on a specific task of answering questions related to Indian regulatory compliance.\nAny use beyond this is not guaranteed to be accurate.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n* Bias: Trained for English language only (as of now).\n* Risk: Guardrails are reliant on the base models Microsoft Phi-2. Finetuning could impact this behaviour.\n* Limitations: Intended to be a small model optimised for Indian regulations (as of now).", "### Recommendations\n\n\n* This model is supposed to be used as an assistive AI technology. Kindly consult and verify with the source documents for decision making.\n* This model should be used with grounding on a set of regulatory documents.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nSample Output\n-------------", "### Question\n\n\nWhat are the skills that a CCO should have?", "### RegLLM respose", "### GPT-4 response", "### Reference\n\n\nFor evalating truthfulness / hallucination of this response, refer to RBI notification\nRBI/2022-23/24\nRef.No.DoS.CO.PPG./SEC.01/11.01.005/2022-23 (page 8)\n\n\nScreenshot below\n\n\n![rbi-gold-answer](CCO_Skills.png)\nAs you can see, RegLLM has identified CCO has identified Chief Compliance Officer, while GPT-4 (Copilot) identifies CCO has Chief Commercial Officer.\nNote, that the response of RegLLM is not backed by any external knowledge.\nWhen coupled with retriever model, RegLLM can provide fairly precise responses to user queries related to regulatory compliance.\n\n\nKeep watching this space for more updates on the model and evaluations.\n\n\nModel Card Authors\n------------------\n\n\n* Niranjan Kakade\n* Atharva Inamdar\n* Tony Tom\n* Nayan Chheda\n* Sourabh Daptardar\n\n\nModel Card Contact\n------------------\n\n\n\"dataeaze systems\" [contactus@URL](mailto:contactus@URL)" ]	[ 62, 125, 35, 17, 116, 69, 14, 7, 6, 238 ]	[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #finance #legal #custom_code #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n### Model Description\n\n\n* Developed by: dataeaze systems pvt ltd\n* Funded by: dataeaze systems pvt ltd\n* Shared by: dataeaze systems pvt ltd\n* Model type: PhiForCausalLM\n* Language(s) (NLP): English\n* License: cc-by-nc-sa-4.0 Model is made available under non-commercial use for research purposes only. For commercial usage please connect at contactus@URL\n* Finetuned from model: miscrosoft-phi-2\n\n\nUses\n----### Direct Use\n\n\nThe model has been crafted crafted to provide precise and insightful answers to a wide array of queries related to Indian Banking regulations.### Downstream Use\n\n\nThis model can be used as core component in RegTech application### Out-of-Scope Use\n\n\nModel has been fine tuned on a specific task of answering questions related to Indian regulatory compliance.\nAny use beyond this is not guaranteed to be accurate.\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\n* Bias: Trained for English language only (as of now).\n* Risk: Guardrails are reliant on the base models Microsoft Phi-2. Finetuning could impact this behaviour.\n* Limitations: Intended to be a small model optimised for Indian regulations (as of now).### Recommendations\n\n\n* This model is supposed to be used as an assistive AI technology. Kindly consult and verify with the source documents for decision making.\n* This model should be used with grounding on a set of regulatory documents.\n\n\nHow to Get Started with the Model\n---------------------------------\n\n\nSample Output\n-------------### Question\n\n\nWhat are the skills that a CCO should have?### RegLLM respose### GPT-4 response" ]	[ -0.10871487855911255, 0.12238007038831711, -0.0018709517316892743, 0.04139100760221481, 0.08349864184856415, -0.027883393689990044, 0.049203891307115555, 0.0759691447019577, 0.045913200825452805, 0.10582701861858368, 0.03407878056168556, 0.0195681843906641, 0.10414538532495499, 0.2080676555633545, 0.022563312202692032, -0.12316418439149857, 0.03982425481081009, -0.10361845046281815, 0.05961955338716507, 0.12728466093540192, 0.1430821269750595, -0.08246467262506485, 0.07389999181032181, -0.032009173184633255, -0.025077717378735542, 0.00046886125346645713, -0.011338821612298489, -0.01991027221083641, 0.0007564043044112623, 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null	null	diffusers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "diffusers"}	null	YutoNishimura-v2/text-to-kanji	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-08T03:40:27+00:00	[ "1910.09700" ]	[]	TAGS #diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 45, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a diffusers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06515942513942719, 0.15121106803417206, -0.004053659737110138, 0.01550832949578762, 0.10413813591003418, 0.007027626037597656, 0.07110057026147842, 0.10866589844226837, -0.016254039481282234, 0.1362447589635849, 0.03217164799571037, 0.10340566188097, 0.11961229145526886, 0.18158376216888428, 0.0006413664668798447, -0.2113182544708252, 0.06048404052853584, -0.11543826758861542, 0.0270865298807621, 0.11970705538988113, 0.14669035375118256, -0.10399326682090759, 0.0754590556025505, -0.028657250106334686, -0.010884147137403488, -0.03274247795343399, -0.06355240941047668, -0.05748795345425606, 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null	null	transformers	<!-- 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. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2259 - Accuracy: 0.5625 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.8751 \| 1.0 \| 20 \| 1.7512 \| 0.3 \| \| 1.3825 \| 2.0 \| 40 \| 1.4946 \| 0.425 \| \| 1.1532 \| 3.0 \| 60 \| 1.3387 \| 0.45 \| \| 0.9865 \| 4.0 \| 80 \| 1.3469 \| 0.4562 \| \| 0.8767 \| 5.0 \| 100 \| 1.2275 \| 0.55 \| \| 0.7586 \| 6.0 \| 120 \| 1.2560 \| 0.5062 \| \| 0.5985 \| 7.0 \| 140 \| 1.2596 \| 0.5062 \| \| 0.5052 \| 8.0 \| 160 \| 1.3010 \| 0.5687 \| \| 0.4243 \| 9.0 \| 180 \| 1.2613 \| 0.5563 \| \| 0.387 \| 10.0 \| 200 \| 1.2750 \| 0.5312 \| \| 0.3529 \| 11.0 \| 220 \| 1.3103 \| 0.55 \| \| 0.218 \| 12.0 \| 240 \| 1.1832 \| 0.55 \| \| 0.2428 \| 13.0 \| 260 \| 1.2527 \| 0.5563 \| \| 0.2399 \| 14.0 \| 280 \| 1.4836 \| 0.5375 \| \| 0.218 \| 15.0 \| 300 \| 1.4056 \| 0.4875 \| \| 0.1784 \| 16.0 \| 320 \| 1.3879 \| 0.5563 \| \| 0.2021 \| 17.0 \| 340 \| 1.4346 \| 0.5375 \| \| 0.1342 \| 18.0 \| 360 \| 1.4666 \| 0.4813 \| \| 0.1499 \| 19.0 \| 380 \| 1.4104 \| 0.5687 \| \| 0.1032 \| 20.0 \| 400 \| 1.5402 \| 0.525 \| \| 0.1214 \| 21.0 \| 420 \| 1.4114 \| 0.55 \| \| 0.153 \| 22.0 \| 440 \| 1.5887 \| 0.525 \| \| 0.1276 \| 23.0 \| 460 \| 1.4588 \| 0.5188 \| \| 0.1114 \| 24.0 \| 480 \| 1.4866 \| 0.5312 \| \| 0.1305 \| 25.0 \| 500 \| 1.4203 \| 0.5687 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "image_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5625, "name": "Accuracy"}]}]}]}	image-classification	ivnvan/image_classification	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T03:41:39+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	image\_classification ===================== This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2259 * Accuracy: 0.5625 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: polynomial * num\_epochs: 25 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* num\\_epochs: 25", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* num\\_epochs: 25", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 83, 100, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* num\\_epochs: 25### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12450146675109863, 0.14959394931793213, -0.0023493298795074224, 0.12168359011411667, 0.1445702463388443, 0.009289516136050224, 0.15629133582115173, 0.12988944351673126, -0.06360328942537308, 0.06989023834466934, 0.14620916545391083, 0.11872652918100357, 0.03254284709692001, 0.17491905391216278, -0.05388465151190758, -0.21510541439056396, 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null	null	diffusers	# LoRA DreamBooth - danaleee/Long_rank10_iter500_valprompt_token These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of omd rc_car using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of omd rc_car", "inference": true}	text-to-image	danaleee/Long_rank10_iter500_valprompt_token	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	2024-02-08T03:48:36+00:00	[]	[]	TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us	# LoRA DreamBooth - danaleee/Long_rank10_iter500_valprompt_token These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of omd rc_car using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.	[ "# LoRA DreamBooth - danaleee/Long_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of omd rc_car using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/Long_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of omd rc_car using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ 70, 113 ]	[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/Long_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of omd rc_car using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ -0.08889421820640564, 0.018802786245942116, -0.002707574050873518, 0.1002320647239685, 0.09751874953508377, 0.01701522059738636, 0.19576209783554077, 0.0836462751030922, 0.03160989284515381, 0.06795106828212738, 0.11370207369327545, 0.07886653393507004, -0.006832351442426443, 0.0679045170545578, -0.006245092488825321, -0.2073817104101181, -0.016247589141130447, -0.016957925632596016, -0.09651001542806625, 0.04543456807732582, 0.05745751038193703, -0.04478585720062256, 0.12995149195194244, -0.009119635447859764, -0.16690592467784882, 0.04837149754166603, -0.02402552403509617, -0.053552739322185516, 0.08801145106554031, 0.0714230164885521, 0.03817569464445114, 0.06366895139217377, 0.05038304254412651, -0.13005490601062775, 0.0369919091463089, 0.027342159301042557, -0.06405281275510788, 0.07333248853683472, -0.023331310600042343, 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null	null	diffusers	# LoRA text2image fine-tuning - diksha13/arrivae-mod-13 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diksha13/13 dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png)	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true}	text-to-image	diksha13/arrivae-mod-13	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	2024-02-08T03:49:51+00:00	[]	[]	TAGS #diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us	# LoRA text2image fine-tuning - diksha13/arrivae-mod-13 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diksha13/13 dataset. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3	[ "# LoRA text2image fine-tuning - diksha13/arrivae-mod-13\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diksha13/13 dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n", "# LoRA text2image fine-tuning - diksha13/arrivae-mod-13\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diksha13/13 dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ 77, 91 ]	[ "passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #region-us \n# LoRA text2image fine-tuning - diksha13/arrivae-mod-13\nThese are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the diksha13/13 dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3" ]	[ -0.1233273446559906, -0.03157535195350647, -0.0033486075699329376, 0.07113195210695267, 0.07521061599254608, -0.007247321307659149, 0.2207566797733307, 0.086891770362854, -0.002763802884146571, 0.06583862006664276, 0.09745555371046066, 0.08152485638856888, -0.019174139946699142, 0.15595340728759766, -0.047084249556064606, -0.1966148465871811, -0.02097918465733528, -0.04529965668916702, -0.11670124530792236, 0.07020208984613419, 0.09224271029233932, -0.04984361678361893, 0.10461654514074326, 0.019583141431212425, -0.09344534575939178, 0.03687271103262901, 0.023757854476571083, -0.08225185424089432, 0.09231268614530563, 0.05949113517999649, 0.061336956918239594, 0.10068375617265701, 0.07919926196336746, -0.13742779195308685, 0.041292622685432434, 0.032438065856695175, -0.004469678271561861, 0.08442377299070358, -0.022786647081375122, -0.032367851585149765, 0.10816913098096848, 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null	null	transformers	## Model Details Original LlamaGuard 7b model can be found [here](https://huggingface.co/meta-llama/LlamaGuard-7b) Llama-Guard is a 7B parameter [Llama 2](https://arxiv.org/abs/2307.09288)-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example: ![](Llama-Guard_example.png) These are exl2 4.65bpw quantized weights. Original 7B model performs on binary classification of 2k toxic chat test examples Precision: 0.9, Recall: 0.277, F1 Score: 0.424 4.0bpw performs Precision: 0.92, Recall: 0.246, F1 Score: 0.389 4.65bpw performs Precision: 0.903, Recall: 0.256, F1 Score: 0.400	{}	text-generation	ek826/LlamaGuard-7b-4.65bpw-exl2	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2307.09288", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T03:58:13+00:00	[ "2307.09288" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## Model Details Original LlamaGuard 7b model can be found here Llama-Guard is a 7B parameter Llama 2-based input-output safeguard model. It can be used for classifying content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories. Here is an example: ![](Llama-Guard_example.png) These are exl2 4.65bpw quantized weights. Original 7B model performs on binary classification of 2k toxic chat test examples Precision: 0.9, Recall: 0.277, F1 Score: 0.424 4.0bpw performs Precision: 0.92, Recall: 0.246, F1 Score: 0.389 4.65bpw performs Precision: 0.903, Recall: 0.256, F1 Score: 0.400	[ "## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.65bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389\n\n4.65bpw performs\nPrecision: 0.903, Recall: 0.256, F1 Score: 0.400" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.65bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389\n\n4.65bpw performs\nPrecision: 0.903, Recall: 0.256, F1 Score: 0.400" ]	[ 59, 244 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-2307.09288 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Model Details\n\nOriginal LlamaGuard 7b model can be found here\n\nLlama-Guard is a 7B parameter Llama 2-based input-output\nsafeguard model. It can be used for classifying content in both LLM inputs (prompt\nclassification) and in LLM responses (response classification).\nIt acts as an LLM: it generates text in its output that indicates whether a given prompt or\nresponse is safe/unsafe, and if unsafe based on a policy, it also lists the violating subcategories.\nHere is an example:\n\n![](Llama-Guard_example.png)\n\nThese are exl2 4.65bpw quantized weights.\nOriginal 7B model performs on binary classification of 2k toxic chat test examples\nPrecision: 0.9, Recall: 0.277, F1 Score: 0.424\n\n4.0bpw performs\nPrecision: 0.92, Recall: 0.246, F1 Score: 0.389\n\n4.65bpw performs\nPrecision: 0.903, Recall: 0.256, F1 Score: 0.400" ]	[ -0.12017029523849487, 0.08184853941202164, -0.00481884740293026, 0.06851574033498764, 0.07204192131757736, -0.015426182188093662, 0.07739448547363281, 0.13602234423160553, 0.10568447411060333, 0.06502975523471832, 0.02692578360438347, 0.08878594636917114, -0.0069702984765172005, 0.17566905915737152, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec_RTSplit0208_14 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0036 - Wer: 0.1953 - Cer: 0.1521 ## 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: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 11.0213 \| 1.0 \| 120 \| 10.0728 \| 1.0 \| 0.8723 \| \| 2.8625 \| 2.0 \| 240 \| 2.4219 \| 1.0 \| 0.7891 \| \| 1.348 \| 3.0 \| 360 \| 1.0345 \| 0.8617 \| 0.5818 \| \| 0.8296 \| 4.0 \| 480 \| 0.7039 \| 0.8163 \| 0.5495 \| \| 0.7386 \| 5.0 \| 600 \| 0.6197 \| 0.7965 \| 0.4895 \| \| 0.6495 \| 6.0 \| 720 \| 0.5783 \| 0.7687 \| 0.4788 \| \| 0.6081 \| 7.0 \| 840 \| 0.5366 \| 0.7646 \| 0.4589 \| \| 0.5526 \| 8.0 \| 960 \| 0.4689 \| 0.7381 \| 0.3334 \| \| 0.5191 \| 9.0 \| 1080 \| 0.3749 \| 0.6308 \| 0.2769 \| \| 0.3753 \| 10.0 \| 1200 \| 0.3082 \| 0.5393 \| 0.2584 \| \| 0.3481 \| 11.0 \| 1320 \| 0.2449 \| 0.4473 \| 0.2169 \| \| 0.3229 \| 12.0 \| 1440 \| 0.2031 \| 0.3984 \| 0.2086 \| \| 0.275 \| 13.0 \| 1560 \| 0.1527 \| 0.3545 \| 0.2117 \| \| 0.2241 \| 14.0 \| 1680 \| 0.1225 \| 0.3048 \| 0.1884 \| \| 0.2428 \| 15.0 \| 1800 \| 0.0948 \| 0.2737 \| 0.1810 \| \| 0.2123 \| 16.0 \| 1920 \| 0.0782 \| 0.2608 \| 0.1659 \| \| 0.1468 \| 17.0 \| 2040 \| 0.0575 \| 0.2429 \| 0.1724 \| \| 0.1533 \| 18.0 \| 2160 \| 0.0456 \| 0.2374 \| 0.1736 \| \| 0.1364 \| 19.0 \| 2280 \| 0.0366 \| 0.2246 \| 0.1705 \| \| 0.1328 \| 20.0 \| 2400 \| 0.0300 \| 0.2188 \| 0.1715 \| \| 0.172 \| 21.0 \| 2520 \| 0.0283 \| 0.2052 \| 0.1544 \| \| 0.1139 \| 22.0 \| 2640 \| 0.0219 \| 0.1943 \| 0.1529 \| \| 0.1069 \| 23.0 \| 2760 \| 0.0185 \| 0.1949 \| 0.1560 \| \| 0.0925 \| 24.0 \| 2880 \| 0.0152 \| 0.1962 \| 0.1605 \| \| 0.0722 \| 25.0 \| 3000 \| 0.0153 \| 0.1938 \| 0.1575 \| \| 0.1003 \| 26.0 \| 3120 \| 0.0133 \| 0.1966 \| 0.1658 \| \| 0.07 \| 27.0 \| 3240 \| 0.0121 \| 0.2014 \| 0.1559 \| \| 0.0552 \| 28.0 \| 3360 \| 0.0103 \| 0.1982 \| 0.1525 \| \| 0.0595 \| 29.0 \| 3480 \| 0.0104 \| 0.1984 \| 0.1545 \| \| 0.0647 \| 30.0 \| 3600 \| 0.0083 \| 0.1934 \| 0.1451 \| \| 0.0719 \| 31.0 \| 3720 \| 0.0081 \| 0.1880 \| 0.1424 \| \| 0.0915 \| 32.0 \| 3840 \| 0.0082 \| 0.1962 \| 0.1518 \| \| 0.0679 \| 33.0 \| 3960 \| 0.0081 \| 0.1954 \| 0.1490 \| \| 0.0515 \| 34.0 \| 4080 \| 0.0071 \| 0.1900 \| 0.1465 \| \| 0.0505 \| 35.0 \| 4200 \| 0.0063 \| 0.1897 \| 0.1384 \| \| 0.0734 \| 36.0 \| 4320 \| 0.0059 \| 0.1861 \| 0.1402 \| \| 0.0546 \| 37.0 \| 4440 \| 0.0060 \| 0.1869 \| 0.1407 \| \| 0.0555 \| 38.0 \| 4560 \| 0.0062 \| 0.1859 \| 0.1393 \| \| 0.0536 \| 39.0 \| 4680 \| 0.0058 \| 0.1943 \| 0.1486 \| \| 0.0574 \| 40.0 \| 4800 \| 0.0056 \| 0.1878 \| 0.1444 \| \| 0.0532 \| 41.0 \| 4920 \| 0.0052 \| 0.1887 \| 0.1473 \| \| 0.0491 \| 42.0 \| 5040 \| 0.0048 \| 0.1941 \| 0.1475 \| \| 0.056 \| 43.0 \| 5160 \| 0.0044 \| 0.1869 \| 0.1397 \| \| 0.0521 \| 44.0 \| 5280 \| 0.0045 \| 0.1869 \| 0.1415 \| \| 0.0423 \| 45.0 \| 5400 \| 0.0045 \| 0.1926 \| 0.1459 \| \| 0.0518 \| 46.0 \| 5520 \| 0.0040 \| 0.1915 \| 0.1467 \| \| 0.0489 \| 47.0 \| 5640 \| 0.0044 \| 0.1936 \| 0.1485 \| \| 0.056 \| 48.0 \| 5760 \| 0.0038 \| 0.1940 \| 0.1502 \| \| 0.0345 \| 49.0 \| 5880 \| 0.0043 \| 0.1919 \| 0.1501 \| \| 0.0335 \| 50.0 \| 6000 \| 0.0042 \| 0.1919 \| 0.1507 \| \| 0.0406 \| 51.0 \| 6120 \| 0.0038 \| 0.1930 \| 0.1501 \| \| 0.0424 \| 52.0 \| 6240 \| 0.0038 \| 0.1923 \| 0.1472 \| \| 0.035 \| 53.0 \| 6360 \| 0.0038 \| 0.1936 \| 0.1476 \| \| 0.0328 \| 54.0 \| 6480 \| 0.0038 \| 0.1912 \| 0.1435 \| \| 0.043 \| 55.0 \| 6600 \| 0.0044 \| 0.1958 \| 0.1475 \| \| 0.0493 \| 56.0 \| 6720 \| 0.0039 \| 0.1949 \| 0.1502 \| \| 0.0311 \| 57.0 \| 6840 \| 0.0040 \| 0.1953 \| 0.1509 \| \| 0.0377 \| 58.0 \| 6960 \| 0.0037 \| 0.1953 \| 0.1518 \| \| 0.0492 \| 59.0 \| 7080 \| 0.0036 \| 0.1951 \| 0.1517 \| \| 0.0398 \| 60.0 \| 7200 \| 0.0036 \| 0.1953 \| 0.1521 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "model-index": [{"name": "wav2vec_RTSplit0208_14", "results": []}]}	automatic-speech-recognition	tndklab/wav2vec_RTSplit0208_14	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-08T03:58:49+00:00	[]	[]	TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us	wav2vec\_RTSplit0208\_14 ======================== This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-japanese on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0036 * Wer: 0.1953 * Cer: 0.1521 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: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 60 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 60", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 60", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ 80, 116, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-japanese #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 60### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ -0.14110855758190155, 0.15272271633148193, -0.0005118511035107076, 0.10018908232450485, 0.11739840358495712, 0.008022250607609749, 0.1758386194705963, 0.15117147564888, -0.040749691426754, 0.11052531749010086, 0.11308898031711578, 0.06102980300784111, 0.05514821410179138, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-3.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T03:59:02+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-4.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:04:28+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	null	# Lora of charlotte/シャルロット/夏洛蒂 (Genshin Impact) ## What Is This? This is the LoRA model of waifu charlotte/シャルロット/夏洛蒂 (Genshin Impact). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/charlotte_genshin](https://huggingface.co/datasets/CyberHarem/charlotte_genshin), which contains 629 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 4, resolution is 720x720, clustering into 20 buckets. * Trained for 6320 steps, 40 checkpoints were saved and evaluated. * Trigger word is `charlotte_genshin`. * Pruned core tags for this waifu are `pink_hair, hat, red_headwear, bangs, breasts, beret, short_hair, green_eyes, blue_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3792, you need to download [`3792/charlotte_genshin.pt`](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/3792/charlotte_genshin.pt) as the embedding and [`3792/charlotte_genshin.safetensors`](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/3792/charlotte_genshin.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 3792. 1600 images (1.68 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: \| Step \| Epoch \| CCIP \| AI Corrupt \| Bikini Plus \| Score \| Download \| pattern_0_0 \| pattern_0_1 \| pattern_0_2 \| pattern_1 \| pattern_2 \| portrait_0 \| portrait_1 \| portrait_2 \| full_body_0 \| full_body_1 \| profile_0 \| profile_1 \| free_0 \| free_1 \| shorts \| maid_0 \| maid_1 \| miko \| yukata \| suit \| china \| bikini_0 \| bikini_1 \| bikini_2 \| sit \| squat \| kneel \| jump \| crossed_arms \| angry \| smile \| cry \| grin \| n_lie_0 \| n_lie_1 \| n_stand_0 \| n_stand_1 \| n_stand_2 \| n_sex_0 \| n_sex_1 \| \|-------:\|--------:\|:----------\|:-------------\|:--------------\|:----------\|:--------------------------------------------------------------------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:--------------------------------\|:------------------------------------\|:--------------------------------\|:----------------------------------\|:----------------------------------------\|:----------------------------------------\|:----------------------------------------\|:------------------------------\|:----------------------------------\|:----------------------------------\|:--------------------------------\|:------------------------------------------------\|:----------------------------------\|:----------------------------------\|:------------------------------\|:--------------------------------\|:--------------------------------------\|:--------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:--------------------------------------\|:--------------------------------------\| \| 3792 \| 25 \| 0.995 \| 0.855 \| 0.822 \| 0.807 \| [Download](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/3792/charlotte_genshin.zip) \| ![pattern_0_0](3792/previews/pattern_0_0.png) \| ![pattern_0_1](3792/previews/pattern_0_1.png) \| ![pattern_0_2](3792/previews/pattern_0_2.png) \| ![pattern_1](3792/previews/pattern_1.png) \| ![pattern_2](3792/previews/pattern_2.png) \| ![portrait_0](3792/previews/portrait_0.png) \| ![portrait_1](3792/previews/portrait_1.png) \| ![portrait_2](3792/previews/portrait_2.png) \| ![full_body_0](3792/previews/full_body_0.png) \| ![full_body_1](3792/previews/full_body_1.png) \| ![profile_0](3792/previews/profile_0.png) \| ![profile_1](3792/previews/profile_1.png) \| ![free_0](3792/previews/free_0.png) \| ![free_1](3792/previews/free_1.png) \| ![shorts](3792/previews/shorts.png) \| ![maid_0](3792/previews/maid_0.png) \| ![maid_1](3792/previews/maid_1.png) \| ![miko](3792/previews/miko.png) \| ![yukata](3792/previews/yukata.png) \| ![suit](3792/previews/suit.png) \| ![china](3792/previews/china.png) \| ![bikini_0](3792/previews/bikini_0.png) \| ![bikini_1](3792/previews/bikini_1.png) \| ![bikini_2](3792/previews/bikini_2.png) \| ![sit](3792/previews/sit.png) \| ![squat](3792/previews/squat.png) \| ![kneel](3792/previews/kneel.png) \| ![jump](3792/previews/jump.png) \| ![crossed_arms](3792/previews/crossed_arms.png) \| ![angry](3792/previews/angry.png) \| ![smile](3792/previews/smile.png) \| ![cry](3792/previews/cry.png) \| ![grin](3792/previews/grin.png) \| ![n_lie_0](3792/previews/n_lie_0.png) \| ![n_lie_1](3792/previews/n_lie_1.png) \| ![n_stand_0](3792/previews/n_stand_0.png) \| ![n_stand_1](3792/previews/n_stand_1.png) \| ![n_stand_2](3792/previews/n_stand_2.png) \| ![n_sex_0](3792/previews/n_sex_0.png) \| ![n_sex_1](3792/previews/n_sex_1.png) \| \| 5846 \| 38 \| 0.970 \| 0.861 \| 0.834 \| 0.726 \| [Download](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/5846/charlotte_genshin.zip) \| ![pattern_0_0](5846/previews/pattern_0_0.png) \| ![pattern_0_1](5846/previews/pattern_0_1.png) \| ![pattern_0_2](5846/previews/pattern_0_2.png) \| ![pattern_1](5846/previews/pattern_1.png) \| ![pattern_2](5846/previews/pattern_2.png) \| ![portrait_0](5846/previews/portrait_0.png) \| ![portrait_1](5846/previews/portrait_1.png) \| ![portrait_2](5846/previews/portrait_2.png) \| ![full_body_0](5846/previews/full_body_0.png) \| ![full_body_1](5846/previews/full_body_1.png) \| ![profile_0](5846/previews/profile_0.png) \| ![profile_1](5846/previews/profile_1.png) \| ![free_0](5846/previews/free_0.png) \| ![free_1](5846/previews/free_1.png) \| ![shorts](5846/previews/shorts.png) \| ![maid_0](5846/previews/maid_0.png) \| ![maid_1](5846/previews/maid_1.png) \| ![miko](5846/previews/miko.png) \| ![yukata](5846/previews/yukata.png) \| ![suit](5846/previews/suit.png) \| ![china](5846/previews/china.png) \| ![bikini_0](5846/previews/bikini_0.png) \| ![bikini_1](5846/previews/bikini_1.png) \| ![bikini_2](5846/previews/bikini_2.png) \| ![sit](5846/previews/sit.png) \| ![squat](5846/previews/squat.png) \| ![kneel](5846/previews/kneel.png) \| ![jump](5846/previews/jump.png) \| ![crossed_arms](5846/previews/crossed_arms.png) \| ![angry](5846/previews/angry.png) \| ![smile](5846/previews/smile.png) \| ![cry](5846/previews/cry.png) \| ![grin](5846/previews/grin.png) \| ![n_lie_0](5846/previews/n_lie_0.png) \| ![n_lie_1](5846/previews/n_lie_1.png) \| ![n_stand_0](5846/previews/n_stand_0.png) \| ![n_stand_1](5846/previews/n_stand_1.png) \| ![n_stand_2](5846/previews/n_stand_2.png) \| ![n_sex_0](5846/previews/n_sex_0.png) \| ![n_sex_1](5846/previews/n_sex_1.png) \| \| 5688 \| 37 \| 0.969 \| 0.870 \| 0.834 \| 0.725 \| [Download](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/5688/charlotte_genshin.zip) \| ![pattern_0_0](5688/previews/pattern_0_0.png) \| ![pattern_0_1](5688/previews/pattern_0_1.png) \| ![pattern_0_2](5688/previews/pattern_0_2.png) \| ![pattern_1](5688/previews/pattern_1.png) \| ![pattern_2](5688/previews/pattern_2.png) \| ![portrait_0](5688/previews/portrait_0.png) \| ![portrait_1](5688/previews/portrait_1.png) \| ![portrait_2](5688/previews/portrait_2.png) \| ![full_body_0](5688/previews/full_body_0.png) \| ![full_body_1](5688/previews/full_body_1.png) \| ![profile_0](5688/previews/profile_0.png) \| ![profile_1](5688/previews/profile_1.png) \| ![free_0](5688/previews/free_0.png) \| ![free_1](5688/previews/free_1.png) \| ![shorts](5688/previews/shorts.png) \| ![maid_0](5688/previews/maid_0.png) \| ![maid_1](5688/previews/maid_1.png) \| ![miko](5688/previews/miko.png) \| ![yukata](5688/previews/yukata.png) \| ![suit](5688/previews/suit.png) \| ![china](5688/previews/china.png) \| ![bikini_0](5688/previews/bikini_0.png) \| ![bikini_1](5688/previews/bikini_1.png) \| ![bikini_2](5688/previews/bikini_2.png) \| ![sit](5688/previews/sit.png) \| ![squat](5688/previews/squat.png) \| ![kneel](5688/previews/kneel.png) \| ![jump](5688/previews/jump.png) \| ![crossed_arms](5688/previews/crossed_arms.png) \| ![angry](5688/previews/angry.png) \| ![smile](5688/previews/smile.png) \| ![cry](5688/previews/cry.png) \| ![grin](5688/previews/grin.png) \| ![n_lie_0](5688/previews/n_lie_0.png) \| ![n_lie_1](5688/previews/n_lie_1.png) \| ![n_stand_0](5688/previews/n_stand_0.png) \| ![n_stand_1](5688/previews/n_stand_1.png) \| ![n_stand_2](5688/previews/n_stand_2.png) \| ![n_sex_0](5688/previews/n_sex_0.png) \| ![n_sex_1](5688/previews/n_sex_1.png) \| \| 6004 \| 39 \| 0.969 \| 0.904 \| 0.833 \| 0.721 \| [Download](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/6004/charlotte_genshin.zip) \| ![pattern_0_0](6004/previews/pattern_0_0.png) \| ![pattern_0_1](6004/previews/pattern_0_1.png) \| ![pattern_0_2](6004/previews/pattern_0_2.png) \| ![pattern_1](6004/previews/pattern_1.png) \| ![pattern_2](6004/previews/pattern_2.png) \| ![portrait_0](6004/previews/portrait_0.png) \| ![portrait_1](6004/previews/portrait_1.png) \| ![portrait_2](6004/previews/portrait_2.png) \| ![full_body_0](6004/previews/full_body_0.png) \| ![full_body_1](6004/previews/full_body_1.png) \| ![profile_0](6004/previews/profile_0.png) \| ![profile_1](6004/previews/profile_1.png) \| ![free_0](6004/previews/free_0.png) \| ![free_1](6004/previews/free_1.png) \| ![shorts](6004/previews/shorts.png) \| ![maid_0](6004/previews/maid_0.png) \| ![maid_1](6004/previews/maid_1.png) \| ![miko](6004/previews/miko.png) \| ![yukata](6004/previews/yukata.png) \| ![suit](6004/previews/suit.png) \| ![china](6004/previews/china.png) \| ![bikini_0](6004/previews/bikini_0.png) \| ![bikini_1](6004/previews/bikini_1.png) \| ![bikini_2](6004/previews/bikini_2.png) \| ![sit](6004/previews/sit.png) \| ![squat](6004/previews/squat.png) \| ![kneel](6004/previews/kneel.png) \| ![jump](6004/previews/jump.png) \| ![crossed_arms](6004/previews/crossed_arms.png) \| ![angry](6004/previews/angry.png) \| ![smile](6004/previews/smile.png) \| ![cry](6004/previews/cry.png) \| ![grin](6004/previews/grin.png) \| ![n_lie_0](6004/previews/n_lie_0.png) \| ![n_lie_1](6004/previews/n_lie_1.png) \| ![n_stand_0](6004/previews/n_stand_0.png) \| ![n_stand_1](6004/previews/n_stand_1.png) \| ![n_stand_2](6004/previews/n_stand_2.png) \| ![n_sex_0](6004/previews/n_sex_0.png) \| ![n_sex_1](6004/previews/n_sex_1.png) \| \| 2528 \| 17 \| 0.970 \| 0.870 \| 0.828 \| 0.720 \| [Download](https://huggingface.co/CyberHarem/charlotte_genshin/resolve/main/2528/charlotte_genshin.zip) \| ![pattern_0_0](2528/previews/pattern_0_0.png) \| ![pattern_0_1](2528/previews/pattern_0_1.png) \| ![pattern_0_2](2528/previews/pattern_0_2.png) \| ![pattern_1](2528/previews/pattern_1.png) \| ![pattern_2](2528/previews/pattern_2.png) \| ![portrait_0](2528/previews/portrait_0.png) \| ![portrait_1](2528/previews/portrait_1.png) \| ![portrait_2](2528/previews/portrait_2.png) \| ![full_body_0](2528/previews/full_body_0.png) \| ![full_body_1](2528/previews/full_body_1.png) \| ![profile_0](2528/previews/profile_0.png) \| ![profile_1](2528/previews/profile_1.png) \| ![free_0](2528/previews/free_0.png) \| ![free_1](2528/previews/free_1.png) \| ![shorts](2528/previews/shorts.png) \| ![maid_0](2528/previews/maid_0.png) \| ![maid_1](2528/previews/maid_1.png) \| ![miko](2528/previews/miko.png) \| ![yukata](2528/previews/yukata.png) \| ![suit](2528/previews/suit.png) \| ![china](2528/previews/china.png) \| ![bikini_0](2528/previews/bikini_0.png) \| ![bikini_1](2528/previews/bikini_1.png) \| ![bikini_2](2528/previews/bikini_2.png) \| ![sit](2528/previews/sit.png) \| ![squat](2528/previews/squat.png) \| ![kneel](2528/previews/kneel.png) \| ![jump](2528/previews/jump.png) \| ![crossed_arms](2528/previews/crossed_arms.png) \| ![angry](2528/previews/angry.png) \| ![smile](2528/previews/smile.png) \| ![cry](2528/previews/cry.png) \| ![grin](2528/previews/grin.png) \| ![n_lie_0](2528/previews/n_lie_0.png) \| ![n_lie_1](2528/previews/n_lie_1.png) \| ![n_stand_0](2528/previews/n_stand_0.png) \| ![n_stand_1](2528/previews/n_stand_1.png) \| ![n_stand_2](2528/previews/n_stand_2.png) \| ![n_sex_0](2528/previews/n_sex_0.png) \| ![n_sex_1](2528/previews/n_sex_1.png) \| ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 4898 to 6320](all/0.md) * [Steps From 3318 to 4740](all/1.md) * [Steps From 1738 to 3160](all/2.md) * [Steps From 158 to 1580](all/3.md)	{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/charlotte_genshin"], "pipeline_tag": "text-to-image"}	text-to-image	CyberHarem/charlotte_genshin	[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/charlotte_genshin", "license:mit", "region:us" ]	2024-02-08T04:07:31+00:00	[]	[]	TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/charlotte_genshin #license-mit #region-us	Lora of charlotte/シャルロット/夏洛蒂 (Genshin Impact) ============================================= What Is This? ------------- This is the LoRA model of waifu charlotte/シャルロット/夏洛蒂 (Genshin Impact). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/charlotte\_genshin, which contains 629 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 4, resolution is 720x720, clustering into 20 buckets. * Trained for 6320 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'charlotte\_genshin'. * Pruned core tags for this waifu are 'pink\_hair, hat, red\_headwear, bangs, breasts, beret, short\_hair, green\_eyes, blue\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 3792, you need to download '3792/charlotte\_genshin.pt' as the embedding and '3792/charlotte\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 3792. 1600 images (1.68 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 4898 to 6320 * Steps From 3318 to 4740 * Steps From 1738 to 3160 * Steps From 158 to 1580	[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 3792, you need to download '3792/charlotte\\_genshin.pt' as the embedding and '3792/charlotte\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 3792.\n\n\n1600 images (1.68 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 4898 to 6320\n* Steps From 3318 to 4740\n* Steps From 1738 to 3160\n* Steps From 158 to 1580" ]	[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/charlotte_genshin #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 3792, you need to download '3792/charlotte\\_genshin.pt' as the embedding and '3792/charlotte\\_genshin.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 3792.\n\n\n1600 images (1.68 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 4898 to 6320\n* Steps From 3318 to 4740\n* Steps From 1738 to 3160\n* Steps From 158 to 1580" ]	[ 43, 38, 470 ]	[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/charlotte_genshin #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. 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null	null	peft	<!-- 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. --> # bertin-gpt-j-6B_8bit_27 This model is a fine-tuned version of [bertin-project/bertin-gpt-j-6B](https://huggingface.co/bertin-project/bertin-gpt-j-6B) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.41e-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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "bertin-project/bertin-gpt-j-6B", "model-index": [{"name": "bertin-gpt-j-6B_8bit_27", "results": []}]}	null	versae/bertin-gpt-j-6B_8bit_27	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bertin-project/bertin-gpt-j-6B", "license:apache-2.0", "region:us" ]	2024-02-08T04:09:07+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us	# bertin-gpt-j-6B_8bit_27 This model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.41e-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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.1	[ "# bertin-gpt-j-6B_8bit_27\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n", "# bertin-gpt-j-6B_8bit_27\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1" ]	[ 52, 47, 6, 12, 8, 3, 104, 4, 39 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-bertin-project/bertin-gpt-j-6B #license-apache-2.0 #region-us \n# bertin-gpt-j-6B_8bit_27\n\nThis model is a fine-tuned version of bertin-project/bertin-gpt-j-6B on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1.41e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.7.1\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.1" ]	[ -0.07580133527517319, 0.10625508427619934, -0.0020771536510437727, 0.07181205600500107, 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null	null	diffusers	# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt_token These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of dits teddybear using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of dits teddybear", "inference": true}	text-to-image	danaleee/CL_rank10_iter500_valprompt_token	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	2024-02-08T04:09:14+00:00	[]	[]	TAGS #diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us	# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt_token These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of dits teddybear using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.	[ "# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of dits teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ "TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of dits teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ 70, 112 ]	[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - danaleee/CL_rank10_iter500_valprompt_token\n\nThese are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were trained on a photo of dits teddybear using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ -0.07449650764465332, 0.004569408018141985, -0.003082951297983527, 0.09509260207414627, 0.09764725714921951, 0.01791216805577278, 0.2020571529865265, 0.08962977677583694, 0.031294647604227066, 0.06958908587694168, 0.10713893920183182, 0.07810276746749878, -0.00851928535848856, 0.07555179297924042, -0.01183327566832304, -0.21527165174484253, -0.019878637045621872, -0.006058662198483944, -0.08154074102640152, 0.04424126073718071, 0.05499556288123131, -0.0570247620344162, 0.12393773347139359, -0.022403322160243988, -0.16698679327964783, 0.04829522594809532, -0.017980830743908882, -0.04805506765842438, 0.08760230988264084, 0.07327590882778168, 0.05364679545164108, 0.06006740406155586, 0.04233305901288986, -0.1283404380083084, 0.038355518132448196, 0.031023520976305008, -0.0600426159799099, 0.08046220988035202, -0.023937927559018135, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-4.65bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:11:25+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	null	# Intro 新年快樂~ 年後補充	{"language": ["zh"], "license": "cc-by-nc-sa-4.0", "tags": ["vits"]}	null	luanyao/SO-VITS-SVC-Model	[ "vits", "zh", "license:cc-by-nc-sa-4.0", "region:us" ]	2024-02-08T04:12:06+00:00	[]	[ "zh" ]	TAGS #vits #zh #license-cc-by-nc-sa-4.0 #region-us	# Intro 新年快樂~ 年後補充	[ "# Intro\n\n新年快樂~ 年後補充" ]	[ "TAGS\n#vits #zh #license-cc-by-nc-sa-4.0 #region-us \n", "# Intro\n\n新年快樂~ 年後補充" ]	[ 24, 10 ]	[ "passage: TAGS\n#vits #zh #license-cc-by-nc-sa-4.0 #region-us \n# Intro\n\n新年快樂~ 年後補充" ]	[ -0.015499141998589039, -0.08593659847974777, -0.009371901862323284, -0.02604190818965435, 0.035903096199035645, 0.05362573266029358, 0.057336945086717606, 0.028698230162262917, 0.16605833172798157, -0.0019203335978090763, 0.12344800680875778, 0.0410696379840374, 0.04835757613182068, -0.016704922541975975, -0.009226648136973381, -0.1421327143907547, 0.06735078990459442, 0.06366591155529022, 0.013223632238805294, 0.03303248807787895, 0.054846424609422684, -0.029948407784104347, 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null	null	transformers	<!-- 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. --> # safety-utcustom-train-SF-RGB-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2236 - Mean Iou: 0.5051 - Mean Accuracy: 0.8441 - Overall Accuracy: 0.9820 - Accuracy Unlabeled: nan - Accuracy Safe: 0.6977 - Accuracy Unsafe: 0.9906 - Iou Unlabeled: 0.0 - Iou Safe: 0.5337 - Iou Unsafe: 0.9816 ## 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-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 130 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 1.2239 \| 0.91 \| 10 \| 0.3992 \| nan \| 0.2951 \| 0.0314 \| 0.0 \| 0.2939 \| 1.1103 \| 0.3472 \| 0.1084 \| 0.2982 \| \| 1.1948 \| 1.82 \| 20 \| 0.5219 \| nan \| 0.3705 \| 0.0440 \| 0.0 \| 0.3689 \| 1.0963 \| 0.4462 \| 0.1376 \| 0.3750 \| \| 1.1661 \| 2.73 \| 30 \| 0.5863 \| nan \| 0.4988 \| 0.0647 \| 0.0 \| 0.4961 \| 1.0516 \| 0.5426 \| 0.1870 \| 0.5014 \| \| 1.1112 \| 3.64 \| 40 \| 0.5459 \| nan \| 0.5794 \| 0.0900 \| 0.0 \| 0.5754 \| 1.0048 \| 0.5626 \| 0.2218 \| 0.5784 \| \| 1.0907 \| 4.55 \| 50 \| 0.5993 \| nan \| 0.6367 \| 0.1094 \| 0.0 \| 0.6321 \| 0.9690 \| 0.6180 \| 0.2472 \| 0.6356 \| \| 1.047 \| 5.45 \| 60 \| 0.6692 \| nan \| 0.6699 \| 0.1159 \| 0.0 \| 0.6656 \| 0.9437 \| 0.6695 \| 0.2605 \| 0.6699 \| \| 1.0112 \| 6.36 \| 70 \| 0.6673 \| nan \| 0.7189 \| 0.1349 \| 0.0 \| 0.7137 \| 0.9084 \| 0.6931 \| 0.2829 \| 0.7173 \| \| 0.9925 \| 7.27 \| 80 \| 0.6842 \| nan \| 0.7665 \| 0.1452 \| 0.0 \| 0.7605 \| 0.8647 \| 0.7254 \| 0.3019 \| 0.7641 \| \| 0.9395 \| 8.18 \| 90 \| 0.6818 \| nan \| 0.7921 \| 0.1620 \| 0.0 \| 0.7856 \| 0.8319 \| 0.7369 \| 0.3159 \| 0.7888 \| \| 0.8902 \| 9.09 \| 100 \| 0.6806 \| nan \| 0.8142 \| 0.1770 \| 0.0 \| 0.8072 \| 0.8014 \| 0.7474 \| 0.3281 \| 0.8102 \| \| 0.9057 \| 10.0 \| 110 \| 0.6984 \| nan \| 0.8179 \| 0.1733 \| 0.0 \| 0.8109 \| 0.7867 \| 0.7581 \| 0.3281 \| 0.8143 \| \| 0.8321 \| 10.91 \| 120 \| 0.6744 \| nan \| 0.8494 \| 0.1862 \| 0.0 \| 0.8413 \| 0.7440 \| 0.7619 \| 0.3425 \| 0.8442 \| \| 0.8152 \| 11.82 \| 130 \| 0.6688 \| nan \| 0.8590 \| 0.2006 \| 0.0 \| 0.8507 \| 0.7270 \| 0.7639 \| 0.3504 \| 0.8534 \| \| 0.7929 \| 12.73 \| 140 \| 0.6660 \| nan \| 0.8657 \| 0.2085 \| 0.0 \| 0.8572 \| 0.7045 \| 0.7658 \| 0.3553 \| 0.8598 \| \| 0.7568 \| 13.64 \| 150 \| 0.6571 \| nan \| 0.8838 \| 0.2185 \| 0.0 \| 0.8748 \| 0.6744 \| 0.7704 \| 0.3644 \| 0.8771 \| \| 0.7085 \| 14.55 \| 160 \| 0.6519 \| nan \| 0.8934 \| 0.2260 \| 0.0 \| 0.8842 \| 0.6556 \| 0.7727 \| 0.3701 \| 0.8863 \| \| 0.7147 \| 15.45 \| 170 \| 0.6561 \| nan \| 0.8964 \| 0.2283 \| 0.0 \| 0.8872 \| 0.6509 \| 0.7762 \| 0.3718 \| 0.8893 \| \| 0.6991 \| 16.36 \| 180 \| 0.6620 \| nan \| 0.8964 \| 0.2267 \| 0.0 \| 0.8874 \| 0.6502 \| 0.7792 \| 0.3714 \| 0.8895 \| \| 0.6357 \| 17.27 \| 190 \| 0.6612 \| nan \| 0.9051 \| 0.2411 \| 0.0 \| 0.8960 \| 0.6230 \| 0.7831 \| 0.3790 \| 0.8979 \| \| 0.6815 \| 18.18 \| 200 \| 0.6484 \| nan \| 0.9178 \| 0.2594 \| 0.0 \| 0.9082 \| 0.5993 \| 0.7831 \| 0.3892 \| 0.9098 \| \| 0.6398 \| 19.09 \| 210 \| 0.6414 \| nan \| 0.9258 \| 0.2682 \| 0.0 \| 0.9159 \| 0.5785 \| 0.7836 \| 0.3947 \| 0.9174 \| \| 0.5845 \| 20.0 \| 220 \| 0.6426 \| nan \| 0.9286 \| 0.2698 \| 0.0 \| 0.9187 \| 0.5641 \| 0.7856 \| 0.3962 \| 0.9202 \| \| 0.6062 \| 20.91 \| 230 \| 0.6520 \| nan \| 0.9252 \| 0.2641 \| 0.0 \| 0.9156 \| 0.5693 \| 0.7886 \| 0.3932 \| 0.9171 \| \| 0.6071 \| 21.82 \| 240 \| 0.6592 \| nan \| 0.9283 \| 0.2675 \| 0.0 \| 0.9188 \| 0.5627 \| 0.7937 \| 0.3955 \| 0.9203 \| \| 0.6209 \| 22.73 \| 250 \| 0.6619 \| nan \| 0.9300 \| 0.2724 \| 0.0 \| 0.9205 \| 0.5632 \| 0.7959 \| 0.3977 \| 0.9220 \| \| 0.5609 \| 23.64 \| 260 \| 0.6505 \| nan \| 0.9379 \| 0.2868 \| 0.0 \| 0.9281 \| 0.5416 \| 0.7942 \| 0.4050 \| 0.9294 \| \| 0.5752 \| 24.55 \| 270 \| 0.6412 \| nan \| 0.9451 \| 0.2983 \| 0.0 \| 0.9350 \| 0.5141 \| 0.7932 \| 0.4111 \| 0.9362 \| \| 0.6004 \| 25.45 \| 280 \| 0.6492 \| nan \| 0.9412 \| 0.2907 \| 0.0 \| 0.9313 \| 0.5255 \| 0.7952 \| 0.4073 \| 0.9326 \| \| 0.5524 \| 26.36 \| 290 \| 0.6588 \| nan \| 0.9387 \| 0.2868 \| 0.0 \| 0.9291 \| 0.5314 \| 0.7987 \| 0.4053 \| 0.9304 \| \| 0.5758 \| 27.27 \| 300 \| 0.6544 \| nan \| 0.9423 \| 0.2913 \| 0.0 \| 0.9326 \| 0.5268 \| 0.7984 \| 0.4080 \| 0.9338 \| \| 0.5598 \| 28.18 \| 310 \| 0.6605 \| nan \| 0.9408 \| 0.2897 \| 0.0 \| 0.9312 \| 0.5240 \| 0.8006 \| 0.4070 \| 0.9325 \| \| 0.5505 \| 29.09 \| 320 \| 0.6582 \| nan \| 0.9421 \| 0.2959 \| 0.0 \| 0.9324 \| 0.5165 \| 0.8002 \| 0.4094 \| 0.9337 \| \| 0.5754 \| 30.0 \| 330 \| 0.6578 \| nan \| 0.9433 \| 0.2959 \| 0.0 \| 0.9336 \| 0.5145 \| 0.8005 \| 0.4098 \| 0.9348 \| \| 0.5284 \| 30.91 \| 340 \| 0.6719 \| nan \| 0.9411 \| 0.2941 \| 0.0 \| 0.9318 \| 0.5175 \| 0.8065 \| 0.4086 \| 0.9331 \| \| 0.5463 \| 31.82 \| 350 \| 0.6684 \| nan \| 0.9448 \| 0.3020 \| 0.0 \| 0.9354 \| 0.5016 \| 0.8066 \| 0.4125 \| 0.9367 \| \| 0.4923 \| 32.73 \| 360 \| 0.6688 \| nan \| 0.9463 \| 0.3066 \| 0.0 \| 0.9369 \| 0.4947 \| 0.8075 \| 0.4145 \| 0.9381 \| \| 0.4922 \| 33.64 \| 370 \| 0.6685 \| nan \| 0.9504 \| 0.3165 \| 0.0 \| 0.9409 \| 0.4738 \| 0.8094 \| 0.4191 \| 0.9420 \| \| 0.4976 \| 34.55 \| 380 \| 0.6748 \| nan \| 0.9535 \| 0.3233 \| 0.0 \| 0.9443 \| 0.4663 \| 0.8142 \| 0.4225 \| 0.9453 \| \| 0.4922 \| 35.45 \| 390 \| 0.6509 \| nan \| 0.9653 \| 0.3484 \| 0.0 \| 0.9552 \| 0.4295 \| 0.8081 \| 0.4345 \| 0.9560 \| \| 0.4608 \| 36.36 \| 400 \| 0.6580 \| nan \| 0.9637 \| 0.3507 \| 0.0 \| 0.9538 \| 0.4434 \| 0.8109 \| 0.4348 \| 0.9547 \| \| 0.4836 \| 37.27 \| 410 \| 0.6522 \| nan \| 0.9662 \| 0.3588 \| 0.0 \| 0.9561 \| 0.4328 \| 0.8092 \| 0.4383 \| 0.9569 \| \| 0.459 \| 38.18 \| 420 \| 0.6477 \| nan \| 0.9691 \| 0.3632 \| 0.0 \| 0.9588 \| 0.4211 \| 0.8084 \| 0.4407 \| 0.9596 \| \| 0.4528 \| 39.09 \| 430 \| 0.6593 \| nan \| 0.9668 \| 0.3574 \| 0.0 \| 0.9569 \| 0.4239 \| 0.8131 \| 0.4381 \| 0.9577 \| \| 0.4202 \| 40.0 \| 440 \| 0.6572 \| nan \| 0.9689 \| 0.3650 \| 0.0 \| 0.9590 \| 0.4141 \| 0.8130 \| 0.4413 \| 0.9597 \| \| 0.4805 \| 40.91 \| 450 \| 0.6470 \| nan \| 0.9724 \| 0.3754 \| 0.0 \| 0.9621 \| 0.4012 \| 0.8097 \| 0.4458 \| 0.9628 \| \| 0.4611 \| 41.82 \| 460 \| 0.6525 \| nan \| 0.9718 \| 0.3716 \| 0.0 \| 0.9617 \| 0.4025 \| 0.8122 \| 0.4444 \| 0.9624 \| \| 0.4339 \| 42.73 \| 470 \| 0.6487 \| nan \| 0.9726 \| 0.3744 \| 0.0 \| 0.9624 \| 0.3951 \| 0.8107 \| 0.4456 \| 0.9631 \| \| 0.4361 \| 43.64 \| 480 \| 0.6448 \| nan \| 0.9740 \| 0.3769 \| 0.0 \| 0.9636 \| 0.3946 \| 0.8094 \| 0.4468 \| 0.9643 \| \| 0.4416 \| 44.55 \| 490 \| 0.6447 \| nan \| 0.9746 \| 0.3783 \| 0.0 \| 0.9642 \| 0.3871 \| 0.8097 \| 0.4475 \| 0.9649 \| \| 0.4524 \| 45.45 \| 500 \| 0.6589 \| nan \| 0.9712 \| 0.3701 \| 0.0 \| 0.9612 \| 0.4025 \| 0.8151 \| 0.4438 \| 0.9620 \| \| 0.4319 \| 46.36 \| 510 \| 0.6730 \| nan \| 0.9673 \| 0.3594 \| 0.0 \| 0.9578 \| 0.4169 \| 0.8202 \| 0.4391 \| 0.9586 \| \| 0.4224 \| 47.27 \| 520 \| 0.6603 \| nan \| 0.9712 \| 0.3716 \| 0.0 \| 0.9613 \| 0.3986 \| 0.8158 \| 0.4443 \| 0.9620 \| \| 0.4333 \| 48.18 \| 530 \| 0.6650 \| nan \| 0.9703 \| 0.3724 \| 0.0 \| 0.9605 \| 0.4038 \| 0.8176 \| 0.4443 \| 0.9612 \| \| 0.3916 \| 49.09 \| 540 \| 0.6624 \| nan \| 0.9724 \| 0.3781 \| 0.0 \| 0.9626 \| 0.3968 \| 0.8174 \| 0.4469 \| 0.9633 \| \| 0.4803 \| 50.0 \| 550 \| 0.6680 \| nan \| 0.9726 \| 0.3809 \| 0.0 \| 0.9629 \| 0.3942 \| 0.8203 \| 0.4479 \| 0.9636 \| \| 0.3543 \| 50.91 \| 560 \| 0.6473 \| nan \| 0.9777 \| 0.3952 \| 0.0 \| 0.9673 \| 0.3697 \| 0.8125 \| 0.4542 \| 0.9680 \| \| 0.3684 \| 51.82 \| 570 \| 0.6515 \| nan \| 0.9772 \| 0.3951 \| 0.0 \| 0.9670 \| 0.3708 \| 0.8143 \| 0.4540 \| 0.9676 \| \| 0.4004 \| 52.73 \| 580 \| 0.6437 \| nan \| 0.9793 \| 0.4014 \| 0.0 \| 0.9688 \| 0.3585 \| 0.8115 \| 0.4567 \| 0.9694 \| \| 0.3656 \| 53.64 \| 590 \| 0.6559 \| nan \| 0.9780 \| 0.4010 \| 0.0 \| 0.9679 \| 0.3654 \| 0.8169 \| 0.4563 \| 0.9685 \| \| 0.3918 \| 54.55 \| 600 \| 0.6432 \| nan \| 0.9809 \| 0.4115 \| 0.0 \| 0.9704 \| 0.3527 \| 0.8121 \| 0.4606 \| 0.9709 \| \| 0.3741 \| 55.45 \| 610 \| 0.6393 \| nan \| 0.9827 \| 0.4185 \| 0.0 \| 0.9720 \| 0.3361 \| 0.8110 \| 0.4635 \| 0.9726 \| \| 0.3656 \| 56.36 \| 620 \| 0.6540 \| nan \| 0.9807 \| 0.4147 \| 0.0 \| 0.9705 \| 0.3473 \| 0.8174 \| 0.4617 \| 0.9710 \| \| 0.3341 \| 57.27 \| 630 \| 0.6258 \| nan \| 0.9845 \| 0.4247 \| 0.0 \| 0.9734 \| 0.3335 \| 0.8052 \| 0.4660 \| 0.9739 \| \| 0.3669 \| 58.18 \| 640 \| 0.6495 \| nan \| 0.9815 \| 0.4190 \| 0.0 \| 0.9712 \| 0.3395 \| 0.8155 \| 0.4634 \| 0.9717 \| \| 0.3347 \| 59.09 \| 650 \| 0.6612 \| nan \| 0.9800 \| 0.4174 \| 0.0 \| 0.9700 \| 0.3416 \| 0.8206 \| 0.4625 \| 0.9706 \| \| 0.4287 \| 60.0 \| 660 \| 0.6673 \| nan \| 0.9797 \| 0.4185 \| 0.0 \| 0.9699 \| 0.3419 \| 0.8235 \| 0.4628 \| 0.9705 \| \| 0.3838 \| 60.91 \| 670 \| 0.6611 \| nan \| 0.9812 \| 0.4227 \| 0.0 \| 0.9712 \| 0.3381 \| 0.8211 \| 0.4646 \| 0.9718 \| \| 0.352 \| 61.82 \| 680 \| 0.6407 \| nan \| 0.9845 \| 0.4318 \| 0.0 \| 0.9738 \| 0.3216 \| 0.8126 \| 0.4685 \| 0.9743 \| \| 0.3343 \| 62.73 \| 690 \| 0.6499 \| nan \| 0.9837 \| 0.4311 \| 0.0 \| 0.9733 \| 0.3275 \| 0.8168 \| 0.4681 \| 0.9738 \| \| 0.3443 \| 63.64 \| 700 \| 0.6528 \| nan \| 0.9836 \| 0.4324 \| 0.0 \| 0.9733 \| 0.3273 \| 0.8182 \| 0.4686 \| 0.9738 \| \| 0.3183 \| 64.55 \| 710 \| 0.6456 \| nan \| 0.9848 \| 0.4367 \| 0.0 \| 0.9743 \| 0.3155 \| 0.8152 \| 0.4703 \| 0.9748 \| \| 0.3346 \| 65.45 \| 720 \| 0.6517 \| nan \| 0.9841 \| 0.4356 \| 0.0 \| 0.9738 \| 0.3212 \| 0.8179 \| 0.4698 \| 0.9743 \| \| 0.3225 \| 66.36 \| 730 \| 0.6367 \| nan \| 0.9863 \| 0.4432 \| 0.0 \| 0.9755 \| 0.3052 \| 0.8115 \| 0.4729 \| 0.9759 \| \| 0.3792 \| 67.27 \| 740 \| 0.6381 \| nan \| 0.9861 \| 0.4429 \| 0.0 \| 0.9753 \| 0.3037 \| 0.8121 \| 0.4728 \| 0.9758 \| \| 0.3177 \| 68.18 \| 750 \| 0.6345 \| nan \| 0.9865 \| 0.4446 \| 0.0 \| 0.9756 \| 0.2989 \| 0.8105 \| 0.4734 \| 0.9761 \| \| 0.3295 \| 69.09 \| 760 \| 0.6404 \| nan \| 0.9859 \| 0.4426 \| 0.0 \| 0.9752 \| 0.3064 \| 0.8131 \| 0.4726 \| 0.9757 \| \| 0.3847 \| 70.0 \| 770 \| 0.6429 \| nan \| 0.9857 \| 0.4439 \| 0.0 \| 0.9751 \| 0.3054 \| 0.8143 \| 0.4730 \| 0.9756 \| \| 0.3406 \| 70.91 \| 780 \| 0.6443 \| nan \| 0.9862 \| 0.4476 \| 0.0 \| 0.9756 \| 0.3075 \| 0.8152 \| 0.4744 \| 0.9761 \| \| 0.3847 \| 71.82 \| 790 \| 0.6343 \| nan \| 0.9877 \| 0.4546 \| 0.0 \| 0.9769 \| 0.2911 \| 0.8110 \| 0.4772 \| 0.9773 \| \| 0.3292 \| 72.73 \| 800 \| 0.6328 \| nan \| 0.9881 \| 0.4567 \| 0.0 \| 0.9771 \| 0.2905 \| 0.8105 \| 0.4779 \| 0.9776 \| \| 0.3156 \| 73.64 \| 810 \| 0.6318 \| nan \| 0.9882 \| 0.4579 \| 0.0 \| 0.9773 \| 0.2865 \| 0.8100 \| 0.4784 \| 0.9777 \| \| 0.3106 \| 74.55 \| 820 \| 0.6333 \| nan \| 0.9884 \| 0.4600 \| 0.0 \| 0.9775 \| 0.2812 \| 0.8109 \| 0.4792 \| 0.9779 \| \| 0.3004 \| 75.45 \| 830 \| 0.6232 \| nan \| 0.9893 \| 0.4632 \| 0.0 \| 0.9781 \| 0.2798 \| 0.8063 \| 0.4804 \| 0.9785 \| \| 0.3336 \| 76.36 \| 840 \| 0.6485 \| nan \| 0.9872 \| 0.4593 \| 0.0 \| 0.9768 \| 0.2954 \| 0.8178 \| 0.4787 \| 0.9772 \| \| 0.299 \| 77.27 \| 850 \| 0.6490 \| nan \| 0.9874 \| 0.4613 \| 0.0 \| 0.9769 \| 0.2909 \| 0.8182 \| 0.4794 \| 0.9774 \| \| 0.292 \| 78.18 \| 860 \| 0.6497 \| nan \| 0.9875 \| 0.4629 \| 0.0 \| 0.9771 \| 0.2853 \| 0.8186 \| 0.4800 \| 0.9775 \| \| 0.2922 \| 79.09 \| 870 \| 0.6586 \| nan \| 0.9866 \| 0.4601 \| 0.0 \| 0.9765 \| 0.2917 \| 0.8226 \| 0.4789 \| 0.9770 \| \| 0.3583 \| 80.0 \| 880 \| 0.6515 \| nan \| 0.9876 \| 0.4644 \| 0.0 \| 0.9772 \| 0.2876 \| 0.8195 \| 0.4805 \| 0.9776 \| \| 0.293 \| 80.91 \| 890 \| 0.6465 \| nan \| 0.9882 \| 0.4674 \| 0.0 \| 0.9777 \| 0.2767 \| 0.8173 \| 0.4817 \| 0.9781 \| \| 0.3287 \| 81.82 \| 900 \| 0.6518 \| nan \| 0.9876 \| 0.4652 \| 0.0 \| 0.9773 \| 0.2858 \| 0.8197 \| 0.4808 \| 0.9777 \| \| 0.3067 \| 82.73 \| 910 \| 0.6528 \| nan \| 0.9875 \| 0.4654 \| 0.0 \| 0.9772 \| 0.2861 \| 0.8202 \| 0.4809 \| 0.9776 \| \| 0.3374 \| 83.64 \| 920 \| 0.6577 \| nan \| 0.9870 \| 0.4631 \| 0.0 \| 0.9768 \| 0.2869 \| 0.8224 \| 0.4800 \| 0.9773 \| \| 0.3171 \| 84.55 \| 930 \| 0.6442 \| nan \| 0.9887 \| 0.4714 \| 0.0 \| 0.9781 \| 0.2706 \| 0.8164 \| 0.4832 \| 0.9785 \| \| 0.3156 \| 85.45 \| 940 \| 0.6321 \| nan \| 0.9899 \| 0.4757 \| 0.0 \| 0.9789 \| 0.2708 \| 0.8110 \| 0.4849 \| 0.9793 \| \| 0.2749 \| 86.36 \| 950 \| 0.6518 \| nan \| 0.9887 \| 0.4765 \| 0.0 \| 0.9783 \| 0.2760 \| 0.8202 \| 0.4850 \| 0.9787 \| \| 0.2725 \| 87.27 \| 960 \| 0.6681 \| nan \| 0.9876 \| 0.4765 \| 0.0 \| 0.9777 \| 0.2780 \| 0.8279 \| 0.4847 \| 0.9782 \| \| 0.2948 \| 88.18 \| 970 \| 0.6565 \| nan \| 0.9891 \| 0.4845 \| 0.0 \| 0.9788 \| 0.2636 \| 0.8228 \| 0.4878 \| 0.9793 \| \| 0.2972 \| 89.09 \| 980 \| 0.6722 \| nan \| 0.9879 \| 0.4829 \| 0.0 \| 0.9782 \| 0.2770 \| 0.8301 \| 0.4870 \| 0.9786 \| \| 0.3101 \| 90.0 \| 990 \| 0.6711 \| nan \| 0.9882 \| 0.4859 \| 0.0 \| 0.9784 \| 0.2765 \| 0.8297 \| 0.4881 \| 0.9788 \| \| 0.2874 \| 90.91 \| 1000 \| 0.6689 \| nan \| 0.9888 \| 0.4899 \| 0.0 \| 0.9789 \| 0.2690 \| 0.8288 \| 0.4896 \| 0.9793 \| \| 0.275 \| 91.82 \| 1010 \| 0.6542 \| nan \| 0.9901 \| 0.4947 \| 0.0 \| 0.9798 \| 0.2593 \| 0.8221 \| 0.4915 \| 0.9802 \| \| 0.2711 \| 92.73 \| 1020 \| 0.6673 \| nan \| 0.9893 \| 0.4957 \| 0.0 \| 0.9794 \| 0.2608 \| 0.8283 \| 0.4917 \| 0.9798 \| \| 0.2691 \| 93.64 \| 1030 \| 0.6819 \| nan \| 0.9884 \| 0.4954 \| 0.0 \| 0.9789 \| 0.2609 \| 0.8352 \| 0.4915 \| 0.9794 \| \| 0.274 \| 94.55 \| 1040 \| 0.6722 \| nan \| 0.9895 \| 0.5007 \| 0.0 \| 0.9797 \| 0.2542 \| 0.8309 \| 0.4935 \| 0.9801 \| \| 0.27 \| 95.45 \| 1050 \| 0.6436 \| nan \| 0.9919 \| 0.5087 \| 0.0 \| 0.9812 \| 0.2357 \| 0.8177 \| 0.4966 \| 0.9816 \| \| 0.255 \| 96.36 \| 1060 \| 0.6671 \| nan \| 0.9903 \| 0.5074 \| 0.0 \| 0.9804 \| 0.2460 \| 0.8287 \| 0.4959 \| 0.9808 \| \| 0.2756 \| 97.27 \| 1070 \| 0.6634 \| nan \| 0.9909 \| 0.5113 \| 0.0 \| 0.9809 \| 0.2411 \| 0.8271 \| 0.4974 \| 0.9812 \| \| 0.2473 \| 98.18 \| 1080 \| 0.6724 \| nan \| 0.9904 \| 0.5116 \| 0.0 \| 0.9806 \| 0.2447 \| 0.8314 \| 0.4974 \| 0.9810 \| \| 0.266 \| 99.09 \| 1090 \| 0.6778 \| nan \| 0.9901 \| 0.5118 \| 0.0 \| 0.9804 \| 0.2455 \| 0.8339 \| 0.4974 \| 0.9808 \| \| 0.2682 \| 100.0 \| 1100 \| 0.6651 \| nan \| 0.9911 \| 0.5156 \| 0.0 \| 0.9811 \| 0.2359 \| 0.8281 \| 0.4989 \| 0.9815 \| \| 0.2607 \| 100.91 \| 1110 \| 0.6739 \| nan \| 0.9905 \| 0.5144 \| 0.0 \| 0.9808 \| 0.2376 \| 0.8322 \| 0.4984 \| 0.9812 \| \| 0.2506 \| 101.82 \| 1120 \| 0.6727 \| nan \| 0.9907 \| 0.5153 \| 0.0 \| 0.9809 \| 0.2380 \| 0.8317 \| 0.4987 \| 0.9813 \| \| 0.2729 \| 102.73 \| 1130 \| 0.6802 \| nan \| 0.9902 \| 0.5146 \| 0.0 \| 0.9806 \| 0.2375 \| 0.8352 \| 0.4984 \| 0.9810 \| \| 0.2348 \| 103.64 \| 1140 \| 0.6731 \| nan \| 0.9908 \| 0.5172 \| 0.0 \| 0.9810 \| 0.2335 \| 0.8319 \| 0.4994 \| 0.9814 \| \| 0.2409 \| 104.55 \| 1150 \| 0.6781 \| nan \| 0.9904 \| 0.5164 \| 0.0 \| 0.9808 \| 0.2385 \| 0.8343 \| 0.4991 \| 0.9812 \| \| 0.2737 \| 105.45 \| 1160 \| 0.6774 \| nan \| 0.9906 \| 0.5177 \| 0.0 \| 0.9809 \| 0.2357 \| 0.8340 \| 0.4995 \| 0.9813 \| \| 0.2857 \| 106.36 \| 1170 \| 0.6659 \| nan \| 0.9915 \| 0.5203 \| 0.0 \| 0.9815 \| 0.2304 \| 0.8287 \| 0.5006 \| 0.9818 \| \| 0.2503 \| 107.27 \| 1180 \| 0.6822 \| nan \| 0.9902 \| 0.5172 \| 0.0 \| 0.9807 \| 0.2397 \| 0.8362 \| 0.4993 \| 0.9811 \| \| 0.2524 \| 108.18 \| 1190 \| 0.6741 \| nan \| 0.9909 \| 0.5199 \| 0.0 \| 0.9812 \| 0.2294 \| 0.8325 \| 0.5004 \| 0.9816 \| \| 0.2722 \| 109.09 \| 1200 \| 0.6928 \| nan \| 0.9894 \| 0.5145 \| 0.0 \| 0.9802 \| 0.2461 \| 0.8411 \| 0.4982 \| 0.9806 \| \| 0.2761 \| 110.0 \| 1210 \| 0.2530 \| 0.4979 \| 0.8435 \| 0.9804 \| nan \| 0.6981 \| 0.9889 \| 0.0 \| 0.5136 \| 0.9799 \| \| 0.2595 \| 110.91 \| 1220 \| 0.2386 \| 0.5002 \| 0.8395 \| 0.9811 \| nan \| 0.6891 \| 0.9900 \| 0.0 \| 0.5197 \| 0.9808 \| \| 0.2877 \| 111.82 \| 1230 \| 0.2260 \| 0.5022 \| 0.8345 \| 0.9819 \| nan \| 0.6779 \| 0.9911 \| 0.0 \| 0.5251 \| 0.9815 \| \| 0.2307 \| 112.73 \| 1240 \| 0.2219 \| 0.5031 \| 0.8317 \| 0.9822 \| nan \| 0.6717 \| 0.9916 \| 0.0 \| 0.5275 \| 0.9818 \| \| 0.2559 \| 113.64 \| 1250 \| 0.2382 \| 0.5011 \| 0.8413 \| 0.9812 \| nan \| 0.6927 \| 0.9900 \| 0.0 \| 0.5224 \| 0.9808 \| \| 0.2668 \| 114.55 \| 1260 \| 0.2275 \| 0.5024 \| 0.8395 \| 0.9816 \| nan \| 0.6883 \| 0.9906 \| 0.0 \| 0.5258 \| 0.9813 \| \| 0.2269 \| 115.45 \| 1270 \| 0.2212 \| 0.5038 \| 0.8356 \| 0.9821 \| nan \| 0.6798 \| 0.9913 \| 0.0 \| 0.5296 \| 0.9818 \| \| 0.2713 \| 116.36 \| 1280 \| 0.2265 \| 0.5041 \| 0.8365 \| 0.9821 \| nan \| 0.6817 \| 0.9913 \| 0.0 \| 0.5305 \| 0.9818 \| \| 0.2351 \| 117.27 \| 1290 \| 0.2113 \| 0.5064 \| 0.8255 \| 0.9832 \| nan \| 0.6580 \| 0.9931 \| 0.0 \| 0.5363 \| 0.9828 \| \| 0.2306 \| 118.18 \| 1300 \| 0.2258 \| 0.5053 \| 0.8354 \| 0.9824 \| nan \| 0.6791 \| 0.9917 \| 0.0 \| 0.5337 \| 0.9821 \| \| 0.2371 \| 119.09 \| 1310 \| 0.2234 \| 0.5057 \| 0.8342 \| 0.9826 \| nan \| 0.6765 \| 0.9919 \| 0.0 \| 0.5348 \| 0.9822 \| \| 0.277 \| 120.0 \| 1320 \| 0.2263 \| 0.5061 \| 0.8340 \| 0.9827 \| nan \| 0.6760 \| 0.9920 \| 0.0 \| 0.5359 \| 0.9823 \| \| 0.2121 \| 120.91 \| 1330 \| 0.2191 \| 0.5065 \| 0.8337 \| 0.9828 \| nan \| 0.6752 \| 0.9921 \| 0.0 \| 0.5370 \| 0.9824 \| \| 0.2426 \| 121.82 \| 1340 \| 0.2237 \| 0.5055 \| 0.8400 \| 0.9822 \| nan \| 0.6889 \| 0.9912 \| 0.0 \| 0.5345 \| 0.9819 \| \| 0.2429 \| 122.73 \| 1350 \| 0.2106 \| 0.5069 \| 0.8340 \| 0.9829 \| nan \| 0.6757 \| 0.9922 \| 0.0 \| 0.5383 \| 0.9825 \| \| 0.244 \| 123.64 \| 1360 \| 0.2133 \| 0.5062 \| 0.8395 \| 0.9824 \| nan \| 0.6876 \| 0.9914 \| 0.0 \| 0.5364 \| 0.9821 \| \| 0.2287 \| 124.55 \| 1370 \| 0.2126 \| 0.5068 \| 0.8371 \| 0.9827 \| nan \| 0.6823 \| 0.9918 \| 0.0 \| 0.5381 \| 0.9823 \| \| 0.2223 \| 125.45 \| 1380 \| 0.1988 \| 0.5083 \| 0.8305 \| 0.9833 \| nan \| 0.6681 \| 0.9929 \| 0.0 \| 0.5420 \| 0.9830 \| \| 0.2275 \| 126.36 \| 1390 \| 0.2164 \| 0.5061 \| 0.8409 \| 0.9823 \| nan \| 0.6906 \| 0.9912 \| 0.0 \| 0.5363 \| 0.9820 \| \| 0.2378 \| 127.27 \| 1400 \| 0.2201 \| 0.5062 \| 0.8405 \| 0.9824 \| nan \| 0.6898 \| 0.9913 \| 0.0 \| 0.5365 \| 0.9820 \| \| 0.2385 \| 128.18 \| 1410 \| 0.2286 \| 0.5046 \| 0.8455 \| 0.9818 \| nan \| 0.7006 \| 0.9903 \| 0.0 \| 0.5324 \| 0.9814 \| \| 0.2787 \| 129.09 \| 1420 \| 0.2157 \| 0.5063 \| 0.8405 \| 0.9824 \| nan \| 0.6896 \| 0.9913 \| 0.0 \| 0.5369 \| 0.9820 \| \| 0.2377 \| 130.0 \| 1430 \| 0.2236 \| 0.5051 \| 0.8441 \| 0.9820 \| nan \| 0.6977 \| 0.9906 \| 0.0 \| 0.5337 \| 0.9816 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF-RGB-b5", "results": []}]}	image-segmentation	sam1120/safety-utcustom-train-SF-RGB-b5	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-08T04:15:05+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	safety-utcustom-train-SF-RGB-b5 =============================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.2236 * Mean Iou: 0.5051 * Mean Accuracy: 0.8441 * Overall Accuracy: 0.9820 * Accuracy Unlabeled: nan * Accuracy Safe: 0.6977 * Accuracy Unsafe: 0.9906 * Iou Unlabeled: 0.0 * Iou Safe: 0.5337 * Iou Unsafe: 0.9816 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-06 * train\_batch\_size: 15 * eval\_batch\_size: 15 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 130 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10489735752344131, 0.03523673117160797, -0.0019801033195108175, 0.11434859782457352, 0.1715484857559204, 0.028419161215424538, 0.11649660021066666, 0.11414402723312378, -0.11054181307554245, 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null	null	transformers	### NLLB-esp-rap This is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M ## Intended uses & limitations The first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture. This model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set. Finally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model. ## How to use	{"language": "rap", "license": "cc-by-nc-4.0", "metrics": ["chrf"], "pipeline_tag": "text2text-generation"}	text2text-generation	HugoZeballos/nllb-esp-rap	[ "transformers", "pytorch", "m2m_100", "text2text-generation", "rap", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2024-02-08T04:15:20+00:00	[]	[ "rap" ]	TAGS #transformers #pytorch #m2m_100 #text2text-generation #rap #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us	### NLLB-esp-rap This is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M ## Intended uses & limitations The first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture. This model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set. Finally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model. ## How to use	[ "### NLLB-esp-rap\nThis is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M", "## Intended uses & limitations\nThe first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture.\n\nThis model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set.\n\nFinally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model.", "## How to use" ]	[ "TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #rap #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### NLLB-esp-rap\nThis is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M", "## Intended uses & limitations\nThe first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture.\n\nThis model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set.\n\nFinally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model.", "## How to use" ]	[ 59, 41, 214, 4 ]	[ "passage: TAGS\n#transformers #pytorch #m2m_100 #text2text-generation #rap #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### NLLB-esp-rap\nThis is the model card of NLLB-esp-rap, which comes from a model nllb-200-distilled-600M## Intended uses & limitations\nThe first intention with this model is for a final project and, on the other hand, to contribute to safeguarding the Rapa Nui language, which is the way in which it transcends its culture.\n\nThis model is only a first version for a Rapa Nui translator and still needs substantial improvements regarding the human evaluation of the translator and a Flores-200 type evaluation set.\n\nFinally, NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations, so the above is also true for this model.## How to use" ]	[ 0.008743061684072018, 0.10674349218606949, -0.0029422705993056297, 0.06646547466516495, 0.03431892395019531, 0.04091882333159447, 0.0899127945303917, 0.12982329726219177, -0.052081625908613205, 0.077854223549366, 0.08038096129894257, -0.05122070014476776, 0.025402303785085678, 0.023707304149866104, 0.08896689116954803, -0.26738208532333374, 0.09403594583272934, 0.01108146458864212, 0.035320159047842026, 0.07450174540281296, 0.035663191229104996, -0.029149450361728668, 0.05114488676190376, -0.04863118380308151, -0.1149042472243309, 0.03488985076546669, 0.020366651937365532, -0.05905592814087868, 0.05782265216112137, 0.07480823993682861, 0.04016660153865814, 0.01888439618051052, 0.0604311041533947, -0.2437000572681427, -0.0043626027181744576, 0.0272047258913517, -0.026286078616976738, 0.018487850204110146, 0.002590089337900281, 0.021967876702547073, 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null	null	transformers	<!-- 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. --> # safety-utcustom-train-SF-RGBD-b0 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1043 - Mean Iou: 0.7188 - Mean Accuracy: 0.7607 - Overall Accuracy: 0.9815 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5261 - Accuracy Unsafe: 0.9953 - Iou Unlabeled: nan - Iou Safe: 0.4564 - Iou Unsafe: 0.9812 ## 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: 0.05 - num_epochs: 130 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 1.0084 \| 1.0 \| 10 \| 0.0368 \| nan \| 0.7845 \| 0.0163 \| 0.0 \| 0.7666 \| 1.0688 \| 0.4107 \| 0.2610 \| 0.7625 \| \| 0.8483 \| 2.0 \| 20 \| 0.0002 \| nan \| 0.9980 \| 0.0002 \| 0.0 \| 0.9687 \| 0.8740 \| 0.4991 \| 0.3230 \| 0.9686 \| \| 0.7058 \| 3.0 \| 30 \| 0.0009 \| nan \| 0.9930 \| 0.0009 \| 0.0 \| 0.9641 \| 0.7416 \| 0.4969 \| 0.3217 \| 0.9637 \| \| 0.578 \| 4.0 \| 40 \| 0.0007 \| nan \| 0.9953 \| 0.0007 \| 0.0 \| 0.9662 \| 0.5969 \| 0.4980 \| 0.3223 \| 0.9659 \| \| 0.5531 \| 5.0 \| 50 \| 0.0061 \| nan \| 0.9974 \| 0.0059 \| 0.0 \| 0.9682 \| 0.5068 \| 0.5018 \| 0.3247 \| 0.9681 \| \| 0.4786 \| 6.0 \| 60 \| 0.0097 \| nan \| 0.9961 \| 0.0092 \| 0.0 \| 0.9671 \| 0.4575 \| 0.5029 \| 0.3254 \| 0.9670 \| \| 0.4681 \| 7.0 \| 70 \| 0.0067 \| nan \| 0.9983 \| 0.0064 \| 0.0 \| 0.9690 \| 0.4382 \| 0.5025 \| 0.3251 \| 0.9690 \| \| 0.4139 \| 8.0 \| 80 \| 0.0017 \| nan \| 0.9980 \| 0.0016 \| 0.0 \| 0.9686 \| 0.3973 \| 0.4998 \| 0.3234 \| 0.9686 \| \| 0.4275 \| 9.0 \| 90 \| 0.0077 \| nan \| 0.9994 \| 0.0076 \| nan \| 0.9701 \| 0.3983 \| 0.5036 \| 0.4888 \| 0.9701 \| \| 0.3975 \| 10.0 \| 100 \| 0.0008 \| nan \| 0.9998 \| 0.0008 \| 0.0 \| 0.9702 \| 0.3398 \| 0.5003 \| 0.3237 \| 0.9702 \| \| 0.4325 \| 11.0 \| 110 \| 0.0941 \| nan \| 0.9993 \| 0.0919 \| 0.0 \| 0.9725 \| 0.3785 \| 0.5467 \| 0.3548 \| 0.9725 \| \| 0.3239 \| 12.0 \| 120 \| 0.0772 \| nan \| 0.9995 \| 0.0759 \| 0.0 \| 0.9722 \| 0.3338 \| 0.5383 \| 0.3493 \| 0.9722 \| \| 0.3733 \| 13.0 \| 130 \| 0.0763 \| nan \| 0.9995 \| 0.0751 \| nan \| 0.9722 \| 0.3013 \| 0.5379 \| 0.5236 \| 0.9722 \| \| 0.3165 \| 14.0 \| 140 \| 0.0800 \| nan \| 0.9994 \| 0.0786 \| nan \| 0.9722 \| 0.2849 \| 0.5397 \| 0.5254 \| 0.9723 \| \| 0.3329 \| 15.0 \| 150 \| 0.1118 \| nan \| 0.9990 \| 0.1083 \| nan \| 0.9727 \| 0.3002 \| 0.5554 \| 0.5405 \| 0.9728 \| \| 0.3214 \| 16.0 \| 160 \| 0.0908 \| nan \| 0.9995 \| 0.0892 \| nan \| 0.9726 \| 0.2725 \| 0.5451 \| 0.5309 \| 0.9726 \| \| 0.2744 \| 17.0 \| 170 \| 0.1573 \| nan \| 0.9986 \| 0.1503 \| nan \| 0.9736 \| 0.2896 \| 0.5780 \| 0.5620 \| 0.9737 \| \| 0.2948 \| 18.0 \| 180 \| 0.1330 \| nan \| 0.9989 \| 0.1282 \| nan \| 0.9732 \| 0.2564 \| 0.5659 \| 0.5507 \| 0.9733 \| \| 0.2653 \| 19.0 \| 190 \| 0.1732 \| nan \| 0.9987 \| 0.1660 \| nan \| 0.9742 \| 0.2518 \| 0.5860 \| 0.5701 \| 0.9743 \| \| 0.3026 \| 20.0 \| 200 \| 0.1408 \| nan \| 0.9990 \| 0.1364 \| nan \| 0.9735 \| 0.2531 \| 0.5699 \| 0.5550 \| 0.9737 \| \| 0.2649 \| 21.0 \| 210 \| 0.1802 \| nan \| 0.9986 \| 0.1722 \| nan \| 0.9743 \| 0.2384 \| 0.5894 \| 0.5732 \| 0.9744 \| \| 0.2431 \| 22.0 \| 220 \| 0.1993 \| nan \| 0.9983 \| 0.1890 \| nan \| 0.9746 \| 0.2390 \| 0.5988 \| 0.5818 \| 0.9747 \| \| 0.2608 \| 23.0 \| 230 \| 0.2317 \| nan \| 0.9981 \| 0.2181 \| nan \| 0.9753 \| 0.2355 \| 0.6149 \| 0.5967 \| 0.9755 \| \| 0.223 \| 24.0 \| 240 \| 0.1697 \| nan \| 0.9989 \| 0.1637 \| nan \| 0.9743 \| 0.2290 \| 0.5843 \| 0.5690 \| 0.9744 \| \| 0.2448 \| 25.0 \| 250 \| 0.2141 \| nan \| 0.9985 \| 0.2037 \| nan \| 0.9751 \| 0.2262 \| 0.6063 \| 0.5894 \| 0.9753 \| \| 0.2547 \| 26.0 \| 260 \| 0.2737 \| nan \| 0.9978 \| 0.2555 \| nan \| 0.9763 \| 0.2281 \| 0.6357 \| 0.6159 \| 0.9764 \| \| 0.2266 \| 27.0 \| 270 \| 0.2391 \| nan \| 0.9981 \| 0.2252 \| nan \| 0.9755 \| 0.2191 \| 0.6186 \| 0.6004 \| 0.9757 \| \| 0.2357 \| 28.0 \| 280 \| 0.2227 \| nan \| 0.9985 \| 0.2122 \| nan \| 0.9754 \| 0.2218 \| 0.6106 \| 0.5938 \| 0.9756 \| \| 0.2563 \| 29.0 \| 290 \| 0.1852 \| nan \| 0.9988 \| 0.1782 \| nan \| 0.9746 \| 0.2096 \| 0.5920 \| 0.5764 \| 0.9748 \| \| 0.226 \| 30.0 \| 300 \| 0.2844 \| nan \| 0.9977 \| 0.2643 \| nan \| 0.9764 \| 0.2121 \| 0.6410 \| 0.6203 \| 0.9766 \| \| 0.2221 \| 31.0 \| 310 \| 0.2718 \| nan \| 0.9978 \| 0.2533 \| nan \| 0.9761 \| 0.2016 \| 0.6348 \| 0.6147 \| 0.9763 \| \| 0.2317 \| 32.0 \| 320 \| 0.2649 \| nan \| 0.9982 \| 0.2499 \| nan \| 0.9763 \| 0.2008 \| 0.6315 \| 0.6131 \| 0.9765 \| \| 0.2643 \| 33.0 \| 330 \| 0.3254 \| nan \| 0.9976 \| 0.3014 \| nan \| 0.9775 \| 0.1989 \| 0.6615 \| 0.6394 \| 0.9777 \| \| 0.2118 \| 34.0 \| 340 \| 0.3347 \| nan \| 0.9977 \| 0.3117 \| nan \| 0.9779 \| 0.1901 \| 0.6662 \| 0.6448 \| 0.9782 \| \| 0.2133 \| 35.0 \| 350 \| 0.3619 \| nan \| 0.9976 \| 0.3350 \| nan \| 0.9785 \| 0.1917 \| 0.6797 \| 0.6568 \| 0.9788 \| \| 0.2064 \| 36.0 \| 360 \| 0.3401 \| nan \| 0.9978 \| 0.3174 \| nan \| 0.9782 \| 0.1860 \| 0.6690 \| 0.6478 \| 0.9784 \| \| 0.2341 \| 37.0 \| 370 \| 0.2704 \| nan \| 0.9983 \| 0.2557 \| nan \| 0.9766 \| 0.1775 \| 0.6343 \| 0.6162 \| 0.9768 \| \| 0.2093 \| 38.0 \| 380 \| 0.3552 \| nan \| 0.9928 \| 0.2874 \| nan \| 0.9737 \| 0.1934 \| 0.6740 \| 0.6306 \| 0.9740 \| \| 0.1958 \| 39.0 \| 390 \| 0.3001 \| nan \| 0.9980 \| 0.2818 \| nan \| 0.9772 \| 0.1755 \| 0.6491 \| 0.6295 \| 0.9774 \| \| 0.1886 \| 40.0 \| 400 \| 0.3881 \| nan \| 0.9969 \| 0.3522 \| nan \| 0.9787 \| 0.1768 \| 0.6925 \| 0.6654 \| 0.9789 \| \| 0.1734 \| 41.0 \| 410 \| 0.3948 \| nan \| 0.9973 \| 0.3626 \| nan \| 0.9793 \| 0.1745 \| 0.6960 \| 0.6709 \| 0.9795 \| \| 0.1795 \| 42.0 \| 420 \| 0.4168 \| nan \| 0.9970 \| 0.3789 \| nan \| 0.9796 \| 0.1710 \| 0.7069 \| 0.6793 \| 0.9798 \| \| 0.222 \| 43.0 \| 430 \| 0.4041 \| nan \| 0.9972 \| 0.3700 \| nan \| 0.9794 \| 0.1706 \| 0.7007 \| 0.6747 \| 0.9797 \| \| 0.1831 \| 44.0 \| 440 \| 0.4044 \| nan \| 0.9972 \| 0.3708 \| nan \| 0.9795 \| 0.1687 \| 0.7008 \| 0.6752 \| 0.9797 \| \| 0.1935 \| 45.0 \| 450 \| 0.4347 \| nan \| 0.9964 \| 0.3889 \| nan \| 0.9796 \| 0.1711 \| 0.7155 \| 0.6842 \| 0.9798 \| \| 0.1728 \| 46.0 \| 460 \| 0.4208 \| nan \| 0.9969 \| 0.3819 \| nan \| 0.9796 \| 0.1714 \| 0.7088 \| 0.6808 \| 0.9799 \| \| 0.1742 \| 47.0 \| 470 \| 0.3898 \| nan \| 0.9974 \| 0.3590 \| nan \| 0.9792 \| 0.1670 \| 0.6936 \| 0.6691 \| 0.9794 \| \| 0.2064 \| 48.0 \| 480 \| 0.4209 \| nan \| 0.9970 \| 0.3827 \| nan \| 0.9797 \| 0.1683 \| 0.7089 \| 0.6812 \| 0.9799 \| \| 0.1946 \| 49.0 \| 490 \| 0.3746 \| nan \| 0.9976 \| 0.3471 \| nan \| 0.9790 \| 0.1659 \| 0.6861 \| 0.6630 \| 0.9792 \| \| 0.1836 \| 50.0 \| 500 \| 0.4487 \| nan \| 0.9965 \| 0.4020 \| nan \| 0.9800 \| 0.1618 \| 0.7226 \| 0.6910 \| 0.9803 \| \| 0.1786 \| 51.0 \| 510 \| 0.4327 \| nan \| 0.9966 \| 0.3896 \| nan \| 0.9797 \| 0.1595 \| 0.7147 \| 0.6846 \| 0.9800 \| \| 0.1867 \| 52.0 \| 520 \| 0.4540 \| nan \| 0.9966 \| 0.4083 \| nan \| 0.9803 \| 0.1555 \| 0.7253 \| 0.6943 \| 0.9806 \| \| 0.1824 \| 53.0 \| 530 \| 0.4386 \| nan \| 0.9966 \| 0.3942 \| nan \| 0.9798 \| 0.1564 \| 0.7176 \| 0.6870 \| 0.9801 \| \| 0.1494 \| 54.0 \| 540 \| 0.4920 \| nan \| 0.9956 \| 0.4299 \| nan \| 0.9804 \| 0.1540 \| 0.7438 \| 0.7052 \| 0.9807 \| \| 0.1583 \| 55.0 \| 550 \| 0.4558 \| nan \| 0.9964 \| 0.4075 \| nan \| 0.9802 \| 0.1502 \| 0.7261 \| 0.6939 \| 0.9804 \| \| 0.1648 \| 56.0 \| 560 \| 0.4791 \| nan \| 0.9958 \| 0.4208 \| nan \| 0.9802 \| 0.1523 \| 0.7374 \| 0.7005 \| 0.9805 \| \| 0.1993 \| 57.0 \| 570 \| 0.4586 \| nan \| 0.9964 \| 0.4103 \| nan \| 0.9803 \| 0.1502 \| 0.7275 \| 0.6953 \| 0.9805 \| \| 0.2243 \| 58.0 \| 580 \| 0.3920 \| nan \| 0.9973 \| 0.3599 \| nan \| 0.9792 \| 0.1474 \| 0.6946 \| 0.6695 \| 0.9794 \| \| 0.1551 \| 59.0 \| 590 \| 0.4687 \| nan \| 0.9961 \| 0.4157 \| nan \| 0.9803 \| 0.1445 \| 0.7324 \| 0.6980 \| 0.9805 \| \| 0.1666 \| 60.0 \| 600 \| 0.4460 \| nan \| 0.9964 \| 0.3986 \| nan \| 0.9799 \| 0.1444 \| 0.7212 \| 0.6892 \| 0.9801 \| \| 0.1632 \| 61.0 \| 610 \| 0.5120 \| nan \| 0.9951 \| 0.4411 \| nan \| 0.9805 \| 0.1504 \| 0.7535 \| 0.7108 \| 0.9808 \| \| 0.1589 \| 62.0 \| 620 \| 0.4059 \| nan \| 0.9971 \| 0.3704 \| nan \| 0.9794 \| 0.1430 \| 0.7015 \| 0.6749 \| 0.9796 \| \| 0.1454 \| 63.0 \| 630 \| 0.4835 \| nan \| 0.9959 \| 0.4260 \| nan \| 0.9805 \| 0.1423 \| 0.7397 \| 0.7032 \| 0.9808 \| \| 0.1635 \| 64.0 \| 640 \| 0.4902 \| nan \| 0.9957 \| 0.4299 \| nan \| 0.9805 \| 0.1424 \| 0.7430 \| 0.7052 \| 0.9808 \| \| 0.1515 \| 65.0 \| 650 \| 0.4775 \| nan \| 0.9962 \| 0.4239 \| nan \| 0.9806 \| 0.1422 \| 0.7368 \| 0.7022 \| 0.9808 \| \| 0.151 \| 66.0 \| 660 \| 0.4718 \| nan \| 0.9962 \| 0.4195 \| nan \| 0.9804 \| 0.1423 \| 0.7340 \| 0.7000 \| 0.9807 \| \| 0.166 \| 67.0 \| 670 \| 0.4721 \| nan \| 0.9963 \| 0.4208 \| nan \| 0.9805 \| 0.1427 \| 0.7342 \| 0.7007 \| 0.9808 \| \| 0.1561 \| 68.0 \| 680 \| 0.4916 \| nan \| 0.9959 \| 0.4332 \| nan \| 0.9807 \| 0.1420 \| 0.7437 \| 0.7070 \| 0.9810 \| \| 0.1501 \| 69.0 \| 690 \| 0.4906 \| nan \| 0.9958 \| 0.4311 \| nan \| 0.9806 \| 0.1437 \| 0.7432 \| 0.7058 \| 0.9809 \| \| 0.1598 \| 70.0 \| 700 \| 0.3445 \| nan \| 0.9977 \| 0.3204 \| nan \| 0.9782 \| 0.1379 \| 0.6711 \| 0.6493 \| 0.9784 \| \| 0.1431 \| 71.0 \| 710 \| 0.4898 \| nan \| 0.9960 \| 0.4325 \| nan \| 0.9807 \| 0.1400 \| 0.7429 \| 0.7066 \| 0.9810 \| \| 0.164 \| 72.0 \| 720 \| 0.4698 \| nan \| 0.9964 \| 0.4196 \| nan \| 0.9805 \| 0.1347 \| 0.7331 \| 0.7001 \| 0.9808 \| \| 0.1555 \| 73.0 \| 730 \| 0.5271 \| nan \| 0.9937 \| 0.4364 \| nan \| 0.9796 \| 0.1368 \| 0.7604 \| 0.7080 \| 0.9799 \| \| 0.1924 \| 74.0 \| 740 \| 0.4638 \| nan \| 0.9965 \| 0.4159 \| nan \| 0.9805 \| 0.1312 \| 0.7301 \| 0.6982 \| 0.9808 \| \| 0.1612 \| 75.0 \| 750 \| 0.5052 \| nan \| 0.9956 \| 0.4409 \| nan \| 0.9808 \| 0.1340 \| 0.7504 \| 0.7108 \| 0.9811 \| \| 0.1234 \| 76.0 \| 760 \| 0.5301 \| nan \| 0.9946 \| 0.4501 \| nan \| 0.9806 \| 0.1354 \| 0.7624 \| 0.7153 \| 0.9809 \| \| 0.1679 \| 77.0 \| 770 \| 0.4644 \| nan \| 0.9964 \| 0.4156 \| nan \| 0.9804 \| 0.1323 \| 0.7304 \| 0.6980 \| 0.9807 \| \| 0.1375 \| 78.0 \| 780 \| 0.4804 \| nan \| 0.9961 \| 0.4263 \| nan \| 0.9806 \| 0.1355 \| 0.7383 \| 0.7035 \| 0.9809 \| \| 0.1839 \| 79.0 \| 790 \| 0.5070 \| nan \| 0.9955 \| 0.4422 \| nan \| 0.9808 \| 0.1319 \| 0.7512 \| 0.7115 \| 0.9811 \| \| 0.155 \| 80.0 \| 800 \| 0.4846 \| nan \| 0.9961 \| 0.4295 \| nan \| 0.9807 \| 0.1298 \| 0.7403 \| 0.7051 \| 0.9810 \| \| 0.1219 \| 81.0 \| 810 \| 0.4671 \| nan \| 0.9963 \| 0.4167 \| nan \| 0.9804 \| 0.1302 \| 0.7317 \| 0.6986 \| 0.9807 \| \| 0.1218 \| 82.0 \| 820 \| 0.4864 \| nan \| 0.9960 \| 0.4300 \| nan \| 0.9807 \| 0.1313 \| 0.7412 \| 0.7054 \| 0.9810 \| \| 0.138 \| 83.0 \| 830 \| 0.5097 \| nan \| 0.9955 \| 0.4445 \| nan \| 0.9809 \| 0.1318 \| 0.7526 \| 0.7127 \| 0.9812 \| \| 0.1399 \| 84.0 \| 840 \| 0.5067 \| nan \| 0.9957 \| 0.4441 \| nan \| 0.9810 \| 0.1290 \| 0.7512 \| 0.7126 \| 0.9813 \| \| 0.1455 \| 85.0 \| 850 \| 0.5024 \| nan \| 0.9957 \| 0.4404 \| nan \| 0.9809 \| 0.1277 \| 0.7491 \| 0.7106 \| 0.9811 \| \| 0.1466 \| 86.0 \| 860 \| 0.4920 \| nan \| 0.9959 \| 0.4341 \| nan \| 0.9808 \| 0.1243 \| 0.7440 \| 0.7074 \| 0.9811 \| \| 0.1769 \| 87.0 \| 870 \| 0.5737 \| nan \| 0.9924 \| 0.4592 \| nan \| 0.9797 \| 0.1317 \| 0.7831 \| 0.7194 \| 0.9800 \| \| 0.1453 \| 88.0 \| 880 \| 0.3341 \| nan \| 0.9978 \| 0.3115 \| nan \| 0.9780 \| 0.1254 \| 0.6659 \| 0.6447 \| 0.9782 \| \| 0.133 \| 89.0 \| 890 \| 0.5257 \| nan \| 0.9950 \| 0.4518 \| nan \| 0.9809 \| 0.1283 \| 0.7603 \| 0.7163 \| 0.9812 \| \| 0.1288 \| 90.0 \| 900 \| 0.5049 \| nan \| 0.9957 \| 0.4420 \| nan \| 0.9809 \| 0.1221 \| 0.7503 \| 0.7115 \| 0.9812 \| \| 0.1318 \| 91.0 \| 910 \| 0.4838 \| nan \| 0.9961 \| 0.4290 \| nan \| 0.9807 \| 0.1219 \| 0.7400 \| 0.7049 \| 0.9810 \| \| 0.1211 \| 92.0 \| 920 \| 0.5355 \| nan \| 0.9950 \| 0.4596 \| nan \| 0.9811 \| 0.1242 \| 0.7652 \| 0.7203 \| 0.9814 \| \| 0.1137 \| 93.0 \| 930 \| 0.5135 \| nan \| 0.9958 \| 0.4517 \| nan \| 0.9813 \| 0.1181 \| 0.7547 \| 0.7165 \| 0.9816 \| \| 0.1312 \| 94.0 \| 940 \| 0.4775 \| nan \| 0.9963 \| 0.4262 \| nan \| 0.9807 \| 0.1199 \| 0.7369 \| 0.7035 \| 0.9810 \| \| 0.1591 \| 95.0 \| 950 \| 0.5115 \| nan \| 0.9956 \| 0.4473 \| nan \| 0.9810 \| 0.1182 \| 0.7536 \| 0.7142 \| 0.9813 \| \| 0.1207 \| 96.0 \| 960 \| 0.5206 \| nan \| 0.9956 \| 0.4544 \| nan \| 0.9812 \| 0.1156 \| 0.7581 \| 0.7178 \| 0.9815 \| \| 0.1203 \| 97.0 \| 970 \| 0.5054 \| nan \| 0.9958 \| 0.4439 \| nan \| 0.9810 \| 0.1165 \| 0.7506 \| 0.7124 \| 0.9813 \| \| 0.1196 \| 98.0 \| 980 \| 0.5296 \| nan \| 0.9953 \| 0.4585 \| nan \| 0.9812 \| 0.1131 \| 0.7624 \| 0.7199 \| 0.9815 \| \| 0.1304 \| 99.0 \| 990 \| 0.5269 \| nan \| 0.9953 \| 0.4568 \| nan \| 0.9812 \| 0.1155 \| 0.7611 \| 0.7190 \| 0.9815 \| \| 0.1058 \| 100.0 \| 1000 \| 0.5163 \| nan \| 0.9955 \| 0.4496 \| nan \| 0.9810 \| 0.1144 \| 0.7559 \| 0.7153 \| 0.9813 \| \| 0.1135 \| 101.0 \| 1010 \| 0.4934 \| nan \| 0.9961 \| 0.4368 \| nan \| 0.9809 \| 0.1113 \| 0.7447 \| 0.7089 \| 0.9812 \| \| 0.1116 \| 102.0 \| 1020 \| 0.5878 \| nan \| 0.9932 \| 0.4799 \| nan \| 0.9808 \| 0.1128 \| 0.7905 \| 0.7304 \| 0.9812 \| \| 0.1036 \| 103.0 \| 1030 \| 0.4826 \| nan \| 0.9963 \| 0.4304 \| nan \| 0.9809 \| 0.1078 \| 0.7394 \| 0.7056 \| 0.9811 \| \| 0.1195 \| 104.0 \| 1040 \| 0.4364 \| nan \| 0.9966 \| 0.3930 \| nan \| 0.9798 \| 0.1110 \| 0.7165 \| 0.6864 \| 0.9801 \| \| 0.1205 \| 105.0 \| 1050 \| 0.5793 \| nan \| 0.9934 \| 0.4762 \| nan \| 0.9808 \| 0.1120 \| 0.7864 \| 0.7285 \| 0.9812 \| \| 0.1453 \| 106.0 \| 1060 \| 0.4707 \| nan \| 0.9964 \| 0.4205 \| nan \| 0.9806 \| 0.1110 \| 0.7336 \| 0.7005 \| 0.9808 \| \| 0.0965 \| 107.0 \| 1070 \| 0.5638 \| nan \| 0.9941 \| 0.4723 \| nan \| 0.9811 \| 0.1091 \| 0.7789 \| 0.7267 \| 0.9814 \| \| 0.1058 \| 108.0 \| 1080 \| 0.4881 \| nan \| 0.9962 \| 0.4337 \| nan \| 0.9809 \| 0.1085 \| 0.7422 \| 0.7073 \| 0.9812 \| \| 0.1163 \| 109.0 \| 1090 \| 0.5128 \| nan \| 0.9957 \| 0.4493 \| nan \| 0.9811 \| 0.1077 \| 0.7542 \| 0.7152 \| 0.9814 \| \| 0.1145 \| 110.0 \| 1100 \| 0.5228 \| nan \| 0.9954 \| 0.4547 \| nan \| 0.9812 \| 0.1081 \| 0.7591 \| 0.7179 \| 0.9815 \| \| 0.1031 \| 111.0 \| 1110 \| 0.5522 \| nan \| 0.9945 \| 0.4673 \| nan \| 0.9811 \| 0.1073 \| 0.7733 \| 0.7242 \| 0.9814 \| \| 0.1042 \| 112.0 \| 1120 \| 0.5490 \| nan \| 0.9947 \| 0.4669 \| nan \| 0.9812 \| 0.1064 \| 0.7718 \| 0.7241 \| 0.9815 \| \| 0.1119 \| 113.0 \| 1130 \| 0.5064 \| nan \| 0.9958 \| 0.4449 \| nan \| 0.9811 \| 0.1063 \| 0.7511 \| 0.7130 \| 0.9813 \| \| 0.1116 \| 114.0 \| 1140 \| 0.5172 \| nan \| 0.9956 \| 0.4520 \| nan \| 0.9812 \| 0.1074 \| 0.7564 \| 0.7166 \| 0.9815 \| \| 0.1063 \| 115.0 \| 1150 \| 0.5163 \| nan \| 0.9956 \| 0.4511 \| nan \| 0.9812 \| 0.1072 \| 0.7560 \| 0.7161 \| 0.9814 \| \| 0.1054 \| 116.0 \| 1160 \| 0.4994 \| nan \| 0.9960 \| 0.4408 \| nan \| 0.9810 \| 0.1065 \| 0.7477 \| 0.7109 \| 0.9813 \| \| 0.1613 \| 117.0 \| 1170 \| 0.5251 \| nan \| 0.9955 \| 0.4576 \| nan \| 0.9813 \| 0.1060 \| 0.7603 \| 0.7195 \| 0.9816 \| \| 0.1542 \| 118.0 \| 1180 \| 0.5454 \| nan \| 0.9947 \| 0.4649 \| nan \| 0.9812 \| 0.1058 \| 0.7701 \| 0.7230 \| 0.9815 \| \| 0.1226 \| 119.0 \| 1190 \| 0.5469 \| nan \| 0.9947 \| 0.4658 \| nan \| 0.9812 \| 0.1064 \| 0.7708 \| 0.7235 \| 0.9815 \| \| 0.1295 \| 120.0 \| 1200 \| 0.5437 \| nan \| 0.9948 \| 0.4646 \| nan \| 0.9812 \| 0.1060 \| 0.7693 \| 0.7229 \| 0.9815 \| \| 0.1438 \| 121.0 \| 1210 \| 0.1076 \| 0.7084 \| 0.7435 \| 0.9812 \| nan \| 0.4909 \| 0.9962 \| nan \| 0.4358 \| 0.9809 \| \| 0.1391 \| 122.0 \| 1220 \| 0.1081 \| 0.7221 \| 0.7683 \| 0.9814 \| nan \| 0.5417 \| 0.9948 \| nan \| 0.4630 \| 0.9811 \| \| 0.1756 \| 123.0 \| 1230 \| 0.1041 \| 0.7233 \| 0.7710 \| 0.9814 \| nan \| 0.5473 \| 0.9947 \| nan \| 0.4655 \| 0.9811 \| \| 0.1174 \| 124.0 \| 1240 \| 0.1029 \| 0.7189 \| 0.7614 \| 0.9815 \| nan \| 0.5275 \| 0.9953 \| nan \| 0.4566 \| 0.9812 \| \| 0.1025 \| 125.0 \| 1250 \| 0.1043 \| 0.7100 \| 0.7470 \| 0.9812 \| nan \| 0.4980 \| 0.9959 \| nan \| 0.4391 \| 0.9809 \| \| 0.0997 \| 126.0 \| 1260 \| 0.1038 \| 0.7211 \| 0.7638 \| 0.9816 \| nan \| 0.5323 \| 0.9953 \| nan \| 0.4609 \| 0.9813 \| \| 0.1768 \| 127.0 \| 1270 \| 0.1037 \| 0.7204 \| 0.7617 \| 0.9817 \| nan \| 0.5279 \| 0.9955 \| nan \| 0.4594 \| 0.9814 \| \| 0.1527 \| 128.0 \| 1280 \| 0.1027 \| 0.7167 \| 0.7564 \| 0.9815 \| nan \| 0.5171 \| 0.9956 \| nan \| 0.4522 \| 0.9812 \| \| 0.1269 \| 129.0 \| 1290 \| 0.1041 \| 0.7178 \| 0.7583 \| 0.9815 \| nan \| 0.5211 \| 0.9955 \| nan \| 0.4543 \| 0.9812 \| \| 0.0968 \| 130.0 \| 1300 \| 0.1043 \| 0.7188 \| 0.7607 \| 0.9815 \| nan \| 0.5261 \| 0.9953 \| nan \| 0.4564 \| 0.9812 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF-RGBD-b0", "results": []}]}	image-segmentation	sam1120/safety-utcustom-train-SF-RGBD-b0	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-08T04:17:24+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	safety-utcustom-train-SF-RGBD-b0 ================================ This model is a fine-tuned version of nvidia/mit-b0 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.1043 * Mean Iou: 0.7188 * Mean Accuracy: 0.7607 * Overall Accuracy: 0.9815 * Accuracy Unlabeled: nan * Accuracy Safe: 0.5261 * Accuracy Unsafe: 0.9953 * Iou Unlabeled: nan * Iou Safe: 0.4564 * Iou Unsafe: 0.9812 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: 0.05 * num\_epochs: 130 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 130### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.1061462014913559, 0.0360175259411335, -0.0020096865482628345, 0.11525905877351761, 0.1711447834968567, 0.02860238216817379, 0.11503864079713821, 0.1143607422709465, -0.1091211661696434, 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null	null	peft	<!-- 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. --> # output_0207 This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1532 ## 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: 8 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 10 \| 1.1975 \| \| No log \| 2.0 \| 20 \| 1.1639 \| \| 1.1986 \| 3.0 \| 30 \| 1.1532 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "openai/whisper-large", "model-index": [{"name": "output_0207", "results": []}]}	null	shtapm/output_0207	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:openai/whisper-large", "license:apache-2.0", "region:us" ]	2024-02-08T04:19:13+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-openai/whisper-large #license-apache-2.0 #region-us	output\_0207 ============ This model is a fine-tuned version of openai/whisper-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.1532 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: 8 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 5 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-openai/whisper-large #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 47, 131, 4, 44 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-openai/whisper-large #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.11936301738023758, 0.11033549159765244, -0.0031707040034234524, 0.09754900634288788, 0.10392286628484726, -0.01588326133787632, 0.14659996330738068, 0.12398331612348557, -0.09092392027378082, 0.0826081782579422, 0.12387820333242416, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-5.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:19:26+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	BrauuHdzM/GPT-J-finetuned-noticias	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T04:22:29+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	peft	<!-- 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: `0.4.0` ```yaml base_model: andysalerno/mistral-sft-v3 model_type: AutoModelForCausalLM load_in_8bit: true load_in_4bit: false strict: false datasets: - path: andysalerno/rainbowfish-v1 type: system_prompt: "" field_system: system field_instruction: input field_output: output format: "{instruction}" no_input_format: "{instruction}" dataset_prepared_path: last_run_prepared val_set_size: 0.005 output_dir: ./lora-out-rainbow7 adapter: lora lora_model_dir: sequence_len: 2048 sample_packing: false # was true eval_sample_packing: false pad_to_sequence_len: false padding_side: left lora_r: 64 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj lora_modules_to_save: - embed_tokens - lm_head wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 2e-5 train_on_inputs: false group_by_length: false bf16: true fp16: tf32: false gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false # early_stopping_patience: 3 local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 hub_strategy: "every_save" hub_model_id: andysalerno/rainbowfish-v7 num_epochs: 2 warmup_steps: 100 # warmup_ratio: 0.1 eval_steps: 200 eval_table_size: eval_table_max_new_tokens: 128 # save_steps: 5 # max_steps: 400 saves_per_epoch: 2 debug: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: bos_token: "<\|im_start\|>" eos_token: "<\|im_end\|>" unk_token: "<unk>" ``` </details><br> # rainbowfish-v7 This model is a fine-tuned version of [andysalerno/mistral-sft-v3](https://huggingface.co/andysalerno/mistral-sft-v3) on the [andysalerno/rainbowfish-v1](https://huggingface.co/datasets/andysalerno/rainbowfish-v1) dataset. It achieves the following results on the evaluation set: - Loss: 0.6464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.6514 \| 0.18 \| 200 \| 0.6828 \| \| 0.6875 \| 0.37 \| 400 \| 0.6691 \| \| 0.6626 \| 0.55 \| 600 \| 0.6625 \| \| 0.688 \| 0.74 \| 800 \| 0.6558 \| \| 0.7143 \| 0.92 \| 1000 \| 0.6520 \| \| 0.5243 \| 1.11 \| 1200 \| 0.6495 \| \| 0.6205 \| 1.29 \| 1400 \| 0.6482 \| \| 0.6159 \| 1.47 \| 1600 \| 0.6469 \| \| 0.6287 \| 1.66 \| 1800 \| 0.6465 \| \| 0.6606 \| 1.84 \| 2000 \| 0.6464 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0	{"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "datasets": ["andysalerno/rainbowfish-v1"], "base_model": "andysalerno/mistral-sft-v3", "model-index": [{"name": "rainbowfish-v7", "results": []}]}	null	andysalerno/rainbowfish-v7	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "dataset:andysalerno/rainbowfish-v1", "base_model:andysalerno/mistral-sft-v3", "license:apache-2.0", "region:us" ]	2024-02-08T04:23:30+00:00	[]	[]	TAGS #peft #safetensors #mistral #axolotl #generated_from_trainer #dataset-andysalerno/rainbowfish-v1 #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #region-us	<img src="URL alt="Built with Axolotl" width="200" height="32"/> See axolotl config axolotl version: '0.4.0' rainbowfish-v7 ============== This model is a fine-tuned version of andysalerno/mistral-sft-v3 on the andysalerno/rainbowfish-v1 dataset. It achieves the following results on the evaluation set: * Loss: 0.6464 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 4 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * total\_eval\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 2 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.2+cu118 * Datasets 2.16.1 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ "TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #dataset-andysalerno/rainbowfish-v1 #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ 70, 179, 4, 44 ]	[ "passage: TAGS\n#peft #safetensors #mistral #axolotl #generated_from_trainer #dataset-andysalerno/rainbowfish-v1 #base_model-andysalerno/mistral-sft-v3 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2+cu118\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ -0.10511558502912521, 0.13951140642166138, -0.004454484209418297, 0.09252028912305832, 0.10675042122602463, 0.042880065739154816, 0.1203436553478241, 0.13572947680950165, -0.06324436515569687, 0.13751158118247986, 0.09940297156572342, 0.06669757515192032, 0.07907430827617645, 0.1891881674528122, -0.001584269804880023, -0.26525330543518066, 0.021937286481261253, -0.04740605130791664, -0.1197536438703537, 0.10683926194906235, 0.04863717779517174, -0.1226203665137291, 0.07451052218675613, -0.03126584738492966, -0.09396018087863922, -0.05445932596921921, -0.04964252933859825, -0.0189801212400198, 0.08532176166772842, 0.008614419028162956, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-6.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:28:03+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"language": ["my"], "library_name": "transformers", "pipeline_tag": "text-generation"}	text-generation	hmone231/mistral_burmese_health_update	[ "transformers", "safetensors", "mistral", "text-generation", "my", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:28:24+00:00	[ "1910.09700" ]	[ "my" ]	TAGS #transformers #safetensors #mistral #text-generation #my #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #my #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 58, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #my #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05109531804919243, 0.16009391844272614, -0.0052794222719967365, 0.020815899595618248, 0.09721443057060242, 0.012691669166088104, 0.07134328037500381, 0.11103275418281555, -0.015157300047576427, 0.11269285529851913, 0.03029373288154602, 0.0967770665884018, 0.11308509111404419, 0.15021316707134247, -0.0008672142284922302, -0.2334829568862915, 0.052584193646907806, -0.12700870633125305, -0.037807878106832504, 0.11701106280088425, 0.14697974920272827, -0.10366447269916534, 0.0775810182094574, -0.030996141955256462, -0.009513135068118572, -0.032482534646987915, -0.06273817270994186, -0.048135414719581604, 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null	null	transformers	Everyone-Coder-33b-v2-Base ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ECrHQnZnv8UM9GUCQtlWW.jpeg) EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` The models that were used in this merger were as follow: - https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct - https://huggingface.co/codefuse-ai/CodeFuse-DeepSeek-33B - https://huggingface.co/WizardLM/WizardCoder-33B-V1.1 Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. 💗 You can find the write up for merging models here: https://docs.google.com/document/d/1_vOftBnrk9NRk5h10UqrfJ5CDih9KBKL61yvrZtVWPE/edit?usp=sharing Config for the merger can be found bellow: ```yaml models: - model: codefuse-ai_CodeFuse-DeepSeek-33B parameters: weight: 1 - model: deepseek-ai_deepseek-coder-33b-instruct parameters: weight: 1 - model: WizardLM_WizardCoder-33B-V1.1 parameters: weight: 1 merge_method: task_arithmetic base_model: deepseek-ai_deepseek-coder-33b-base parameters: normalize: true int8_mask: true dtype: float16 ```	{"license": "other", "tags": ["merge"], "license_name": "deepseek", "license_link": "https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL"}	text-generation	LoneStriker/Everyone-Coder-33b-v2-Base-8.0bpw-h8-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:38:20+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Everyone-Coder-33b-v2-Base !image/jpeg EveryoneLLM series of models made by the community, for the community. This is a coding specific model made using fine-tunes of deekseekcoder-33b-base. This Version 2 of the Everything-Coder-33b model uses the task_arithmetic merging method which has major increases in coding performance as opposed to the ties method. You should find this version having much better coding performance than Version 1, without any of the negative that merging has on the integrity of the model. Prompt template: Alpaca The models that were used in this merger were as follow: - URL - URL - URL Thank you to the creators of the above ai models, they have full credit for the EveryoneLLM series of models. Without their hard work we wouldnt be able to achieve the great success we have in the open source community. You can find the write up for merging models here: URL Config for the merger can be found bellow:	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 55 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.00496361730620265, 0.013435622677206993, -0.0048941983841359615, -0.007948110811412334, 0.11774998903274536, 0.010081208311021328, 0.20247454941272736, 0.10724741965532303, -0.0081727784126997, -0.02794553153216839, 0.11637203395366669, 0.19246558845043182, -0.020575184375047684, 0.0786113366484642, -0.11944705247879028, -0.19450408220291138, 0.08193118125200272, 0.01286437176167965, 0.019434256479144096, 0.07955759763717651, 0.10736028850078583, -0.052583519369363785, 0.08838794380426407, -0.04033555090427399, -0.10580462217330933, 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null	null	setfit	# SetFit with firqaaa/indo-sentence-bert-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 6 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:----------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| kesedihan \| <ul><li>'Saya merasa agak kecewa, saya rasa harus menyerahkan sesuatu yang tidak menarik hanya untuk memenuhi tenggat waktu'</li><li>'Aku merasa seperti aku telah cukup lalai terhadap blogku dan aku hanya mengatakan bahwa kita di sini hidup dan bahagia'</li><li>'Aku tahu dan aku selalu terkoyak karenanya karena aku merasa tidak berdaya dan tidak berguna'</li></ul> \| \| sukacita \| <ul><li>'aku mungkin tidak merasa begitu keren'</li><li>'saya merasa baik-baik saja'</li><li>'saya merasa seperti saya seorang ibu dengan mengorbankan produktivitas'</li></ul> \| \| cinta \| <ul><li>'aku merasa mencintaimu'</li><li>'aku akan merasa sangat nostalgia di usia yang begitu muda'</li><li>'Saya merasa diberkati bahwa saya tinggal di Amerika memiliki keluarga yang luar biasa dan Dorothy Kelsey adalah bagian dari hidup saya'</li></ul> \| \| amarah \| <ul><li>'Aku terlalu memikirkan cara dudukku, suaraku terdengar jika ada makanan di mulutku, dan perasaan bahwa aku harus berjalan ke semua orang agar tidak bersikap kasar'</li><li>'aku merasa memberontak sedikit kesal gila terkurung'</li><li>'Aku merasakan perasaan itu muncul kembali dari perasaan paranoid dan cemburu yang penuh kebencian yang selalu menyiksaku tanpa henti'</li></ul> \| \| takut \| <ul><li>'aku merasa seperti diserang oleh landak titanium'</li><li>'Aku membiarkan diriku memikirkan perilakuku terhadapmu saat kita masih kecil. Aku merasakan campuran aneh antara rasa bersalah dan kekaguman atas ketangguhanmu'</li><li>'saya marah karena majikan saya tidak berinvestasi pada kami sama sekali, gaji pelatihan, kenaikan hari libur bank dan rasanya seperti ketidakadilan sehingga saya merasa tidak berdaya'</li></ul> \| \| kejutan \| <ul><li>'Aku membaca bagian ol feefyefo Aku merasa takjub melihat betapa aku bisa mengoceh dan betapa transparannya aku dalam hidupku'</li><li>'saya menemukan seni di sisi lain saya merasa sangat terkesan dengan karya saya'</li><li>'aku merasa penasaran, bersemangat dan tidak sabar'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.718 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("firqaaa/indo-setfit-bert-base-p3") # Run inference preds = model("Aku melihat ke dalam dompetku dan aku merasakan hawa dingin") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 2 \| 16.7928 \| 56 \| \| Label \| Training Sample Count \| \|:----------\|:----------------------\| \| kesedihan \| 300 \| \| sukacita \| 300 \| \| cinta \| 300 \| \| amarah \| 300 \| \| takut \| 300 \| \| kejutan \| 300 \| ### Training Hyperparameters - batch_size: (128, 128) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: True ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:---------:\|:-------------:\|:---------------:\| \| 0.0000 \| 1 \| 0.2927 \| - \| \| 0.0024 \| 50 \| 0.2605 \| - \| \| 0.0047 \| 100 \| 0.2591 \| - \| \| 0.0071 \| 150 \| 0.2638 \| - \| \| 0.0095 \| 200 \| 0.245 \| - \| \| 0.0119 \| 250 \| 0.226 \| - \| \| 0.0142 \| 300 \| 0.222 \| - \| \| 0.0166 \| 350 \| 0.1968 \| - \| \| 0.0190 \| 400 \| 0.1703 \| - \| \| 0.0213 \| 450 \| 0.1703 \| - \| \| 0.0237 \| 500 \| 0.1587 \| - \| \| 0.0261 \| 550 \| 0.1087 \| - \| \| 0.0284 \| 600 \| 0.1203 \| - \| \| 0.0308 \| 650 \| 0.0844 \| - \| \| 0.0332 \| 700 \| 0.0696 \| - \| \| 0.0356 \| 750 \| 0.0606 \| - \| \| 0.0379 \| 800 \| 0.0333 \| - \| \| 0.0403 \| 850 \| 0.0453 \| - \| \| 0.0427 \| 900 \| 0.033 \| - \| \| 0.0450 \| 950 \| 0.0142 \| - \| \| 0.0474 \| 1000 \| 0.004 \| - \| \| 0.0498 \| 1050 \| 0.0097 \| - \| \| 0.0521 \| 1100 \| 0.0065 \| - \| \| 0.0545 \| 1150 \| 0.0081 \| - \| \| 0.0569 \| 1200 \| 0.0041 \| - \| \| 0.0593 \| 1250 \| 0.0044 \| - \| \| 0.0616 \| 1300 \| 0.0013 \| - \| \| 0.0640 \| 1350 \| 0.0024 \| - \| \| 0.0664 \| 1400 \| 0.001 \| - \| \| 0.0687 \| 1450 \| 0.0012 \| - \| \| 0.0711 \| 1500 \| 0.0013 \| - \| \| 0.0735 \| 1550 \| 0.0006 \| - \| \| 0.0759 \| 1600 \| 0.0033 \| - \| \| 0.0782 \| 1650 \| 0.0006 \| - \| \| 0.0806 \| 1700 \| 0.0013 \| - \| \| 0.0830 \| 1750 \| 0.0008 \| - \| \| 0.0853 \| 1800 \| 0.0006 \| - \| \| 0.0877 \| 1850 \| 0.0008 \| - \| \| 0.0901 \| 1900 \| 0.0004 \| - \| \| 0.0924 \| 1950 \| 0.0005 \| - \| \| 0.0948 \| 2000 \| 0.0004 \| - \| \| 0.0972 \| 2050 \| 0.0002 \| - \| \| 0.0996 \| 2100 \| 0.0002 \| - \| \| 0.1019 \| 2150 \| 0.0003 \| - \| \| 0.1043 \| 2200 \| 0.0006 \| - \| \| 0.1067 \| 2250 \| 0.0005 \| - \| \| 0.1090 \| 2300 \| 0.0003 \| - \| \| 0.1114 \| 2350 \| 0.0018 \| - \| \| 0.1138 \| 2400 \| 0.0003 \| - \| \| 0.1161 \| 2450 \| 0.0002 \| - \| \| 0.1185 \| 2500 \| 0.0018 \| - \| \| 0.1209 \| 2550 \| 0.0003 \| - \| \| 0.1233 \| 2600 \| 0.0008 \| - \| \| 0.1256 \| 2650 \| 0.0002 \| - \| \| 0.1280 \| 2700 \| 0.0007 \| - \| \| 0.1304 \| 2750 \| 0.006 \| - \| \| 0.1327 \| 2800 \| 0.0002 \| - \| \| 0.1351 \| 2850 \| 0.0001 \| - \| \| 0.1375 \| 2900 \| 0.0001 \| - \| \| 0.1399 \| 2950 \| 0.0001 \| - \| \| 0.1422 \| 3000 \| 0.0001 \| - \| \| 0.1446 \| 3050 \| 0.0001 \| - \| \| 0.1470 \| 3100 \| 0.0001 \| - \| \| 0.1493 \| 3150 \| 0.0001 \| - \| \| 0.1517 \| 3200 \| 0.0002 \| - \| \| 0.1541 \| 3250 \| 0.0003 \| - \| \| 0.1564 \| 3300 \| 0.0004 \| - \| \| 0.1588 \| 3350 \| 0.0001 \| - \| \| 0.1612 \| 3400 \| 0.0001 \| - \| \| 0.1636 \| 3450 \| 0.0014 \| - \| \| 0.1659 \| 3500 \| 0.0005 \| - \| \| 0.1683 \| 3550 \| 0.0003 \| - \| \| 0.1707 \| 3600 \| 0.0001 \| - \| \| 0.1730 \| 3650 \| 0.0001 \| - \| \| 0.1754 \| 3700 \| 0.0001 \| - \| \| 0.1778 \| 3750 \| 0.0001 \| - \| \| 0.1801 \| 3800 \| 0.0001 \| - \| \| 0.1825 \| 3850 \| 0.0001 \| - \| \| 0.1849 \| 3900 \| 0.0001 \| - \| \| 0.1873 \| 3950 \| 0.0001 \| - \| \| 0.1896 \| 4000 \| 0.0001 \| - \| \| 0.1920 \| 4050 \| 0.0001 \| - \| \| 0.1944 \| 4100 \| 0.0003 \| - \| \| 0.1967 \| 4150 \| 0.0006 \| - \| \| 0.1991 \| 4200 \| 0.0001 \| - \| \| 0.2015 \| 4250 \| 0.0 \| - \| \| 0.2038 \| 4300 \| 0.0 \| - \| \| 0.2062 \| 4350 \| 0.0001 \| - \| \| 0.2086 \| 4400 \| 0.0 \| - \| \| 0.2110 \| 4450 \| 0.0 \| - \| \| 0.2133 \| 4500 \| 0.0001 \| - \| \| 0.2157 \| 4550 \| 0.0002 \| - \| \| 0.2181 \| 4600 \| 0.0003 \| - \| \| 0.2204 \| 4650 \| 0.0018 \| - \| \| 0.2228 \| 4700 \| 0.0003 \| - \| \| 0.2252 \| 4750 \| 0.0145 \| - \| \| 0.2276 \| 4800 \| 0.0001 \| - \| \| 0.2299 \| 4850 \| 0.0006 \| - \| \| 0.2323 \| 4900 \| 0.0001 \| - \| \| 0.2347 \| 4950 \| 0.0007 \| - \| \| 0.2370 \| 5000 \| 0.0001 \| - \| \| 0.2394 \| 5050 \| 0.0 \| - \| \| 0.2418 \| 5100 \| 0.0 \| - \| \| 0.2441 \| 5150 \| 0.0001 \| - \| \| 0.2465 \| 5200 \| 0.0003 \| - \| \| 0.2489 \| 5250 \| 0.0 \| - \| \| 0.2513 \| 5300 \| 0.0 \| - \| \| 0.2536 \| 5350 \| 0.0 \| - \| \| 0.2560 \| 5400 \| 0.0 \| - \| \| 0.2584 \| 5450 \| 0.0004 \| - \| \| 0.2607 \| 5500 \| 0.0 \| - \| \| 0.2631 \| 5550 \| 0.0 \| - \| \| 0.2655 \| 5600 \| 0.0 \| - \| \| 0.2678 \| 5650 \| 0.0 \| - \| \| 0.2702 \| 5700 \| 0.0 \| - \| \| 0.2726 \| 5750 \| 0.0002 \| - \| \| 0.2750 \| 5800 \| 0.0 \| - \| \| 0.2773 \| 5850 \| 0.0 \| - \| \| 0.2797 \| 5900 \| 0.0 \| - \| \| 0.2821 \| 5950 \| 0.0 \| - \| \| 0.2844 \| 6000 \| 0.0 \| - \| \| 0.2868 \| 6050 \| 0.0 \| - \| \| 0.2892 \| 6100 \| 0.0 \| - \| \| 0.2916 \| 6150 \| 0.0 \| - \| \| 0.2939 \| 6200 \| 0.0 \| - \| \| 0.2963 \| 6250 \| 0.0 \| - \| \| 0.2987 \| 6300 \| 0.0001 \| - \| \| 0.3010 \| 6350 \| 0.0003 \| - \| \| 0.3034 \| 6400 \| 0.0048 \| - \| \| 0.3058 \| 6450 \| 0.0 \| - \| \| 0.3081 \| 6500 \| 0.0 \| - \| \| 0.3105 \| 6550 \| 0.0 \| - \| \| 0.3129 \| 6600 \| 0.0 \| - \| \| 0.3153 \| 6650 \| 0.0 \| - \| \| 0.3176 \| 6700 \| 0.0 \| - \| \| 0.3200 \| 6750 \| 0.0 \| - \| \| 0.3224 \| 6800 \| 0.0 \| - \| \| 0.3247 \| 6850 \| 0.0 \| - \| \| 0.3271 \| 6900 \| 0.0 \| - \| \| 0.3295 \| 6950 \| 0.0 \| - \| \| 0.3318 \| 7000 \| 0.0 \| - \| \| 0.3342 \| 7050 \| 0.0 \| - \| \| 0.3366 \| 7100 \| 0.0 \| - \| \| 0.3390 \| 7150 \| 0.0011 \| - \| \| 0.3413 \| 7200 \| 0.0002 \| - \| \| 0.3437 \| 7250 \| 0.0 \| - \| \| 0.3461 \| 7300 \| 0.0 \| - \| \| 0.3484 \| 7350 \| 0.0001 \| - \| \| 0.3508 \| 7400 \| 0.0001 \| - \| \| 0.3532 \| 7450 \| 0.0002 \| - \| \| 0.3556 \| 7500 \| 0.0 \| - \| \| 0.3579 \| 7550 \| 0.0 \| - \| \| 0.3603 \| 7600 \| 0.0 \| - \| \| 0.3627 \| 7650 \| 0.0 \| - \| \| 0.3650 \| 7700 \| 0.0 \| - \| \| 0.3674 \| 7750 \| 0.0 \| - \| \| 0.3698 \| 7800 \| 0.0001 \| - \| \| 0.3721 \| 7850 \| 0.0 \| - \| \| 0.3745 \| 7900 \| 0.0 \| - \| \| 0.3769 \| 7950 \| 0.0 \| - \| \| 0.3793 \| 8000 \| 0.0 \| - \| \| 0.3816 \| 8050 \| 0.0 \| - \| \| 0.3840 \| 8100 \| 0.0 \| - \| \| 0.3864 \| 8150 \| 0.0 \| - \| \| 0.3887 \| 8200 \| 0.0 \| - \| \| 0.3911 \| 8250 \| 0.0 \| - \| \| 0.3935 \| 8300 \| 0.0 \| - \| \| 0.3958 \| 8350 \| 0.0 \| - \| \| 0.3982 \| 8400 \| 0.0 \| - \| \| 0.4006 \| 8450 \| 0.0 \| - \| \| 0.4030 \| 8500 \| 0.0 \| - \| \| 0.4053 \| 8550 \| 0.0001 \| - \| \| 0.4077 \| 8600 \| 0.0001 \| - \| \| 0.4101 \| 8650 \| 0.0008 \| - \| \| 0.4124 \| 8700 \| 0.0001 \| - \| \| 0.4148 \| 8750 \| 0.0 \| - \| \| 0.4172 \| 8800 \| 0.0 \| - \| \| 0.4196 \| 8850 \| 0.0001 \| - \| \| 0.4219 \| 8900 \| 0.0 \| - \| \| 0.4243 \| 8950 \| 0.0 \| - \| \| 0.4267 \| 9000 \| 0.0 \| - \| \| 0.4290 \| 9050 \| 0.0 \| - \| \| 0.4314 \| 9100 \| 0.0 \| - \| \| 0.4338 \| 9150 \| 0.0 \| - \| \| 0.4361 \| 9200 \| 0.0 \| - \| \| 0.4385 \| 9250 \| 0.0 \| - \| \| 0.4409 \| 9300 \| 0.0 \| - \| \| 0.4433 \| 9350 \| 0.0 \| - \| \| 0.4456 \| 9400 \| 0.0 \| - \| \| 0.4480 \| 9450 \| 0.0 \| - \| \| 0.4504 \| 9500 \| 0.0 \| - \| \| 0.4527 \| 9550 \| 0.0 \| - \| \| 0.4551 \| 9600 \| 0.0 \| - \| \| 0.4575 \| 9650 \| 0.0 \| - \| \| 0.4598 \| 9700 \| 0.0 \| - \| \| 0.4622 \| 9750 \| 0.0001 \| - \| \| 0.4646 \| 9800 \| 0.0 \| - \| \| 0.4670 \| 9850 \| 0.0 \| - \| \| 0.4693 \| 9900 \| 0.0 \| - \| \| 0.4717 \| 9950 \| 0.0 \| - \| \| 0.4741 \| 10000 \| 0.0 \| - \| \| 0.4764 \| 10050 \| 0.0 \| - \| \| 0.4788 \| 10100 \| 0.0006 \| - \| \| 0.4812 \| 10150 \| 0.0 \| - \| \| 0.4835 \| 10200 \| 0.0 \| - \| \| 0.4859 \| 10250 \| 0.0 \| - \| \| 0.4883 \| 10300 \| 0.0 \| - \| \| 0.4907 \| 10350 \| 0.0 \| - \| \| 0.4930 \| 10400 \| 0.0 \| - \| \| 0.4954 \| 10450 \| 0.0 \| - \| \| 0.4978 \| 10500 \| 0.0 \| - \| \| 0.5001 \| 10550 \| 0.0 \| - \| \| 0.5025 \| 10600 \| 0.0 \| - \| \| 0.5049 \| 10650 \| 0.0 \| - \| \| 0.5073 \| 10700 \| 0.0 \| - \| \| 0.5096 \| 10750 \| 0.0 \| - \| \| 0.5120 \| 10800 \| 0.0 \| - \| \| 0.5144 \| 10850 \| 0.0 \| - \| \| 0.5167 \| 10900 \| 0.0 \| - \| \| 0.5191 \| 10950 \| 0.0 \| - \| \| 0.5215 \| 11000 \| 0.0 \| - \| \| 0.5238 \| 11050 \| 0.0 \| - \| \| 0.5262 \| 11100 \| 0.0 \| - \| \| 0.5286 \| 11150 \| 0.0 \| - \| \| 0.5310 \| 11200 \| 0.0 \| - \| \| 0.5333 \| 11250 \| 0.0 \| - \| \| 0.5357 \| 11300 \| 0.0 \| - \| \| 0.5381 \| 11350 \| 0.0 \| - \| \| 0.5404 \| 11400 \| 0.0 \| - \| \| 0.5428 \| 11450 \| 0.0 \| - \| \| 0.5452 \| 11500 \| 0.0 \| - \| \| 0.5475 \| 11550 \| 0.0 \| - \| \| 0.5499 \| 11600 \| 0.0 \| - \| \| 0.5523 \| 11650 \| 0.0001 \| - \| \| 0.5547 \| 11700 \| 0.0 \| - \| \| 0.5570 \| 11750 \| 0.0043 \| - \| \| 0.5594 \| 11800 \| 0.0 \| - \| \| 0.5618 \| 11850 \| 0.0 \| - \| \| 0.5641 \| 11900 \| 0.0 \| - \| \| 0.5665 \| 11950 \| 0.0 \| - \| \| 0.5689 \| 12000 \| 0.0 \| - \| \| 0.5713 \| 12050 \| 0.0 \| - \| \| 0.5736 \| 12100 \| 0.0 \| - \| \| 0.5760 \| 12150 \| 0.0 \| - \| \| 0.5784 \| 12200 \| 0.0 \| - \| \| 0.5807 \| 12250 \| 0.0029 \| - \| \| 0.5831 \| 12300 \| 0.0 \| - \| \| 0.5855 \| 12350 \| 0.0 \| - \| \| 0.5878 \| 12400 \| 0.0 \| - \| \| 0.5902 \| 12450 \| 0.0 \| - \| \| 0.5926 \| 12500 \| 0.0 \| - \| \| 0.5950 \| 12550 \| 0.0 \| - \| \| 0.5973 \| 12600 \| 0.0 \| - \| \| 0.5997 \| 12650 \| 0.0 \| - \| \| 0.6021 \| 12700 \| 0.0 \| - \| \| 0.6044 \| 12750 \| 0.0 \| - \| \| 0.6068 \| 12800 \| 0.0 \| - \| \| 0.6092 \| 12850 \| 0.0 \| - \| \| 0.6115 \| 12900 \| 0.0 \| - \| \| 0.6139 \| 12950 \| 0.0 \| - \| \| 0.6163 \| 13000 \| 0.0 \| - \| \| 0.6187 \| 13050 \| 0.0 \| - \| \| 0.6210 \| 13100 \| 0.0 \| - \| \| 0.6234 \| 13150 \| 0.0001 \| - \| \| 0.6258 \| 13200 \| 0.0 \| - \| \| 0.6281 \| 13250 \| 0.0 \| - \| \| 0.6305 \| 13300 \| 0.0 \| - \| \| 0.6329 \| 13350 \| 0.0 \| - \| \| 0.6353 \| 13400 \| 0.0001 \| - \| \| 0.6376 \| 13450 \| 0.0 \| - \| \| 0.6400 \| 13500 \| 0.0 \| - \| \| 0.6424 \| 13550 \| 0.0 \| - \| \| 0.6447 \| 13600 \| 0.0 \| - \| \| 0.6471 \| 13650 \| 0.0 \| - \| \| 0.6495 \| 13700 \| 0.0 \| - \| \| 0.6518 \| 13750 \| 0.0 \| - \| \| 0.6542 \| 13800 \| 0.0 \| - \| \| 0.6566 \| 13850 \| 0.0 \| - \| \| 0.6590 \| 13900 \| 0.0 \| - \| \| 0.6613 \| 13950 \| 0.0 \| - \| \| 0.6637 \| 14000 \| 0.0 \| - \| \| 0.6661 \| 14050 \| 0.0 \| - \| \| 0.6684 \| 14100 \| 0.0 \| - \| \| 0.6708 \| 14150 \| 0.0 \| - \| \| 0.6732 \| 14200 \| 0.0 \| - \| \| 0.6755 \| 14250 \| 0.0 \| - \| \| 0.6779 \| 14300 \| 0.0 \| - \| \| 0.6803 \| 14350 \| 0.0 \| - \| \| 0.6827 \| 14400 \| 0.0 \| - \| \| 0.6850 \| 14450 \| 0.0 \| - \| \| 0.6874 \| 14500 \| 0.0 \| - \| \| 0.6898 \| 14550 \| 0.0 \| - \| \| 0.6921 \| 14600 \| 0.0 \| - \| \| 0.6945 \| 14650 \| 0.0 \| - \| \| 0.6969 \| 14700 \| 0.0 \| - \| \| 0.6993 \| 14750 \| 0.0 \| - \| \| 0.7016 \| 14800 \| 0.0 \| - \| \| 0.7040 \| 14850 \| 0.0 \| - \| \| 0.7064 \| 14900 \| 0.0 \| - \| \| 0.7087 \| 14950 \| 0.0 \| - \| \| 0.7111 \| 15000 \| 0.0 \| - \| \| 0.7135 \| 15050 \| 0.0 \| - \| \| 0.7158 \| 15100 \| 0.0 \| - \| \| 0.7182 \| 15150 \| 0.0 \| - \| \| 0.7206 \| 15200 \| 0.0 \| - \| \| 0.7230 \| 15250 \| 0.0 \| - \| \| 0.7253 \| 15300 \| 0.0 \| - \| \| 0.7277 \| 15350 \| 0.0 \| - \| \| 0.7301 \| 15400 \| 0.0 \| - \| \| 0.7324 \| 15450 \| 0.0 \| - \| \| 0.7348 \| 15500 \| 0.0 \| - \| \| 0.7372 \| 15550 \| 0.0 \| - \| \| 0.7395 \| 15600 \| 0.0 \| - \| \| 0.7419 \| 15650 \| 0.0 \| - \| \| 0.7443 \| 15700 \| 0.0 \| - \| \| 0.7467 \| 15750 \| 0.0 \| - \| \| 0.7490 \| 15800 \| 0.0 \| - \| \| 0.7514 \| 15850 \| 0.0 \| - \| \| 0.7538 \| 15900 \| 0.0 \| - \| \| 0.7561 \| 15950 \| 0.0 \| - \| \| 0.7585 \| 16000 \| 0.0 \| - \| \| 0.7609 \| 16050 \| 0.0 \| - \| \| 0.7633 \| 16100 \| 0.0 \| - \| \| 0.7656 \| 16150 \| 0.0 \| - \| \| 0.7680 \| 16200 \| 0.0 \| - \| \| 0.7704 \| 16250 \| 0.0 \| - \| \| 0.7727 \| 16300 \| 0.0 \| - \| \| 0.7751 \| 16350 \| 0.0 \| - \| \| 0.7775 \| 16400 \| 0.0 \| - \| \| 0.7798 \| 16450 \| 0.0 \| - \| \| 0.7822 \| 16500 \| 0.0 \| - \| \| 0.7846 \| 16550 \| 0.0 \| - \| \| 0.7870 \| 16600 \| 0.0 \| - \| \| 0.7893 \| 16650 \| 0.0 \| - \| \| 0.7917 \| 16700 \| 0.0 \| - \| \| 0.7941 \| 16750 \| 0.0 \| - \| \| 0.7964 \| 16800 \| 0.0 \| - \| \| 0.7988 \| 16850 \| 0.0 \| - \| \| 0.8012 \| 16900 \| 0.0 \| - \| \| 0.8035 \| 16950 \| 0.0 \| - \| \| 0.8059 \| 17000 \| 0.0 \| - \| \| 0.8083 \| 17050 \| 0.0 \| - \| \| 0.8107 \| 17100 \| 0.0 \| - \| \| 0.8130 \| 17150 \| 0.0 \| - \| \| 0.8154 \| 17200 \| 0.0 \| - \| \| 0.8178 \| 17250 \| 0.0 \| - \| \| 0.8201 \| 17300 \| 0.0 \| - \| \| 0.8225 \| 17350 \| 0.0 \| - \| \| 0.8249 \| 17400 \| 0.0 \| - \| \| 0.8272 \| 17450 \| 0.0 \| - \| \| 0.8296 \| 17500 \| 0.0 \| - \| \| 0.8320 \| 17550 \| 0.0 \| - \| \| 0.8344 \| 17600 \| 0.0 \| - \| \| 0.8367 \| 17650 \| 0.0 \| - \| \| 0.8391 \| 17700 \| 0.0 \| - \| \| 0.8415 \| 17750 \| 0.0 \| - \| \| 0.8438 \| 17800 \| 0.0 \| - \| \| 0.8462 \| 17850 \| 0.0 \| - \| \| 0.8486 \| 17900 \| 0.0 \| - \| \| 0.8510 \| 17950 \| 0.0 \| - \| \| 0.8533 \| 18000 \| 0.0 \| - \| \| 0.8557 \| 18050 \| 0.0 \| - \| \| 0.8581 \| 18100 \| 0.0 \| - \| \| 0.8604 \| 18150 \| 0.0 \| - \| \| 0.8628 \| 18200 \| 0.0 \| - \| \| 0.8652 \| 18250 \| 0.0 \| - \| \| 0.8675 \| 18300 \| 0.0 \| - \| \| 0.8699 \| 18350 \| 0.0 \| - \| \| 0.8723 \| 18400 \| 0.0 \| - \| \| 0.8747 \| 18450 \| 0.0 \| - \| \| 0.8770 \| 18500 \| 0.0 \| - \| \| 0.8794 \| 18550 \| 0.0 \| - \| \| 0.8818 \| 18600 \| 0.0 \| - \| \| 0.8841 \| 18650 \| 0.0 \| - \| \| 0.8865 \| 18700 \| 0.0 \| - \| \| 0.8889 \| 18750 \| 0.0 \| - \| \| 0.8912 \| 18800 \| 0.0 \| - \| \| 0.8936 \| 18850 \| 0.0 \| - \| \| 0.8960 \| 18900 \| 0.0 \| - \| \| 0.8984 \| 18950 \| 0.0 \| - \| \| 0.9007 \| 19000 \| 0.0 \| - \| \| 0.9031 \| 19050 \| 0.0 \| - \| \| 0.9055 \| 19100 \| 0.0 \| - \| \| 0.9078 \| 19150 \| 0.0 \| - \| \| 0.9102 \| 19200 \| 0.0 \| - \| \| 0.9126 \| 19250 \| 0.0 \| - \| \| 0.9150 \| 19300 \| 0.0 \| - \| \| 0.9173 \| 19350 \| 0.0 \| - \| \| 0.9197 \| 19400 \| 0.0 \| - \| \| 0.9221 \| 19450 \| 0.0 \| - \| \| 0.9244 \| 19500 \| 0.0 \| - \| \| 0.9268 \| 19550 \| 0.0 \| - \| \| 0.9292 \| 19600 \| 0.0 \| - \| \| 0.9315 \| 19650 \| 0.0 \| - \| \| 0.9339 \| 19700 \| 0.0 \| - \| \| 0.9363 \| 19750 \| 0.0 \| - \| \| 0.9387 \| 19800 \| 0.0 \| - \| \| 0.9410 \| 19850 \| 0.0 \| - \| \| 0.9434 \| 19900 \| 0.0 \| - \| \| 0.9458 \| 19950 \| 0.0 \| - \| \| 0.9481 \| 20000 \| 0.0 \| - \| \| 0.9505 \| 20050 \| 0.0 \| - \| \| 0.9529 \| 20100 \| 0.0 \| - \| \| 0.9552 \| 20150 \| 0.0 \| - \| \| 0.9576 \| 20200 \| 0.0 \| - \| \| 0.9600 \| 20250 \| 0.0 \| - \| \| 0.9624 \| 20300 \| 0.0 \| - \| \| 0.9647 \| 20350 \| 0.0 \| - \| \| 0.9671 \| 20400 \| 0.0 \| - \| \| 0.9695 \| 20450 \| 0.0 \| - \| \| 0.9718 \| 20500 \| 0.0 \| - \| \| 0.9742 \| 20550 \| 0.0 \| - \| \| 0.9766 \| 20600 \| 0.0 \| - \| \| 0.9790 \| 20650 \| 0.0 \| - \| \| 0.9813 \| 20700 \| 0.0 \| - \| \| 0.9837 \| 20750 \| 0.0 \| - \| \| 0.9861 \| 20800 \| 0.0 \| - \| \| 0.9884 \| 20850 \| 0.0 \| - \| \| 0.9908 \| 20900 \| 0.0 \| - \| \| 0.9932 \| 20950 \| 0.0 \| - \| \| 0.9955 \| 21000 \| 0.0 \| - \| \| 0.9979 \| 21050 \| 0.0 \| - \| \| 1.0 \| 21094 \| - \| 0.2251 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.13 - SetFit: 1.0.3 - Sentence Transformers: 2.2.2 - Transformers: 4.36.2 - PyTorch: 2.1.2+cu121 - Datasets: 2.16.1 - Tokenizers: 0.15.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Aku sudah lebih tua dan hidupku sangat berbeda. Aku bisa merasakan betapa takjubnya aku pagi itu"}, {"text": "Saya merasa cukup href http kata-kata yang tak terucapkan disimpan di dalam"}, {"text": "Aku melihat ke dalam dompetku dan aku merasakan hawa dingin"}, {"text": "Aku menurunkan Erik dengan perasaan agak tidak puas dengan malam itu"}, {"text": "Aku bertanya-tanya apa yang siswa lain di kelasku rasakan ketika aku tidak takut untuk memberikan jawaban di luar sana"}], "pipeline_tag": "text-classification", "inference": true, "base_model": "firqaaa/indo-sentence-bert-base", "model-index": [{"name": "SetFit with firqaaa/indo-sentence-bert-base", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "firqaaa/emotion-bahasa", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.718, "name": "Accuracy"}]}]}]}	text-classification	firqaaa/indo-setfit-bert-base-p3	[ "setfit", "safetensors", "bert", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:firqaaa/indo-sentence-bert-base", "model-index", "region:us" ]	2024-02-08T04:41:47+00:00	[ "2209.11055" ]	[]	TAGS #setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-firqaaa/indo-sentence-bert-base #model-index #region-us	SetFit with firqaaa/indo-sentence-bert-base =========================================== This is a SetFit model that can be used for Text Classification. This SetFit model uses firqaaa/indo-sentence-bert-base as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. Model Details ------------- ### Model Description * Model Type: SetFit * Sentence Transformer body: firqaaa/indo-sentence-bert-base * Classification head: a LogisticRegression instance * Maximum Sequence Length: 512 tokens * Number of Classes: 6 classes ### Model Sources * Repository: SetFit on GitHub * Paper: Efficient Few-Shot Learning Without Prompts * Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts ### Model Labels Evaluation ---------- ### Metrics Uses ---- ### Direct Use for Inference First install the SetFit library: Then you can load this model and run inference. Training Details ---------------- ### Training Set Metrics ### Training Hyperparameters * batch\_size: (128, 128) * num\_epochs: (1, 1) * max\_steps: -1 * sampling\_strategy: oversampling * body\_learning\_rate: (2e-05, 1e-05) * head\_learning\_rate: 0.01 * loss: CosineSimilarityLoss * distance\_metric: cosine\_distance * margin: 0.25 * end\_to\_end: False * use\_amp: False * warmup\_proportion: 0.1 * seed: 42 * eval\_max\_steps: -1 * load\_best\_model\_at\_end: True ### Training Results * The bold row denotes the saved checkpoint. ### Framework Versions * Python: 3.10.13 * SetFit: 1.0.3 * Sentence Transformers: 2.2.2 * Transformers: 4.36.2 * PyTorch: 2.1.2+cu121 * Datasets: 2.16.1 * Tokenizers: 0.15.0 ### BibTeX	[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: firqaaa/indo-sentence-bert-base\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 6 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (128, 128)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: True", "### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.2.2\n* Transformers: 4.36.2\n* PyTorch: 2.1.2+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.0", "### BibTeX" ]	[ "TAGS\n#setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-firqaaa/indo-sentence-bert-base #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: firqaaa/indo-sentence-bert-base\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 6 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (128, 128)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: True", "### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.2.2\n* Transformers: 4.36.2\n* PyTorch: 2.1.2+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.0", "### BibTeX" ]	[ 67, 61, 52, 8, 8, 31, 7, 168, 19, 58, 6 ]	[ "passage: TAGS\n#setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-firqaaa/indo-sentence-bert-base #model-index #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: firqaaa/indo-sentence-bert-base\n* Classification head: a LogisticRegression instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 6 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts### Model Labels\n\n\n\nEvaluation\n----------### Metrics\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (128, 128)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: True### Training Results\n\n\n\n* The bold row denotes the saved checkpoint.### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.2.2\n* Transformers: 4.36.2\n* PyTorch: 2.1.2+cu121\n* Datasets: 2.16.1\n* Tokenizers: 0.15.0### BibTeX" ]	[ -0.08604343980550766, 0.1477956473827362, -0.005874059163033962, 0.07293445616960526, 0.11215012520551682, 0.06304974853992462, 0.059823643416166306, 0.14388321340084076, -0.044660888612270355, 0.14424565434455872, 0.05866021290421486, 0.09288549423217773, 0.11243151128292084, 0.18489621579647064, -0.0023268689401447773, -0.2917645573616028, 0.027103308588266373, -0.1108960434794426, -0.04907190799713135, 0.09164220839738846, 0.09973931312561035, -0.0773247554898262, 0.05188029631972313, -0.049563147127628326, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	shtapm/whisper-large_0207	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T04:43:05+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_billsum_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5668 - Rouge1: 0.1412 - Rouge2: 0.053 - Rougel: 0.1189 - Rougelsum: 0.1186 - 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 62 \| 2.8480 \| 0.1306 \| 0.0403 \| 0.1097 \| 0.1097 \| 19.0 \| \| No log \| 2.0 \| 124 \| 2.6457 \| 0.1376 \| 0.051 \| 0.116 \| 0.1157 \| 19.0 \| \| No log \| 3.0 \| 186 \| 2.5829 \| 0.1389 \| 0.0521 \| 0.1168 \| 0.1167 \| 19.0 \| \| No log \| 4.0 \| 248 \| 2.5668 \| 0.1412 \| 0.053 \| 0.1189 \| 0.1186 \| 19.0 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_billsum_model", "results": []}]}	text2text-generation	seabornresponsibility/my_awesome_billsum_model	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:45:16+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_awesome\_billsum\_model =========================== This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5668 * Rouge1: 0.1412 * Rouge2: 0.053 * Rougel: 0.1189 * Rougelsum: 0.1186 * 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 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 77, 113, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10266303271055222, 0.09942521154880524, -0.0027460495475679636, 0.08506198227405548, 0.09827578812837601, -0.016694581136107445, 0.17796874046325684, 0.15241588652133942, 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null	null	peft	# 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"}	null	noza-kit/Adapter_llama2_translate_Q_enjppt_ex2-1epoch	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]	2024-02-08T04:46:02+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.13237035274505615, 0.20393601059913635, -0.002511046128347516, 0.02874687872827053, 0.07912357151508331, 0.019634027034044266, 0.04979075863957405, 0.13531364500522614, 0.020043307915329933, 0.10580451786518097, 0.0737132653594017, 0.11718367785215378, 0.10926163196563721, 0.20654499530792236, 0.003890186781063676, -0.15440793335437775, 0.024214256554841995, -0.08298544585704803, 0.006814117077738047, 0.1290476769208908, 0.14319083094596863, -0.10468140989542007, 0.0831538662314415, -0.014203370548784733, 0.0008161105797626078, -0.03246506303548813, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-2.4bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:46:45+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	null	# FactFT <!-- Provide a quick summary of what the model is/does. --> LoRA adapter model for paper [On the Intractability to Synthesize Factual Inconsistencies in Summarization](https://openreview.net/forum?id=bVpIOOgXKpS) ## Model Details https://github.com/NKWBTB/FactFT ### 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]	{"language": ["en"], "license": "apache-2.0", "pipeline_tag": "text-classification"}	text-classification	nkwbtb/FactFT	[ "safetensors", "text-classification", "en", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	2024-02-08T04:47:16+00:00	[ "1910.09700" ]	[ "en" ]	TAGS #safetensors #text-classification #en #arxiv-1910.09700 #license-apache-2.0 #region-us	# FactFT LoRA adapter model for paper On the Intractability to Synthesize Factual Inconsistencies in Summarization ## Model Details URL ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# FactFT\n\n\n\nLoRA adapter model for paper On the Intractability to Synthesize Factual Inconsistencies in Summarization", "## Model Details\nURL", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#safetensors #text-classification #en #arxiv-1910.09700 #license-apache-2.0 #region-us \n", "# FactFT\n\n\n\nLoRA adapter model for paper On the Intractability to Synthesize Factual Inconsistencies in Summarization", "## Model Details\nURL", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 35, 28, 4, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#safetensors #text-classification #en #arxiv-1910.09700 #license-apache-2.0 #region-us \n# FactFT\n\n\n\nLoRA adapter model for paper On the Intractability to Synthesize Factual Inconsistencies in Summarization## Model Details\nURL### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06559611856937408, 0.23686550557613373, -0.002509780926629901, 0.022145545110106468, 0.10755586624145508, -0.004186440259218216, 0.04503323510289192, 0.13394731283187866, -0.01727777160704136, 0.10822748392820358, 0.03661790117621422, 0.08086273074150085, 0.0988415777683258, 0.18628861010074615, 0.03385162353515625, -0.1911250203847885, -0.0025558085180819035, -0.10125695914030075, 0.024339577183127403, 0.11378954350948334, 0.14503642916679382, -0.09393768012523651, 0.07014091312885284, -0.051946911960840225, 0.0013698055408895016, 0.00032732522231526673, -0.10157448798418045, -0.07211805135011673, 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null	null	diffusers	### modern_08_feb Dreambooth model trained by livingbox with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:	{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]}	text-to-image	livingbox/modern-08-feb	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-08T04:50:28+00:00	[]	[]	TAGS #diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	### modern_08_feb Dreambooth model trained by livingbox with TheLastBen's fast-DreamBooth notebook Test the concept via A1111 Colab fast-Colab-A1111 Sample pictures of this concept:	[ "### modern_08_feb Dreambooth model trained by livingbox with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ "TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### modern_08_feb Dreambooth model trained by livingbox with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ 56, 52 ]	[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### modern_08_feb Dreambooth model trained by livingbox with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ 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null	null	transformers	## WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF) <p style="font-size:28px;" align="center"> 🏠 <a href="https://wizardlm.github.io/" target="_blank">Home Page</a> </p> <p align="center"> <p align="center"> 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> </p> <p align="center"> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## News [12/19/2023] 🔥 We released WizardMath-7B-V1.1 trained from Mistral-7B, the SOTA 7B math LLM, achieves 83.2 pass@1 on GSM8k, and 33.0 pass@1 on MATH. Use this [[Demo](http://47.103.63.15:50083/)] to chat with it. [12/19/2023] 🔥 WizardMath-7B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, Mixtral MOE, and Claude Instant on GSM8K pass@1. [12/19/2023] 🔥 WizardMath-7B-V1.1 is comparable with ChatGPT 3.5, Gemini Pro, and surpasses Mixtral MOE on MATH pass@1. \| Model \| Checkpoint \| Paper \| GSM8k \| MATH \| Demo\| \| ----- \|------\| ---- \|------\|-------\|-------\| \| WizardMath-7B-V1.1 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.1" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 83.2 \| 33.0 \|[[Demo](http://47.103.63.15:50083/)] \| \| WizardMath-70B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 81.6 \| 22.7 \|\| \| WizardMath-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 63.9 \| 14.0 \|\| \| WizardMath-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 54.9 \| 10.7 \| \| ## [12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs. \| Model \| GSM8k Pass@1 \| MATH Pass@1 \| \| ----- \|------\| ---- \| \| MPT-7B \| 6.8 \| 3.0 \| \|Llama 1-7B \| 11.0 \| 2.9 \| \|Llama 2-7B\|12.3 \|2.8 \| \|Yi-6b\| 32.6 \|5.8 \| \|Mistral-7B\|37.8 \|9.1 \| \|Qwen-7b\|47.8 \|9.3 \| \| RFT-7B \| 50.3 \| -- \| \| MAmmoTH-7B (COT) \| 50.5 \| 10.4 \| \| WizardMath-7B-V1.0 \| 54.9 \| 10.7 \| \|Abel-7B-001 \|59.7 \|13 \| \| MetaMath-7B \| 66.5 \| 19.8 \| \| Arithmo-Mistral-7B \| 74.7 \| 25.3 \| \|MetaMath-Mistral-7B\|77.7 \|28.2 \| \|Abel-7B-002 \| 80.4 \| 29.5 \| \| WizardMath-7B-V1.1 \| 83.2 \| 33.0 \| ## [12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs. \| Model \| GSM8k Pass@1 \| MATH Pass@1 \| \| ----- \|------\| ---- \| \| Llemma-34B \| 51.5 \| 25.0 \| \| Minerva-62B \| 52.4 \| 27.6 \| \| Llama 2-70B \| 56.8 \| 13.5 \| \| DeepSeek 67B \| 63.4 \| -- \| \| Gork 33B \| 62.9 \| 23.9 \| \| MAmmoTH-70B \| 72.4 \| 21.1 \| \| Yi-34B \| 67.9 \| 15.9 \| \| Mixtral 8x7B \| 74.4 \| 28.4 \| \| MetaMath-70B \| 82.3 \| 26.6 \| \| WizardMath-7B-V1.1 \| 83.2 \| 33.0 \| ## ❗ Data Contamination Check: Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set. 🔥 ❗<b>Note for model system prompts usage:</b> Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions. Default version: ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" ``` CoT Version: （❗For the simple math questions, we do NOT recommend to use the CoT prompt.） ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step." ``` ## Inference WizardMath Demo Script We provide the WizardMath inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo). ## Citation Please cite the repo if you use the data, method or code in this repo. ``` @article{luo2023wizardmath, title={WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct}, author={Luo, Haipeng and Sun, Qingfeng and Xu, Can and Zhao, Pu and Lou, Jianguang and Tao, Chongyang and Geng, Xiubo and Lin, Qingwei and Chen, Shifeng and Zhang, Dongmei}, journal={arXiv preprint arXiv:2308.09583}, year={2023} } ```	{"language": ["en"], "inference": false, "pipeline_tag": "text-generation"}	text-generation	SagarKeshave/wizard_math_	[ "transformers", "pytorch", "mistral", "text-generation", "en", "arxiv:2304.12244", "arxiv:2306.08568", "arxiv:2308.09583", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-08T04:50:47+00:00	[ "2304.12244", "2306.08568", "2308.09583" ]	[ "en" ]	TAGS #transformers #pytorch #mistral #text-generation #en #arxiv-2304.12244 #arxiv-2306.08568 #arxiv-2308.09583 #autotrain_compatible #text-generation-inference #region-us	WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF) ------------------------------------------------------------------------------------------------------------ [Home Page](URL target=) [HF Repo](URL target=) • [Github Repo](URL target=) • [Twitter](URL target=) [[WizardLM]](URL target=) • [[WizardCoder]](URL target=) • [[WizardMath]](URL target=) Join our [Discord](URL target=) News ---- [12/19/2023] We released WizardMath-7B-V1.1 trained from Mistral-7B, the SOTA 7B math LLM, achieves 83.2 pass@1 on GSM8k, and 33.0 pass@1 on MATH. Use this [Demo] to chat with it. [12/19/2023] WizardMath-7B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, Mixtral MOE, and Claude Instant on GSM8K pass@1. [12/19/2023] WizardMath-7B-V1.1 is comparable with ChatGPT 3.5, Gemini Pro, and surpasses Mixtral MOE on MATH pass@1. [12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs. ----------------------------------------------------------------------------------- Model: MPT-7B, GSM8k Pass@1: 6.8, MATH Pass@1: 3.0 Model: Llama 1-7B, GSM8k Pass@1: 11.0, MATH Pass@1: 2.9 Model: Llama 2-7B, GSM8k Pass@1: 12.3, MATH Pass@1: 2.8 Model: Yi-6b, GSM8k Pass@1: 32.6, MATH Pass@1: 5.8 Model: Mistral-7B, GSM8k Pass@1: 37.8, MATH Pass@1: 9.1 Model: Qwen-7b, GSM8k Pass@1: 47.8, MATH Pass@1: 9.3 Model: RFT-7B, GSM8k Pass@1: 50.3, MATH Pass@1: -- Model: MAmmoTH-7B (COT), GSM8k Pass@1: 50.5, MATH Pass@1: 10.4 Model: WizardMath-7B-V1.0, GSM8k Pass@1: 54.9, MATH Pass@1: 10.7 Model: Abel-7B-001, GSM8k Pass@1: 59.7, MATH Pass@1: 13 Model: MetaMath-7B, GSM8k Pass@1: 66.5, MATH Pass@1: 19.8 Model: Arithmo-Mistral-7B, GSM8k Pass@1: 74.7, MATH Pass@1: 25.3 Model: MetaMath-Mistral-7B, GSM8k Pass@1: 77.7, MATH Pass@1: 28.2 Model: Abel-7B-002, GSM8k Pass@1: 80.4, MATH Pass@1: 29.5 Model: WizardMath-7B-V1.1, GSM8k Pass@1: 83.2, MATH Pass@1: 33.0 [12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs. -------------------------------------------------------------------------------- Model: Llemma-34B, GSM8k Pass@1: 51.5, MATH Pass@1: 25.0 Model: Minerva-62B, GSM8k Pass@1: 52.4, MATH Pass@1: 27.6 Model: Llama 2-70B, GSM8k Pass@1: 56.8, MATH Pass@1: 13.5 Model: DeepSeek 67B, GSM8k Pass@1: 63.4, MATH Pass@1: -- Model: Gork 33B, GSM8k Pass@1: 62.9, MATH Pass@1: 23.9 Model: MAmmoTH-70B, GSM8k Pass@1: 72.4, MATH Pass@1: 21.1 Model: Yi-34B, GSM8k Pass@1: 67.9, MATH Pass@1: 15.9 Model: Mixtral 8x7B, GSM8k Pass@1: 74.4, MATH Pass@1: 28.4 Model: MetaMath-70B, GSM8k Pass@1: 82.3, MATH Pass@1: 26.6 Model: WizardMath-7B-V1.1, GSM8k Pass@1: 83.2, MATH Pass@1: 33.0 Data Contamination Check: ------------------------- Before model training, we carefully and rigorously checked all the training data, and used multiple deduplication methods to verify and prevent data leakage on GSM8k and MATH test set. Note for model system prompts usage: Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions. Default version: CoT Version: （For the simple math questions, we do NOT recommend to use the CoT prompt.） Inference WizardMath Demo Script -------------------------------- We provide the WizardMath inference demo code here. Please cite the repo if you use the data, method or code in this repo.	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null	null	peft	# 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "unsloth/llama-2-13b-bnb-4bit"}	null	lmg-anon/vntl-13b-v0.2-qlora	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-2-13b-bnb-4bit", "region:us" ]	2024-02-08T04:52:45+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-unsloth/llama-2-13b-bnb-4bit #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-unsloth/llama-2-13b-bnb-4bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 42, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-unsloth/llama-2-13b-bnb-4bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.12453377991914749, 0.21093545854091644, -0.0028214736375957727, 0.0290839746594429, 0.0809982419013977, 0.018378613516688347, 0.05512875318527222, 0.1306380182504654, 0.026453014463186264, 0.11573038250207901, 0.07425287365913391, 0.12261075526475906, 0.11124251037836075, 0.20841574668884277, 0.007121732458472252, -0.16033422946929932, 0.026212913915514946, -0.07947352528572083, 0.01885686255991459, 0.12731729447841644, 0.13896465301513672, -0.10140690207481384, 0.07727691531181335, -0.02126898244023323, 0.004357269499450922, -0.032517869025468826, 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null	null	transformers	<!-- 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. --> # Fine-Tuned_Model2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "Fine-Tuned_Model2", "results": []}]}	image-classification	arpanl/Fine-Tuned_Model2	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T04:56:44+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# Fine-Tuned_Model2 This model is a fine-tuned version of google/vit-base-patch16-224 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# Fine-Tuned_Model2\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Fine-Tuned_Model2\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 79, 37, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Fine-Tuned_Model2\n\nThis model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 32\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 100### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.09499125182628632, 0.1827688068151474, -0.0016954183811321855, 0.11282956600189209, 0.14734643697738647, 0.013401983305811882, 0.10694930702447891, 0.15498211979866028, -0.08908739686012268, 0.07138904929161072, 0.06431407481431961, 0.019147498533129692, 0.06005696952342987, 0.149776428937912, -0.0035386651288717985, -0.24733728170394897, -0.004315583501011133, -0.017485300078988075, -0.03462639078497887, 0.09390384703874588, 0.08984998613595963, -0.09850428253412247, 0.08918920904397964, -0.012402412481606007, -0.13958972692489624, 0.039795637130737305, -0.02379923313856125, -0.053221553564071655, 0.0854109674692154, 0.008912541903555393, 0.060432303696870804, 0.007031342014670372, 0.14941804111003876, -0.22229832410812378, -0.00032054699840955436, 0.0931549146771431, 0.015342728234827518, 0.07563983649015427, 0.05506181716918945, 0.0005333408480510116, 0.03962258994579315, -0.17945916950702667, 0.08804066479206085, 0.020519185811281204, -0.0519731305539608, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-3.5bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:00:02+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	null	# 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	{"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": "18.00 +/- 15.89", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	wladimir/Reinforce-Pixelcopter-PLE-v0	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-08T05:04:13+00:00	[]	[]	TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# 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: URL	[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 41, 58 ]	[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.0073175891302526, -0.2259262204170227, -0.0017347558168694377, 0.05054566636681557, 0.0658537745475769, -0.055378563702106476, 0.1412602812051773, 0.05916554853320122, -0.04990595206618309, 0.059261854737997055, 0.14166708290576935, 0.03996060788631439, 0.022112762555480003, 0.1513713151216507, 0.09764605015516281, -0.2469022423028946, 0.07438477873802185, 0.01641594059765339, 0.008152224123477936, 0.09583204984664917, 0.060265738517045975, -0.1405058205127716, 0.037032704800367355, -0.01332044042646885, -0.13650871813297272, 0.0010478810872882605, -0.021802188828587532, -0.03625129908323288, 0.15681709349155426, 0.006844013463705778, 0.09602472931146622, -0.001560068572871387, 0.06475798785686493, -0.12438877671957016, 0.05466329678893089, 0.06455880403518677, -0.06293967366218567, 0.058029334992170334, -0.057374246418476105, 0.11959903687238693, 0.04641333222389221, -0.01578129455447197, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "274.95 +/- 16.24", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	dlee-13/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-08T05:07:02+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	transformers	<!-- 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. --> # TrOCR_0208-1 This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8777 - Cer: 0.0931 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 1.5571 \| 0.68 \| 200 \| 1.6487 \| 0.2024 \| \| 0.9405 \| 1.37 \| 400 \| 1.2816 \| 0.1666 \| \| 0.6927 \| 2.05 \| 600 \| 1.0319 \| 0.1199 \| \| 1.0794 \| 2.74 \| 800 \| 0.8777 \| 0.0931 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.13.0 - Tokenizers 0.15.0	{"tags": ["generated_from_trainer"], "base_model": "microsoft/trocr-base-stage1", "model-index": [{"name": "TrOCR_0208-1", "results": []}]}	null	yoon1000/TrOCR_0208-1	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:microsoft/trocr-base-stage1", "endpoints_compatible", "region:us" ]	2024-02-08T05:10:40+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us	TrOCR\_0208-1 ============= This model is a fine-tuned version of microsoft/trocr-base-stage1 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.8777 * Cer: 0.0931 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.13.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]	[ 56, 113, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.13.0\n* Tokenizers 0.15.0" ]	[ -0.09946054965257645, 0.04089779034256935, -0.0013119349023327231, 0.0928853377699852, 0.1553080976009369, 0.011895016767084599, 0.12249614298343658, 0.0926666259765625, -0.10396905243396759, 0.0533798411488533, 0.12088591605424881, 0.11361321806907654, 0.022177845239639282, 0.14082275331020355, -0.04729761928319931, -0.2591608166694641, 0.019180556759238243, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-4.0bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:13:11+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	transformers	This is an English and Chinese chat model for Coding but also general purpose. example output 写一个python程序抓取youtube最流行的内容：从网页获取数据，以下是一个python脚本，可以从Youtube上抓取最受欢迎的视频并将其保存到本地文件中。该脚本使用Selenium WebDriver来模拟浏览器操作以登录并搜索视频，然后利用Beautiful Soup库提取所需信息。如果您需要任何帮助或修改建议，请随时告诉我！ ``` from selenium import webdriver from bs4 import BeautifulSoup as BS import os # 设置ChromeDriver路径 CHROMEDRIVER_PATH = './chromedriver.exe' # 更换为实际路径 def main(): # 启动Chrome浏览器并登录Youtube browser = webdriver.Chrome(executable_path=CHROMEDRIVER_PATH) browser.get('https://www.youtube.com/') login_url = 'https://accounts.google.com/signin' browser.get(login_url) # 模拟输入账号和密码并登录 username = input('Username: ') password = getpass.getpass() # 防止密码显示在终端上 submit_button = browser.find_element_by_class_name('whsLW', timeout=5) submit_button.click() # 在搜索栏中输入关键词并查找结果 search_query = 'most popular youtube videos today' search_box = browser.find_element_by_id('search_query') search_box.send_keys(search_query) enter_key = browser.find_element_by_class_name('ytp-suggest-renderer__input-icon') enter_key.click() results = BS(browser.page_source, 'html.parser') # 从搜索结果中提取出最受欢迎的视频链接并保存到本地文件 videos = results.find('ol', {'class': 'r'}) video_urls = [a['href'] for a in videos.find_all('a')] saved_path = 'popular_videos/' + os.getcwd().split('/')[-2] + '_' + str(int(time.time())) + '/' if not os.path.exists(saved_path): os.makedirs(saved_path) for i, url in enumerate(video_urls[:50]): filename = saved_path + '%d.mp4' % (i+1) urllib.request.urlretrieve(url, filename) print('Downloaded top 50 videos to:', saved_path) except Exception as e: print(e) ```	{"license": "other", "tags": ["yi", "moe"], "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE"}	text-generation	cloudyu/60B_MoE_Coder_v3	[ "transformers", "safetensors", "mixtral", "text-generation", "yi", "moe", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:17:53+00:00	[]	[]	TAGS #transformers #safetensors #mixtral #text-generation #yi #moe #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	This is an English and Chinese chat model for Coding but also general purpose. example output 写一个python程序抓取youtube最流行的内容：从网页获取数据，以下是一个python脚本，可以从Youtube上抓取最受欢迎的视频并将其保存到本地文件中。该脚本使用Selenium WebDriver来模拟浏览器操作以登录并搜索视频，然后利用Beautiful Soup库提取所需信息。如果您需要任何帮助或修改建议，请随时告诉我！	[]	[ "TAGS\n#transformers #safetensors #mixtral #text-generation #yi #moe #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 61 ]	[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #yi #moe #conversational #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 0.01445821113884449, 0.008185355924069881, -0.0057961768470704556, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	text-generation	hotsuyuki/gpt_0.125B_global_step4000_openassistant	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:22:30+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05622259899973869, 0.16002345085144043, -0.004987028427422047, 0.023115945979952812, 0.0962471067905426, 0.011845538392663002, 0.06785304099321365, 0.11496778577566147, -0.020396295934915543, 0.11142492294311523, 0.03292480856180191, 0.0972127765417099, 0.11474913358688354, 0.16215258836746216, 0.004439093638211489, -0.23455148935317993, 0.04782992601394653, -0.12695099413394928, -0.033447545021772385, 0.11785799264907837, 0.14491069316864014, -0.10402194410562515, 0.07766910642385483, -0.030544815585017204, -0.009361269883811474, -0.03290390968322754, -0.06365230679512024, -0.05152205005288124, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	text-generation	IBM-DTT/sap_finetunemodel_codegeneration_misteral_10k	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:23:29+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04571164771914482, 0.1637648642063141, -0.005522117950022221, 0.017756497487425804, 0.09821303188800812, 0.01318030059337616, 0.06541220843791962, 0.1127115860581398, -0.017605241388082504, 0.1127321794629097, 0.030432263389229774, 0.09820804744958878, 0.1134178638458252, 0.14702944457530975, -0.003594378475099802, -0.22472713887691498, 0.052083637565374374, -0.12124937027692795, -0.03241228312253952, 0.1181139275431633, 0.14941681921482086, -0.09871039539575577, 0.07234785705804825, -0.030714161694049835, -0.01334790326654911, -0.03167412802577019, -0.05947697162628174, -0.045681875199079514, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This model is for used for our thesis, Dynamic Quest Generation. - Developed by: Carlo Vii - 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]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	cvzion/mistral-dqg-v6	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T05:24:27+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This model is for used for our thesis, Dynamic Quest Generation. - Developed by: Carlo Vii - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2 ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis model is for used for our thesis, Dynamic Quest Generation.\n\n- Developed by: Carlo Vii\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis model is for used for our thesis, Dynamic Quest Generation.\n\n- Developed by: Carlo Vii\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 85, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis model is for used for our thesis, Dynamic Quest Generation.\n\n- Developed by: Carlo Vii\n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]: mistralai/Mistral-7B-Instruct-v0.2### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05607038363814354, 0.21808165311813354, -0.003011707216501236, 0.025472493842244148, 0.12555564939975739, 0.0025999341160058975, 0.03457632660865784, 0.12712626159191132, -0.024995185434818268, 0.10229407250881195, 0.03139004111289978, 0.0954960361123085, 0.1022351011633873, 0.1575213521718979, 0.03137965127825737, -0.22601863741874695, 0.006480363663285971, -0.09621047973632812, 0.013627239502966404, 0.10862281918525696, 0.13652390241622925, -0.1015954539179802, 0.07293689996004105, -0.03637291491031647, -0.01424424722790718, -0.004057562444359064, -0.09777285158634186, -0.06617556512355804, 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null	null	transformers	<!-- 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. --> # pretrained_gpt2 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: - Loss: 5.4616 ## 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 6.48 \| 1.17 \| 500 \| 6.1099 \| \| 5.5792 \| 2.35 \| 1000 \| 5.7804 \| \| 5.1247 \| 3.52 \| 1500 \| 5.6199 \| \| 4.7774 \| 4.69 \| 2000 \| 5.5176 \| \| 4.4701 \| 5.87 \| 2500 \| 5.4713 \| \| 4.1836 \| 7.04 \| 3000 \| 5.4591 \| \| 3.9183 \| 8.22 \| 3500 \| 5.4620 \| \| 3.7163 \| 9.39 \| 4000 \| 5.4616 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "pretrained_gpt2", "results": []}]}	text-generation	Anish13/pretrained_gpt2	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:26:23+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	pretrained\_gpt2 ================ This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 5.4616 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: 0.0005 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 72, 112, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.09112178534269333, 0.0655236691236496, -0.0035305856727063656, 0.08389535546302795, 0.12653855979442596, 0.01508148480206728, 0.16209271550178528, 0.12821859121322632, -0.06391997635364532, 0.061594363301992416, 0.1269487738609314, 0.11914882063865662, 0.03571315109729767, 0.1414848119020462, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-4.65bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:28:09+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	transformers	<!-- 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. --> # TesterGG/act_classifier This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3861 - Validation Loss: 0.5086 - Train Accuracy: 0.8073 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 9080, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.5062 \| 0.5242 \| 0.7969 \| 0 \| \| 0.4096 \| 0.5086 \| 0.8073 \| 1 \| \| 0.3861 \| 0.5086 \| 0.8073 \| 2 \| ### Framework versions - Transformers 4.38.0.dev0 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "TesterGG/act_classifier", "results": []}]}	text-classification	TesterGG/act_classifier	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T05:28:51+00:00	[]	[]	TAGS #transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	TesterGG/act\_classifier ======================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.3861 * Validation Loss: 0.5086 * Train Accuracy: 0.8073 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 9080, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * TensorFlow 2.15.0 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 70, 304, 4, 36 ]	[ "passage: TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 9080, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.07663873583078384, 0.08986556529998779, -0.008487056009471416, 0.0724228098988533, 0.12175950407981873, 0.04083836078643799, 0.09483479708433151, 0.12496300786733627, -0.0758526474237442, 0.1557338386774063, 0.1059238612651825, 0.15150544047355652, 0.037131063640117645, 0.13350623846054077, -0.06639955937862396, -0.14093494415283203, 0.05740964412689209, -0.036766767501831055, -0.04073693975806236, 0.07434383779764175, 0.06369990855455399, -0.06048324331641197, 0.07965539395809174, -0.021063320338726044, -0.06953240185976028, -0.006596391089260578, 0.02444688230752945, -0.04740916192531586, 0.06788547337055206, 0.05879714712500572, 0.03951220214366913, 0.016151968389749527, -0.0059505645185709, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	humung/polyglot-ko-12.8b-vlending-v0.4	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T05:36:19+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	peft	<!-- 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. --> # logs This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7854 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.9083 \| 0.16 \| 100 \| 0.9578 \| \| 0.7997 \| 0.32 \| 200 \| 0.9078 \| \| 0.7535 \| 0.48 \| 300 \| 0.8746 \| \| 0.7232 \| 0.64 \| 400 \| 0.8580 \| \| 0.6994 \| 0.8 \| 500 \| 0.8348 \| \| 0.6882 \| 0.96 \| 600 \| 0.8228 \| \| 0.6433 \| 1.12 \| 700 \| 0.8008 \| \| 0.5995 \| 1.28 \| 800 \| 0.7912 \| \| 0.5908 \| 1.44 \| 900 \| 0.7864 \| \| 0.5902 \| 1.6 \| 1000 \| 0.7836 \| \| 0.5671 \| 1.76 \| 1100 \| 0.7850 \| \| 0.5749 \| 1.92 \| 1200 \| 0.7854 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "logs", "results": []}]}	null	nhatminh/TinyLLama-Chat-CoT	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	2024-02-08T05:36:45+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us	logs ==== This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7854 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: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * num\_epochs: 2 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.1.2 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 55, 141, 4, 41 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #generated_from_trainer #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.13321538269519806, 0.08248830586671829, 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null	null	transformers	<!-- 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: `0.4.0` ```yaml adapter: qlora additional_layers: 2 base_model: ahxt/LiteLlama-460M-1T bf16: false dataset_prepared_path: null datasets: - path: OEvortex/vortex-mini type: alpaca debug: null deepspeed: null early_stopping_patience: null embedding_size: 256 evals_per_epoch: null flash_attention: false fp16: true fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true group_by_length: false hidden_size: 512 is_llama_derived_model: false learning_rate: 0.0002 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 16 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lora_target_modules: null lr_scheduler: cosine max_steps: 20 micro_batch_size: 1 mlflow_experiment_name: colab-example model_type: LlamaForCausalLM num_epochs: 4 optimizer: paged_adamw_32bit output_dir: ./qlora-out pad_to_sequence_len: true resume_from_checkpoint: null sample_packing: true saves_per_epoch: null sequence_len: 1096 special_tokens: null strict: false tf32: false tokenizer_type: GPT2Tokenizer train_on_inputs: false val_set_size: 0.05 wandb_entity: null wandb_log_model: null wandb_name: null wandb_project: null wandb_watch: null warmup_steps: 10 weight_decay: 0.0 xformers_attention: null ``` </details><br> # qlora-out This model is a fine-tuned version of [ahxt/LiteLlama-460M-1T](https://huggingface.co/ahxt/LiteLlama-460M-1T) on the None dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - training_steps: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4442 \| 0.0 \| 20 \| nan \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["OEvortex/vortex-mini"], "base_model": "ahxt/LiteLlama-460M-1T", "model-index": [{"name": "qlora-out", "results": []}]}	text-generation	Aarifkhan/lite-vortex	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "dataset:OEvortex/vortex-mini", "base_model:ahxt/LiteLlama-460M-1T", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:38:08+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #generated_from_trainer #dataset-OEvortex/vortex-mini #base_model-ahxt/LiteLlama-460M-1T #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	<img src="URL alt="Built with Axolotl" width="200" height="32"/> See axolotl config axolotl version: '0.4.0' qlora-out ========= This model is a fine-tuned version of ahxt/LiteLlama-460M-1T on the None dataset. It achieves the following results on the evaluation set: * Loss: nan 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 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 10 * training\_steps: 20 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.0.1+cu117 * Datasets 2.16.1 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #dataset-OEvortex/vortex-mini #base_model-ahxt/LiteLlama-460M-1T #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ 88, 130, 4, 44 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #generated_from_trainer #dataset-OEvortex/vortex-mini #base_model-ahxt/LiteLlama-460M-1T #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* training\\_steps: 20\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* 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null	null	transformers	## Description This is ai-forever/FRED-T5-large model trained on Question-Answering, Question-Generation and Answer-Aware Question Generation tasks on russian dataset (hivaze/ru-AAQG-QA-QG) ### Prompts ```python AAQG_PROMPT = "Сгенерируй вопрос по тексту, используя известный ответ. Текст: '{context}'. Ответ: '{answer}'." QG_PROMPT = "Сгенерируй вопрос по тексту. Текст: '{context}'." QA_PROMPT = "Сгенерируй ответ на вопрос по тексту. Текст: '{context}'. Вопрос: '{question}'." ``` ### Examples and code ```python from transformers import AutoTokenizer, T5ForConditionalGeneration from functools import partial saved_checkpoint = 'hivaze/AAQG-QA-QG-FRED-T5-large' tokenizer = AutoTokenizer.from_pretrained(saved_checkpoint) model = T5ForConditionalGeneration.from_pretrained(saved_checkpoint).cuda() def generate_text(prompt, tokenizer, model, n=1, temperature=0.8, num_beams=3): encoded_input = tokenizer.encode_plus(prompt, return_tensors='pt') encoded_input = {k: v.to(model.device) for k, v in encoded_input.items()} resulted_tokens = model.generate(**encoded_input, max_new_tokens=64, do_sample=True, num_beams=num_beams, num_return_sequences=n, temperature=temperature, top_p=0.9, top_k=50) resulted_texts = tokenizer.batch_decode(resulted_tokens, skip_special_tokens=True) return resulted_texts generate_text = partial(generate_text, tokenizer=tokenizer, model=model) test_context = "Путешественник Федор Конюхов и пилот Игорь Потапкин установили мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова" ``` #### AAQG ```python generate_text(AAQG_PROMPT.format( context=test_context, answer='на паралёте' ), n=1) ``` > "На чём установили мировой рекорд высоты полета Федор Конюхов и пилот Игорь Потапкин?" ```python generate_text(AAQG_PROMPT.format( context=test_context, answer='рекорд высоты полета' ), n=1) ``` > "Что установили Конюхов и Потапкин?" #### QA ```python generate_text(QA_PROMPT.format( context=test_context, question='Что установили путешественник Федор Конюхов и пилот Игорь Потапкин?' ), n=1) ``` > "мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова" #### QG ```python generate_text(QG_PROMPT.format(context=test_context), n=1) ``` > "Кто установил мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров?" ## Metrics \| Step \| Training Loss \| Validation Loss \| Sbleu \| Chr F \| Rouge1 \| Rouge2 \| Rougel \| \|------\|---------------\|-----------------\|-------\|-------\|--------\|--------\|--------\| \| 500 \| 1.183100 \| 1.188049 \| 40.114700 \| 62.147000 \| 0.104600 \| 0.034500 \| 0.104300 \| \| 1000 \| 1.193000 \| 1.125300 \| 40.722300 \| 62.661400 \| 0.104700 \| 0.033900 \| 0.104300 \| \| 1500 \| 1.114300 \| 1.097496 \| 41.416600 \| 63.060300 \| 0.106100 \| 0.033800 \| 0.105800 \| \| 2000 \| 1.081300 \| 1.080900 \| 41.600200 \| 63.260500 \| 0.106200 \| 0.033700 \| 0.105900 \| \| 2500 \| 1.076900 \| 1.070221 \| 41.722300 \| 63.315300 \| 0.106300 \| 0.034100 \| 0.106000 \| \| 3000 \| 1.125600 \| 1.062671 \| 41.744500 \| 63.409400 \| 0.106400 \| 0.034200 \| 0.106200 \| ## Authors - Sergei Bratchikov (https://t.me/nlpwanderer)	{"language": ["ru"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["hivaze/ru-AAQG-QA-QG"], "pipeline_tag": "text2text-generation"}	text2text-generation	hivaze/AAQG-QA-QG-FRED-T5-large	[ "transformers", "safetensors", "t5", "text2text-generation", "ru", "dataset:hivaze/ru-AAQG-QA-QG", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:38:22+00:00	[]	[ "ru" ]	TAGS #transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Description ----------- This is ai-forever/FRED-T5-large model trained on Question-Answering, Question-Generation and Answer-Aware Question Generation tasks on russian dataset (hivaze/ru-AAQG-QA-QG) ### Prompts ### Examples and code #### AAQG > > "На чём установили мировой рекорд высоты полета Федор Конюхов и пилот Игорь Потапкин?" > > > > > "Что установили Конюхов и Потапкин?" > > > #### QA > > "мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова" > > > #### QG > > "Кто установил мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров?" > > > Metrics ------- Authors ------- * Sergei Bratchikov (https://t.me/nlpwanderer)	[ "### Prompts", "### Examples and code", "#### AAQG\n\n\n\n> \n> \"На чём установили мировой рекорд высоты полета Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили Конюхов и Потапкин?\"\n> \n> \n>", "#### QA\n\n\n\n> \n> \"мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова\"\n> \n> \n>", "#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Prompts", "### Examples and code", "#### AAQG\n\n\n\n> \n> \"На чём установили мировой рекорд высоты полета Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили Конюхов и Потапкин?\"\n> \n> \n>", "#### QA\n\n\n\n> \n> \"мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова\"\n> \n> \n>", "#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ 77, 5, 6, 50, 38, 63 ]	[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #ru #dataset-hivaze/ru-AAQG-QA-QG #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Prompts### Examples and code#### AAQG\n\n\n\n> \n> \"На чём установили мировой рекорд высоты полета Федор Конюхов и пилот Игорь Потапкин?\"\n> \n> \n> \n\n\n\n> \n> \"Что установили Конюхов и Потапкин?\"\n> \n> \n>#### QA\n\n\n\n> \n> \"мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров — сайт Конюхова\"\n> \n> \n>#### QG\n\n\n\n> \n> \"Кто установил мировой рекорд высоты полета на паралёте, поднявшись на высоту 4728 метров?\"\n> \n> \n> \n\n\nMetrics\n-------\n\n\n\nAuthors\n-------\n\n\n* Sergei Bratchikov (https://t.me/nlpwanderer)" ]	[ 0.008694023825228214, 0.07239315658807755, -0.008878832682967186, 0.07727925479412079, 0.03500809893012047, -0.009105120785534382, 0.10201002657413483, 0.06133727356791496, 0.030415182933211327, 0.05924495309591293, 0.17193832993507385, 0.0017726188525557518, 0.044173020869493484, 0.13238155841827393, -0.008937506936490536, -0.1832474172115326, -0.011835862882435322, -0.09428244084119797, -0.1328388899564743, 0.11587566882371902, 0.08074899762868881, -0.09187222272157669, 0.02720683068037033, -0.0793626606464386, 0.028131185099482536, 0.005356821231544018, 0.0670243576169014, -0.08806393295526505, 0.09125467389822006, 0.019460055977106094, 0.019644396379590034, 0.05467665567994118, 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null	null	peft	<!-- 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. --> # code-llama-7b-text-to-sql This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the generator 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: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "HuggingFaceH4/zephyr-7b-beta", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]}	null	charleschen2022/code-llama-7b-text-to-sql	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]	2024-02-08T05:38:45+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us	# code-llama-7b-text-to-sql This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator 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: 3 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.7.2.dev0 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n", "# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 59, 42, 6, 12, 8, 3, 128, 4, 42 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #dataset-generator #base_model-HuggingFaceH4/zephyr-7b-beta #license-mit #region-us \n# code-llama-7b-text-to-sql\n\nThis model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 3\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 6\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- num_epochs: 3### Training results### Framework versions\n\n- PEFT 0.7.2.dev0\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.09624605625867844, 0.0813027173280716, -0.0036917049437761307, 0.08221740275621414, 0.1360018104314804, 0.027185237035155296, 0.1334851086139679, 0.11090151965618134, -0.06843243539333344, 0.09375786036252975, 0.035841427743434906, 0.021312672644853592, 0.055649496614933014, 0.14697164297103882, -0.0399676114320755, -0.20877255499362946, 0.01289305929094553, -0.024164782837033272, -0.073714978992939, 0.09618699550628662, 0.09982776641845703, -0.09642899036407471, 0.06324788182973862, 0.01798507198691368, -0.17073692381381989, 0.018285144120454788, -0.017748191952705383, -0.02616720274090767, 0.11649713665246964, 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null	null	adapter-transformers	![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435f27b2d0ed796668ffd8b/NI5slu-granu9cxA5d97r.png) MHENN cat (generated by pixlr) the primary base of all models is mistral-instruct-v0.1 this is a continuous fine-tune of mistral and is MHENN4 fine-tuned further. all models are continuous finetunes and started at the original MHENN version	{"language": ["en"], "license": "mit", "library_name": "adapter-transformers", "datasets": ["netcat420/MHENN5"], "pipeline_tag": "text-classification"}	text-classification	netcat420/MHENN5	[ "adapter-transformers", "safetensors", "mistral", "text-classification", "en", "dataset:netcat420/MHENN5", "license:mit", "region:us" ]	2024-02-08T05:42:03+00:00	[]	[ "en" ]	TAGS #adapter-transformers #safetensors #mistral #text-classification #en #dataset-netcat420/MHENN5 #license-mit #region-us	!image/png MHENN cat (generated by pixlr) the primary base of all models is mistral-instruct-v0.1 this is a continuous fine-tune of mistral and is MHENN4 fine-tuned further. all models are continuous finetunes and started at the original MHENN version	[]	[ "TAGS\n#adapter-transformers #safetensors #mistral #text-classification #en #dataset-netcat420/MHENN5 #license-mit #region-us \n" ]	[ 43 ]	[ "passage: TAGS\n#adapter-transformers #safetensors #mistral #text-classification #en #dataset-netcat420/MHENN5 #license-mit #region-us \n" ]	[ -0.060381270945072174, 0.03346438705921173, -0.006650677882134914, 0.0035642043221741915, 0.08513599634170532, 0.01716635935008526, 0.21930906176567078, 0.024824893102049828, 0.0764135792851448, -0.07767511159181595, 0.1073313057422638, 0.11568155884742737, -0.012417417950928211, 0.10075514018535614, -0.06012512743473053, -0.11139017343521118, 0.10256293416023254, -0.024335406720638275, -0.053095147013664246, 0.09732303023338318, 0.11243278533220291, -0.041596777737140656, 0.08095023781061172, -0.04644138365983963, -0.06472776085138321, 0.011149147525429726, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-5.0bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:45:32+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	transformers	# Severusectum-7B-DPO - AWQ - Model creator: [s3nh](https://huggingface.co/s3nh) - Original model: [Severusectum-7B-DPO](https://huggingface.co/s3nh/Severusectum-7B-DPO) ![image](severus.png)	{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["mergekit", "merge"], "model_name": "Severusectum-7B-DPO", "base_model": ["FelixChao/Sectumsempra-7B-DPO", "FelixChao/WestSeverus-7B-DPO-v2"], "model_creator": "s3nh", "model_type": "mistral", "pipeline_tag": "text-generation", "prompt_template": "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant\n", "quantized_by": "Suparious"}	text-generation	solidrust/Severusectum-7B-DPO-AWQ	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "en", "base_model:FelixChao/Sectumsempra-7B-DPO", "base_model:FelixChao/WestSeverus-7B-DPO-v2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	2024-02-08T05:50:02+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #en #base_model-FelixChao/Sectumsempra-7B-DPO #base_model-FelixChao/WestSeverus-7B-DPO-v2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	# Severusectum-7B-DPO - AWQ - Model creator: s3nh - Original model: Severusectum-7B-DPO !image	[ "# Severusectum-7B-DPO - AWQ\n\n- Model creator: s3nh\n- Original model: Severusectum-7B-DPO\n\n!image" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #en #base_model-FelixChao/Sectumsempra-7B-DPO #base_model-FelixChao/WestSeverus-7B-DPO-v2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Severusectum-7B-DPO - AWQ\n\n- Model creator: s3nh\n- Original model: Severusectum-7B-DPO\n\n!image" ]	[ 105, 33 ]	[ "passage: TAGS\n#transformers #safetensors 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "base_model": "deepset/roberta-base-squad2", "model-index": [{"name": "roberta-finetuned-subjqa-movies_2", "results": []}]}	question-answering	akshayugale/roberta-finetuned-subjqa-movies_2	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	2024-02-08T05:52:21+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us	# roberta-finetuned-subjqa-movies_2 This model is a fine-tuned version of deepset/roberta-base-squad2 on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# roberta-finetuned-subjqa-movies_2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us \n", "# roberta-finetuned-subjqa-movies_2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 67, 44, 6, 12, 8, 3, 103, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #question-answering #generated_from_trainer #base_model-deepset/roberta-base-squad2 #license-cc-by-4.0 #endpoints_compatible #region-us \n# roberta-finetuned-subjqa-movies_2\n\nThis model is a fine-tuned version of deepset/roberta-base-squad2 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.35.2\n- 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	Jaerim/bloom-7b1-lora-tagger_2	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T05:52:28+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	## 训练细节 - [Firefly](https://github.com/yangjianxin1/Firefly) QLoRA - 基座：Yi-6B - 数据：341千中文字符（103 个对话串），来自前四作中去掉了长篇描写后的内容 - 时长：60 steps, ~9.2 epochs ## 技术决策 - 少量的数据：LIMA: Less Is More for Alignment 的宗旨在于，模型的生成内容的质量几乎完全由基座模型的预训练决定，微调训练只会影响生成内容的风格，而且只需要极少量但高质量的训练数据就可以达到效果。 - Yi-6B 为基座：在微调训练的早期测试阶段，我曾以 ChatGLM3-6B-Base 作为基座模型。后来决定使用 Yi-6B 主要是因为它的 Llama2 架构在现今依旧是主流实现，部署选择要多得多（特别是 GGUF 生态）。最终成品比较时，基于 Yi-6B 的比基于 ChatGLM3-6B-Base 对文风的还原度要高一些，不过在创造力上略逊一筹。 - Firefly 微调框架：他们的训练方法是为多轮对话设计的，而超长上下文正是我的训练目标。`yoruno-vn` 数据集的对话串平均长度是现今大多问答数据集的数倍。对于未来的写作模型，我会认真研究如何制作有真正有效 32k 上下文的模型。	{"language": ["zh"], "license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences"], "datasets": ["nenekochan/yoruno-vn"], "base_model": "01-ai/Yi-6B", "inference": false}	text-generation	nenekochan/Yi-6B-yoruno	[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "conversational", "zh", "dataset:nenekochan/yoruno-vn", "base_model:01-ai/Yi-6B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-08T05:54:09+00:00	[]	[ "zh" ]	TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #zh #dataset-nenekochan/yoruno-vn #base_model-01-ai/Yi-6B #license-cc-by-nc-4.0 #autotrain_compatible #text-generation-inference #region-us	## 训练细节 - Firefly QLoRA - 基座：Yi-6B - 数据：341千中文字符（103 个对话串），来自前四作中去掉了长篇描写后的内容 - 时长：60 steps, ~9.2 epochs ## 技术决策 - 少量的数据：LIMA: Less Is More for Alignment 的宗旨在于，模型的生成内容的质量几乎完全由基座模型的预训练决定，微调训练只会影响生成内容的风格，而且只需要极少量但高质量的训练数据就可以达到效果。 - Yi-6B 为基座：在微调训练的早期测试阶段，我曾以 ChatGLM3-6B-Base 作为基座模型。后来决定使用 Yi-6B 主要是因为它的 Llama2 架构在现今依旧是主流实现，部署选择要多得多（特别是 GGUF 生态）。最终成品比较时，基于 Yi-6B 的比基于 ChatGLM3-6B-Base 对文风的还原度要高一些，不过在创造力上略逊一筹。 - Firefly 微调框架：他们的训练方法是为多轮对话设计的，而超长上下文正是我的训练目标。'yoruno-vn' 数据集的对话串平均长度是现今大多问答数据集的数倍。对于未来的写作模型，我会认真研究如何制作有真正有效 32k 上下文的模型。	[ "## 训练细节\n\n- Firefly QLoRA\n- 基座：Yi-6B\n- 数据：341千中文字符（103 个对话串），来自前四作中去掉了长篇描写后的内容\n- 时长：60 steps, ~9.2 epochs", "## 技术决策\n\n- 少量的数据：LIMA: Less Is More for Alignment 的宗旨在于，模型的生成内容的质量几乎完全由基座模型的预训练决定，微调训练只会影响生成内容的风格，而且只需要极少量但高质量的训练数据就可以达到效果。\n- Yi-6B 为基座：在微调训练的早期测试阶段，我曾以 ChatGLM3-6B-Base 作为基座模型。后来决定使用 Yi-6B 主要是因为它的 Llama2 架构在现今依旧是主流实现，部署选择要多得多（特别是 GGUF 生态）。最终成品比较时，基于 Yi-6B 的比基于 ChatGLM3-6B-Base 对文风的还原度要高一些，不过在创造力上略逊一筹。\n- Firefly 微调框架：他们的训练方法是为多轮对话设计的，而超长上下文正是我的训练目标。'yoruno-vn' 数据集的对话串平均长度是现今大多问答数据集的数倍。对于未来的写作模型，我会认真研究如何制作有真正有效 32k 上下文的模型。" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #zh #dataset-nenekochan/yoruno-vn #base_model-01-ai/Yi-6B #license-cc-by-nc-4.0 #autotrain_compatible #text-generation-inference #region-us \n", "## 训练细节\n\n- Firefly QLoRA\n- 基座：Yi-6B\n- 数据：341千中文字符（103 个对话串），来自前四作中去掉了长篇描写后的内容\n- 时长：60 steps, ~9.2 epochs", "## 技术决策\n\n- 少量的数据：LIMA: Less Is More for Alignment 的宗旨在于，模型的生成内容的质量几乎完全由基座模型的预训练决定，微调训练只会影响生成内容的风格，而且只需要极少量但高质量的训练数据就可以达到效果。\n- Yi-6B 为基座：在微调训练的早期测试阶段，我曾以 ChatGLM3-6B-Base 作为基座模型。后来决定使用 Yi-6B 主要是因为它的 Llama2 架构在现今依旧是主流实现，部署选择要多得多（特别是 GGUF 生态）。最终成品比较时，基于 Yi-6B 的比基于 ChatGLM3-6B-Base 对文风的还原度要高一些，不过在创造力上略逊一筹。\n- Firefly 微调框架：他们的训练方法是为多轮对话设计的，而超长上下文正是我的训练目标。'yoruno-vn' 数据集的对话串平均长度是现今大多问答数据集的数倍。对于未来的写作模型，我会认真研究如何制作有真正有效 32k 上下文的模型。" ]	[ 88, 62, 261 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #zh #dataset-nenekochan/yoruno-vn #base_model-01-ai/Yi-6B #license-cc-by-nc-4.0 #autotrain_compatible #text-generation-inference #region-us \n## 训练细节\n\n- Firefly QLoRA\n- 基座：Yi-6B\n- 数据：341千中文字符（103 个对话串），来自前四作中去掉了长篇描写后的内容\n- 时长：60 steps, ~9.2 epochs## 技术决策\n\n- 少量的数据：LIMA: Less Is More for Alignment 的宗旨在于，模型的生成内容的质量几乎完全由基座模型的预训练决定，微调训练只会影响生成内容的风格，而且只需要极少量但高质量的训练数据就可以达到效果。\n- Yi-6B 为基座：在微调训练的早期测试阶段，我曾以 ChatGLM3-6B-Base 作为基座模型。后来决定使用 Yi-6B 主要是因为它的 Llama2 架构在现今依旧是主流实现，部署选择要多得多（特别是 GGUF 生态）。最终成品比较时，基于 Yi-6B 的比基于 ChatGLM3-6B-Base 对文风的还原度要高一些，不过在创造力上略逊一筹。\n- Firefly 微调框架：他们的训练方法是为多轮对话设计的，而超长上下文正是我的训练目标。'yoruno-vn' 数据集的对话串平均长度是现今大多问答数据集的数倍。对于未来的写作模型，我会认真研究如何制作有真正有效 32k 上下文的模型。" ]	[ -0.011790980584919453, 0.007738648913800716, -0.013419604860246181, -0.009386024437844753, 0.015370509587228298, 0.009469719603657722, 0.13104233145713806, 0.04376479610800743, -0.009364457800984383, 0.012080669403076172, 0.028459548950195312, 0.042154133319854736, 0.0423777736723423, 0.05730497092008591, -0.018174774944782257, -0.09189070016145706, 0.06191030889749527, -0.008775608614087105, -0.04679960757493973, 0.07701734453439713, 0.08621729165315628, -0.05724519491195679, 0.03245865926146507, 0.026119621470570564, -0.08712366968393326, -0.0028306986205279827, -0.0035827052779495716, -0.01413644291460514, 0.10101931542158127, 0.039808161556720734, 0.08765145391225815, 0.09045873582363129, 0.011270474642515182, -0.11183765530586243, 0.0390973836183548, 0.03609234839677811, 0.021588655188679695, 0.03906683623790741, -0.04340391978621483, 0.06485100090503693, 0.14393900334835052, 0.01967744342982769, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	Amanaccessassist/adhar	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T05:58:03+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- 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. --> # SMIDS_3x_beit_large_Adamax_lr00001_fold3 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6808 - Accuracy: 0.92 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2087 \| 1.0 \| 450 \| 0.2348 \| 0.9067 \| \| 0.1169 \| 2.0 \| 900 \| 0.2554 \| 0.9217 \| \| 0.0903 \| 3.0 \| 1350 \| 0.2877 \| 0.925 \| \| 0.0618 \| 4.0 \| 1800 \| 0.3385 \| 0.925 \| \| 0.0633 \| 5.0 \| 2250 \| 0.3914 \| 0.92 \| \| 0.0007 \| 6.0 \| 2700 \| 0.4614 \| 0.9267 \| \| 0.0013 \| 7.0 \| 3150 \| 0.4789 \| 0.92 \| \| 0.0053 \| 8.0 \| 3600 \| 0.5496 \| 0.9217 \| \| 0.0003 \| 9.0 \| 4050 \| 0.5869 \| 0.9167 \| \| 0.0124 \| 10.0 \| 4500 \| 0.5174 \| 0.93 \| \| 0.0006 \| 11.0 \| 4950 \| 0.5812 \| 0.9217 \| \| 0.0002 \| 12.0 \| 5400 \| 0.6247 \| 0.9183 \| \| 0.0 \| 13.0 \| 5850 \| 0.6077 \| 0.9217 \| \| 0.0283 \| 14.0 \| 6300 \| 0.6220 \| 0.9267 \| \| 0.0 \| 15.0 \| 6750 \| 0.6313 \| 0.9217 \| \| 0.0 \| 16.0 \| 7200 \| 0.7747 \| 0.9167 \| \| 0.0 \| 17.0 \| 7650 \| 0.6285 \| 0.93 \| \| 0.0 \| 18.0 \| 8100 \| 0.6993 \| 0.92 \| \| 0.0001 \| 19.0 \| 8550 \| 0.7453 \| 0.9183 \| \| 0.0 \| 20.0 \| 9000 \| 0.6432 \| 0.9183 \| \| 0.0 \| 21.0 \| 9450 \| 0.6754 \| 0.9233 \| \| 0.0 \| 22.0 \| 9900 \| 0.7004 \| 0.9233 \| \| 0.0009 \| 23.0 \| 10350 \| 0.6678 \| 0.9217 \| \| 0.0 \| 24.0 \| 10800 \| 0.6245 \| 0.9233 \| \| 0.0 \| 25.0 \| 11250 \| 0.7048 \| 0.9283 \| \| 0.0001 \| 26.0 \| 11700 \| 0.6328 \| 0.925 \| \| 0.0 \| 27.0 \| 12150 \| 0.6790 \| 0.9267 \| \| 0.0 \| 28.0 \| 12600 \| 0.6801 \| 0.9183 \| \| 0.0001 \| 29.0 \| 13050 \| 0.6765 \| 0.9217 \| \| 0.0 \| 30.0 \| 13500 \| 0.6608 \| 0.93 \| \| 0.0002 \| 31.0 \| 13950 \| 0.6659 \| 0.925 \| \| 0.0046 \| 32.0 \| 14400 \| 0.6531 \| 0.92 \| \| 0.0 \| 33.0 \| 14850 \| 0.6941 \| 0.9217 \| \| 0.0 \| 34.0 \| 15300 \| 0.6617 \| 0.9217 \| \| 0.0 \| 35.0 \| 15750 \| 0.7299 \| 0.92 \| \| 0.0 \| 36.0 \| 16200 \| 0.6682 \| 0.9233 \| \| 0.0 \| 37.0 \| 16650 \| 0.7125 \| 0.92 \| \| 0.0 \| 38.0 \| 17100 \| 0.6705 \| 0.92 \| \| 0.0 \| 39.0 \| 17550 \| 0.7142 \| 0.9233 \| \| 0.0 \| 40.0 \| 18000 \| 0.6802 \| 0.92 \| \| 0.0 \| 41.0 \| 18450 \| 0.7057 \| 0.92 \| \| 0.0 \| 42.0 \| 18900 \| 0.6914 \| 0.92 \| \| 0.0 \| 43.0 \| 19350 \| 0.6897 \| 0.9167 \| \| 0.0 \| 44.0 \| 19800 \| 0.6920 \| 0.9183 \| \| 0.0 \| 45.0 \| 20250 \| 0.6844 \| 0.9183 \| \| 0.0 \| 46.0 \| 20700 \| 0.6742 \| 0.9217 \| \| 0.0 \| 47.0 \| 21150 \| 0.6784 \| 0.9217 \| \| 0.0 \| 48.0 \| 21600 \| 0.6783 \| 0.92 \| \| 0.0 \| 49.0 \| 22050 \| 0.6805 \| 0.92 \| \| 0.0 \| 50.0 \| 22500 \| 0.6808 \| 0.92 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_Adamax_lr00001_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.92, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_Adamax_lr00001_fold3	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T06:02:17+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_Adamax\_lr00001\_fold3 ============================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.6808 * Accuracy: 0.92 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 116, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1292150914669037, 0.17132072150707245, -0.002415567170828581, 0.13183215260505676, 0.11657863855361938, 0.020753253251314163, 0.1335890144109726, 0.16620413959026337, -0.08238927274942398, 0.04929587244987488, 0.13697229325771332, 0.1357421576976776, 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null	null	transformers	## MiquMaid v2 DPO Check out our blogpost about this model series [Here!](https://ikaridevgit.github.io/index.html?blog=blogid-6&bo=true#Miqu-base) - Join our Discord server [Here!](https://discord.gg/Bb8pRUXy3Z) <center>[<a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B">V2-70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO">V2-70B-DPO</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B">V2-2x70B</a> - <a href="https://huggingface.co/NeverSleep/MiquMaid-v2-2x70B-DPO">V2-2x70B-DPO</a>] </br> <div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63ab1241ad514ca8d1430003/tPFdudSae6SCDNvhe1lC9.png" style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: [FP16](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO) - [GGUF](https://huggingface.co/NeverSleep/MiquMaid-v2-70B-DPO-GGUF) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## DPO training data used: - [ToxicDPOqa](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicDPOqa) - [toxic-dpo-v0.1-NoWarning](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-NoWarning) ### Custom format: ``` ### Instruction: {system prompt} ### Input: {input} ### Response: {reply} ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek	{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}	text-generation	LoneStriker/MiquMaid-v2-70B-DPO-6.0bpw-h6-exl2	[ "transformers", "pytorch", "llama", "text-generation", "not-for-all-audiences", "nsfw", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T06:04:09+00:00	[]	[]	TAGS #transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## MiquMaid v2 DPO Check out our blogpost about this model series Here! - Join our Discord server Here! <center>[<a href="URL - <a href="URL - <a href="URL - <a href="URL </br> <div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Alpaca prompting format Model trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of MiquMaid-v2-70B-DPO. Switch: FP16 - GGUF ## Training data used: - Aesir datasets - NoRobots - limarp - toxic-dpo-v0.1-sharegpt - ToxicQAFinal ## DPO training data used: - ToxicDPOqa - toxic-dpo-v0.1-NoWarning ### Custom format: ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek	[ "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ "TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.", "## Credits:\n- Undi\n- IkariDev", "## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF", "## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal", "## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning", "### Custom format:", "## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ 74, 134, 11, 33, 40, 27, 5, 32 ]	[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## MiquMaid v2 DPO\n\nCheck out our blogpost about this model series Here! - Join our Discord server Here!\n\n<center>[<a href=\"URL - <a href=\"URL - <a href=\"URL - <a href=\"URL\n</br>\n<div style=\"width: 100%;\">\n <img src=\"URL style=\"display: block; margin: auto;\">\n</div></center>\n\nThis model uses the Alpaca prompting format\n\nModel trained for RP conversation on Miqu-70B with our magic sauce, then trained on DPO for uncensoring.## Credits:\n- Undi\n- IkariDev## Description\n\nThis repo contains FP16 files of MiquMaid-v2-70B-DPO.\n\nSwitch: FP16 - GGUF## Training data used:\n- Aesir datasets\n- NoRobots\n- limarp\n- toxic-dpo-v0.1-sharegpt\n- ToxicQAFinal## DPO training data used:\n- ToxicDPOqa\n- toxic-dpo-v0.1-NoWarning### Custom format:## Others\n\nUndi: If you want to support us, you can here.\n\nIkariDev: Visit my retro/neocities style website please kek" ]	[ -0.0706348717212677, 0.1641329675912857, -0.0070732529275119305, 0.031934548169374466, 0.11278533190488815, 0.045274849981069565, 0.12522536516189575, 0.1828782707452774, 0.04978856444358826, 0.07901106774806976, 0.031935933977365494, 0.15931794047355652, 0.0837186947464943, 0.17052061855793, 0.014745748601853848, -0.20975269377231598, 0.045130982995033264, -0.00788366049528122, -0.037658512592315674, 0.11343381553888321, 0.10146518051624298, -0.040018610656261444, 0.06850190460681915, 0.0027182099875062704, -0.11080321669578552, -0.05066491290926933, -0.016220586374402046, -0.060478195548057556, 0.04256656765937805, -0.03278227522969246, 0.047798555344343185, 0.04292993247509003, 0.021812420338392258, -0.20568275451660156, 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null	null	peft	# 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2.dev0	{"library_name": "peft", "base_model": "intelsense/IntelsenseMistral1stPhase"}	null	RadAlienware/mis_mod_bn_2nd_phase	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:intelsense/IntelsenseMistral1stPhase", "region:us" ]	2024-02-08T06:05:04+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-intelsense/IntelsenseMistral1stPhase #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2.dev0	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2.dev0" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-intelsense/IntelsenseMistral1stPhase #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2.dev0" ]	[ 40, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 14 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-intelsense/IntelsenseMistral1stPhase #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2.dev0" ]	[ -0.12417774647474289, 0.21463528275489807, -0.002713459311053157, 0.031501516699790955, 0.08154761046171188, 0.022615941241383553, 0.0514797568321228, 0.13042709231376648, 0.007755563128739595, 0.10719957202672958, 0.06883455812931061, 0.10996545106172562, 0.11360393464565277, 0.21151776611804962, 0.0050641754642128944, -0.1687309592962265, 0.029621383175253868, -0.08972493559122086, 0.0027659728657454252, 0.1254180669784546, 0.1426415741443634, -0.10099056363105774, 0.08502715826034546, -0.013852105475962162, -0.006456129718571901, -0.04074561595916748, 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null	null	transformers	<!-- 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. --> # SMIDS_3x_beit_large_Adamax_lr001_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.4180 - Accuracy: 0.8397 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.8804 \| 1.0 \| 451 \| 0.8973 \| 0.5225 \| \| 0.7303 \| 2.0 \| 902 \| 0.8264 \| 0.5643 \| \| 0.7942 \| 3.0 \| 1353 \| 0.7224 \| 0.6528 \| \| 0.7079 \| 4.0 \| 1804 \| 0.6958 \| 0.6878 \| \| 0.5454 \| 5.0 \| 2255 \| 0.6747 \| 0.7195 \| \| 0.5974 \| 6.0 \| 2706 \| 0.5994 \| 0.7446 \| \| 0.5779 \| 7.0 \| 3157 \| 0.6768 \| 0.7162 \| \| 0.4467 \| 8.0 \| 3608 \| 0.5963 \| 0.7429 \| \| 0.4741 \| 9.0 \| 4059 \| 0.5081 \| 0.7947 \| \| 0.4634 \| 10.0 \| 4510 \| 0.4793 \| 0.8197 \| \| 0.4198 \| 11.0 \| 4961 \| 0.5560 \| 0.7713 \| \| 0.4751 \| 12.0 \| 5412 \| 0.5440 \| 0.7746 \| \| 0.3199 \| 13.0 \| 5863 \| 0.4422 \| 0.8247 \| \| 0.3305 \| 14.0 \| 6314 \| 0.4885 \| 0.8114 \| \| 0.4518 \| 15.0 \| 6765 \| 0.4133 \| 0.8297 \| \| 0.4686 \| 16.0 \| 7216 \| 0.5003 \| 0.8214 \| \| 0.2471 \| 17.0 \| 7667 \| 0.4503 \| 0.8314 \| \| 0.2945 \| 18.0 \| 8118 \| 0.4459 \| 0.8464 \| \| 0.1835 \| 19.0 \| 8569 \| 0.6086 \| 0.8264 \| \| 0.1922 \| 20.0 \| 9020 \| 0.4739 \| 0.8381 \| \| 0.261 \| 21.0 \| 9471 \| 0.5819 \| 0.8364 \| \| 0.1169 \| 22.0 \| 9922 \| 0.6000 \| 0.8464 \| \| 0.105 \| 23.0 \| 10373 \| 0.6420 \| 0.8347 \| \| 0.2086 \| 24.0 \| 10824 \| 0.6934 \| 0.8414 \| \| 0.1873 \| 25.0 \| 11275 \| 0.7943 \| 0.8431 \| \| 0.0966 \| 26.0 \| 11726 \| 0.7505 \| 0.8397 \| \| 0.0874 \| 27.0 \| 12177 \| 0.8484 \| 0.8381 \| \| 0.1363 \| 28.0 \| 12628 \| 0.8159 \| 0.8397 \| \| 0.0335 \| 29.0 \| 13079 \| 0.8190 \| 0.8414 \| \| 0.0524 \| 30.0 \| 13530 \| 1.0374 \| 0.8364 \| \| 0.0481 \| 31.0 \| 13981 \| 1.0532 \| 0.8414 \| \| 0.0013 \| 32.0 \| 14432 \| 1.2170 \| 0.8548 \| \| 0.0139 \| 33.0 \| 14883 \| 1.0477 \| 0.8564 \| \| 0.0588 \| 34.0 \| 15334 \| 1.3113 \| 0.8481 \| \| 0.0946 \| 35.0 \| 15785 \| 1.1127 \| 0.8314 \| \| 0.0004 \| 36.0 \| 16236 \| 1.2097 \| 0.8397 \| \| 0.0019 \| 37.0 \| 16687 \| 1.2006 \| 0.8447 \| \| 0.0019 \| 38.0 \| 17138 \| 1.3255 \| 0.8414 \| \| 0.001 \| 39.0 \| 17589 \| 1.5015 \| 0.8497 \| \| 0.038 \| 40.0 \| 18040 \| 1.5393 \| 0.8497 \| \| 0.0001 \| 41.0 \| 18491 \| 1.2404 \| 0.8514 \| \| 0.0276 \| 42.0 \| 18942 \| 1.3550 \| 0.8481 \| \| 0.0001 \| 43.0 \| 19393 \| 1.4737 \| 0.8397 \| \| 0.0002 \| 44.0 \| 19844 \| 1.5451 \| 0.8381 \| \| 0.0192 \| 45.0 \| 20295 \| 1.4037 \| 0.8314 \| \| 0.0048 \| 46.0 \| 20746 \| 1.4161 \| 0.8347 \| \| 0.0095 \| 47.0 \| 21197 \| 1.4999 \| 0.8397 \| \| 0.0003 \| 48.0 \| 21648 \| 1.4224 \| 0.8381 \| \| 0.0254 \| 49.0 \| 22099 \| 1.4136 \| 0.8381 \| \| 0.0037 \| 50.0 \| 22550 \| 1.4180 \| 0.8397 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_Adamax_lr001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8397328881469115, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_Adamax_lr001_fold1	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T06:15:01+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_Adamax\_lr001\_fold1 ============================================ This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.4180 * Accuracy: 0.8397 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 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: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 115, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1293599158525467, 0.1724882274866104, -0.0023454553447663784, 0.13587836921215057, 0.11250235140323639, 0.015344180166721344, 0.13944171369075775, 0.16905122995376587, -0.08231265097856522, 0.04725457355380058, 0.1399839073419571, 0.13659004867076874, 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null	null	transformers	<!-- 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. --> # billsum_3272_t5-large This model is a fine-tuned version of [google-t5/t5-large](https://huggingface.co/google-t5/t5-large) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 28 - eval_batch_size: 28 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 448 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-t5/t5-large", "model-index": [{"name": "billsum_3272_t5-large", "results": []}]}	text2text-generation	ryusangwon/billsum_3272_t5-large	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-large", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T06:19:57+00:00	[]	[]	TAGS #transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# billsum_3272_t5-large This model is a fine-tuned version of google-t5/t5-large on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 28 - eval_batch_size: 28 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 448 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "# billsum_3272_t5-large\n\nThis model is a fine-tuned version of google-t5/t5-large on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 28\n- eval_batch_size: 28\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 448\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# billsum_3272_t5-large\n\nThis model is a fine-tuned version of google-t5/t5-large on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 28\n- eval_batch_size: 28\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 448\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 78, 38, 6, 12, 8, 3, 129, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google-t5/t5-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# billsum_3272_t5-large\n\nThis model is a fine-tuned version of google-t5/t5-large on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 28\n- eval_batch_size: 28\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 448\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 10### Training results### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ -0.09805596619844437, 0.16354826092720032, -0.0021821612026542425, 0.0790504440665245, 0.14505229890346527, 0.03185230866074562, 0.10785118490457535, 0.1296234130859375, -0.10365453362464905, 0.0903489887714386, 0.07707890123128891, 0.04357912763953209, 0.06710829585790634, 0.14403489232063293, -0.00749865872785449, -0.23864886164665222, -0.016341308131814003, -0.01045874785631895, -0.06708169728517532, 0.11995323747396469, 0.10374369472265244, -0.08886494487524033, 0.06000719219446182, -0.002442115219309926, -0.12511402368545532, 0.009006564505398273, -0.049916043877601624, -0.06307071447372437, 0.09181027114391327, -0.00004045240712002851, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	text-generation	ssoh/llama-2-7b-all-strings	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T06:20:26+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	null	- Chen Chen et al. "It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition"	{"language": ["en", "zh"], "license": "mit", "tags": ["generative error correction", "large language model", "LLaMA"], "pipeline_tag": "text-generation", "inference": false, "fine-tuning": true}	text-generation	PeacefulData/UADFusionGER	[ "generative error correction", "large language model", "LLaMA", "text-generation", "en", "zh", "license:mit", "region:us" ]	2024-02-08T06:20:42+00:00	[]	[ "en", "zh" ]	TAGS #generative error correction #large language model #LLaMA #text-generation #en #zh #license-mit #region-us	- Chen Chen et al. "It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition"	[]	[ "TAGS\n#generative error correction #large language model #LLaMA #text-generation #en #zh #license-mit #region-us \n" ]	[ 35 ]	[ "passage: TAGS\n#generative error correction #large language model #LLaMA #text-generation #en #zh #license-mit #region-us \n" ]	[ -0.039720043540000916, -0.08373261988162994, -0.004831989295780659, -0.003009588224813342, 0.12170226871967316, 0.028427448123693466, 0.11337880790233612, 0.08887510746717453, 0.14910566806793213, 0.005132314283400774, 0.1728801280260086, 0.05956155061721802, 0.028022557497024536, -0.08280755579471588, -0.0602680966258049, -0.18929550051689148, 0.04040159285068512, -0.015298154205083847, 0.02077183872461319, 0.01561056636273861, 0.10015617311000824, -0.024980606511235237, 0.09386330097913742, 0.0020020862575620413, -0.07263320684432983, 0.040054939687252045, 0.05249924585223198, -0.03385332226753235, 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null	null	transformers	# Mistraou-slerp Mistraou-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [openchat/openchat-3.5-1210](https://huggingface.co/openchat/openchat-3.5-1210) * [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP) ## 🧩 Configuration ```yaml slices: - sources: - model: openchat/openchat-3.5-1210 layer_range: [0, 32] - model: maywell/PiVoT-0.1-Starling-LM-RP layer_range: [0, 32] merge_method: slerp base_model: openchat/openchat-3.5-1210 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 = "baconnier/Mistraou-slerp" 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"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-1210", "maywell/PiVoT-0.1-Starling-LM-RP"], "base_model": ["openchat/openchat-3.5-1210", "maywell/PiVoT-0.1-Starling-LM-RP"]}	text-generation	baconnier/Mistraou-slerp	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "openchat/openchat-3.5-1210", "maywell/PiVoT-0.1-Starling-LM-RP", "conversational", "base_model:openchat/openchat-3.5-1210", "base_model:maywell/PiVoT-0.1-Starling-LM-RP", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T06:22:25+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #maywell/PiVoT-0.1-Starling-LM-RP #conversational #base_model-openchat/openchat-3.5-1210 #base_model-maywell/PiVoT-0.1-Starling-LM-RP #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Mistraou-slerp Mistraou-slerp is a merge of the following models using LazyMergekit: * openchat/openchat-3.5-1210 * maywell/PiVoT-0.1-Starling-LM-RP ## Configuration ## Usage	[ "# Mistraou-slerp\n\nMistraou-slerp is a merge of the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* maywell/PiVoT-0.1-Starling-LM-RP", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #maywell/PiVoT-0.1-Starling-LM-RP #conversational #base_model-openchat/openchat-3.5-1210 #base_model-maywell/PiVoT-0.1-Starling-LM-RP #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Mistraou-slerp\n\nMistraou-slerp is a merge of the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* maywell/PiVoT-0.1-Starling-LM-RP", "## Configuration", "## Usage" ]	[ 124, 53, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #openchat/openchat-3.5-1210 #maywell/PiVoT-0.1-Starling-LM-RP #conversational #base_model-openchat/openchat-3.5-1210 #base_model-maywell/PiVoT-0.1-Starling-LM-RP #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mistraou-slerp\n\nMistraou-slerp is a merge of the following models using LazyMergekit:\n* openchat/openchat-3.5-1210\n* maywell/PiVoT-0.1-Starling-LM-RP## Configuration## Usage" ]	[ -0.12911270558834076, 0.003962496295571327, -0.0013952597510069609, -0.008962009102106094, 0.052651770412921906, 0.0311090350151062, 0.11843643337488174, 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null	null	transformers	<!-- 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4564 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7024 \| 1.0 \| 157 \| 2.4965 \| \| 2.5792 \| 2.0 \| 314 \| 2.4280 \| \| 2.5354 \| 3.0 \| 471 \| 2.4508 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]}	fill-mask	kumarme072/distilbert-base-uncased-finetuned-imdb	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-08T06:22:29+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-imdb ====================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.4564 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: 3.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 72, 113, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #fill-mask #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.1032542735338211, 0.10541015863418579, -0.00422185892239213, 0.0945814847946167, 0.09204947203397751, 0.003585381433367729, 0.1808249056339264, 0.13241694867610931, -0.07312146574258804, 0.07217897474765778, 0.1414647400379181, 0.10869461297988892, 0.037004854530096054, 0.1872505098581314, 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null	null	sentence-transformers	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="https://arxiv.org/abs/2402.05930">📄Paper</a></div> <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> <div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div> <div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div> </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ [Click here to access the original model.](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) # Sentence Transformers Details This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('McGill-NLP/MiniLM-L6-dmr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('McGill-NLP/MiniLM-L6-dmr') model = AutoModel.from_pretrained('McGill-NLP/MiniLM-L6-dmr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=McGill-NLP/MiniLM-L6-dmr) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2560 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 3e-05 }, "scheduler": "warmuplinear", "steps_per_epoch": null, "warmup_steps": 500, "weight_decay": 0.0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	sentence-similarity	McGill-NLP/MiniLM-L6-dmr	[ "sentence-transformers", "pytorch", "feature-extraction", "sentence-similarity", "transformers", "arxiv:2402.05930", "endpoints_compatible", "region:us" ]	2024-02-08T06:22:51+00:00	[ "2402.05930" ]	[]	TAGS #sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #arxiv-2402.05930 #endpoints_compatible #region-us	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ Click here to access the original model. # Sentence Transformers Details This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 2560 with parameters: Loss: 'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors	[ "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ "TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #arxiv-2402.05930 #endpoints_compatible #region-us \n", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ 49, 34, 50, 38, 64, 29, 77, 5, 6 ]	[ "passage: TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #arxiv-2402.05930 #endpoints_compatible #region-us \n## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]	[ -0.059469614177942276, 0.11502932757139206, -0.006387036293745041, 0.031418830156326294, 0.1322484314441681, 0.056125346571207047, 0.13531944155693054, 0.08909301459789276, -0.04548428952693939, 0.059622447937726974, -0.04075724259018898, 0.08898594975471497, 0.028684068471193314, 0.07063653320074081, 0.06520317494869232, 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null	null	sentence-transformers	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> <div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div> <div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div> </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ [Click here to access the original model.](https://huggingface.co/BAAI/bge-small-en-v1.5) # Sentence Transformers Details This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('McGill-NLP/bge-small-dmr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('McGill-NLP/bge-small-dmr') model = AutoModel.from_pretrained('McGill-NLP/bge-small-dmr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=McGill-NLP/bge-small-dmr) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2560 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 3e-05 }, "scheduler": "warmuplinear", "steps_per_epoch": null, "warmup_steps": 500, "weight_decay": 0.0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	sentence-similarity	McGill-NLP/bge-small-dmr	[ "sentence-transformers", "pytorch", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]	2024-02-08T06:25:20+00:00	[]	[]	TAGS #sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ Click here to access the original model. # Sentence Transformers Details This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 2560 with parameters: Loss: 'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors	[ "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ "TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ 40, 34, 50, 38, 64, 29, 77, 5, 6 ]	[ "passage: TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]	[ -0.036701809614896774, 0.09706444293260574, -0.007352575194090605, 0.03516235947608948, 0.11260554194450378, 0.05130456015467644, 0.10155311971902847, 0.10055414587259293, -0.013014450669288635, 0.08958380669355392, -0.007838234305381775, 0.12385739386081696, 0.015424596145749092, 0.08369217813014984, 0.04050145298242569, -0.2557029724121094, 0.012574565596878529, -0.06930525600910187, 0.008898996748030186, 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null	null	transformers	<!-- 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. --> # first_qa This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7386 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.2933 \| \| 2.6286 \| 2.0 \| 500 \| 1.8314 \| \| 2.6286 \| 3.0 \| 750 \| 1.7386 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "first_qa", "results": []}]}	question-answering	mattwanjia/first_qa	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-08T06:25:33+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us	first\_qa ========= This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.7386 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: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 61, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.10436774045228958, 0.08735478669404984, -0.0014565602177754045, 0.10540106147527695, 0.13632909953594208, 0.01078457199037075, 0.14080867171287537, 0.11553538590669632, -0.07250584661960602, 0.04973730817437172, 0.13890103995800018, 0.12373152375221252, -0.0033676757011562586, 0.07990606874227524, -0.05838661640882492, -0.19592304527759552, 0.005195692181587219, 0.034011147916316986, -0.09485674649477005, 0.11690908670425415, 0.08091920614242554, -0.14218325912952423, 0.08041191846132278, -0.006524901371449232, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-squad-model1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 9 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-squad-model1", "results": []}]}	question-answering	varun-v-rao/bert-base-cased-squad-model1	[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-08T06:25:46+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us	# bert-base-cased-squad-model1 This model is a fine-tuned version of bert-base-cased on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 9 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "# bert-base-cased-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n", "# bert-base-cased-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 73, 38, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n# bert-base-cased-squad-model1\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 64\n- eval_batch_size: 16\n- seed: 9\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ -0.0854194238781929, 0.14664305746555328, -0.0016958309570327401, 0.09856688231229782, 0.13848517835140228, 0.007714211009442806, 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null	null	ml-agents	# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: turgutburak01/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}	reinforcement-learning	turgutburak01/ppo-SnowballTarget	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	2024-02-08T06:26:05+00:00	[]	[]	TAGS #ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us	# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL 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: URL - A longer tutorial to understand how works ML-Agents: URL ### Resume the training ### 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 URL 2. Step 1: Find your model_id: turgutburak01/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play	[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: turgutburak01/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: turgutburak01/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ 50, 208 ]	[ "passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: turgutburak01/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. 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null	null	diffusers	# DreamBooth trained by AutoTrain Text encoder was not trained.	{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<dr4wing>", "inference": true}	text-to-image	kg-09/autotrain-test	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "has_space", "region:us" ]	2024-02-08T06:26:19+00:00	[]	[]	TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us	# DreamBooth trained by AutoTrain Text encoder was not trained.	[ "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ 45, 19 ]	[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ -0.02063869684934616, 0.12998254597187042, -0.00014558587281499058, 0.05282456427812576, 0.16523675620555878, 0.04722703993320465, 0.16625140607357025, 0.08092519640922546, -0.021600954234600067, 0.06268861889839172, 0.19911405444145203, -0.005327701102942228, 0.005592701490968466, 0.22998546063899994, -0.094501793384552, -0.15147385001182556, 0.05843960493803024, -0.017813973128795624, 0.08953600376844406, 0.04556926712393761, 0.01589704304933548, -0.08332102000713348, 0.06851272284984589, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (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]	{"library_name": "transformers", "tags": []}	null	RajuEEE/GPT2_FineTunedModel	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-08T06:26:52+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	sentence-transformers	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="https://mcgill-nlp.github.io/weblinx">🌐Website</a></div> <div><a href="https://huggingface.co/spaces/McGill-NLP/weblinx-explorer">💻Explorer</a></div> <div><a href="https://huggingface.co/datasets/McGill-NLP/WebLINX">🤗Dataset</a></div> <div><a href="https://github.com/McGill-NLP/weblinx">💾Code</a></div> </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ [Click here to access the original model.](https://huggingface.co/thenlper/gte-base) # Sentence Transformers Details This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('McGill-NLP/gte-base-dmr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('McGill-NLP/gte-base-dmr') model = AutoModel.from_pretrained('McGill-NLP/gte-base-dmr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=McGill-NLP/gte-base-dmr) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2560 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 3e-05 }, "scheduler": "warmuplinear", "steps_per_epoch": null, "warmup_steps": 500, "weight_decay": 0.0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->	{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}	sentence-similarity	McGill-NLP/gte-base-dmr	[ "sentence-transformers", "pytorch", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]	2024-02-08T06:27:51+00:00	[]	[]	TAGS #sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us	<div align="center"> <h1 style="margin-bottom: 0.5em;">WebLINX: Real-World Website Navigation with Multi-Turn Dialogue</h1> <em>Xing Han Lù, Zdeněk Kasner, Siva Reddy</em> </div> <div style="margin-bottom: 2em"></div> <div style="display: flex; justify-content: space-around; align-items: center; font-size: 120%;"> <div><a href="URL <div><a href="URL <div><a href="URL <div><a href="URL </div> <div style="margin-bottom: 2em"></div> ## Original Model This model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\ Click here to access the original model. # Sentence Transformers Details This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 2560 with parameters: Loss: 'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors	[ "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ "TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.", "# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]	[ 40, 34, 50, 38, 64, 29, 77, 5, 6 ]	[ "passage: TAGS\n#sentence-transformers #pytorch #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n## Original Model\n\nThis model is finetuned on WebLINX using checkpoints previously published on Huggingface Hub.\\\nClick here to access the original model.# Sentence Transformers Details\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the Sentence Embeddings Benchmark: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 2560 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors" ]	[ -0.036950789391994476, 0.09406091272830963, -0.007307460531592369, 0.03593264892697334, 0.11363522708415985, 0.05086836591362953, 0.10121886432170868, 0.10044735670089722, -0.011028890497982502, 0.08896368741989136, -0.007244700565934181, 0.12596344947814941, 0.016006404533982277, 0.08153305947780609, 0.041473206132650375, -0.2550666332244873, 0.013464033603668213, -0.0685899406671524, 0.010352288372814655, 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null	null	transformers	<!-- 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. --> # billsum_2118_t5-v1_1-base This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5157 - Rouge1: 0.1576 - Rouge2: 0.0565 - Rougel: 0.128 - Rougelsum: 0.1279 - Gen Len: 18.9871 ## 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 - gradient_accumulation_steps: 16 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.9403 \| 6.75 \| 500 \| 2.5157 \| 0.1576 \| 0.0565 \| 0.128 \| 0.1279 \| 18.9871 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/t5-v1_1-base", "model-index": [{"name": "billsum_2118_t5-v1_1-base", "results": []}]}	text2text-generation	ryusangwon/billsum_2118_t5-v1_1-base	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/t5-v1_1-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-08T06:28:37+00:00	[]	[]	TAGS #transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	billsum\_2118\_t5-v1\_1-base ============================ This model is a fine-tuned version of google/t5-v1\_1-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5157 * Rouge1: 0.1576 * Rouge2: 0.0565 * Rougel: 0.128 * Rougelsum: 0.1279 * Gen Len: 18.9871 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 * gradient\_accumulation\_steps: 16 * total\_train\_batch\_size: 256 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.0.1+cu117 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 78, 144, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-google/t5-v1_1-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 256\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.1275639683008194, 0.12823355197906494, -0.0018175727454945445, 0.0744837298989296, 0.13789352774620056, 0.00661179143935442, 0.12641900777816772, 0.12078261375427246, -0.09753711521625519, 0.10416188091039658, 0.13126979768276215, 0.11467836797237396, 0.053801488131284714, 0.17841282486915588, -0.03728345036506653, -0.2814277708530426, 0.017786182463169098, -0.01210835762321949, -0.1295153647661209, 0.13292472064495087, 0.09010131657123566, -0.11791207641363144, 0.0793006494641304, -0.003468223614618182, -0.10573238879442215, -0.011987483128905296, -0.026558954268693924, -0.0565762035548687, 0.11799199134111404, 0.03421138599514961, 0.08992408215999603, 0.044957660138607025, 0.08613418787717819, -0.22954684495925903, 0.006641846150159836, 0.07393798977136612, 0.011278366670012474, 0.08865370601415634, 0.08770476281642914, -0.027469662949442863, 0.11986611038446426, -0.09064806252717972, 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