librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-30 18:26:50	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 530 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-30 18:26:48	card stringlengths 11 1.01M
HeySatyam/my_awesome_model	HeySatyam	2023-05-06T00:45:59Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T22:36:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93128 --- <!-- 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_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Accuracy: 0.9313 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2348 \| 1.0 \| 1563 \| 0.1867 \| 0.9289 \| \| 0.1523 \| 2.0 \| 3126 \| 0.2315 \| 0.9313 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
GraphicStylz/Stylz	GraphicStylz	2023-05-06T00:40:22Z	0	0	adapter-transformers	[ "adapter-transformers", "conversational", "en", "es", "fr", "sm", "dataset:OpenAssistant/oasst1", "dataset:wikipedia", "dataset:google/fleurs", "dataset:google/xtreme_s", "dataset:google/wit", "dataset:google_wellformed_query", "dataset:cornell_movie_dialog", "license:cc-by-nc-sa-4.0", "region:us" ]	text-generation	2023-05-05T23:43:52Z	--- license: cc-by-nc-sa-4.0 datasets: - OpenAssistant/oasst1 - wikipedia - google/fleurs - google/xtreme_s - google/wit - google_wellformed_query - cornell_movie_dialog language: - en - es - fr - sm metrics: - character - bertscore library_name: adapter-transformers pipeline_tag: conversational ---
Bainbridge/gpt2-kl_001_07_hscnspecial-hs_cn	Bainbridge	2023-05-06T00:40:01Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T23:44:28Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_07_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_001_07_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5264 \| 0.02 \| 10 \| 69.5797 \| \| 46.0831 \| 0.04 \| 20 \| 32.9588 \| \| 13.5437 \| 0.06 \| 30 \| 10.6394 \| \| 6.7905 \| 0.08 \| 40 \| 4.1876 \| \| 3.5385 \| 0.1 \| 50 \| 2.0711 \| \| 1.5427 \| 0.12 \| 60 \| 1.1384 \| \| 1.1141 \| 0.14 \| 70 \| 0.8584 \| \| 0.926 \| 0.16 \| 80 \| 0.7673 \| \| 0.7432 \| 0.18 \| 90 \| 0.6549 \| \| 0.7195 \| 0.2 \| 100 \| 0.6203 \| \| 0.6079 \| 0.22 \| 110 \| 0.5966 \| \| 0.6356 \| 0.24 \| 120 \| 0.5855 \| \| 0.6443 \| 0.26 \| 130 \| 0.5837 \| \| 0.6171 \| 0.28 \| 140 \| 0.5791 \| \| 0.6031 \| 0.3 \| 150 \| 0.5869 \| \| 0.587 \| 0.32 \| 160 \| 0.5674 \| \| 0.518 \| 0.34 \| 170 \| 0.5665 \| \| 0.6313 \| 0.36 \| 180 \| 0.5698 \| \| 0.6331 \| 0.38 \| 190 \| 0.5624 \| \| 0.5768 \| 0.4 \| 200 \| 0.5575 \| \| 0.6768 \| 0.42 \| 210 \| 0.5587 \| \| 0.6288 \| 0.44 \| 220 \| 0.5500 \| \| 0.5371 \| 0.46 \| 230 \| 0.5510 \| \| 0.5499 \| 0.48 \| 240 \| 0.5578 \| \| 0.548 \| 0.5 \| 250 \| 0.5583 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
Bainbridge/gpt2-kl_001_06_hscnspecial-hs_cn	Bainbridge	2023-05-05T23:43:42Z	7	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T22:42:39Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_06_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_001_06_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5489 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5243 \| 0.02 \| 10 \| 69.5733 \| \| 46.0785 \| 0.04 \| 20 \| 32.9424 \| \| 13.5445 \| 0.06 \| 30 \| 10.6459 \| \| 6.772 \| 0.08 \| 40 \| 4.2120 \| \| 3.5618 \| 0.1 \| 50 \| 2.0539 \| \| 1.4416 \| 0.12 \| 60 \| 1.0919 \| \| 1.0628 \| 0.14 \| 70 \| 0.8710 \| \| 0.8751 \| 0.16 \| 80 \| 0.7180 \| \| 0.7178 \| 0.18 \| 90 \| 0.7504 \| \| 0.7072 \| 0.2 \| 100 \| 0.6374 \| \| 0.5923 \| 0.22 \| 110 \| 0.6286 \| \| 0.6271 \| 0.24 \| 120 \| 0.6355 \| \| 0.6407 \| 0.26 \| 130 \| 0.6217 \| \| 0.6474 \| 0.28 \| 140 \| 0.5978 \| \| 0.5946 \| 0.3 \| 150 \| 0.5985 \| \| 0.6073 \| 0.32 \| 160 \| 0.5887 \| \| 0.5149 \| 0.34 \| 170 \| 0.5773 \| \| 0.6478 \| 0.36 \| 180 \| 0.5671 \| \| 0.6186 \| 0.38 \| 190 \| 0.5593 \| \| 0.5708 \| 0.4 \| 200 \| 0.5572 \| \| 0.6788 \| 0.42 \| 210 \| 0.5545 \| \| 0.6316 \| 0.44 \| 220 \| 0.5503 \| \| 0.5317 \| 0.46 \| 230 \| 0.5508 \| \| 0.5464 \| 0.48 \| 240 \| 0.5475 \| \| 0.548 \| 0.5 \| 250 \| 0.5466 \| \| 0.5488 \| 0.52 \| 260 \| 0.5473 \| \| 0.5551 \| 0.54 \| 270 \| 0.5469 \| \| 0.5867 \| 0.56 \| 280 \| 0.5489 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
reem1997/depressionDetectionArabert	reem1997	2023-05-05T23:34:23Z	119	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-04T10:37:37Z	--- tags: - generated_from_trainer metrics: - precision - recall - accuracy model-index: - name: depressionDetectionArabert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # depressionDetectionArabert This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4586 - Macro F1: 0.9279 - Precision: 0.9281 - Recall: 0.9279 - Kappa: 0.8558 - Accuracy: 0.9279 ## Model description More information needed ## Intended uses & 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: 128 - seed: 25 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Macro F1 \| Precision \| Recall \| Kappa \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 407 \| 0.2285 \| 0.9143 \| 0.9181 \| 0.9144 \| 0.8289 \| 0.9144 \| \| 0.2459 \| 2.0 \| 815 \| 0.2124 \| 0.9264 \| 0.9269 \| 0.9264 \| 0.8528 \| 0.9264 \| \| 0.1306 \| 3.0 \| 1222 \| 0.2439 \| 0.9285 \| 0.9287 \| 0.9285 \| 0.8570 \| 0.9285 \| \| 0.0747 \| 4.0 \| 1630 \| 0.3240 \| 0.9270 \| 0.9272 \| 0.9270 \| 0.8540 \| 0.9270 \| \| 0.0328 \| 5.0 \| 2037 \| 0.3850 \| 0.9273 \| 0.9275 \| 0.9273 \| 0.8546 \| 0.9273 \| \| 0.0328 \| 6.0 \| 2445 \| 0.4129 \| 0.9279 \| 0.9280 \| 0.9279 \| 0.8558 \| 0.9279 \| \| 0.0213 \| 7.0 \| 2852 \| 0.4541 \| 0.9273 \| 0.9273 \| 0.9273 \| 0.8545 \| 0.9273 \| \| 0.0108 \| 7.99 \| 3256 \| 0.4586 \| 0.9279 \| 0.9281 \| 0.9279 \| 0.8558 \| 0.9279 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3
ratish/DBERT_CleanDesc_Collision_v2.1.4	ratish	2023-05-05T23:28:07Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T23:24:37Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.4 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ratish/DBERT_CleanDesc_Collision_v2.1.4 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: 0.3438 - Validation Loss: 1.4467 - Train Accuracy: 0.5897 - Epoch: 11 ## Model description More information needed ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4575, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.6148 \| 1.7151 \| 0.3077 \| 0 \| \| 1.4783 \| 1.7263 \| 0.3077 \| 1 \| \| 1.3926 \| 1.6779 \| 0.4103 \| 2 \| \| 1.2462 \| 1.5778 \| 0.4359 \| 3 \| \| 1.0592 \| 1.5154 \| 0.4359 \| 4 \| \| 0.8814 \| 1.5370 \| 0.4615 \| 5 \| \| 0.7554 \| 1.4250 \| 0.5385 \| 6 \| \| 0.6303 \| 1.4385 \| 0.5641 \| 7 \| \| 0.5458 \| 1.3870 \| 0.4872 \| 8 \| \| 0.4808 \| 1.3459 \| 0.5385 \| 9 \| \| 0.4098 \| 1.5049 \| 0.5385 \| 10 \| \| 0.3438 \| 1.4467 \| 0.5897 \| 11 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
ratish/DBERT_CleanDesc_Collision_v2.1.3	ratish	2023-05-05T23:22:32Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T23:18:12Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.3 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ratish/DBERT_CleanDesc_Collision_v2.1.3 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: 0.4790 - Validation Loss: 1.3887 - Train Accuracy: 0.6154 - Epoch: 9 ## Model description More information needed ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.6006 \| 1.7368 \| 0.3077 \| 0 \| \| 1.4578 \| 1.7548 \| 0.3077 \| 1 \| \| 1.3664 \| 1.6852 \| 0.4103 \| 2 \| \| 1.1923 \| 1.5441 \| 0.4615 \| 3 \| \| 1.0295 \| 1.4601 \| 0.5385 \| 4 \| \| 0.8769 \| 1.4388 \| 0.5897 \| 5 \| \| 0.7829 \| 1.4165 \| 0.5897 \| 6 \| \| 0.6690 \| 1.4263 \| 0.5897 \| 7 \| \| 0.5765 \| 1.3252 \| 0.6154 \| 8 \| \| 0.4790 \| 1.3887 \| 0.6154 \| 9 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
ratish/DBERT_CleanDesc_Collision_v2.1.2	ratish	2023-05-05T23:16:03Z	61	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T23:15:13Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.2 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ratish/DBERT_CleanDesc_Collision_v2.1.2 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: 1.6317 - Validation Loss: 1.7171 - Train Accuracy: 0.3077 - Epoch: 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: - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.6317 \| 1.7171 \| 0.3077 \| 0 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
ratish/DBERT_CleanDesc_Collision_v2.1.1	ratish	2023-05-05T23:12:27Z	63	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T23:11:43Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.1 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # ratish/DBERT_CleanDesc_Collision_v2.1.1 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: 1.4183 - Validation Loss: 1.1286 - Train Accuracy: 0.6923 - Epoch: 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: - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 1.4183 \| 1.1286 \| 0.6923 \| 0 \| ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3
Bainbridge/gpt2-kl_001_05_hscnspecial-hs_cn	Bainbridge	2023-05-05T22:41:52Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T21:41:54Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_05_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_001_05_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5513 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5241 \| 0.02 \| 10 \| 69.5822 \| \| 46.0925 \| 0.04 \| 20 \| 33.0099 \| \| 13.5527 \| 0.06 \| 30 \| 10.6424 \| \| 6.8151 \| 0.08 \| 40 \| 4.2013 \| \| 3.5806 \| 0.1 \| 50 \| 2.0893 \| \| 1.4772 \| 0.12 \| 60 \| 1.1276 \| \| 1.1166 \| 0.14 \| 70 \| 0.8410 \| \| 0.8952 \| 0.16 \| 80 \| 0.7324 \| \| 0.7517 \| 0.18 \| 90 \| 0.6448 \| \| 0.7044 \| 0.2 \| 100 \| 0.6761 \| \| 0.6069 \| 0.22 \| 110 \| 0.6436 \| \| 0.6184 \| 0.24 \| 120 \| 0.6057 \| \| 0.6394 \| 0.26 \| 130 \| 0.5877 \| \| 0.6243 \| 0.28 \| 140 \| 0.5719 \| \| 0.598 \| 0.3 \| 150 \| 0.5675 \| \| 0.5848 \| 0.32 \| 160 \| 0.5645 \| \| 0.5161 \| 0.34 \| 170 \| 0.5662 \| \| 0.6247 \| 0.36 \| 180 \| 0.5665 \| \| 0.6243 \| 0.38 \| 190 \| 0.5592 \| \| 0.5768 \| 0.4 \| 200 \| 0.5569 \| \| 0.68 \| 0.42 \| 210 \| 0.5583 \| \| 0.627 \| 0.44 \| 220 \| 0.5539 \| \| 0.5369 \| 0.46 \| 230 \| 0.5576 \| \| 0.5449 \| 0.48 \| 240 \| 0.5576 \| \| 0.5456 \| 0.5 \| 250 \| 0.5494 \| \| 0.55 \| 0.52 \| 260 \| 0.5559 \| \| 0.5595 \| 0.54 \| 270 \| 0.5525 \| \| 0.5821 \| 0.56 \| 280 \| 0.5513 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
vinesmsuic/bg-visualnovel-v02	vinesmsuic	2023-05-05T22:21:14Z	42	11	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-18T08:24:37Z	--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # BG-VisualNovel-v02 A prototype project of generating Visual Novel backgrounds for game developers/artists. This model is intended to produce visual novel backgrounds with just a few prompts. ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "vinesmsuic/bg-visualnovel-v02" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a classroom" image = pipe(prompt).images[0] image.save("./classroom.png") prompt = "a hospital building, two trees" image = pipe(prompt).images[0] image.save("./hospital.png") prompt = "a street at night with nobody around" image = pipe(prompt).images[0] image.save("./nightstreet.png") ``` ## Examples a classroom ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/classroom.png) a hospital building, two trees ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/hospital.png) a street at night with nobody around ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/nightstreet.png) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
tarek23/flan-t5-qg-test-LQ	tarek23	2023-05-05T22:17:04Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T15:44:51Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-qg-test-LQ results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # flan-t5-qg-test-LQ This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4480 - Rouge1: 22.9065 - Rouge2: 6.1197 - Rougel: 20.6542 - Rougelsum: 20.6524 - Gen Len: 16.0202 ## Model description More information needed ## Intended uses & 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.472 \| 1.0 \| 23583 \| 2.4480 \| 22.9065 \| 6.1197 \| 20.6542 \| 20.6524 \| 16.0202 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
NightOcean/cat-blip-datasets	NightOcean	2023-05-05T22:08:17Z	0	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-05-05T03:50:18Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - NightOcean/cat-blip-datasets These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the ethers/cat-blip-datasets 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)
WildPress/model2	WildPress	2023-05-05T22:05:09Z	30	0	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-05-05T21:54:58Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of simbimbi cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - WildPress/model2 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of simbimbi cat using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
Monero/Pygmalion-Metharme-7b-4bit-TopScore	Monero	2023-05-05T22:03:08Z	1,355	45	transformers	[ "transformers", "llama", "text-generation", "text generation", "instruct", "en", "autotrain_compatible", "region:us" ]	text-generation	2023-04-30T03:04:16Z	--- language: - en thumbnail: null tags: - text generation - instruct pipeline_tag: text-generation inference: false --- <h1 style="text-align: center">Metharme 7B 4bit</h1> <h2 style="text-align: center">An instruction-tuned LLaMA biased towards fiction writing and conversation.</h2> ## Model Details Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/metharme-7b Quantized for KoboldAI use using https://github.com/0cc4m/GPTQ-for-LLaMa I created several dozen quantized variations of this model and believe this variation to be "best." <br> <html> <head> <style> table { border:1px solid #b3adad; border-collapse:collapse; padding:5px; } table th { border:1px solid #b3adad; padding:5px; background: #f0f0f0; color: #313030; } table td { border:1px solid #b3adad; text-align:center; padding:5px; background: #ffffff; color: #313030; } </style> </head> <body> <table> <thead> <tr> <th>Model:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme 7b - 16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-AO-TS-Trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> </tbody> </table> </body> </html> <br><b>Other benchmark scores at the bottom of readme.</b> <hr> Metharme 7B is an instruct model based on Meta's LLaMA-7B. This is an experiment to try and get a model that is usable for conversation, roleplaying and storywriting, but which can be guided using natural language like other instruct models. See the [prompting](#prompting) section below for examples. It was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories and conversations with synthetically generated instructions attached. ## Prompting The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<\|system\|>`, `<\|user\|>` and `<\|model\|>`. The `<\|system\|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<\|user\|>` prompt should be used to indicate user input. The `<\|model\|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history. ### Prompting example Here's a concrete example. Let's say you have the following system prompt: > This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn. And the user has typed in the following message: > Start! To perform inference against the model, the prompt should be formatted like this: ``` <\|system\|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<\|user\|>Start!<\|model\|> ``` The model might generate something like: > You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!' > > - Go inside the house. > - Ignore the call and move away. > - Run as fast as you can. It will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete. Pretend then that the user has replied with `go inside`. The next prompt should then be: ``` <\|system\|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<\|user\|>Start!<\|model\|>You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!' - Go inside the house. - Ignore the call and move away. - Run as fast as you can.<\|user\|>go inside<\|model\|> ``` Which might generate something like: > You enter the house, and immediately, a wave of unsettling energy washes over you. Your senses seem heightened, but your mind feels foggy. A figure appears before you - a tall man with piercing eyes. He speaks to you in a language you don't understand. > > - Ask him what he wants. > - Attack him. > - Run away. Same process applies. Usually, it is best to do a sliding window over the user and model turns, but keep the system prompt fixed at the start of the context window. ## Limitations and biases The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. As such, it was not fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading. <hr> <p><strong><font size="5">Click to Expand Benchmarks of different quantized variations</font></strong></p> <strong><font size="4">The lower the number, the better the score.</font></strong> <html> <body> <details> <summary>Benchmarks Sorted by C4-New score</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted by Wikitext2</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted by PTB-new Score</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted in Alphabetical Order</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> </tbody> </table> </details> </body> </html>
keminglu/pivoine-7b	keminglu	2023-05-05T21:59:20Z	24	2	transformers	[ "transformers", "pytorch", "bloom", "text-generation", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-04T21:53:46Z	--- license: bigscience-bloom-rail-1.0 --- # Tokenizer used for all BLOOM models Tokenizer information are provided at [https://huggingface.co/bigscience/bloom#preprocessing](https://huggingface.co/bigscience/bloom#preprocessing) TODO: point to paper once it comes out with extra details on the tokenizer
wavymulder/portraitplus	wavymulder	2023-05-05T21:59:07Z	257,281	297	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-23T16:04:26Z	--- language: - en thumbnail: "https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page1.jpg" license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - safetensors - diffusers inference: true --- Portrait+ ![Header](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page1.jpg) [CKPT DOWNLOAD LINK](https://huggingface.co/wavymulder/portraitplus/resolve/main/portrait%2B1.0.ckpt) - this is a dreambooth model trained on a diverse set of close to medium range portraits of people. Use `portrait+ style` in your prompt (I recommend at the start) The goal was to create a model with a consistent portrait composition and consistent eyes. See the batch example below for the consistency of the model's eyes. This model can do several styles, so you'll want to guide it along depending on your goals. Note below in the document that prompting celebrities works a bit differently than prompting generic characters, since real people have a more photoreal presence in the base 1.5 model. Also note that fantasy concepts, like cyberpunk people or wizards, will require more rigid prompting for photoreal styles than something common like a person in a park. Portrait+ works best at a 1:1 aspect ratio, though I've had success with tall aspect ratios as well. Please see [this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images above.](https://huggingface.co/wavymulder/portraitplus/resolve/main/parameters_for_samples.txt) We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run portraitplus: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/wavymulder/portraitplus) ![Modification example](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page2.jpg) ![Batch example](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/batchgrid.jpg)
chribeiro/a2c-PandaReachDense-v2	chribeiro	2023-05-05T21:58:33Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T21:53:27Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.34 +/- 0.18 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
lckidwell/album-cover-style	lckidwell	2023-05-05T21:51:46Z	41	5	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-13T20:13:37Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Album-Cover-Style Dreambooth model > trained by lckidwell with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Trained on ~80 album covers, mostly from the 50s and 60s, a mix of Jazz, pop, polka, religious, children's and other genres. ## Sample Prompts: * Kanye plays jazz, albumcover style * Swingin' with Henry Kissinger, albumcover style * Jay Z Children's album, albumcover style * Polka Party with Machine Gun Kelly, albumcover style ## Sample pictures of this concept: ![0](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02503-4178330406-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![2](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02512-2122051129-Polka_Party_with_Henry_Kissinger,_albumcover_style.png) ![3](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02493-407743854-Kanye_goes_country,_albumcover_style.png) ![4](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02387-1542142160-albumcover_style,_albumcover_style.png) ![5](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02521-1024797607-Polka_Party_with_Henry_Kissinger_and_Weird_Al,_albumcover_style.png) ![6](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02491-407743852-Kanye_goes_country,_albumcover_style.png) ![7](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02509-4178330412-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![8](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02529-3942483747-Jayz_Childrens_Album,_albumcover_style.png) ![9](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02507-4178330410-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![10](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02395-1542142168-albumcover_style,_albumcover_style.png) ![11](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02494-1810968449-Kanye_plays_Jazz,_albumcover_style.png) ![12](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02537-2335869042-Polka_Party_with_Machine_Gun_Kelly,_albumcover_style.png) ![13](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02412-1542142185-albumcover_style,_albumcover_style.png) ![14](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02403-1542142176-albumcover_style,_albumcover_style.png) ## Moar Samples ![0](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00095.png) ![1](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00101.png) ![2](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00104.png) ![3](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00111.png) ![4](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00113.png)
MirageML/lowpoly-cyberpunk	MirageML	2023-05-05T21:32:43Z	40	30	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T07:50:09Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Cyberpunk on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Cyberpunk concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of lowpoly_cyberpunk # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-cyberpunk/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/search/images?similarStyleImagesId=847895439)
MirageML/lowpoly-environment	MirageML	2023-05-05T21:32:29Z	36	8	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T08:25:58Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Environment on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Environment concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of lowpoly_environment # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-environment/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/gallery/76095417/Game-Environments?tracking_source=search_projects%7Cisometric+building)
mHossain/bangla-para-v1-410000	mHossain	2023-05-05T21:19:39Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T20:20:53Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla-para-v1-410000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bangla-para-v1-410000 This model is a fine-tuned version of [mHossain/bangla-para-v1-380000](https://huggingface.co/mHossain/bangla-para-v1-380000) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9209 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 18.2867 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.1627 \| 1.0 \| 3375 \| 0.9209 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 18.2867 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
HelloSecurity/BootLeggerAI	HelloSecurity	2023-05-05T21:19:12Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-05-05T21:13:11Z	--- license: mit --- # BootLeggerAI A python based chat bot for coders BootLegger AI is a Python script that uses deep learning, neural networking, and natural language processing to create a chatbot that can help users with their programming needs. The chatbot can understand natural language and generate appropriate responses based on the user's input. (A → E, E → I, I → O, O → U, U → A) ## Instructions for Use 1. Install the necessary libraries: Tensorflow and Numpy. You can do this using pip or any package manager of your choice. 2. Copy the code in the BootLeggerAI.py file into your Python editor of choice. 3. Run the code in your Python environment. 4. The chatbot will start running and prompt the user to enter their request. 5. Enter your request, and the chatbot will generate a response based on the predicted output. 6. If you encounter any issues, please see the Troubleshooting section below. ## Detailed instructions 1. Install Required Libraries: Before running the script, make sure you have installed all the required libraries. This script requires `tensorflow` and `numpy`. You can install them via pip or conda: ``` pip install tensorflow numpy ``` 2. Prepare Input and Output Data: The script takes in an array of input data and an array of output data. Each element of the input array should be a string that represents a programming task you want help with. The output array should contain the corresponding output for each input task. For example, you can create a numpy array for the input and output data like this: ``` import numpy as np # Define input and output data input_data = np.array(['create a python script', 'build a program', 'generate a code']) output_data = np.array([['create', 'python', 'script'], ['build', 'program'], ['generate', 'code']]) ``` 3. Tokenize Input Data: To use the input data with the model, we need to tokenize it first. Tokenization is the process of converting text into numerical values. The function `tokenize_input` in the script takes in the input data and returns the tokenizer object, the tokenized input sequence, the maximum length of the input sequence, and the vocabulary size. You can tokenize the input data like this: ``` from bootlegger_ai import tokenize_input tokenizer, input_seq, max_len, vocab_size = tokenize_input(input_data) ``` 4. Define the Neural Network Model: The next step is to define the neural network model. The function `define_model` in the script takes in the vocabulary size and maximum length of the input sequence and returns the model object. You can define the model like this: ``` from bootlegger_ai import define_model model = define_model(vocab_size, max_len) ``` 5. Train the Neural Network Model: After defining the model, we need to train it with the input and output data. The function `train_model` in the script takes in the model object, input sequence, output data, and number of epochs to train the model. It returns the trained model object. You can train the model like this: ``` from bootlegger_ai import train_model model = train_model(model, input_seq, output_data) ``` 6. Test the Model: After training the model, we can test it on new input data. The function `test_model` in the script takes in the model object, test data, tokenizer object, and maximum length of the input sequence. It returns the predictions for the test data. You can test the model like this: ``` from bootlegger_ai import test_model test_data = np.array(['I want to create a new website']) predictions = test_model(model, test_data, tokenizer, max_len) ``` 7. Generate Response: Finally, we can generate a response based on the predicted output. The function `generate_response` in the script takes in the predictions and tokenizer object and returns a response string. You can generate a response like this: ``` from bootlegger_ai import generate_response response = generate_response(predictions, tokenizer) print(response) ``` And that's it! By following these steps, you can use the BootLegger AI script to generate responses to programming-related requests. ## Developed By This script was developed by Adam Rivers and Hello Security LLC. ## Troubleshooting If the chatbot is not generating appropriate responses, please ensure that the input data is relevant to the context of programming. Additionally, you can try retraining the neural network model by modifying the input and output data in the script. If you encounter any other issues, please feel free to reach out for assistance.
Hentai00/aurakasih	Hentai00	2023-05-05T20:59:47Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-05T20:50:29Z	--- license: creativeml-openrail-m ---
radames/instruct-pix2pix-img2img	radames	2023-05-05T20:59:08Z	12	19	diffusers	[ "diffusers", "safetensors", "image-to-image", "license:mit", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]	image-to-image	2023-05-05T20:58:32Z	--- license: mit duplicated_from: timbrooks/instruct-pix2pix library_name: diffusers tags: - image-to-image --- # InstructPix2Pix: Learning to Follow Image Editing Instructions GitHub: https://github.com/timothybrooks/instruct-pix2pix <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg" def download_image(url): image = PIL.Image.open(requests.get(url, stream=True).raw) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image image = download_image(URL) prompt = "turn him into cyborg" images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images images[0] ```
MirageML/lowpoly-game-building	MirageML	2023-05-05T20:53:17Z	27	16	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T08:52:37Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Game Building on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Game Building concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of lowpoly_game_building # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-game-building/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/guutv)
MirageML/lowpoly-landscape	MirageML	2023-05-05T20:53:06Z	11	5	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T08:57:57Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Landscape on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Landscape concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of lowpoly_landscape # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-landscape/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.deviantart.com/kautsar211086/art/Long-Time-No-See-457162094)
MirageML/lowpoly-world	MirageML	2023-05-05T20:52:48Z	50	84	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T09:20:48Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly World on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly World concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of lowpoly_world # Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run lowpoly-world: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/MirageML/lowpoly-world) # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-world/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/gallery/89934319/Low-Poly-Worlds)
Ahmedrai/Starfire	Ahmedrai	2023-05-05T20:52:43Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-05T20:06:32Z	--- license: creativeml-openrail-m ---
MirageML/fantasy-scene	MirageML	2023-05-05T20:52:36Z	39	20	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T09:25:54Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Fantasy Scene on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Scene concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of fantasy_scene # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/fantasy-scene/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source #1](https://www.behance.net/search/images?tracking_source=typeahead_search_direct&similarStyleImagesId=647409143) [Image Source #2](https://www.behance.net/romanklco)
MirageML/fantasy-sword	MirageML	2023-05-05T20:52:28Z	16	13	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "text-to-image", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-28T09:26:54Z	--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Fantasy Sword on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Sword concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of fantasy_sword # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/fantasy-sword/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source]( https://www.artstation.com/search?sort_by=relevance&query=fantasy%20sword)
amankishore/hpchapter	amankishore	2023-05-05T20:52:16Z	34	0	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-08T00:01:38Z	--- license: creativeml-openrail-m library_name: diffusers ---
jdmartinev/CREMA_D_Model	jdmartinev	2023-05-05T20:50:56Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-05-03T14:45:30Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: CREMA_D_Model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CREMA_D_Model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8221 - Accuracy: 0.7322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.7381 \| 0.99 \| 37 \| 1.6700 \| 0.3359 \| \| 1.4143 \| 1.99 \| 74 \| 1.4013 \| 0.4878 \| \| 1.1738 \| 2.98 \| 111 \| 1.1820 \| 0.6029 \| \| 1.0229 \| 4.0 \| 149 \| 1.0244 \| 0.6532 \| \| 0.8688 \| 4.99 \| 186 \| 0.9101 \| 0.7036 \| \| 0.7578 \| 5.99 \| 223 \| 0.8787 \| 0.7112 \| \| 0.705 \| 6.98 \| 260 \| 0.8292 \| 0.7229 \| \| 0.6469 \| 8.0 \| 298 \| 0.8509 \| 0.7179 \| \| 0.5684 \| 8.99 \| 335 \| 0.8412 \| 0.7288 \| \| 0.5611 \| 9.93 \| 370 \| 0.8221 \| 0.7322 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
bilginn/bert-base-uncased-finetuned-cola	bilginn	2023-05-05T20:49:47Z	6	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-03T19:32:34Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5678267214677118 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5922 - Matthews Correlation: 0.5678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.207256119784435e-06 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------------------:\| \| 0.5811 \| 1.0 \| 2138 \| 0.6179 \| 0.4846 \| \| 0.4698 \| 2.0 \| 4276 \| 0.8083 \| 0.5495 \| \| 0.3161 \| 3.0 \| 6414 \| 1.1152 \| 0.5389 \| \| 0.2499 \| 4.0 \| 8552 \| 1.0719 \| 0.5624 \| \| 0.1755 \| 5.0 \| 10690 \| 1.1734 \| 0.5709 \| \| 0.1511 \| 6.0 \| 12828 \| 1.2383 \| 0.5699 \| \| 0.0738 \| 7.0 \| 14966 \| 1.3802 \| 0.5598 \| \| 0.0677 \| 8.0 \| 17104 \| 1.4711 \| 0.5599 \| \| 0.0509 \| 9.0 \| 19242 \| 1.5751 \| 0.5678 \| \| 0.0397 \| 10.0 \| 21380 \| 1.5922 \| 0.5678 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3
marcoliu/sd-pokemon-model-lora	marcoliu	2023-05-05T20:33:57Z	1	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-05-05T13:29:07Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - marcoliu/sd-pokemon-model-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions 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)
seena18/tier3_satellite_image_classification	seena18	2023-05-05T20:21:35Z	218	1	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-05T20:00:47Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: tier3_satellite_image_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tier3_satellite_image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4309 - Accuracy: 0.8085 ## Model description More information needed ## Intended uses & 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: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1878 \| 1.0 \| 125 \| 2.0795 \| 0.6905 \| \| 1.5347 \| 2.0 \| 250 \| 1.5727 \| 0.776 \| \| 1.3524 \| 3.0 \| 375 \| 1.4309 \| 0.8085 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
jeremyvictor/mt5-large-gecid-e8-b8	jeremyvictor	2023-05-05T20:05:15Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T17:31:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-large-gecid-e8-b8 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-large-gecid-e8-b8 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3000 - Rouge1: 64.4729 - Rouge2: 57.8072 - Rougel: 64.3868 - Rougelsum: 64.3569 - Gen Len: 18.7495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 0.8319 \| 0.57 \| 500 \| 0.4310 \| 61.7619 \| 53.4157 \| 61.6684 \| 61.6628 \| 18.7567 \| \| 0.4258 \| 1.13 \| 1000 \| 0.3541 \| 62.8056 \| 55.1747 \| 62.7392 \| 62.7231 \| 18.7601 \| \| 0.2777 \| 1.7 \| 1500 \| 0.3231 \| 63.4739 \| 56.1433 \| 63.366 \| 63.3544 \| 18.7546 \| \| 0.2023 \| 2.26 \| 2000 \| 0.3068 \| 64.1314 \| 57.5343 \| 64.0453 \| 64.024 \| 18.7546 \| \| 0.1432 \| 2.83 \| 2500 \| 0.3000 \| 64.4729 \| 57.8072 \| 64.3868 \| 64.3569 \| 18.7495 \| \| 0.0976 \| 3.39 \| 3000 \| 0.3257 \| 64.7215 \| 58.3266 \| 64.6223 \| 64.5957 \| 18.7601 \| \| 0.0811 \| 3.96 \| 3500 \| 0.3112 \| 64.7518 \| 58.4888 \| 64.6487 \| 64.6454 \| 18.7648 \| \| 0.0472 \| 4.52 \| 4000 \| 0.3389 \| 64.9658 \| 58.822 \| 64.8741 \| 64.8621 \| 18.7592 \| \| 0.0413 \| 5.09 \| 4500 \| 0.3557 \| 64.9468 \| 58.8286 \| 64.8609 \| 64.8501 \| 18.7592 \| \| 0.0248 \| 5.66 \| 5000 \| 0.3452 \| 65.2004 \| 59.2566 \| 65.0876 \| 65.0889 \| 18.7605 \| \| 0.0195 \| 6.22 \| 5500 \| 0.3719 \| 65.1043 \| 59.083 \| 65.0369 \| 65.026 \| 18.7541 \| \| 0.013 \| 6.79 \| 6000 \| 0.3947 \| 65.3124 \| 59.486 \| 65.2434 \| 65.2324 \| 18.7571 \| \| 0.0084 \| 7.35 \| 6500 \| 0.4056 \| 65.4053 \| 59.6589 \| 65.3249 \| 65.3115 \| 18.7580 \| \| 0.0055 \| 7.92 \| 7000 \| 0.4216 \| 65.3303 \| 59.5344 \| 65.2475 \| 65.2284 \| 18.7567 \| ### Framework versions - Transformers 4.28.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.2
bazyl/gtsrb-model	bazyl	2023-05-05T20:05:05Z	298	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "vision", "generated_from_trainer", "dataset:gtsrb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-07-02T10:39:06Z	--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer datasets: - gtsrb metrics: - accuracy model-index: - name: gtsrb-model results: - task: name: Image Classification type: image-classification dataset: name: bazyl/GTSRB type: gtsrb args: gtsrb metrics: - name: Accuracy type: accuracy value: 0.9993199591975519 --- <!-- 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. --> # gtsrb-model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the bazyl/GTSRB dataset. It achieves the following results on the evaluation set: - Loss: 0.0034 - Accuracy: 0.9993 ## Model description The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2593 \| 1.0 \| 4166 \| 0.1585 \| 0.9697 \| \| 0.2659 \| 2.0 \| 8332 \| 0.0472 \| 0.9900 \| \| 0.2825 \| 3.0 \| 12498 \| 0.0155 \| 0.9971 \| \| 0.0953 \| 4.0 \| 16664 \| 0.0113 \| 0.9983 \| \| 0.1277 \| 5.0 \| 20830 \| 0.0076 \| 0.9985 \| \| 0.0816 \| 6.0 \| 24996 \| 0.0047 \| 0.9988 \| \| 0.0382 \| 7.0 \| 29162 \| 0.0041 \| 0.9990 \| \| 0.0983 \| 8.0 \| 33328 \| 0.0059 \| 0.9990 \| \| 0.1746 \| 9.0 \| 37494 \| 0.0034 \| 0.9993 \| \| 0.1153 \| 10.0 \| 41660 \| 0.0038 \| 0.9990 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.12.0 - Datasets 2.3.2 - Tokenizers 0.12.1
gdecarlo/fabric_test	gdecarlo	2023-05-05T19:38:50Z	243	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-05T19:27:00Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: fabric_test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.3731343150138855 --- # fabric_test Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### fabric linen ![fabric linen](images/fabric_linen.jpg) #### fabric morley ![fabric morley ](images/fabric_morley_.jpg) #### fabric saten ![fabric saten](images/fabric_saten.jpg)
Bainbridge/gpt2-kl_01_07_hscnspecial-hs_cn	Bainbridge	2023-05-05T19:35:19Z	11	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T18:20:56Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_07_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_01_07_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5377 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5948 \| 0.02 \| 10 \| 69.5786 \| \| 46.1586 \| 0.04 \| 20 \| 32.9619 \| \| 13.6007 \| 0.06 \| 30 \| 10.6513 \| \| 6.8042 \| 0.08 \| 40 \| 4.2289 \| \| 2.8577 \| 0.1 \| 50 \| 2.1080 \| \| 1.447 \| 0.12 \| 60 \| 1.1006 \| \| 1.2972 \| 0.14 \| 70 \| 0.9296 \| \| 0.9482 \| 0.16 \| 80 \| 0.7053 \| \| 0.7817 \| 0.18 \| 90 \| 0.7118 \| \| 0.7763 \| 0.2 \| 100 \| 0.6232 \| \| 0.6719 \| 0.22 \| 110 \| 0.5972 \| \| 0.6852 \| 0.24 \| 120 \| 0.5835 \| \| 0.7033 \| 0.26 \| 130 \| 0.5850 \| \| 0.6782 \| 0.28 \| 140 \| 0.5815 \| \| 0.6635 \| 0.3 \| 150 \| 0.5757 \| \| 0.6405 \| 0.32 \| 160 \| 0.5796 \| \| 0.5739 \| 0.34 \| 170 \| 0.5705 \| \| 0.7139 \| 0.36 \| 180 \| 0.5606 \| \| 0.6883 \| 0.38 \| 190 \| 0.5592 \| \| 0.6429 \| 0.4 \| 200 \| 0.5586 \| \| 0.7397 \| 0.42 \| 210 \| 0.5511 \| \| 0.6993 \| 0.44 \| 220 \| 0.5484 \| \| 0.5946 \| 0.46 \| 230 \| 0.5515 \| \| 0.6172 \| 0.48 \| 240 \| 0.5473 \| \| 0.6077 \| 0.5 \| 250 \| 0.5442 \| \| 0.6148 \| 0.52 \| 260 \| 0.5435 \| \| 0.6213 \| 0.54 \| 270 \| 0.5425 \| \| 0.6431 \| 0.56 \| 280 \| 0.5414 \| \| 0.6459 \| 0.58 \| 290 \| 0.5392 \| \| 0.604 \| 0.6 \| 300 \| 0.5394 \| \| 0.603 \| 0.62 \| 310 \| 0.5368 \| \| 0.7207 \| 0.64 \| 320 \| 0.5387 \| \| 0.5689 \| 0.66 \| 330 \| 0.5407 \| \| 0.5721 \| 0.68 \| 340 \| 0.5377 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
cuadron11/5	cuadron11	2023-05-05T19:16:37Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-05T17:44:27Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: '5' results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 5 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2255 - Precision: 0.6432 - Recall: 0.595 - F1: 0.6182 - Accuracy: 0.9709 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.5e-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: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 29 \| 0.3210 \| 0.0 \| 0.0 \| 0.0 \| 0.9324 \| \| No log \| 2.0 \| 58 \| 0.2694 \| 0.0 \| 0.0 \| 0.0 \| 0.9324 \| \| No log \| 3.0 \| 87 \| 0.2216 \| 0.0 \| 0.0 \| 0.0 \| 0.9316 \| \| No log \| 4.0 \| 116 \| 0.2115 \| 0.25 \| 0.035 \| 0.0614 \| 0.9403 \| \| No log \| 5.0 \| 145 \| 0.1740 \| 0.3465 \| 0.175 \| 0.2326 \| 0.9512 \| \| No log \| 6.0 \| 174 \| 0.1589 \| 0.42 \| 0.315 \| 0.36 \| 0.9566 \| \| No log \| 7.0 \| 203 \| 0.1514 \| 0.4797 \| 0.295 \| 0.3653 \| 0.9584 \| \| No log \| 8.0 \| 232 \| 0.1686 \| 0.4576 \| 0.405 \| 0.4297 \| 0.9624 \| \| No log \| 9.0 \| 261 \| 0.1840 \| 0.5971 \| 0.415 \| 0.4897 \| 0.9646 \| \| No log \| 10.0 \| 290 \| 0.1571 \| 0.5505 \| 0.545 \| 0.5477 \| 0.9646 \| \| No log \| 11.0 \| 319 \| 0.1809 \| 0.6158 \| 0.545 \| 0.5782 \| 0.9700 \| \| No log \| 12.0 \| 348 \| 0.1763 \| 0.6129 \| 0.57 \| 0.5907 \| 0.9681 \| \| No log \| 13.0 \| 377 \| 0.1902 \| 0.5571 \| 0.61 \| 0.5823 \| 0.9655 \| \| No log \| 14.0 \| 406 \| 0.1916 \| 0.5842 \| 0.555 \| 0.5692 \| 0.9673 \| \| No log \| 15.0 \| 435 \| 0.1895 \| 0.6335 \| 0.605 \| 0.6189 \| 0.9697 \| \| No log \| 16.0 \| 464 \| 0.1951 \| 0.5880 \| 0.635 \| 0.6106 \| 0.9667 \| \| No log \| 17.0 \| 493 \| 0.1918 \| 0.6324 \| 0.585 \| 0.6078 \| 0.9702 \| \| 0.0838 \| 18.0 \| 522 \| 0.1957 \| 0.6020 \| 0.605 \| 0.6035 \| 0.9699 \| \| 0.0838 \| 19.0 \| 551 \| 0.1886 \| 0.6 \| 0.6 \| 0.6 \| 0.9681 \| \| 0.0838 \| 20.0 \| 580 \| 0.1992 \| 0.6158 \| 0.585 \| 0.6 \| 0.9702 \| \| 0.0838 \| 21.0 \| 609 \| 0.2043 \| 0.625 \| 0.6 \| 0.6122 \| 0.9706 \| \| 0.0838 \| 22.0 \| 638 \| 0.2110 \| 0.6243 \| 0.59 \| 0.6067 \| 0.9707 \| \| 0.0838 \| 23.0 \| 667 \| 0.2121 \| 0.6421 \| 0.61 \| 0.6256 \| 0.9714 \| \| 0.0838 \| 24.0 \| 696 \| 0.2112 \| 0.6455 \| 0.61 \| 0.6272 \| 0.9713 \| \| 0.0838 \| 25.0 \| 725 \| 0.2150 \| 0.6392 \| 0.62 \| 0.6294 \| 0.9711 \| \| 0.0838 \| 26.0 \| 754 \| 0.2229 \| 0.6264 \| 0.57 \| 0.5969 \| 0.9702 \| \| 0.0838 \| 27.0 \| 783 \| 0.2219 \| 0.6339 \| 0.58 \| 0.6057 \| 0.9706 \| \| 0.0838 \| 28.0 \| 812 \| 0.2239 \| 0.6429 \| 0.585 \| 0.6126 \| 0.9707 \| \| 0.0838 \| 29.0 \| 841 \| 0.2211 \| 0.6402 \| 0.605 \| 0.6221 \| 0.9713 \| \| 0.0838 \| 30.0 \| 870 \| 0.2230 \| 0.6364 \| 0.595 \| 0.6150 \| 0.9709 \| \| 0.0838 \| 31.0 \| 899 \| 0.2244 \| 0.6432 \| 0.595 \| 0.6182 \| 0.9709 \| \| 0.0838 \| 32.0 \| 928 \| 0.2255 \| 0.6432 \| 0.595 \| 0.6182 \| 0.9709 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
Parcurcik/code	Parcurcik	2023-05-05T19:07:26Z	147	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T19:03:52Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: code results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # code This model is a fine-tuned version of [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1352 - Accuracy: 0.7530 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
pcalhoun/LLaMA-7b-JonathanSwift	pcalhoun	2023-05-05T18:58:30Z	0	0	Transformers	[ "Transformers", "text2text-generation", "license:apache-2.0", "region:us" ]	text2text-generation	2023-05-05T15:28:54Z	--- license: apache-2.0 library_name: Transformers pipeline_tag: text2text-generation --- Uses PEFT weights: https://huggingface.co/blog/peft Finetuning on about 4500 strings of [modern translation of Jonathan Swift string] + " \|SWIFTIFY\| " + [Jonathan Swift text] + " \|END\|" Some dataset examples: > How has he been bombarded, harassed, and battered by just one smart aleck who promises never to leave him, dead or alive! \|SWIFTIFY\| How hath he been pelted, pestered, and pounded by one single wag, who promiseth never to forsake him living or dead! \|END\| > The third advantage is the convenience of secure harbors and ports for transporting their own finished goods and importing those of others that are minimally processed, as mutual trade demands. \|SWIFTIFY\| The third, is the conveniency of safe ports and havens, to carry out their own goods, as much manufactured, and bring in those of others, as little manufactured as the nature of mutual commerce will allow. \|END\| Some example output for novel input sentences: > The spectroscopy part of what you do — spectroscopes — you can use them to judge if food is rotten, if a painting is authentic, if something is an explosive. What do you use them to do? \|SWIFTIFY\| That branch of your art which you call the Spectra-physics; -- Spectroscope: You may apply those instruments in judging whether meat be putrid or not, whether a picture be genuine or counterfeit, and so on with other things that are called Explosives. Now, what service doth all this perform? \|END\| > Colorado Parks and Wildlife has a comprehensive list of animals you can own without a permit in Colorado, from dogs and cats to red kangaroos and wallaroos. If it isn’t on the list, you need a permit. Capybaras are not on the list. \|SWIFTIFY\| The Colorado Division of Parks & Wildlife lists all domesticated animals that may be kept within the state's borders without a license: Dogs, Cats, Red Kangaroo, Wallaroo, etc., but no capybara is listed. \|END\| > And I think that's the true beauty of a good film: the allegory, the moral, the true message is at the core – it's not fed to us directly – and it's up to the viewer to take what they need from it. \|SWIFTIFY\| And this, I believe, is the real excellence in all good pictures; for the allegorical, the moral, or the true lesson lies at the bottom -- it is not poured down our throats -- but left to be taken as we please. \|END\|
vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo	vg055	2023-05-05T18:37:12Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T17:06:08Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo This model is a fine-tuned version of [vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation](https://huggingface.co/vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0472 - F1: 0.9902 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.0479 \| 1.0 \| 14159 \| 0.0521 \| 0.9878 \| \| 0.0154 \| 2.0 \| 28318 \| 0.0472 \| 0.9902 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
AppledaMapple/Reenatest	AppledaMapple	2023-05-05T18:21:22Z	43	0	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-14T14:30:31Z	--- language: - en tags: - stable-diffusion - text-to-image - diffusers inference: true --- ## Reena Character Model A model trained on OC character Reena ![00290-2068285911-A_drawing_of_a_robot_badass_Reena._By_greg_Rutkowski.png](https://s3.amazonaws.com/moonup/production/uploads/1673719587816-635eafb49f24f6db0a1eafd1.png)
mHossain/bangla-para-v1-350000	mHossain	2023-05-05T18:19:59Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T14:25:45Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla-para-v1-350000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bangla-para-v1-350000 This model is a fine-tuned version of [mHossain/bangla-para-v1-320000](https://huggingface.co/mHossain/bangla-para-v1-320000) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9279 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 18.3427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.1838 \| 1.0 \| 6750 \| 0.9279 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 18.3427 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
danny3/codehelper-ds	danny3	2023-05-05T18:11:00Z	146	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-04T21:41:08Z	--- license: mit tags: - generated_from_trainer model-index: - name: codehelper-ds results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # codehelper-ds 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: 2.1453 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
safwaan21/q-FrozenLake-v1-4x4-noSlippery	safwaan21	2023-05-05T17:52:51Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T17:52:49Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="safwaan21/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
thejuuuuu/torgo_b6_2	thejuuuuu	2023-05-05T17:36:46Z	106	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-05T17:35:52Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_b6_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # torgo_b6_2 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) 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: 0.0001 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
pnparam/torgo_hel2	pnparam	2023-05-05T17:31:40Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-05T15:36:18Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_hel2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # torgo_hel2 This model is a fine-tuned version of [pnparam/torgo_healthy_2_40](https://huggingface.co/pnparam/torgo_healthy_2_40) 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
EExe/my_ppo_LunarLand	EExe	2023-05-05T17:25:42Z	0	0	null	[ "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T17:25:20Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -30.85 +/- 140.48 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'debug' 'gym_id': 'LunarLander-v2' 'hface_repo': 'EExe/my_ppo_LunarLand' 'learning_rate': 0.00025 'seed': 42 'total_timesteps': 2000000 'num_envs': 4 'num_steps': 1024 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 64 'num_epochs': 10 'clip_coeff': 0.2 'ent_coeff': 0.1 'vf_coeff': 0.1 'max_gradient_norm': 0.5 'target_kl': 0.015} ```
safwaan21/ppo-LunarLander-v2	safwaan21	2023-05-05T17:24:18Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-04-26T20:48:33Z	--- 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: 246.01 +/- 17.65 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
RamAnanth1/sd-1-5-ram	RamAnanth1	2023-05-05T17:17:40Z	46	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-08T04:42:30Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### SD-1-5-Ram Dreambooth model trained by RamAnanth1 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:
Bainbridge/gpt2-kl_01_05_hscnspecial-hs_cn	Bainbridge	2023-05-05T17:15:14Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T16:10:18Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_05_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_01_05_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5409 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5512 \| 0.02 \| 10 \| 69.5664 \| \| 46.0783 \| 0.04 \| 20 \| 32.9183 \| \| 13.5625 \| 0.06 \| 30 \| 10.6417 \| \| 6.8251 \| 0.08 \| 40 \| 4.2115 \| \| 3.5615 \| 0.1 \| 50 \| 2.0291 \| \| 1.4471 \| 0.12 \| 60 \| 1.0824 \| \| 1.1156 \| 0.14 \| 70 \| 0.9043 \| \| 0.9839 \| 0.16 \| 80 \| 0.7788 \| \| 0.75 \| 0.18 \| 90 \| 0.6556 \| \| 0.7311 \| 0.2 \| 100 \| 0.6302 \| \| 0.6331 \| 0.22 \| 110 \| 0.6142 \| \| 0.6426 \| 0.24 \| 120 \| 0.6279 \| \| 0.7058 \| 0.26 \| 130 \| 0.5762 \| \| 0.6443 \| 0.28 \| 140 \| 0.5702 \| \| 0.6437 \| 0.3 \| 150 \| 0.5679 \| \| 0.6041 \| 0.32 \| 160 \| 0.5654 \| \| 0.5403 \| 0.34 \| 170 \| 0.5669 \| \| 0.6511 \| 0.36 \| 180 \| 0.5710 \| \| 0.651 \| 0.38 \| 190 \| 0.5604 \| \| 0.6074 \| 0.4 \| 200 \| 0.5608 \| \| 0.7023 \| 0.42 \| 210 \| 0.5603 \| \| 0.6567 \| 0.44 \| 220 \| 0.5581 \| \| 0.5573 \| 0.46 \| 230 \| 0.5606 \| \| 0.5748 \| 0.48 \| 240 \| 0.5573 \| \| 0.5728 \| 0.5 \| 250 \| 0.5559 \| \| 0.6418 \| 0.52 \| 260 \| 0.5447 \| \| 0.5855 \| 0.54 \| 270 \| 0.5406 \| \| 0.6127 \| 0.56 \| 280 \| 0.5423 \| \| 0.6065 \| 0.58 \| 290 \| 0.5415 \| \| 0.5687 \| 0.6 \| 300 \| 0.5409 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
Norod78/sd15-fluentui-emoji	Norod78	2023-05-05T17:14:27Z	42	7	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/microsoft-fluentui-emoji-512-whitebg", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-02T05:31:23Z	--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd15-fluentui-emoji/resolve/main/sample_images/sd15-fluentui-emoji-Thumbnail.png" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/microsoft-fluentui-emoji-512-whitebg inference: true widget: - text: A cute dog flat - text: A cute dog high contrast - text: A cute dog color - text: A cute dog --- # SDv1.5 sd15-fluentui-emoji model ### Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/microsoft-fluentui-emoji-512-whitebg](https://huggingface.co/datasets/Norod78/microsoft-fluentui-emoji-512-whitebg) # The Emoji file names were converted to become the text descriptions. It made the model learn a few special words: "flat", "high contrast" and "color" ![thumbnail](https://huggingface.co/Norod78/sd15-fluentui-emoji/resolve/main/sample_images/sd15-fluentui-emoji-Thumbnail.png) ## A few sample pictures generated with this model are available [here](https://huggingface.co/Norod78/sd15-fluentui-emoji/tree/main/sample_images)
Norod78/SD15-VinageStyle	Norod78	2023-05-05T17:14:21Z	42	15	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/vintage-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-21T21:01:18Z	--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/SD15-VintageStyle-Thumbnail.jpg" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/vintage-blip-captions inference: true widget: - text: A Pulp Cover featuring Gal Gadot, very detailed, clean, high quality, sharp image, Saturno Butto - text: A photo of an astronaut riding a horse on mars, Vintage style, Pulp Cover, very detailed, clean, high quality, sharp image, Dave Dorman - text: A beatiful person, Vintage face - text: A Vintage style commercial for cat food --- # SDv1.5 SD15-VinageStyle model, trained by Norod78 in two parts. ### First Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/vintage-blip-captions](https://huggingface.co/datasets/Norod78/vintage-blip-captions) then it underwent further fine tuning with Dreambooth using the same images as the ones in the dataset but rather then having it blip-captioned, it was split into "Vintage style", "Vintage face" and "Pulp cover" concepts. ### Dreambooth model was trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ## Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch # Trigger-Words are: "Vintage", "Vintage style", "Vintage face", "Pulp cover" ![thumbnail](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/SD15-VintageStyle-Thumbnail.jpg) ## A few sample pictures generated with this mode (more available [here](https://huggingface.co/Norod78/SD15-VinageStyle/tree/main/sample_images)): A photo of Gal Gadot as wonderwoman, Vintage style, very detailed, clean, high quality, sharp image.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3486356206, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 2 ![1](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/00186-3486356206-A%20photo%20of%20Gal%20Gadot%20as%20wonderwoman%2C%20Vintage%20style%2C%20very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image.jpeg) A photo of Gal Gadot as wonderwoman fighting against Cthulhu, Vintage, very detailed, clean, high quality, sharp image, ,Naoto Hattori.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3408435550, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 3 ![2](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/00199-3408435550-A%20photo%20of%20Gal%20Gadot%20as%20wonderwoman%20fighting%20against%20Cthulhu%2C%20Vintage%2C%20very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image%2C%20%2CNaoto%20H.jpeg)
Norod78/claymationxmas	Norod78	2023-05-05T17:14:10Z	36	7	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/ChristmasClaymation-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-12-14T17:51:54Z	--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0167-3362370631-A_TARDIS_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/ChristmasClaymation-blip-captions inference: true widget: - text: A Gal Gadot in the snow with Santa ClaymationXmas, very detailed, clean, high quality, sharp image - text: A TARDIS in the snow with Santa ClaymationXmas, very detailed, clean, high quality, sharp image --- ### SDv1.5 ClaymationXmas Dreambooth model trained by Norod78 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ## Use ClaymationXmas in your prompt 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) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0167-3362370631-A_TARDIS_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg) ![1](https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0168-2731336261-A_Gal_Gadot_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg)
Norod78/sd-simpsons-model	Norod78	2023-05-05T17:14:04Z	39	17	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "dataset:Norod78/simpsons-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-10T16:41:01Z	--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00496-2202810362-A%20beautiful%20hungry%20demon%20girl,%20John%20Philip%20Falter,%20Very%20detailed%20painting,%20Mark%20Ryden.jpg" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image datasets: - Norod78/simpsons-blip-captions inference: true --- # Simpsons diffusion Stable Diffusion fine tuned on images related to "The Simpsons" If you want more details on how to generate your own blip cpationed dataset see this [colab](https://colab.research.google.com/gist/Norod/ee6ee3c4bf11c2d2be531d728ec30824/buildimagedatasetwithblipcaptionsanduploadtohf.ipynb) Training was done using a slightly modified version of Hugging-Face's text to image training [example script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) ## About Put in a text prompt and generate cartoony/simpsony images A beautiful hungry demon girl, John Philip Falter, Very detailed painting, Mark Ryden* ![A beautiful hungry demon girl, John Philip Falter, Very detailed painting, Mark Ryden](https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00496-2202810362-A%20beautiful%20hungry%20demon%20girl,%20John%20Philip%20Falter,%20Very%20detailed%20painting,%20Mark%20Ryden.jpg) Gal Gadot, cartoon ![Gal Gadot, cartoon](https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00323-2574793241-Gal%20Gadot,%20cartoon.jpg) ## More examples The [examples](https://huggingface.co/Norod78/sd-simpsons-model/tree/main/examples) folder contains a few images generated by this model's ckpt file using [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which means their EXIF info contain the parameter used to generate them ## Sample code ```py from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler import torch # this will substitute the default PNDM scheduler for K-LMS lms = LMSDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) guidance_scale=9 seed=7777 steps=100 model_id = "Norod78/sd-simpsons-model" pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=lms, torch_dtype=torch.float16) pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ## Dataset and Training Finetuned for 10,000 iterations upon [Runway ML's Stable-Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on [BLIP captioned Simpsons images](https://huggingface.co/datasets/Norod78/simpsons-blip-captions) using 1xA5000 GPU on my home desktop computer Trained by [@Norod78](https://twitter.com/Norod78)
Norod78/sd21-hearthstone-cards	Norod78	2023-05-05T17:13:58Z	15	2	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/hearthstone-cards-512", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-01-06T12:02:06Z	--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd21-hearthstone-cards/resolve/main/sample_images/00005-166904889-Snoop%20Dogg%20music%20power%20Hearthstone%20card.png" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/hearthstone-cards-512 inference: true widget: - text: 3 Cute dog, Fluff. Hearthstone card - text: Gal Gadot Super Wonderwoman power. Hearthstone card - text: Cute Pikachu Pokemon Electricity buzzzz Hearthstone card - text: 4 Snoop Dogg music power Hearthstone card library_name: diffusers pipeline_tag: text-to-image --- # SDv2.1 sd21-hearthstone-cards model ### Stable-Diffusion v2.1 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/hearthstone-cards-512](https://huggingface.co/datasets/Norod78/hearthstone-cards-512) # Stable-Diffusion Hearthstone card generator. First digit in prompt controls the Mana-cost (pretty well) then card name, then special ability and description, then "Hearthstone card". ![thumbnail](https://huggingface.co/Norod78/sd21-hearthstone-cards/resolve/main/sample_images/sd21-hearthstone-cards-animation-GalGadot.gif) ## A few sample pictures generated with this model are available [here](https://huggingface.co/Norod78/sd21-hearthstone-cards/tree/main/sample_images) Please note that the entire training set contains actual Hearthstone card images which are copyrighted by Blizzard So it is possible that the generated images contain copyrighted elements and should only be use for your private entertainment Trained by [@Norod78](https://twitter.com/Norod78)
nolanaatama/skmchnstyl	nolanaatama	2023-05-05T17:04:24Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-05-05T16:59:48Z	--- license: creativeml-openrail-m ---
rifatul123/Primary_doctor_v1	rifatul123	2023-05-05T16:57:39Z	0	0	adapter-transformers	[ "adapter-transformers", "pytorch", "gpt2", "biology", "medical", "chemistry", "text-generation-inference", "text-generation", "en", "region:us" ]	text-generation	2023-05-05T08:35:44Z	--- language: - en metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-generation tags: - biology - medical - chemistry - text-generation-inference --- ![Screenshot 2023-05-05 092541.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/9gMBxc270uN8agP8n6-5m.png) ![Screenshot 2023-05-05 094102.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/kqOUgU2wyxLDP1gKnCKPC.png) ![Screenshot 2023-05-05 094303.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/WpNXVBwbLCNNvWJ65dJI8.png) ![Screenshot 2023-05-05 094409.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/HZ1YdlwfZAi8CPlvrcqDr.png) ![Screenshot 2023-05-05 094542.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/h9EJw9fRNMBpOwJVVw6zI.png) # Model Card for Model ID This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset. The model is intended for text generation in the medical research domain. ## Model Details This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset. The model is intended for text generation in the medical research domain. ### Model Description The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens. - Developed by: [OpenAI] - Shared by [optional]: [More Information Needed] - Model type: [Language Model] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [GPT-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 ### Direct Use This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries. ### Downstream Use [optional] The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification. ### Out-of-Scope Use This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use. ## Bias, Risks, and Limitations This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information. ### Recommendations To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations: 1)Evaluate the input data for potential bias and ensure that it is diverse and representative. 2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias. 3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose. 4)Provide clear and transparent documentation of the model's limitations and potential biases to users and stakeholders. ## How to Get Started with the Model To use the model, load it in your preferred programming language using the transformers library, and pass in the input text. The model will generate text based on the input, using the task-specific parameters that have been set.
4bd4774h/bert-base-uncased-finetuned-cola	4bd4774h	2023-05-05T16:54:05Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T15:20:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5815775806078913 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0375 - Matthews Correlation: 0.5816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.999174630178768e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.4594 \| 1.0 \| 1069 \| 0.4619 \| 0.5155 \| \| 0.3105 \| 2.0 \| 2138 \| 0.5069 \| 0.5807 \| \| 0.2003 \| 3.0 \| 3207 \| 1.0033 \| 0.5524 \| \| 0.1074 \| 4.0 \| 4276 \| 1.0375 \| 0.5816 \| \| 0.0715 \| 5.0 \| 5345 \| 1.1228 \| 0.5743 \| \| 0.0355 \| 6.0 \| 6414 \| 1.3127 \| 0.5728 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad	vg055	2023-05-05T16:52:06Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-05-05T10:27:51Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad This model is a fine-tuned version of [vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation](https://huggingface.co/vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5996 - F1: 0.7468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.5823 \| 1.0 \| 14159 \| 0.5671 \| 0.7452 \| \| 0.4536 \| 2.0 \| 28318 \| 0.5996 \| 0.7468 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
cuadron11/modelBsc5	cuadron11	2023-05-05T16:43:35Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-05T15:52:41Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: modelBsc5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # modelBsc5 This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1546 - Precision: 0.5567 - Recall: 0.6075 - F1: 0.5810 - Accuracy: 0.9708 ## Model description More information needed ## Intended uses & 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: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 29 \| 0.2697 \| 0.0 \| 0.0 \| 0.0 \| 0.9446 \| \| No log \| 2.0 \| 58 \| 0.2357 \| 0.0 \| 0.0 \| 0.0 \| 0.9446 \| \| No log \| 3.0 \| 87 \| 0.2176 \| 0.0 \| 0.0 \| 0.0 \| 0.9446 \| \| No log \| 4.0 \| 116 \| 0.1889 \| 0.0541 \| 0.0108 \| 0.0179 \| 0.9406 \| \| No log \| 5.0 \| 145 \| 0.1800 \| 0.1613 \| 0.0269 \| 0.0461 \| 0.9477 \| \| No log \| 6.0 \| 174 \| 0.1625 \| 0.2745 \| 0.0753 \| 0.1181 \| 0.9532 \| \| No log \| 7.0 \| 203 \| 0.1502 \| 0.4306 \| 0.1667 \| 0.2403 \| 0.9571 \| \| No log \| 8.0 \| 232 \| 0.1426 \| 0.3810 \| 0.2581 \| 0.3077 \| 0.9576 \| \| No log \| 9.0 \| 261 \| 0.1459 \| 0.5586 \| 0.3333 \| 0.4175 \| 0.9638 \| \| No log \| 10.0 \| 290 \| 0.1288 \| 0.5732 \| 0.5054 \| 0.5371 \| 0.9636 \| \| No log \| 11.0 \| 319 \| 0.1241 \| 0.4769 \| 0.5538 \| 0.5124 \| 0.9638 \| \| No log \| 12.0 \| 348 \| 0.1282 \| 0.5189 \| 0.5161 \| 0.5175 \| 0.9664 \| \| No log \| 13.0 \| 377 \| 0.1335 \| 0.4232 \| 0.6667 \| 0.5177 \| 0.9595 \| \| No log \| 14.0 \| 406 \| 0.1314 \| 0.5854 \| 0.5161 \| 0.5486 \| 0.9693 \| \| No log \| 15.0 \| 435 \| 0.1357 \| 0.4772 \| 0.6183 \| 0.5386 \| 0.9653 \| \| No log \| 16.0 \| 464 \| 0.1382 \| 0.4398 \| 0.6290 \| 0.5177 \| 0.9638 \| \| No log \| 17.0 \| 493 \| 0.1362 \| 0.5634 \| 0.6452 \| 0.6015 \| 0.9705 \| \| 0.1141 \| 18.0 \| 522 \| 0.1466 \| 0.5 \| 0.6505 \| 0.5654 \| 0.9669 \| \| 0.1141 \| 19.0 \| 551 \| 0.1497 \| 0.5441 \| 0.5968 \| 0.5692 \| 0.9703 \| \| 0.1141 \| 20.0 \| 580 \| 0.1375 \| 0.5520 \| 0.6559 \| 0.5995 \| 0.9700 \| \| 0.1141 \| 21.0 \| 609 \| 0.1373 \| 0.5707 \| 0.6290 \| 0.5985 \| 0.9722 \| \| 0.1141 \| 22.0 \| 638 \| 0.1490 \| 0.5777 \| 0.6398 \| 0.6071 \| 0.9710 \| \| 0.1141 \| 23.0 \| 667 \| 0.1550 \| 0.5314 \| 0.6828 \| 0.5976 \| 0.9679 \| \| 0.1141 \| 24.0 \| 696 \| 0.1506 \| 0.5397 \| 0.6935 \| 0.6071 \| 0.9679 \| \| 0.1141 \| 25.0 \| 725 \| 0.1528 \| 0.5346 \| 0.6237 \| 0.5757 \| 0.9703 \| \| 0.1141 \| 26.0 \| 754 \| 0.1503 \| 0.5764 \| 0.6290 \| 0.6015 \| 0.9715 \| \| 0.1141 \| 27.0 \| 783 \| 0.1446 \| 0.5767 \| 0.5860 \| 0.5813 \| 0.9722 \| \| 0.1141 \| 28.0 \| 812 \| 0.1486 \| 0.5377 \| 0.6129 \| 0.5729 \| 0.9701 \| \| 0.1141 \| 29.0 \| 841 \| 0.1538 \| 0.5577 \| 0.6237 \| 0.5888 \| 0.9703 \| \| 0.1141 \| 30.0 \| 870 \| 0.1543 \| 0.5545 \| 0.6022 \| 0.5773 \| 0.9701 \| \| 0.1141 \| 31.0 \| 899 \| 0.1551 \| 0.5517 \| 0.6022 \| 0.5758 \| 0.9708 \| \| 0.1141 \| 32.0 \| 928 \| 0.1546 \| 0.5567 \| 0.6075 \| 0.5810 \| 0.9708 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
cuadron11/modelBeto5	cuadron11	2023-05-05T16:43:23Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-05T15:52:22Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: modelBeto5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # modelBeto5 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1686 - Precision: 0.5990 - Recall: 0.6541 - F1: 0.6253 - Accuracy: 0.9727 ## Model description More information needed ## Intended uses & 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: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 29 \| 0.2706 \| 0.0 \| 0.0 \| 0.0 \| 0.9451 \| \| No log \| 2.0 \| 58 \| 0.3328 \| 0.0 \| 0.0 \| 0.0 \| 0.9451 \| \| No log \| 3.0 \| 87 \| 0.1872 \| 0.0476 \| 0.0108 \| 0.0176 \| 0.9320 \| \| No log \| 4.0 \| 116 \| 0.1428 \| 0.3971 \| 0.1459 \| 0.2134 \| 0.9551 \| \| No log \| 5.0 \| 145 \| 0.1169 \| 0.4690 \| 0.2865 \| 0.3557 \| 0.9614 \| \| No log \| 6.0 \| 174 \| 0.1259 \| 0.5414 \| 0.5297 \| 0.5355 \| 0.9629 \| \| No log \| 7.0 \| 203 \| 0.1166 \| 0.4575 \| 0.6108 \| 0.5231 \| 0.9604 \| \| No log \| 8.0 \| 232 \| 0.1240 \| 0.6149 \| 0.4919 \| 0.5465 \| 0.9693 \| \| No log \| 9.0 \| 261 \| 0.1145 \| 0.5276 \| 0.5676 \| 0.5469 \| 0.9681 \| \| No log \| 10.0 \| 290 \| 0.1377 \| 0.5612 \| 0.5946 \| 0.5774 \| 0.9688 \| \| No log \| 11.0 \| 319 \| 0.1321 \| 0.5833 \| 0.6432 \| 0.6118 \| 0.9700 \| \| No log \| 12.0 \| 348 \| 0.1549 \| 0.6581 \| 0.5514 \| 0.6 \| 0.9717 \| \| No log \| 13.0 \| 377 \| 0.1482 \| 0.6080 \| 0.6541 \| 0.6302 \| 0.9713 \| \| No log \| 14.0 \| 406 \| 0.1589 \| 0.5348 \| 0.6649 \| 0.5928 \| 0.9675 \| \| No log \| 15.0 \| 435 \| 0.1507 \| 0.6178 \| 0.6378 \| 0.6277 \| 0.9720 \| \| No log \| 16.0 \| 464 \| 0.1554 \| 0.6082 \| 0.6378 \| 0.6227 \| 0.9720 \| \| No log \| 17.0 \| 493 \| 0.1658 \| 0.5918 \| 0.6270 \| 0.6089 \| 0.9708 \| \| 0.0785 \| 18.0 \| 522 \| 0.1616 \| 0.5792 \| 0.6919 \| 0.6305 \| 0.9715 \| \| 0.0785 \| 19.0 \| 551 \| 0.1632 \| 0.6059 \| 0.6649 \| 0.6340 \| 0.9717 \| \| 0.0785 \| 20.0 \| 580 \| 0.1638 \| 0.6103 \| 0.6432 \| 0.6263 \| 0.9726 \| \| 0.0785 \| 21.0 \| 609 \| 0.1603 \| 0.6010 \| 0.6432 \| 0.6214 \| 0.9724 \| \| 0.0785 \| 22.0 \| 638 \| 0.1652 \| 0.6078 \| 0.6703 \| 0.6375 \| 0.9722 \| \| 0.0785 \| 23.0 \| 667 \| 0.1577 \| 0.6440 \| 0.6649 \| 0.6543 \| 0.9738 \| \| 0.0785 \| 24.0 \| 696 \| 0.1600 \| 0.6492 \| 0.6703 \| 0.6596 \| 0.9743 \| \| 0.0785 \| 25.0 \| 725 \| 0.1663 \| 0.6256 \| 0.6595 \| 0.6421 \| 0.9733 \| \| 0.0785 \| 26.0 \| 754 \| 0.1686 \| 0.6106 \| 0.6865 \| 0.6463 \| 0.9713 \| \| 0.0785 \| 27.0 \| 783 \| 0.1691 \| 0.5951 \| 0.6595 \| 0.6256 \| 0.9720 \| \| 0.0785 \| 28.0 \| 812 \| 0.1668 \| 0.61 \| 0.6595 \| 0.6338 \| 0.9731 \| \| 0.0785 \| 29.0 \| 841 \| 0.1679 \| 0.5931 \| 0.6541 \| 0.6221 \| 0.9724 \| \| 0.0785 \| 30.0 \| 870 \| 0.1678 \| 0.6162 \| 0.6595 \| 0.6371 \| 0.9734 \| \| 0.0785 \| 31.0 \| 899 \| 0.1683 \| 0.6040 \| 0.6595 \| 0.6305 \| 0.9729 \| \| 0.0785 \| 32.0 \| 928 \| 0.1686 \| 0.5990 \| 0.6541 \| 0.6253 \| 0.9727 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
stanford-rl/PPO-LunarLander-v2	stanford-rl	2023-05-05T16:40:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T16:39:55Z	--- 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: 259.73 +/- 15.96 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
apurik-parv/abstract-nature-pattern-v1-2	apurik-parv	2023-05-05T16:25:27Z	34	1	diffusers	[ "diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-26T17:16:27Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### abstract_nature_pattern_v1.2 Dreambooth model trained by apurik-parv with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ##### This model is trained with more data but unfortunately it overfits the image. I am not aware of the extent of overfitting some images are good some are really bad. Please leave suggestions in comment. Inference Prompt: abnapa The model is trained on 1024x1024 images for a total step of 3000.
tang11/tang	tang11	2023-05-05T16:20:35Z	0	0	null	[ "medical", "translation", "dataset:BAAI/COIG", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	translation	2023-05-05T16:19:01Z	--- license: apache-2.0 datasets: - BAAI/COIG metrics: - bleurt pipeline_tag: translation tags: - medical --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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 Data 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 Data 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]
hannahh7/ppo-LunarLander-v2	hannahh7	2023-05-05T16:15:56Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T16:15:34Z	--- 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: 260.14 +/- 19.62 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mbshaikh/my_awesome_mind_model	mbshaikh	2023-05-05T16:10:59Z	162	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2023-05-05T16:05:16Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6548 - Accuracy: 0.0619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.8 \| 3 \| 2.6339 \| 0.0708 \| \| No log \| 1.87 \| 7 \| 2.6405 \| 0.0531 \| \| 2.6345 \| 2.93 \| 11 \| 2.6415 \| 0.0354 \| \| 2.6345 \| 4.0 \| 15 \| 2.6437 \| 0.0531 \| \| 2.6345 \| 4.8 \| 18 \| 2.6504 \| 0.0354 \| \| 2.6263 \| 5.87 \| 22 \| 2.6528 \| 0.0531 \| \| 2.6263 \| 6.93 \| 26 \| 2.6548 \| 0.0531 \| \| 2.6155 \| 8.0 \| 30 \| 2.6548 \| 0.0619 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
ayan4m1/trinart_diffusers_v2	ayan4m1	2023-05-05T16:10:04Z	9	6	diffusers	[ "diffusers", "license:apache-2.0", "diffusers:LDMTextToImagePipeline", "region:us" ]	null	2022-09-14T15:40:03Z	--- license: apache-2.0 --- This is a copy of [trinart_stable_diffusion_v2](https://huggingface.co/naclbit/trinart_stable_diffusion_v2) ported for use with the (diffusers)[https://github.com/huggingface/diffusers]) library. All credit for this model goes to [naclbit](https://huggingface.co/naclbit).
Bainbridge/gpt2-kl_01_04_hscnspecial-hs_cn	Bainbridge	2023-05-05T16:09:30Z	8	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T14:39:55Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_04_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_01_04_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5419 \| 0.02 \| 10 \| 69.5826 \| \| 46.0912 \| 0.04 \| 20 \| 32.9478 \| \| 13.5577 \| 0.06 \| 30 \| 10.6427 \| \| 6.8429 \| 0.08 \| 40 \| 4.2253 \| \| 3.5596 \| 0.1 \| 50 \| 2.0345 \| \| 1.4308 \| 0.12 \| 60 \| 1.0810 \| \| 1.0569 \| 0.14 \| 70 \| 0.8577 \| \| 1.0017 \| 0.16 \| 80 \| 0.8291 \| \| 0.7285 \| 0.18 \| 90 \| 0.6919 \| \| 0.7232 \| 0.2 \| 100 \| 0.6110 \| \| 0.5979 \| 0.22 \| 110 \| 0.6133 \| \| 0.6368 \| 0.24 \| 120 \| 0.6051 \| \| 0.6606 \| 0.26 \| 130 \| 0.6075 \| \| 0.6428 \| 0.28 \| 140 \| 0.5920 \| \| 0.6106 \| 0.3 \| 150 \| 0.5754 \| \| 0.6218 \| 0.32 \| 160 \| 0.5741 \| \| 0.534 \| 0.34 \| 170 \| 0.5633 \| \| 0.6411 \| 0.36 \| 180 \| 0.5685 \| \| 0.6353 \| 0.38 \| 190 \| 0.5805 \| \| 0.5922 \| 0.4 \| 200 \| 0.5556 \| \| 0.6924 \| 0.42 \| 210 \| 0.5595 \| \| 0.6429 \| 0.44 \| 220 \| 0.5601 \| \| 0.5428 \| 0.46 \| 230 \| 0.5583 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
crumb/eva-fusion-v2.22	crumb	2023-05-05T16:01:46Z	13	7	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-10-19T07:07:47Z	--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 --- # EVA-Fusion 2.22 - You are (Not) HD for an art project of mine \| model \| init model \| lr \| bs \| acc \| total steps \| training samples \| training resolution \| models trained \| \|--- \|--- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| eva-fusion-v2 \|compvis 1-4\| 2.057e-06 \| 1 \| 1 \| 8,000 \| 1,143 (captioned just "picture in the style of evangelion") \| 512x384 (4:3) \| unet \| \| eva-fusion-v2.1 \| eva-fusion-v2 \| 1.313e-06 \| 1 \| 1 \| 10,000 \| + 560 (hand captioned) \| 512x384 (4:3) \| unet \| \| eva-fusion-v2.22 "you are (not) HD" \| previous models + compvis1-4 + wd1.3 at various ratios \| x \| x \| x \| x \| x \| x \| x \| for generating let me suggest [Whatchamacallit](https://colab.research.google.com/github/aicrumb/whatchamacallit/blob/main/Whatchamacallit.ipynb), suggested in the negative prompt: "blurry jpeg, multiple views, simple background, black and white reference sheet". it wasn't trained on black and white character sheets, it was barely trained on any grayscale images, I do not know why it leans heavily towards them sometimes. link to ckpt file: https://huggingface.co/crumb/eva-model-ckpt/blob/main/test%20model%205.ckpt I also recommend "in the style of Evangelion" in the prompt
doohickey/doohickey-mega-v1.01	doohickey	2023-05-05T16:01:31Z	70	4	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-14T22:30:13Z	--- tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image --- Models better suited for High-Resolution Image Synthesis. The main model (doohickey/doohickey-mega) has been finetuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) near a resolution of 768x768 (suggested method of generating from model is with [Doohickey](https://colab.research.google.com/github/aicrumb/doohickey/blob/main/Doohickey_Diffusion.ipynb)). This is a version of Stable Diffusion finetuned to use [laion/CLIP-ViT-L-14-laion2B-s32B-b82K](https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K) instead of the stock openai clip model. While doohickey-mega finetuned the CLIP model as well, this ckpt was finetuned without the CLIP model being trained. In total 6000 steps. _Limitations and Biases from Stable Diffusion also apply to this model._ <div style="font-size:10px"> This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license </div>
doohickey/doohickey-mega	doohickey	2023-05-05T16:01:30Z	118	4	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-12T17:40:10Z	--- tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image --- Models better suited for High-Resolution Image Synthesis. The main model (doohickey/doohickey-mega) has been finetuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) near a resolution of 768x768 (suggested method of generating from model is with [Doohickey](https://colab.research.google.com/github/aicrumb/doohickey/blob/main/Doohickey_Diffusion.ipynb)). Current models: \| name \| description \| datasets used \| \| --- \| --- \| --- \| \| doohickey/doohickey-mega/v1-3000steps.ckpt \| first try, rlly good hd, bad results w/ other aspect ratios than 1:1 trained at 704x704 \| A-1k\| \| doohickey/doohickey-mega/v2-3000steps.ckpt \| same as last one but worse \| A-1k + ~1k samples from LAION-2b-En-Aesthetic >=768x768 \| \| doohickey/doohickey-mega/v3-3000.ckpt \| with new CLIP model ([laion/CLIP-ViT-L-14-laion2B-s32B-b82K](https://hf.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K)) (CLIP model also finetuned the 3k steps), models past this point were trained with various aspect ratios from 640x640 min to 768x768 max resolution. (examples 768x640 or 704x768) \| A-1k + E-10k \| \| doohickey/doohickey-mega/v3-6000.ckpt \| 3k steps on top of v3-3000.ckpt, better at hands! (just UNet finetune, added a RandomHorizontalFlip operation at 50%) \| A-1k \| \| doohickey/doohickey-mega/v3-7000.ckpt \| continuation of last model, I thought Colab would crash after 3k steps but it kept going for a little while saving ckpts every 1k steps. \| A-1k \| \| doohickey/doohickey-mega/v3-8000.ckpt \| see last description, v3-6000 + 2k steps \| A-1k \| The currently loaded model for diffusers is doohickey/doohickey-mega/v3-8000.ckpt Datasets: \| name \| description \| \| --- \| --- \| \| A-1K \| 1k scraped images, captioned with BLIP (more refined aesthetic) \| \| E-10k \| 10k scraped images captioned with BLIP (less refined aesthetic) \| _Limitations and Biases from Stable Diffusion also apply to this model._ <div style="font-size:10px"> This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license </div>
doohickey/doodad-v1-2	doohickey	2023-05-05T16:01:22Z	33	2	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-09-28T17:38:09Z	--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 inference: false --- # Doodad <p style="color:green"> <b> This is the 1st part of a 2 (3?) part project meant to be used with <a href="https://huggingface.co/doohickey">Doohickey</a> </b> </p> The style was trained with [Dreambooth-Stable](https://www.reddit.com/r/StableDiffusion/comments/xphaiw/dreambooth_stable_diffusion_training_in_just_125/) and is used with "<doodad>". Tt mixes especially well with the <midj-strong> style included in Doohickey. It's a finetuned version of the Trinart-Waifu-diffusion-50-50 included in this organization's models and was trained on 48 images from the author's ([crumb](https://huggingface.co/crumb)'s) Pinterest feed. \| Parameter \| Value \| \|----------------------\|------------\| \| resolution \| 512 \| \| train_batch_size \| 1 \| \| gradient_accumulation_steps \| 2 \| \| learning_rate \| 5e-6 \| \| num_class_images \| 120 \| \| max_train_steps \| 1200 \| Example outputs:
doohickey/doodad-v1-1	doohickey	2023-05-05T16:01:20Z	32	3	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-09-28T09:40:37Z	--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 inference: false --- # Doodad <p style="color:green"> <b> This is the 1st part of a 2 (3?) part project meant to be used with <a href="https://huggingface.co/doohickey">Doohickey</a> </b> </p> The style was trained with [Dreambooth-Stable](https://www.reddit.com/r/StableDiffusion/comments/xphaiw/dreambooth_stable_diffusion_training_in_just_125/) and is used with "imv" (an uncommonly used token recommended by the author I think) and it mixes especially well with the <midj-strong> style included in Doohickey. It's a finetuned version of the Trinart-Waifu-diffusion-50-50 included in this organization's models. \| Parameter \| Value \| \|----------------------\|------------\| \| resolution \| 512 \| \| train_batch_size \| 1 \| \| gradient_accumulation_steps \| 2 \| \| learning_rate \| 5e-6 \| \| num_class_images \| 120 \| \| max_train_steps \| 1200 \| Example outputs: "portal into another dimension" ![](https://cdn.discordapp.com/attachments/1024588665596411944/1024618996479311892/unknown.png) "portrait of a dying god" ![](https://pbs.twimg.com/media/Fdu1c9XWQAA4v2g?format=png&name=900x900) "photograph" ![](https://pbs.twimg.com/media/Fdu6hg4X0AI5aNI?format=png&name=900x900) "The alchemist's laboratory by Greg Rutkowski and Claude Monet, oil on canvas" ![](https://pbs.twimg.com/media/FdvB2ZiWYAQHg-7?format=png&name=900x900)
krasserm/perceiver-ar-sam-giant-midi	krasserm	2023-05-05T15:56:09Z	54	12	transformers	[ "transformers", "pytorch", "perceiver-ar-symbolic-audio-model", "audio-to-audio", "arxiv:2202.07765", "arxiv:2104.09864", "arxiv:1809.04281", "license:apache-2.0", "region:us" ]	audio-to-audio	2023-05-03T08:26:15Z	--- license: apache-2.0 inference: false pipeline_tag: audio-to-audio --- # Perceiver AR symbolic audio model This model is a [Perceiver AR](https://arxiv.org/abs/2202.07765) symbolic audio model (134M parameters) pretrained on the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset for 27 epochs (157M tokens). It uses [rotary embedding](https://arxiv.org/abs/2104.09864) for relative position encoding. It is a [training example](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#giantmidi-piano) of the [perceiver-io](https://github.com/krasserm/perceiver-io) library. ## Model description Perceiver AR is a simple extension of a plain decoder-only transformer such as GPT-2, for example. A core building block of both is the decoder layer consisting of a self-attention layer followed by a position-wise MLP. Self-attention uses a causal attention mask. Perceiver AR additionally cross-attends to a longer prefix of the input sequence in its first attention layer. This layer is a hybrid self- and cross-attention layer. Self-attention is over the last n positions of the input sequence, with a causal attention mask, cross-attention is from the last n positions to the first m positions. The length of the input sequence is m + n. This allows a Perceiver AR to process a much larger context than decoder-only transformers which are based on self-attention only. <p align="center"> <img src="https://krasserm.github.io/img/2023-01-23/perceiver-ar.png" alt="Perceiver AR" width="600"/><br/> <i>Fig. 1</i>. Attention in Perceiver AR with m=8 prefix tokens and n=3 latent tokens. <p/> The output of the hybrid attention layer are n latent arrays corresponding to the last n tokens of the input sequence. These are further processed by a stack of L-1 decoder layers where the total number of attention layers is L. A final layer (not shown in Fig. 1) predicts the target token for each latent position. The weights of the final layer are shared with the input embedding layer. Except for the initial cross-attention to the prefix sequence, a Perceiver AR is architecturally identical to a decoder-only transformer. ## Model training The model was [trained](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#giantmidi-piano) with the task of symbolic audio modeling on the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset for 27 epochs (157M tokens). This dataset consists of [MIDI](https://en.wikipedia.org/wiki/MIDI) files, tokenized using the approach from the [Perceiver AR paper](https://arxiv.org/pdf/2202.07765.pdf), which is described in detail in Section A.2 of [Huang et al (2019)](https://arxiv.org/abs/1809.04281). All hyperparameters are summarized in the [training script](https://github.com/krasserm/perceiver-io/blob/main/examples/training/sam/giantmidi/train.sh). The context length was set to 6144 tokens with 2048 latent positions, resulting in a maximal prefix length of 4096. The actual prefix length per example was randomly chosen between 0 and 4096. Training was done with [PyTorch Lightning](https://www.pytorchlightning.ai/index.html) and the resulting checkpoint was converted to this 🤗 model with a library-specific [conversion utility](#checkpoint-conversion). ## Intended use and limitations This model can be used for audio generation with a user-defined initial number of latent tokens. It mainly exists for demonstration purposes on how to train Perceiver AR models with the [perceiver-io library](https://github.com/krasserm/perceiver-io). To improve on the quality of the generated audio samples a much larger dataset than [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) is required for training. ## Usage examples To use this model you first need to [install](https://github.com/krasserm/perceiver-io/blob/main/README.md#installation) the `perceiver-io` library with extension `audio`. ```shell pip install perceiver-io[audio] ``` Then the model can be used with PyTorch. Either use the model directly to generate MIDI files: ```python import torch from perceiver.model.audio.symbolic import PerceiverSymbolicAudioModel from perceiver.data.audio.midi_processor import decode_midi, encode_midi from pretty_midi import PrettyMIDI repo_id = "krasserm/perceiver-ar-sam-giant-midi" model = PerceiverSymbolicAudioModel.from_pretrained(repo_id) prompt_midi = PrettyMIDI("prompt.mid") prompt = torch.tensor(encode_midi(prompt_midi)).unsqueeze(0) output = model.generate(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0) output_midi = decode_midi(output[0].cpu().numpy()) type(output_midi) ``` ``` pretty_midi.pretty_midi.PrettyMIDI ``` use a `symbolic-audio-generation` pipeline to generate a MIDI output: ```python from transformers import pipeline from pretty_midi import PrettyMIDI from perceiver.model.audio import symbolic # auto-class registration repo_id = "krasserm/perceiver-ar-sam-giant-midi" prompt = PrettyMIDI("prompt.mid") audio_generator = pipeline("symbolic-audio-generation", model=repo_id) output = audio_generator(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0) type(output["generated_audio_midi"]) ``` ``` pretty_midi.pretty_midi.PrettyMIDI ``` or generate WAV output by rendering the MIDI symbols using [fluidsynth](https://www.fluidsynth.org/) (Note: fluidsynth must be installed in order for the following example to work): ```python from transformers import pipeline from pretty_midi import PrettyMIDI from perceiver.model.audio import symbolic # auto-class registration repo_id = "krasserm/perceiver-ar-sam-giant-midi" prompt = PrettyMIDI("prompt.mid") audio_generator = pipeline("symbolic-audio-generation", model=repo_id) output = audio_generator(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0, render=True) with open("generated_audio.wav", "wb") as f: f.write(output["generated_audio_wav"]) ``` ## Audio samples The following (hand-picked) audio samples were generated using various prompts from the validation subset of the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset. The input prompts are not included in the audio output. <table> <tr> <th>Audio sample</th> <th>Top-K</th> <th>Top-p</th> <th>Temperature</th> <th>Prefix length</th> <th>Latents</th> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/01_nehrlich_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">1</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/02_eduardo_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">64</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/03_membree_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">1024</td> <td style="vertical-align: top;">1</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/04_membree_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">15</td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">16</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/05_kinscella_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">1</td> </tr> </table> ## Checkpoint conversion The `krasserm/perceiver-ar-sam-giant-midi` model has been created from a training checkpoint with: ```python from perceiver.model.audio.symbolic import convert_checkpoint convert_checkpoint( save_dir="krasserm/perceiver-ar-sam-giant-midi", ckpt_url="https://martin-krasser.com/perceiver/logs-0.8.0/sam/version_1/checkpoints/epoch=027-val_loss=1.944.ckpt", push_to_hub=True, ) ``` ## Citation ```bibtex @inproceedings{hawthorne2022general, title={General-purpose, long-context autoregressive modeling with perceiver ar}, author={Hawthorne, Curtis and Jaegle, Andrew and Cangea, C{\u{a}}t{\u{a}}lina and Borgeaud, Sebastian and Nash, Charlie and Malinowski, Mateusz and Dieleman, Sander and Vinyals, Oriol and Botvinick, Matthew and Simon, Ian and others}, booktitle={International Conference on Machine Learning}, pages={8535--8558}, year={2022}, organization={PMLR} } ```
sd-dreambooth-library/magikarp-pokemon	sd-dreambooth-library	2023-05-05T15:49:09Z	34	2	diffusers	[ "diffusers", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-23T21:12:46Z	--- license: mit --- ### Magikarp pokemon on Stable Diffusion via Dreambooth #### model by BenjiKan This your the Stable Diffusion model fine-tuned the Magikarp pokemon concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: a photo of sks pokemon You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/4.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/3.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/2.jpeg)
jasonsurya0/BART_SIX	jasonsurya0	2023-05-05T15:49:02Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T15:28:44Z	BART MODEL #6 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM
mbshaikh/videomae-base-finetuned-ucf101-subset	mbshaikh	2023-05-05T15:33:34Z	59	0	transformers	[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2023-05-05T14:48:13Z	--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0024 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0171 \| 0.25 \| 75 \| 0.0063 \| 1.0 \| \| 0.0391 \| 1.25 \| 150 \| 0.0048 \| 1.0 \| \| 0.0019 \| 2.25 \| 225 \| 0.0032 \| 1.0 \| \| 0.1704 \| 3.25 \| 300 \| 0.0024 \| 1.0 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
huggingtweets/mildlysomewhat	huggingtweets	2023-05-05T15:32:30Z	146	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T15:32:23Z	--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1559746526234263558/n8RqkkaD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">☆♪sheeks☆彡</div> <div style="text-align: center; font-size: 14px;">@mildlysomewhat</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ☆♪sheeks☆彡. \| Data \| ☆♪sheeks☆彡 \| \| --- \| --- \| \| Tweets downloaded \| 705 \| \| Retweets \| 207 \| \| Short tweets \| 32 \| \| Tweets kept \| 466 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zuthsw7a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mildlysomewhat's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kq2dk2t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kq2dk2t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mildlysomewhat') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
pcuenq/k-upscaler	pcuenq	2023-05-05T15:27:49Z	6	1	diffusers	[ "diffusers", "diffusers:StableDiffusionUpscalerPipeline", "region:us" ]	null	2022-11-14T17:47:59Z	# k-diffusion latent upscaler By Katherine Crowson. This is a temporary model repository that I created for convenience. I just copied the weights and configuration from this base URL: https://models.rivershavewings.workers.dev/
AkshatSurolia/ICD-10-Code-Prediction	AkshatSurolia	2023-05-05T15:24:14Z	977	45	transformers	[ "transformers", "pytorch", "bert", "text-classification", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - text-classification --- # Clinical BERT for ICD-10 Prediction The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. --- ## How to use the model Load the model via the transformers library: from transformers import AutoTokenizer, BertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") config = model.config Run the model with clinical diagonosis text: text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) Return the Top-5 predicted ICD-10 codes: results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5] return [ config.id2label[ids] for ids in results]
AkshatSurolia/ViT-FaceMask-Finetuned	AkshatSurolia	2023-05-05T15:22:03Z	293	1	transformers	[ "transformers", "pytorch", "safetensors", "vit", "image-classification", "dataset:Face-Mask18K", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - image-classification datasets: - Face-Mask18K --- # Vision Transformer (ViT) for Face Mask Detection Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Training Metrics epoch = 0.89 total_flos = 923776502GF train_loss = 0.057 train_runtime = 0:40:10.40 train_samples_per_second = 23.943 train_steps_per_second = 1.497 --- ## Evaluation Metrics epoch = 0.89 eval_accuracy = 0.9894 eval_loss = 0.0395 eval_runtime = 0:00:36.81 eval_samples_per_second = 97.685 eval_steps_per_second = 12.224
gsaivinay/OpenAssistant-SFT-7-Llama-30B-HF	gsaivinay	2023-05-05T15:17:18Z	5	2	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2304.07327", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T15:17:17Z	--- license: other duplicated_from: TheBloke/OpenAssistant-SFT-7-Llama-30B-HF --- # OpenAssistant LLaMA 30B SFT 7 HF This in HF format repo of [OpenAssistant's LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor). It is the result of merging the XORs from the above repo with the original Llama 30B weights. This is epoch 7 of OpenAssistant's training of a Llama 30B model. # Original model card ``` llama-30b-sft-7: dtype: fp16 log_dir: "llama_log_30b" learning_rate: 1e-5 model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500 #model_name: OpenAssistant/llama-30b-super-pretrain output_dir: llama_model_30b deepspeed_config: configs/zero3_config_sft.json weight_decay: 0.0 residual_dropout: 0.0 max_length: 2048 use_flash_attention: true warmup_steps: 20 gradient_checkpointing: true gradient_accumulation_steps: 12 per_device_train_batch_size: 2 per_device_eval_batch_size: 3 eval_steps: 101 save_steps: 485 num_train_epochs: 4 save_total_limit: 3 use_custom_sampler: true sort_by_length: false #save_strategy: steps save_strategy: epoch datasets: - oasst_export: lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz val_split: 0.05 - vicuna: val_split: 0.05 max_val_set: 800 fraction: 1.0 - dolly15k: val_split: 0.05 max_val_set: 300 - grade_school_math_instructions: val_split: 0.05 - code_alpaca: val_split: 0.05 max_val_set: 250 ``` - OASST dataset paper: https://arxiv.org/abs/2304.07327
jasonsurya0/BART_FIVE	jasonsurya0	2023-05-05T15:05:15Z	105	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-05T14:36:24Z	BART MODEL #5 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM
ameyachitnis/ppo-Huggy	ameyachitnis	2023-05-05T15:00:33Z	15	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-05-05T09:54:27Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: ameyachitnis/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
hsyntemiz/turcoins-classifier	hsyntemiz	2023-05-05T14:57:17Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-05T12:06:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: turcoins-classifier results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: hsyntemiz--turcoins split: test args: hsyntemiz--turcoins metrics: - name: Accuracy type: accuracy value: 0.9548611111111112 --- <!-- 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. --> # turcoins-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - Accuracy: 0.9549 ## Model description More information needed ## Intended uses & 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: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9277 \| 1.0 \| 146 \| 1.9660 \| 0.7726 \| \| 1.6627 \| 2.0 \| 292 \| 1.7154 \| 0.7917 \| \| 1.4071 \| 2.99 \| 438 \| 1.4120 \| 0.8079 \| \| 1.09 \| 4.0 \| 585 \| 1.1225 \| 0.8362 \| \| 0.8086 \| 5.0 \| 731 \| 0.8917 \| 0.8675 \| \| 0.7636 \| 6.0 \| 877 \| 0.7596 \| 0.8709 \| \| 0.611 \| 6.99 \| 1023 \| 0.6493 \| 0.8883 \| \| 0.4605 \| 8.0 \| 1170 \| 0.5899 \| 0.8872 \| \| 0.37 \| 9.0 \| 1316 \| 0.4978 \| 0.9045 \| \| 0.3882 \| 10.0 \| 1462 \| 0.4424 \| 0.9132 \| \| 0.3139 \| 10.99 \| 1608 \| 0.3969 \| 0.9115 \| \| 0.3178 \| 12.0 \| 1755 \| 0.3525 \| 0.9294 \| \| 0.2796 \| 13.0 \| 1901 \| 0.3552 \| 0.9161 \| \| 0.2571 \| 14.0 \| 2047 \| 0.3189 \| 0.9265 \| \| 0.2481 \| 14.99 \| 2193 \| 0.2945 \| 0.9358 \| \| 0.1875 \| 16.0 \| 2340 \| 0.2647 \| 0.9392 \| \| 0.1861 \| 17.0 \| 2486 \| 0.2404 \| 0.9410 \| \| 0.1839 \| 18.0 \| 2632 \| 0.2556 \| 0.9421 \| \| 0.173 \| 18.99 \| 2778 \| 0.2387 \| 0.9462 \| \| 0.1837 \| 20.0 \| 2925 \| 0.2049 \| 0.9485 \| \| 0.1724 \| 21.0 \| 3071 \| 0.2065 \| 0.9525 \| \| 0.1399 \| 22.0 \| 3217 \| 0.2089 \| 0.9404 \| \| 0.1696 \| 22.99 \| 3363 \| 0.1957 \| 0.9497 \| \| 0.1405 \| 24.0 \| 3510 \| 0.1848 \| 0.9554 \| \| 0.1009 \| 25.0 \| 3656 \| 0.1912 \| 0.9520 \| \| 0.1126 \| 26.0 \| 3802 \| 0.1717 \| 0.9560 \| \| 0.1336 \| 26.99 \| 3948 \| 0.1699 \| 0.9589 \| \| 0.1046 \| 28.0 \| 4095 \| 0.1600 \| 0.9601 \| \| 0.126 \| 29.0 \| 4241 \| 0.1839 \| 0.9520 \| \| 0.0882 \| 29.95 \| 4380 \| 0.1763 \| 0.9549 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
SharKRippeR/xlm-roberta-base-finetuned-panx-it	SharKRippeR	2023-05-05T14:56:02Z	103	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-05T14:52:11Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: validation args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8149959250203749 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2579 - F1: 0.8150 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.9299 \| 1.0 \| 53 \| 0.3701 \| 0.7122 \| \| 0.313 \| 2.0 \| 106 \| 0.2874 \| 0.7881 \| \| 0.2174 \| 3.0 \| 159 \| 0.2579 \| 0.8150 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
SanketJadhav/PlantDiseaseClassifier	SanketJadhav	2023-05-05T14:47:43Z	0	0	fastai	[ "fastai", "region:us" ]	null	2023-03-22T14:59:49Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
SharKRippeR/xlm-roberta-base-finetuned-panx-de-fr	SharKRippeR	2023-05-05T14:40:45Z	136	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-05-05T14:27:26Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1624 - F1: 0.8577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2988 \| 1.0 \| 537 \| 0.1743 \| 0.8300 \| \| 0.1472 \| 2.0 \| 1074 \| 0.1616 \| 0.8470 \| \| 0.0987 \| 3.0 \| 1611 \| 0.1624 \| 0.8577 \| ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
marianodo/MegaBatchMarginLoss-light-model-10	marianodo	2023-05-05T14:36:13Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-05-05T14:35:49Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 16 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` 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": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, '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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
thejuuuuu/torgo_b4_2	thejuuuuu	2023-05-05T14:33:34Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-05-05T14:32:15Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_b4_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # torgo_b4_2 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
marianodo/MegaBatchMarginLoss-10	marianodo	2023-05-05T14:28:48Z	9	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-05-05T14:27:55Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(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={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 16 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` 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": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
GeneralAwareness/MirrorMask	GeneralAwareness	2023-05-05T14:25:59Z	16	5	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "image-to-image", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-23T08:48:54Z	--- license: cc-by-nc-sa-4.0 language: - en thumbnail: "https://huggingface.co/GeneralAwareness/Mirrormask/resolve/main/mm1.png" tags: - stable-diffusion - text-to-image - image-to-image - diffusers --- ### Mirror Mask Welcome to MirrorMask - This is a fine-tuned Stable Diffusion model trained from the 2005 dark fantasy film MirrorMask. Use the token mirrormask in your prompts for the style to show. Examples down below: ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm1.png)![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm2.png)![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm3.png) #### Prompt An example of calling mirrormask before the prompt (mirrormask a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm.png) An example of calling mirrormask with a comma before the prompt (mirrormask, a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmcomma.png) An example of calling mirrormask with the additional word of "by" before the prompt (mirrormask by a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmby.png) An example of calling mirrormask with the additional word of "in" before the prompt (mirrormask in a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmin.png) An example of calling the prompt then mirrormask (a woman mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/_mm.png) An example of calling the prompt then adding a comma before mirrormask (a woman, mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/commamm.png) An example of calling the prompt then adding the word "in" before mirrormask (a woman in mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/inmm.png) An example of calling the prompt then adding the word "by" before mirrormask (a woman by mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/bymm.png) For more variety try adding the word "style" after the token word mirrormask (no examples given to save space). ### 🧨 Diffusers This model can be used just as you would any other Stable Diffusion style model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "GeneralAwareness/MirrorMask" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a woman by mirrormask" image = pipe(prompt).images[0] image.save("./awoman.png") ``` ## License This model is under a creative commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). To see what rights you have under this licence follow this link - https://creativecommons.org/licenses/by-nc-sa/4.0/
cornut/a2c-PandaReachDense-v2	cornut	2023-05-05T14:24:58Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-05-05T14:21:56Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.66 +/- 0.47 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-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 ... ```
rozek/OpenLLaMA_7B_300BT_q4	rozek	2023-05-05T14:23:02Z	0	1	null	[ "open-llama", "ggml", "7b", "quantized", "text-generation", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "region:us" ]	text-generation	2023-05-05T13:17:46Z	--- license: apache-2.0 datasets: - togethercomputer/RedPajama-Data-1T tags: - open-llama - ggml - 7b - quantized - text-generation --- This repository contains the [300BT preview](https://huggingface.co/openlm-research/open_llama_7b_preview_300bt) of the [Open LLaMA](https://github.com/openlm-research/open_llama) model based on the [RedPajama 1T dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T), converted to GGML and quantized to 4 bit, ready to be used with [llama.cpp](https://github.com/rozek/llama.cpp) ## Usage ## In order to use this model with llama.cpp * install llama.cpp as [described in the docs](https://github.com/ggerganov/llama.cpp#usage) * download this model * move it into the `models` subfolder of llama.cpp * run inferences with the additional parameter `-m ./models/7B/ggml-openllama-7b-300bt-q4_0.bin` For example, ``` ./main -m ./models/7B/ggml-openllama-7b-300bt-q4_0.bin -n 128 -p "who was Joseph Weizenbaum?" ``` ## License ## Since the underlying model is itself Apache 2.0 licensed, this repo is as well. --- license: apache-2.0 ---
Bainbridge/gpt2-kl_01_03_hscnspecial-hs_cn	Bainbridge	2023-05-05T14:11:46Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-05-05T13:20:34Z	--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_03_hscnspecial-hs_cn results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gpt2-kl_01_03_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5608 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 73.5326 \| 0.02 \| 10 \| 69.5739 \| \| 46.0984 \| 0.04 \| 20 \| 32.9644 \| \| 13.5448 \| 0.06 \| 30 \| 10.6411 \| \| 6.7693 \| 0.08 \| 40 \| 4.2565 \| \| 2.6755 \| 0.1 \| 50 \| 2.0724 \| \| 1.4407 \| 0.12 \| 60 \| 1.1299 \| \| 1.082 \| 0.14 \| 70 \| 0.9249 \| \| 0.8847 \| 0.16 \| 80 \| 0.7332 \| \| 0.7248 \| 0.18 \| 90 \| 0.6492 \| \| 0.7073 \| 0.2 \| 100 \| 0.6516 \| \| 0.6199 \| 0.22 \| 110 \| 0.6608 \| \| 0.6244 \| 0.24 \| 120 \| 0.6069 \| \| 0.6289 \| 0.26 \| 130 \| 0.6602 \| \| 0.6198 \| 0.28 \| 140 \| 0.5919 \| \| 0.5988 \| 0.3 \| 150 \| 0.6068 \| \| 0.6156 \| 0.32 \| 160 \| 0.5664 \| \| 0.5252 \| 0.34 \| 170 \| 0.5626 \| \| 0.6351 \| 0.36 \| 180 \| 0.5663 \| \| 0.6246 \| 0.38 \| 190 \| 0.5599 \| \| 0.5791 \| 0.4 \| 200 \| 0.5609 \| \| 0.6755 \| 0.42 \| 210 \| 0.5627 \| \| 0.6367 \| 0.44 \| 220 \| 0.5608 \| ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3
tomofi/trocr-captcha	tomofi	2023-05-05T13:59:12Z	613	5	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2022-03-11T12:04:59Z	--- license: mit --- CER: 0.0019 training code https://colab.research.google.com/drive/14MfFkhgPS63RJcP7rpBOK6OII_y34jx_?usp=sharing

HeySatyam/my_awesome_model

HeySatyam

2023-05-06T00:45:59Z

103

0

transformers

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

text-classification

2023-05-05T22:36:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93128 ---  # my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Accuracy: 0.9313 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2348 | 1.0 | 1563 | 0.1867 | 0.9289 | | 0.1523 | 2.0 | 3126 | 0.2315 | 0.9313 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.13.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

GraphicStylz/Stylz

GraphicStylz

2023-05-06T00:40:22Z

0

adapter-transformers

[ "adapter-transformers", "conversational", "en", "es", "fr", "sm", "dataset:OpenAssistant/oasst1", "dataset:wikipedia", "dataset:google/fleurs", "dataset:google/xtreme_s", "dataset:google/wit", "dataset:google_wellformed_query", "dataset:cornell_movie_dialog", "license:cc-by-nc-sa-4.0", "region:us" ]

text-generation

2023-05-05T23:43:52Z

--- license: cc-by-nc-sa-4.0 datasets: - OpenAssistant/oasst1 - wikipedia - google/fleurs - google/xtreme_s - google/wit - google_wellformed_query - cornell_movie_dialog language: - en - es - fr - sm metrics: - character - bertscore library_name: adapter-transformers pipeline_tag: conversational ---

Bainbridge/gpt2-kl_001_07_hscnspecial-hs_cn

Bainbridge

2023-05-06T00:40:01Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T23:44:28Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_07_hscnspecial-hs_cn results: [] ---  # gpt2-kl_001_07_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5264 | 0.02 | 10 | 69.5797 | | 46.0831 | 0.04 | 20 | 32.9588 | | 13.5437 | 0.06 | 30 | 10.6394 | | 6.7905 | 0.08 | 40 | 4.1876 | | 3.5385 | 0.1 | 50 | 2.0711 | | 1.5427 | 0.12 | 60 | 1.1384 | | 1.1141 | 0.14 | 70 | 0.8584 | | 0.926 | 0.16 | 80 | 0.7673 | | 0.7432 | 0.18 | 90 | 0.6549 | | 0.7195 | 0.2 | 100 | 0.6203 | | 0.6079 | 0.22 | 110 | 0.5966 | | 0.6356 | 0.24 | 120 | 0.5855 | | 0.6443 | 0.26 | 130 | 0.5837 | | 0.6171 | 0.28 | 140 | 0.5791 | | 0.6031 | 0.3 | 150 | 0.5869 | | 0.587 | 0.32 | 160 | 0.5674 | | 0.518 | 0.34 | 170 | 0.5665 | | 0.6313 | 0.36 | 180 | 0.5698 | | 0.6331 | 0.38 | 190 | 0.5624 | | 0.5768 | 0.4 | 200 | 0.5575 | | 0.6768 | 0.42 | 210 | 0.5587 | | 0.6288 | 0.44 | 220 | 0.5500 | | 0.5371 | 0.46 | 230 | 0.5510 | | 0.5499 | 0.48 | 240 | 0.5578 | | 0.548 | 0.5 | 250 | 0.5583 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

Bainbridge/gpt2-kl_001_06_hscnspecial-hs_cn

Bainbridge

2023-05-05T23:43:42Z

7

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T22:42:39Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_06_hscnspecial-hs_cn results: [] ---  # gpt2-kl_001_06_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5489 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5243 | 0.02 | 10 | 69.5733 | | 46.0785 | 0.04 | 20 | 32.9424 | | 13.5445 | 0.06 | 30 | 10.6459 | | 6.772 | 0.08 | 40 | 4.2120 | | 3.5618 | 0.1 | 50 | 2.0539 | | 1.4416 | 0.12 | 60 | 1.0919 | | 1.0628 | 0.14 | 70 | 0.8710 | | 0.8751 | 0.16 | 80 | 0.7180 | | 0.7178 | 0.18 | 90 | 0.7504 | | 0.7072 | 0.2 | 100 | 0.6374 | | 0.5923 | 0.22 | 110 | 0.6286 | | 0.6271 | 0.24 | 120 | 0.6355 | | 0.6407 | 0.26 | 130 | 0.6217 | | 0.6474 | 0.28 | 140 | 0.5978 | | 0.5946 | 0.3 | 150 | 0.5985 | | 0.6073 | 0.32 | 160 | 0.5887 | | 0.5149 | 0.34 | 170 | 0.5773 | | 0.6478 | 0.36 | 180 | 0.5671 | | 0.6186 | 0.38 | 190 | 0.5593 | | 0.5708 | 0.4 | 200 | 0.5572 | | 0.6788 | 0.42 | 210 | 0.5545 | | 0.6316 | 0.44 | 220 | 0.5503 | | 0.5317 | 0.46 | 230 | 0.5508 | | 0.5464 | 0.48 | 240 | 0.5475 | | 0.548 | 0.5 | 250 | 0.5466 | | 0.5488 | 0.52 | 260 | 0.5473 | | 0.5551 | 0.54 | 270 | 0.5469 | | 0.5867 | 0.56 | 280 | 0.5489 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

reem1997/depressionDetectionArabert

reem1997

2023-05-05T23:34:23Z

119

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-04T10:37:37Z

--- tags: - generated_from_trainer metrics: - precision - recall - accuracy model-index: - name: depressionDetectionArabert results: [] ---  # depressionDetectionArabert This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4586 - Macro F1: 0.9279 - Precision: 0.9281 - Recall: 0.9279 - Kappa: 0.8558 - Accuracy: 0.9279 ## Model description More information needed ## Intended uses & 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: 128 - seed: 25 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 407 | 0.2285 | 0.9143 | 0.9181 | 0.9144 | 0.8289 | 0.9144 | | 0.2459 | 2.0 | 815 | 0.2124 | 0.9264 | 0.9269 | 0.9264 | 0.8528 | 0.9264 | | 0.1306 | 3.0 | 1222 | 0.2439 | 0.9285 | 0.9287 | 0.9285 | 0.8570 | 0.9285 | | 0.0747 | 4.0 | 1630 | 0.3240 | 0.9270 | 0.9272 | 0.9270 | 0.8540 | 0.9270 | | 0.0328 | 5.0 | 2037 | 0.3850 | 0.9273 | 0.9275 | 0.9273 | 0.8546 | 0.9273 | | 0.0328 | 6.0 | 2445 | 0.4129 | 0.9279 | 0.9280 | 0.9279 | 0.8558 | 0.9279 | | 0.0213 | 7.0 | 2852 | 0.4541 | 0.9273 | 0.9273 | 0.9273 | 0.8545 | 0.9273 | | 0.0108 | 7.99 | 3256 | 0.4586 | 0.9279 | 0.9281 | 0.9279 | 0.8558 | 0.9279 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Tokenizers 0.13.3

ratish/DBERT_CleanDesc_Collision_v2.1.4

ratish

2023-05-05T23:28:07Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T23:24:37Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.4 results: [] ---  # ratish/DBERT_CleanDesc_Collision_v2.1.4 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: 0.3438 - Validation Loss: 1.4467 - Train Accuracy: 0.5897 - Epoch: 11 ## Model description More information needed ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4575, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 1.6148 | 1.7151 | 0.3077 | 0 | | 1.4783 | 1.7263 | 0.3077 | 1 | | 1.3926 | 1.6779 | 0.4103 | 2 | | 1.2462 | 1.5778 | 0.4359 | 3 | | 1.0592 | 1.5154 | 0.4359 | 4 | | 0.8814 | 1.5370 | 0.4615 | 5 | | 0.7554 | 1.4250 | 0.5385 | 6 | | 0.6303 | 1.4385 | 0.5641 | 7 | | 0.5458 | 1.3870 | 0.4872 | 8 | | 0.4808 | 1.3459 | 0.5385 | 9 | | 0.4098 | 1.5049 | 0.5385 | 10 | | 0.3438 | 1.4467 | 0.5897 | 11 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

ratish/DBERT_CleanDesc_Collision_v2.1.3

ratish

2023-05-05T23:22:32Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T23:18:12Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.3 results: [] ---  # ratish/DBERT_CleanDesc_Collision_v2.1.3 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: 0.4790 - Validation Loss: 1.3887 - Train Accuracy: 0.6154 - Epoch: 9 ## Model description More information needed ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 1.6006 | 1.7368 | 0.3077 | 0 | | 1.4578 | 1.7548 | 0.3077 | 1 | | 1.3664 | 1.6852 | 0.4103 | 2 | | 1.1923 | 1.5441 | 0.4615 | 3 | | 1.0295 | 1.4601 | 0.5385 | 4 | | 0.8769 | 1.4388 | 0.5897 | 5 | | 0.7829 | 1.4165 | 0.5897 | 6 | | 0.6690 | 1.4263 | 0.5897 | 7 | | 0.5765 | 1.3252 | 0.6154 | 8 | | 0.4790 | 1.3887 | 0.6154 | 9 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

ratish/DBERT_CleanDesc_Collision_v2.1.2

ratish

2023-05-05T23:16:03Z

61

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T23:15:13Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.2 results: [] ---  # ratish/DBERT_CleanDesc_Collision_v2.1.2 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: 1.6317 - Validation Loss: 1.7171 - Train Accuracy: 0.3077 - Epoch: 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: - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 1.6317 | 1.7171 | 0.3077 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

ratish/DBERT_CleanDesc_Collision_v2.1.1

ratish

2023-05-05T23:12:27Z

63

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T23:11:43Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: ratish/DBERT_CleanDesc_Collision_v2.1.1 results: [] ---  # ratish/DBERT_CleanDesc_Collision_v2.1.1 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: 1.4183 - Validation Loss: 1.1286 - Train Accuracy: 0.6923 - Epoch: 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: - 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, '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 | |:----------:|:---------------:|:--------------:|:-----:| | 1.4183 | 1.1286 | 0.6923 | 0 | ### Framework versions - Transformers 4.28.1 - TensorFlow 2.12.0 - Datasets 2.12.0 - Tokenizers 0.13.3

Bainbridge/gpt2-kl_001_05_hscnspecial-hs_cn

Bainbridge

2023-05-05T22:41:52Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T21:41:54Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_001_05_hscnspecial-hs_cn results: [] ---  # gpt2-kl_001_05_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5513 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5241 | 0.02 | 10 | 69.5822 | | 46.0925 | 0.04 | 20 | 33.0099 | | 13.5527 | 0.06 | 30 | 10.6424 | | 6.8151 | 0.08 | 40 | 4.2013 | | 3.5806 | 0.1 | 50 | 2.0893 | | 1.4772 | 0.12 | 60 | 1.1276 | | 1.1166 | 0.14 | 70 | 0.8410 | | 0.8952 | 0.16 | 80 | 0.7324 | | 0.7517 | 0.18 | 90 | 0.6448 | | 0.7044 | 0.2 | 100 | 0.6761 | | 0.6069 | 0.22 | 110 | 0.6436 | | 0.6184 | 0.24 | 120 | 0.6057 | | 0.6394 | 0.26 | 130 | 0.5877 | | 0.6243 | 0.28 | 140 | 0.5719 | | 0.598 | 0.3 | 150 | 0.5675 | | 0.5848 | 0.32 | 160 | 0.5645 | | 0.5161 | 0.34 | 170 | 0.5662 | | 0.6247 | 0.36 | 180 | 0.5665 | | 0.6243 | 0.38 | 190 | 0.5592 | | 0.5768 | 0.4 | 200 | 0.5569 | | 0.68 | 0.42 | 210 | 0.5583 | | 0.627 | 0.44 | 220 | 0.5539 | | 0.5369 | 0.46 | 230 | 0.5576 | | 0.5449 | 0.48 | 240 | 0.5576 | | 0.5456 | 0.5 | 250 | 0.5494 | | 0.55 | 0.52 | 260 | 0.5559 | | 0.5595 | 0.54 | 270 | 0.5525 | | 0.5821 | 0.56 | 280 | 0.5513 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

vinesmsuic/bg-visualnovel-v02

vinesmsuic

2023-05-05T22:21:14Z

42

11

diffusers

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

text-to-image

2022-11-18T08:24:37Z

--- language: - en license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- # BG-VisualNovel-v02 A prototype project of generating Visual Novel backgrounds for game developers/artists. This model is intended to produce visual novel backgrounds with just a few prompts. ## 🧨 Diffusers This model can be used just like any other Stable Diffusion model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "vinesmsuic/bg-visualnovel-v02" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a classroom" image = pipe(prompt).images[0] image.save("./classroom.png") prompt = "a hospital building, two trees" image = pipe(prompt).images[0] image.save("./hospital.png") prompt = "a street at night with nobody around" image = pipe(prompt).images[0] image.save("./nightstreet.png") ``` ## Examples a classroom ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/classroom.png) a hospital building, two trees ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/hospital.png) a street at night with nobody around ![](https://huggingface.co/vinesmsuic/bg-visualnovel-v02/resolve/main/_examples/nightstreet.png) ## License This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

tarek23/flan-t5-qg-test-LQ

tarek23

2023-05-05T22:17:04Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T15:44:51Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: flan-t5-qg-test-LQ results: [] ---  # flan-t5-qg-test-LQ This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4480 - Rouge1: 22.9065 - Rouge2: 6.1197 - Rougel: 20.6542 - Rougelsum: 20.6524 - Gen Len: 16.0202 ## Model description More information needed ## Intended uses & 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.472 | 1.0 | 23583 | 2.4480 | 22.9065 | 6.1197 | 20.6542 | 20.6524 | 16.0202 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

NightOcean/cat-blip-datasets

NightOcean

2023-05-05T22:08:17Z

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-05-05T03:50:18Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - NightOcean/cat-blip-datasets These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the ethers/cat-blip-datasets 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)

WildPress/model2

WildPress

2023-05-05T22:05:09Z

30

0

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-05-05T21:54:58Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of simbimbi cat tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - WildPress/model2 This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of simbimbi cat using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

Monero/Pygmalion-Metharme-7b-4bit-TopScore

Monero

2023-05-05T22:03:08Z

1,355

45

transformers

[ "transformers", "llama", "text-generation", "text generation", "instruct", "en", "autotrain_compatible", "region:us" ]

text-generation

2023-04-30T03:04:16Z

--- language: - en thumbnail: null tags: - text generation - instruct pipeline_tag: text-generation inference: false --- <h1 style="text-align: center">Metharme 7B 4bit</h1> <h2 style="text-align: center">An instruction-tuned LLaMA biased towards fiction writing and conversation.</h2> ## Model Details Converted from the XORs weights from PygmalionAI's release https://huggingface.co/PygmalionAI/metharme-7b Quantized for KoboldAI use using https://github.com/0cc4m/GPTQ-for-LLaMa I created several dozen quantized variations of this model and believe this variation to be "best." <br> <html> <head> <style> table { border:1px solid #b3adad; border-collapse:collapse; padding:5px; } table th { border:1px solid #b3adad; padding:5px; background: #f0f0f0; color: #313030; } table td { border:1px solid #b3adad; text-align:center; padding:5px; background: #ffffff; color: #313030; } </style> </head> <body> <table> <thead> <tr> <th>Model:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme 7b - 16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-AO-TS-Trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> </tbody> </table> </body> </html> <br><b>Other benchmark scores at the bottom of readme.</b> <hr> Metharme 7B is an instruct model based on Meta's LLaMA-7B. This is an experiment to try and get a model that is usable for conversation, roleplaying and storywriting, but which can be guided using natural language like other instruct models. See the [prompting](#prompting) section below for examples. It was trained by doing supervised fine-tuning over a mixture of regular instruction data alongside roleplay, fictional stories and conversations with synthetically generated instructions attached. ## Prompting The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`. The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history. ### Prompting example Here's a concrete example. Let's say you have the following system prompt: > This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn. And the user has typed in the following message: > Start! To perform inference against the model, the prompt should be formatted like this: ``` <|system|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<|user|>Start!<|model|> ``` The model might generate something like: > You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!' > > - Go inside the house. > - Ignore the call and move away. > - Run as fast as you can. It will automatically emit an end-of-text token (`</s>`) when it judges that the response is complete. Pretend then that the user has replied with `go inside`. The next prompt should then be: ``` <|system|>This is a text adventure game. Describe the scenario to the user and give him three options to pick from on each turn.<|user|>Start!<|model|>You are standing in front of an old, abandoned house. The windows are boarded up, and there's no sign of life around it. As you approach, you notice a strange feeling emanating from within. Suddenly, you hear a voice calling out to you... 'Come inside!' - Go inside the house. - Ignore the call and move away. - Run as fast as you can.<|user|>go inside<|model|> ``` Which might generate something like: > You enter the house, and immediately, a wave of unsettling energy washes over you. Your senses seem heightened, but your mind feels foggy. A figure appears before you - a tall man with piercing eyes. He speaks to you in a language you don't understand. > > - Ask him what he wants. > - Attack him. > - Run away. Same process applies. Usually, it is best to do a sliding window over the user and model turns, but keep the system prompt fixed at the start of the context window. ## Limitations and biases The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. As such, it was **not** fine-tuned to be safe and harmless: the base model _and_ this fine-tune have been trained on data known to contain profanity and texts that are lewd or otherwise offensive. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading. <hr> <p><strong><font size="5">Click to Expand Benchmarks of different quantized variations</font></strong></p> <strong><font size="4">The lower the number, the better the score.</font></strong> <html> <body> <details> <summary>Benchmarks Sorted by C4-New score</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted by Wikitext2</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted by PTB-new Score</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> </tbody> </table> </details> <details> <summary>Benchmarks Sorted in Alphabetical Order</summary> <table> <thead> <tr> <th>GPTQ Variation:</th> <th>Wikitext2</th> <th>Ptb-New</th> <th>C4-New</th> </tr> </thead> <tbody> <tr> <td>Metharme-7b-16bit</td> <td>5.7208476066589355</td> <td>41.61103439331055</td> <td>7.512405872344971</td> </tr> <tr> <td>Metharme-7b-4bit-128g-damp0.1</td> <td>6.242006301879883</td> <td>45.30938720703125</td> <td>8.14249324798584</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-damp0.1</td> <td>6.379064083099365</td> <td>52.17233657836914</td> <td>8.316649436950684</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits-damp0.1</td> <td>6.585468769073486</td> <td>51.55869674682617</td> <td>8.2803316116333</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits-damp0.1</td> <td>6.262309551239014</td> <td>47.80055618286133</td> <td>8.192194938659668</td> </tr> <tr> <td>Metharme-7b-4bit-128g</td> <td>10.206376075744629</td> <td>49.00401306152344</td> <td>8.198845863342285</td> </tr> <tr> <td>Metharme-7b-4bit-32g-damp0.1</td> <td>6.107605934143066</td> <td>47.91380310058594</td> <td>8.068695068359375</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-damp0.1</td> <td>6.566899299621582</td> <td>48.0670166015625</td> <td>8.095841407775879</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits-damp0.1</td> <td>6.109438896179199</td> <td>47.35737228393555</td> <td>8.075060844421387</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits-damp0.1</td> <td>6.128157138824463</td> <td>48.04175567626953</td> <td>8.0708646774292</td> </tr> <tr> <td>Metharme-7b-4bit-32g</td> <td>6.902080535888672</td> <td>50.237754821777344</td> <td>8.081602096557617</td> </tr> <tr> <td>Metharme-7b-4bit-alone</td> <td>6.997134685516357</td> <td>58.87525177001953</td> <td>8.824191093444824</td> </tr> <tr> <td>Metharme-7b-4bit-ao-128g</td> <td>251321.265625</td> <td>250117.859375</td> <td>232929.234375</td> </tr> <tr> <td>Metharme-7b-4bit-ao-32g</td> <td>275425.5</td> <td>267733.25</td> <td>254506.71875</td> </tr> <tr> <td>Metharme-7b-4bit-ao-damp0.1</td> <td>6.220707893371582</td> <td>47.82929611206055</td> <td>7.884565353393555</td> </tr> <tr> <td>Metharme-7b-4bit-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-7b-4bit-ao</td> <td>6.281311511993408</td> <td>46.79158401489258</td> <td>7.906069755554199</td> </tr> <tr> <td>Metharme-7b-4bit-damp0.1</td> <td>6.495014190673828</td> <td>49.39763641357422</td> <td>8.68167781829834</td> </tr> <tr> <td>Metharme-7b-4bit-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-damp0.1</td> <td>6.17774772644043</td> <td>46.47630310058594</td> <td>8.20170783996582</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-damp0.1</td> <td>6.584965705871582</td> <td>55.20026397705078</td> <td>8.268644332885742</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits-damp0.1</td> <td>6.225503921508789</td> <td>53.12746047973633</td> <td>8.240595817565918</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits-damp0.1</td> <td>6.289113521575928</td> <td>46.06787109375</td> <td>8.122251510620117</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g</td> <td>9.475017547607422</td> <td>52.358829498291016</td> <td>8.340700149536133</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-damp0.1</td> <td>6.219024658203125</td> <td>45.834869384765625</td> <td>8.071272850036621</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-damp0.1</td> <td>6.274552822113037</td> <td>47.35737228393555</td> <td>8.06270980834961</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits-damp0.1</td> <td>6.266031265258789</td> <td>47.346702575683594</td> <td>8.068148612976074</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits-damp0.1</td> <td>6.118431568145752</td> <td>45.67333221435547</td> <td>8.077078819274902</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g</td> <td>6.424218654632568</td> <td>48.48588943481445</td> <td>8.089512825012207</td> </tr> <tr> <td>Metharme-7b-4bit-ts-damp0.1</td> <td>6.7783050537109375</td> <td>51.81301498413086</td> <td>8.621373176574707</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-damp0.1</td> <td>6.7517595291137695</td> <td>54.06147384643555</td> <td>8.821818351745605</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits-damp0.1</td> <td>6.886506080627441</td> <td>64.72743225097656</td> <td>8.880627632141113</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits-damp0.1</td> <td>6.631694793701172</td> <td>51.7371711730957</td> <td>8.656966209411621</td> </tr> <tr> <td>Metharme-7b-4bit-ts</td> <td>7.735969543457031</td> <td>62.92238235473633</td> <td>8.913650512695312</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym-trits</td> <td>7.056288242340088</td> <td>48.983768463134766</td> <td>8.339276313781738</td> </tr> <tr> <td>Metharme-7b-4bit-128g-sym</td> <td>6.9575653076171875</td> <td>49.356834411621094</td> <td>8.35644817352295</td> </tr> <tr> <td>Metharme-7b-4bit-128g-trits</td> <td>8.68796443939209</td> <td>49.73833465576172</td> <td>8.2406587600708</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym-trits</td> <td>6.206963539123535</td> <td>46.88833999633789</td> <td>8.13610553741455</td> </tr> <tr> <td>Metharme-7b-4bit-32g-sym</td> <td>6.518134117126465</td> <td>49.66925811767578</td> <td>8.13516616821289</td> </tr> <tr> <td>Metharme-7b-4bit-32g-trits</td> <td>7.017086029052734</td> <td>45.04129409790039</td> <td>8.074845314025879</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym-trits</td> <td>6.756448745727539</td> <td>51.510311126708984</td> <td>8.292160987854004</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-sym</td> <td>6.819341659545898</td> <td>55.28740310668945</td> <td>8.377721786499023</td> </tr> <tr> <td>Metharme-7b-4bit-ts-128g-trits</td> <td>7.350858688354492</td> <td>44.25314712524414</td> <td>8.274221420288086</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym-trits</td> <td>6.82415771484375</td> <td>48.82029724121094</td> <td>8.090987205505371</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-sym</td> <td>6.387663841247559</td> <td>48.07244110107422</td> <td>8.173730850219727</td> </tr> <tr> <td>Metharme-7b-4bit-ts-32g-trits</td> <td>6.632943153381348</td> <td>47.973228454589844</td> <td>8.013848304748535</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym-trits</td> <td>7.599876403808594</td> <td>55.75454330444336</td> <td>8.932201385498047</td> </tr> <tr> <td>Metharme-7b-4bit-ts-sym</td> <td>7.270263671875</td> <td>54.35262680053711</td> <td>8.787986755371094</td> </tr> <tr> <td>Metharme-7b-4bit-ts-trits</td> <td>7.2306809425354</td> <td>66.78710174560547</td> <td>8.879831314086914</td> </tr> <tr> <td>Metharme-ao-sym-trits-damp0.1</td> <td>6.329384803771973</td> <td>48.06882858276367</td> <td>7.959168910980225</td> </tr> <tr> <td>Metharme-ao-sym-trits</td> <td>6.390106678009033</td> <td>48.15375900268555</td> <td>7.9804582595825195</td> </tr> <tr> <td>Metharme-ao-sym</td> <td>6.477842807769775</td> <td>48.53507614135742</td> <td>7.993765354156494</td> </tr> <tr> <td>Metharme-ao-trits-damp0.1</td> <td>6.163661956787109</td> <td>46.50249099731445</td> <td>7.877425193786621</td> </tr> <tr> <td>Metharme-ao-trits</td> <td>6.283935546875</td> <td>46.57590103149414</td> <td>7.907411575317383</td> </tr> <tr> <td>Metharme-ao-ts-damp0.1</td> <td>6.184001445770264</td> <td>46.17180633544922</td> <td>7.880400657653809</td> </tr> <tr> <td>Metharme-ao-ts-sym-damp0.1</td> <td>6.210323333740234</td> <td>47.66789245605469</td> <td>7.952476978302002</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits-damp0.1</td> <td>6.232576370239258</td> <td>48.081459045410156</td> <td>7.95023250579834</td> </tr> <tr> <td>Metharme-ao-ts-sym-trits</td> <td>6.471063137054443</td> <td>49.650611877441406</td> <td>7.969552040100098</td> </tr> <tr> <td>Metharme-ao-ts-sym</td> <td>6.460526943206787</td> <td>47.190460205078125</td> <td>7.9732160568237305</td> </tr> <tr> <td>Metharme-ao-ts-trits-damp0.1</td> <td>6.172733783721924</td> <td>45.19890213012695</td> <td>7.872506141662598</td> </tr> <tr> <td>Metharme-ao-ts-trits</td> <td>6.310682773590088</td> <td>46.4483757019043</td> <td>7.898126602172852</td> </tr> <tr> <td>Metharme-ao-ts</td> <td>6.329496383666992</td> <td>46.88129806518555</td> <td>7.910323143005371</td> </tr> <tr> <td>Metharme-sym-damp0.1</td> <td>6.896804332733154</td> <td>57.4250602722168</td> <td>8.703770637512207</td> </tr> <tr> <td>Metharme-sym-trits-damp0.1</td> <td>7.075908184051514</td> <td>59.13897705078125</td> <td>8.919178009033203</td> </tr> <tr> <td>Metharme-sym-trits</td> <td>7.494253635406494</td> <td>63.320709228515625</td> <td>8.969240188598633</td> </tr> <tr> <td>Metharme-sym</td> <td>7.585672855377197</td> <td>61.01168441772461</td> <td>9.032520294189453</td> </tr> <tr> <td>Metharme-trits</td> <td>7.832409858703613</td> <td>55.383026123046875</td> <td>8.806737899780273</td> </tr> </tbody> </table> </details> </body> </html>

keminglu/pivoine-7b

keminglu

2023-05-05T21:59:20Z

24

2

transformers

[ "transformers", "pytorch", "bloom", "text-generation", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-04T21:53:46Z

--- license: bigscience-bloom-rail-1.0 --- # Tokenizer used for all BLOOM models Tokenizer information are provided at [https://huggingface.co/bigscience/bloom#preprocessing](https://huggingface.co/bigscience/bloom#preprocessing) TODO: point to paper once it comes out with extra details on the tokenizer

wavymulder/portraitplus

wavymulder

2023-05-05T21:59:07Z

257,281

297

diffusers

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

text-to-image

2022-12-23T16:04:26Z

--- language: - en thumbnail: "https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page1.jpg" license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - safetensors - diffusers inference: true --- **Portrait+** ![Header](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page1.jpg) [*CKPT DOWNLOAD LINK*](https://huggingface.co/wavymulder/portraitplus/resolve/main/portrait%2B1.0.ckpt) - this is a dreambooth model trained on a diverse set of close to medium range portraits of people. Use `portrait+ style` in your prompt (I recommend at the start) The goal was to create a model with a consistent portrait composition and consistent eyes. See the batch example below for the consistency of the model's eyes. This model can do several styles, so you'll want to guide it along depending on your goals. Note below in the document that prompting celebrities works a bit differently than prompting generic characters, since real people have a more photoreal presence in the base 1.5 model. Also note that fantasy concepts, like cyberpunk people or wizards, will require more rigid prompting for photoreal styles than something common like a person in a park. Portrait+ works best at a 1:1 aspect ratio, though I've had success with tall aspect ratios as well. Please see [this document where I share the parameters (prompt, sampler, seed, etc.) used for all example images above.](https://huggingface.co/wavymulder/portraitplus/resolve/main/parameters_for_samples.txt) We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run portraitplus: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/wavymulder/portraitplus) ![Modification example](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/page2.jpg) ![Batch example](https://huggingface.co/wavymulder/portraitplus/resolve/main/imgs/batchgrid.jpg)

chribeiro/a2c-PandaReachDense-v2

chribeiro

2023-05-05T21:58:33Z

0

stable-baselines3

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

reinforcement-learning

2023-05-05T21:53:27Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -0.34 +/- 0.18 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

lckidwell/album-cover-style

lckidwell

2023-05-05T21:51:46Z

41

5

diffusers

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

text-to-image

2023-01-13T20:13:37Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Album-Cover-Style Dreambooth model > trained by lckidwell with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Trained on ~80 album covers, mostly from the 50s and 60s, a mix of Jazz, pop, polka, religious, children's and other genres. ## Sample Prompts: * Kanye plays jazz, albumcover style * Swingin' with Henry Kissinger, albumcover style * Jay Z Children's album, albumcover style * Polka Party with Machine Gun Kelly, albumcover style ## Sample pictures of this concept: ![0](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02503-4178330406-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![2](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02512-2122051129-Polka_Party_with_Henry_Kissinger,_albumcover_style.png) ![3](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02493-407743854-Kanye_goes_country,_albumcover_style.png) ![4](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02387-1542142160-albumcover_style,_albumcover_style.png) ![5](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02521-1024797607-Polka_Party_with_Henry_Kissinger_and_Weird_Al,_albumcover_style.png) ![6](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02491-407743852-Kanye_goes_country,_albumcover_style.png) ![7](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02509-4178330412-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![8](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02529-3942483747-Jayz_Childrens_Album,_albumcover_style.png) ![9](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02507-4178330410-Swingin'_with_Henry_Kissinger,_albumcover_style.png) ![10](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02395-1542142168-albumcover_style,_albumcover_style.png) ![11](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02494-1810968449-Kanye_plays_Jazz,_albumcover_style.png) ![12](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02537-2335869042-Polka_Party_with_Machine_Gun_Kelly,_albumcover_style.png) ![13](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02412-1542142185-albumcover_style,_albumcover_style.png) ![14](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/02403-1542142176-albumcover_style,_albumcover_style.png) ## Moar Samples ![0](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00095.png) ![1](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00101.png) ![2](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00104.png) ![3](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00111.png) ![4](https://huggingface.co/lckidwell/album-cover-style/resolve/main/sample_images/00113.png)

MirageML/lowpoly-cyberpunk

MirageML

2023-05-05T21:32:43Z

40

30

diffusers

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

text-to-image

2022-11-28T07:50:09Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Cyberpunk on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Cyberpunk concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_cyberpunk** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-cyberpunk/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/search/images?similarStyleImagesId=847895439)

MirageML/lowpoly-environment

MirageML

2023-05-05T21:32:29Z

36

8

diffusers

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

text-to-image

2022-11-28T08:25:58Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Environment on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Environment concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_environment** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-environment/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/gallery/76095417/Game-Environments?tracking_source=search_projects%7Cisometric+building)

mHossain/bangla-para-v1-410000

mHossain

2023-05-05T21:19:39Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T20:20:53Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla-para-v1-410000 results: [] ---  # bangla-para-v1-410000 This model is a fine-tuned version of [mHossain/bangla-para-v1-380000](https://huggingface.co/mHossain/bangla-para-v1-380000) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9209 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 18.2867 ## Model description More information needed ## Intended uses & 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 - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.1627 | 1.0 | 3375 | 0.9209 | 0.0 | 0.0 | 0.0 | 0.0 | 18.2867 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

HelloSecurity/BootLeggerAI

HelloSecurity

2023-05-05T21:19:12Z

0

null

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

null

2023-05-05T21:13:11Z

--- license: mit --- # BootLeggerAI A python based chat bot for coders BootLegger AI is a Python script that uses deep learning, neural networking, and natural language processing to create a chatbot that can help users with their programming needs. The chatbot can understand natural language and generate appropriate responses based on the user's input. (A → E, E → I, I → O, O → U, U → A) ## Instructions for Use 1. Install the necessary libraries: Tensorflow and Numpy. You can do this using pip or any package manager of your choice. 2. Copy the code in the BootLeggerAI.py file into your Python editor of choice. 3. Run the code in your Python environment. 4. The chatbot will start running and prompt the user to enter their request. 5. Enter your request, and the chatbot will generate a response based on the predicted output. 6. If you encounter any issues, please see the Troubleshooting section below. ## Detailed instructions 1. Install Required Libraries: Before running the script, make sure you have installed all the required libraries. This script requires `tensorflow` and `numpy`. You can install them via pip or conda: ``` pip install tensorflow numpy ``` 2. Prepare Input and Output Data: The script takes in an array of input data and an array of output data. Each element of the input array should be a string that represents a programming task you want help with. The output array should contain the corresponding output for each input task. For example, you can create a numpy array for the input and output data like this: ``` import numpy as np # Define input and output data input_data = np.array(['create a python script', 'build a program', 'generate a code']) output_data = np.array([['create', 'python', 'script'], ['build', 'program'], ['generate', 'code']]) ``` 3. Tokenize Input Data: To use the input data with the model, we need to tokenize it first. Tokenization is the process of converting text into numerical values. The function `tokenize_input` in the script takes in the input data and returns the tokenizer object, the tokenized input sequence, the maximum length of the input sequence, and the vocabulary size. You can tokenize the input data like this: ``` from bootlegger_ai import tokenize_input tokenizer, input_seq, max_len, vocab_size = tokenize_input(input_data) ``` 4. Define the Neural Network Model: The next step is to define the neural network model. The function `define_model` in the script takes in the vocabulary size and maximum length of the input sequence and returns the model object. You can define the model like this: ``` from bootlegger_ai import define_model model = define_model(vocab_size, max_len) ``` 5. Train the Neural Network Model: After defining the model, we need to train it with the input and output data. The function `train_model` in the script takes in the model object, input sequence, output data, and number of epochs to train the model. It returns the trained model object. You can train the model like this: ``` from bootlegger_ai import train_model model = train_model(model, input_seq, output_data) ``` 6. Test the Model: After training the model, we can test it on new input data. The function `test_model` in the script takes in the model object, test data, tokenizer object, and maximum length of the input sequence. It returns the predictions for the test data. You can test the model like this: ``` from bootlegger_ai import test_model test_data = np.array(['I want to create a new website']) predictions = test_model(model, test_data, tokenizer, max_len) ``` 7. Generate Response: Finally, we can generate a response based on the predicted output. The function `generate_response` in the script takes in the predictions and tokenizer object and returns a response string. You can generate a response like this: ``` from bootlegger_ai import generate_response response = generate_response(predictions, tokenizer) print(response) ``` And that's it! By following these steps, you can use the BootLegger AI script to generate responses to programming-related requests. ## Developed By This script was developed by Adam Rivers and Hello Security LLC. ## Troubleshooting If the chatbot is not generating appropriate responses, please ensure that the input data is relevant to the context of programming. Additionally, you can try retraining the neural network model by modifying the input and output data in the script. If you encounter any other issues, please feel free to reach out for assistance.

Hentai00/aurakasih

Hentai00

2023-05-05T20:59:47Z

0

null

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

null

2023-05-05T20:50:29Z

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

radames/instruct-pix2pix-img2img

radames

2023-05-05T20:59:08Z

12

19

diffusers

[ "diffusers", "safetensors", "image-to-image", "license:mit", "diffusers:StableDiffusionInstructPix2PixPipeline", "region:us" ]

image-to-image

2023-05-05T20:58:32Z

--- license: mit duplicated_from: timbrooks/instruct-pix2pix library_name: diffusers tags: - image-to-image --- # InstructPix2Pix: Learning to Follow Image Editing Instructions GitHub: https://github.com/timothybrooks/instruct-pix2pix <img src='https://instruct-pix2pix.timothybrooks.com/teaser.jpg'/> ## Example To use `InstructPix2Pix`, install `diffusers` using `main` for now. The pipeline will be available in the next release ```bash pip install diffusers accelerate safetensors transformers ``` ```python import PIL import requests import torch from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler model_id = "timbrooks/instruct-pix2pix" pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) url = "https://raw.githubusercontent.com/timothybrooks/instruct-pix2pix/main/imgs/example.jpg" def download_image(url): image = PIL.Image.open(requests.get(url, stream=True).raw) image = PIL.ImageOps.exif_transpose(image) image = image.convert("RGB") return image image = download_image(URL) prompt = "turn him into cyborg" images = pipe(prompt, image=image, num_inference_steps=10, image_guidance_scale=1).images images[0] ```

MirageML/lowpoly-game-building

MirageML

2023-05-05T20:53:17Z

27

16

diffusers

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

text-to-image

2022-11-28T08:52:37Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Game Building on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Game Building concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_game_building** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-game-building/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/guutv)

MirageML/lowpoly-landscape

MirageML

2023-05-05T20:53:06Z

11

5

diffusers

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

text-to-image

2022-11-28T08:57:57Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly Landscape on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly Landscape concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_landscape** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-landscape/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.deviantart.com/kautsar211086/art/Long-Time-No-See-457162094)

MirageML/lowpoly-world

MirageML

2023-05-05T20:52:48Z

50

84

diffusers

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

text-to-image

2022-11-28T09:20:48Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Low Poly World on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Low Poly World concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of lowpoly_world** # Gradio We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run lowpoly-world: [![Open In Spaces](https://camo.githubusercontent.com/00380c35e60d6b04be65d3d94a58332be5cc93779f630bcdfc18ab9a3a7d3388/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f25463025394625413425393725323048756767696e67253230466163652d5370616365732d626c7565)](https://huggingface.co/spaces/MirageML/lowpoly-world) # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/lowpoly-world/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source](https://www.behance.net/gallery/89934319/Low-Poly-Worlds)

Ahmedrai/Starfire

Ahmedrai

2023-05-05T20:52:43Z

0

null

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

null

2023-05-05T20:06:32Z

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

MirageML/fantasy-scene

MirageML

2023-05-05T20:52:36Z

39

20

diffusers

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

text-to-image

2022-11-28T09:25:54Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Fantasy Scene on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Scene concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of fantasy_scene** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/fantasy-scene/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source #1](https://www.behance.net/search/images?tracking_source=typeahead_search_direct&similarStyleImagesId=647409143) [Image Source #2](https://www.behance.net/romanklco)

MirageML/fantasy-sword

MirageML

2023-05-05T20:52:28Z

16

13

diffusers

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

text-to-image

2022-11-28T09:26:54Z

--- language: - en tags: - stable-diffusion - text-to-image license: creativeml-openrail-m inference: true --- # Fantasy Sword on Stable Diffusion via Dreambooth This the Stable Diffusion model fine-tuned the Fantasy Sword concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of fantasy_sword** # Run on [Mirage](https://app.mirageml.com) Run this model and explore text-to-3D on [Mirage](https://app.mirageml.com)! Here are is a sample output for this model: ![image 0](https://huggingface.co/MirageML/fantasy-sword/resolve/main/output.png) # Share your Results and Reach us on [Discord](https://discord.gg/9B2Pu2bEvj)! [![Discord Server](https://discord.com/api/guilds/1022387303022338058/widget.png?style=banner2)](https://discord.gg/9B2Pu2bEvj) [Image Source]( https://www.artstation.com/search?sort_by=relevance&query=fantasy%20sword)

amankishore/hpchapter

amankishore

2023-05-05T20:52:16Z

34

0

diffusers

[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-01-08T00:01:38Z

--- license: creativeml-openrail-m library_name: diffusers ---

jdmartinev/CREMA_D_Model

jdmartinev

2023-05-05T20:50:56Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-05-03T14:45:30Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: CREMA_D_Model results: [] ---  # CREMA_D_Model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8221 - Accuracy: 0.7322 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.7381 | 0.99 | 37 | 1.6700 | 0.3359 | | 1.4143 | 1.99 | 74 | 1.4013 | 0.4878 | | 1.1738 | 2.98 | 111 | 1.1820 | 0.6029 | | 1.0229 | 4.0 | 149 | 1.0244 | 0.6532 | | 0.8688 | 4.99 | 186 | 0.9101 | 0.7036 | | 0.7578 | 5.99 | 223 | 0.8787 | 0.7112 | | 0.705 | 6.98 | 260 | 0.8292 | 0.7229 | | 0.6469 | 8.0 | 298 | 0.8509 | 0.7179 | | 0.5684 | 8.99 | 335 | 0.8412 | 0.7288 | | 0.5611 | 9.93 | 370 | 0.8221 | 0.7322 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

bilginn/bert-base-uncased-finetuned-cola

bilginn

2023-05-05T20:49:47Z

6

0

transformers

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

text-classification

2023-05-03T19:32:34Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5678267214677118 ---  # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.5922 - Matthews Correlation: 0.5678 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.207256119784435e-06 - train_batch_size: 4 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:-----:|:---------------:|:--------------------:| | 0.5811 | 1.0 | 2138 | 0.6179 | 0.4846 | | 0.4698 | 2.0 | 4276 | 0.8083 | 0.5495 | | 0.3161 | 3.0 | 6414 | 1.1152 | 0.5389 | | 0.2499 | 4.0 | 8552 | 1.0719 | 0.5624 | | 0.1755 | 5.0 | 10690 | 1.1734 | 0.5709 | | 0.1511 | 6.0 | 12828 | 1.2383 | 0.5699 | | 0.0738 | 7.0 | 14966 | 1.3802 | 0.5598 | | 0.0677 | 8.0 | 17104 | 1.4711 | 0.5599 | | 0.0509 | 9.0 | 19242 | 1.5751 | 0.5678 | | 0.0397 | 10.0 | 21380 | 1.5922 | 0.5678 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0 - Datasets 2.12.0 - Tokenizers 0.13.3

marcoliu/sd-pokemon-model-lora

marcoliu

2023-05-05T20:33:57Z

1

diffusers

[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:CompVis/stable-diffusion-v1-4", "base_model:adapter:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-05-05T13:29:07Z

--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA text2image fine-tuning - marcoliu/sd-pokemon-model-lora These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions 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)

seena18/tier3_satellite_image_classification

seena18

2023-05-05T20:21:35Z

218

1

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-05T20:00:47Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: tier3_satellite_image_classification results: [] ---  # tier3_satellite_image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4309 - Accuracy: 0.8085 ## Model description More information needed ## Intended uses & 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: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1878 | 1.0 | 125 | 2.0795 | 0.6905 | | 1.5347 | 2.0 | 250 | 1.5727 | 0.776 | | 1.3524 | 3.0 | 375 | 1.4309 | 0.8085 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

jeremyvictor/mt5-large-gecid-e8-b8

jeremyvictor

2023-05-05T20:05:15Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T17:31:38Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: mt5-large-gecid-e8-b8 results: [] ---  # mt5-large-gecid-e8-b8 This model is a fine-tuned version of [google/mt5-large](https://huggingface.co/google/mt5-large) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3000 - Rouge1: 64.4729 - Rouge2: 57.8072 - Rougel: 64.3868 - Rougelsum: 64.3569 - Gen Len: 18.7495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.8319 | 0.57 | 500 | 0.4310 | 61.7619 | 53.4157 | 61.6684 | 61.6628 | 18.7567 | | 0.4258 | 1.13 | 1000 | 0.3541 | 62.8056 | 55.1747 | 62.7392 | 62.7231 | 18.7601 | | 0.2777 | 1.7 | 1500 | 0.3231 | 63.4739 | 56.1433 | 63.366 | 63.3544 | 18.7546 | | 0.2023 | 2.26 | 2000 | 0.3068 | 64.1314 | 57.5343 | 64.0453 | 64.024 | 18.7546 | | 0.1432 | 2.83 | 2500 | 0.3000 | 64.4729 | 57.8072 | 64.3868 | 64.3569 | 18.7495 | | 0.0976 | 3.39 | 3000 | 0.3257 | 64.7215 | 58.3266 | 64.6223 | 64.5957 | 18.7601 | | 0.0811 | 3.96 | 3500 | 0.3112 | 64.7518 | 58.4888 | 64.6487 | 64.6454 | 18.7648 | | 0.0472 | 4.52 | 4000 | 0.3389 | 64.9658 | 58.822 | 64.8741 | 64.8621 | 18.7592 | | 0.0413 | 5.09 | 4500 | 0.3557 | 64.9468 | 58.8286 | 64.8609 | 64.8501 | 18.7592 | | 0.0248 | 5.66 | 5000 | 0.3452 | 65.2004 | 59.2566 | 65.0876 | 65.0889 | 18.7605 | | 0.0195 | 6.22 | 5500 | 0.3719 | 65.1043 | 59.083 | 65.0369 | 65.026 | 18.7541 | | 0.013 | 6.79 | 6000 | 0.3947 | 65.3124 | 59.486 | 65.2434 | 65.2324 | 18.7571 | | 0.0084 | 7.35 | 6500 | 0.4056 | 65.4053 | 59.6589 | 65.3249 | 65.3115 | 18.7580 | | 0.0055 | 7.92 | 7000 | 0.4216 | 65.3303 | 59.5344 | 65.2475 | 65.2284 | 18.7567 | ### Framework versions - Transformers 4.28.1 - Pytorch 1.11.0a0+b6df043 - Datasets 2.12.0 - Tokenizers 0.13.2

bazyl/gtsrb-model

bazyl

2023-05-05T20:05:05Z

298

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "vision", "generated_from_trainer", "dataset:gtsrb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-07-02T10:39:06Z

--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer datasets: - gtsrb metrics: - accuracy model-index: - name: gtsrb-model results: - task: name: Image Classification type: image-classification dataset: name: bazyl/GTSRB type: gtsrb args: gtsrb metrics: - name: Accuracy type: accuracy value: 0.9993199591975519 ---  # gtsrb-model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the bazyl/GTSRB dataset. It achieves the following results on the evaluation set: - Loss: 0.0034 - Accuracy: 0.9993 ## Model description The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties: - Single-image, multi-class classification problem - More than 40 classes - More than 50,000 images in total - Large, lifelike database ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.2593 | 1.0 | 4166 | 0.1585 | 0.9697 | | 0.2659 | 2.0 | 8332 | 0.0472 | 0.9900 | | 0.2825 | 3.0 | 12498 | 0.0155 | 0.9971 | | 0.0953 | 4.0 | 16664 | 0.0113 | 0.9983 | | 0.1277 | 5.0 | 20830 | 0.0076 | 0.9985 | | 0.0816 | 6.0 | 24996 | 0.0047 | 0.9988 | | 0.0382 | 7.0 | 29162 | 0.0041 | 0.9990 | | 0.0983 | 8.0 | 33328 | 0.0059 | 0.9990 | | 0.1746 | 9.0 | 37494 | 0.0034 | 0.9993 | | 0.1153 | 10.0 | 41660 | 0.0038 | 0.9990 | ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.12.0 - Datasets 2.3.2 - Tokenizers 0.12.1

gdecarlo/fabric_test

gdecarlo

2023-05-05T19:38:50Z

243

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-05T19:27:00Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: fabric_test results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.3731343150138855 --- # fabric_test Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### fabric linen ![fabric linen](images/fabric_linen.jpg) #### fabric morley ![fabric morley ](images/fabric_morley_.jpg) #### fabric saten ![fabric saten](images/fabric_saten.jpg)

Bainbridge/gpt2-kl_01_07_hscnspecial-hs_cn

Bainbridge

2023-05-05T19:35:19Z

11

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T18:20:56Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_07_hscnspecial-hs_cn results: [] ---  # gpt2-kl_01_07_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5377 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5948 | 0.02 | 10 | 69.5786 | | 46.1586 | 0.04 | 20 | 32.9619 | | 13.6007 | 0.06 | 30 | 10.6513 | | 6.8042 | 0.08 | 40 | 4.2289 | | 2.8577 | 0.1 | 50 | 2.1080 | | 1.447 | 0.12 | 60 | 1.1006 | | 1.2972 | 0.14 | 70 | 0.9296 | | 0.9482 | 0.16 | 80 | 0.7053 | | 0.7817 | 0.18 | 90 | 0.7118 | | 0.7763 | 0.2 | 100 | 0.6232 | | 0.6719 | 0.22 | 110 | 0.5972 | | 0.6852 | 0.24 | 120 | 0.5835 | | 0.7033 | 0.26 | 130 | 0.5850 | | 0.6782 | 0.28 | 140 | 0.5815 | | 0.6635 | 0.3 | 150 | 0.5757 | | 0.6405 | 0.32 | 160 | 0.5796 | | 0.5739 | 0.34 | 170 | 0.5705 | | 0.7139 | 0.36 | 180 | 0.5606 | | 0.6883 | 0.38 | 190 | 0.5592 | | 0.6429 | 0.4 | 200 | 0.5586 | | 0.7397 | 0.42 | 210 | 0.5511 | | 0.6993 | 0.44 | 220 | 0.5484 | | 0.5946 | 0.46 | 230 | 0.5515 | | 0.6172 | 0.48 | 240 | 0.5473 | | 0.6077 | 0.5 | 250 | 0.5442 | | 0.6148 | 0.52 | 260 | 0.5435 | | 0.6213 | 0.54 | 270 | 0.5425 | | 0.6431 | 0.56 | 280 | 0.5414 | | 0.6459 | 0.58 | 290 | 0.5392 | | 0.604 | 0.6 | 300 | 0.5394 | | 0.603 | 0.62 | 310 | 0.5368 | | 0.7207 | 0.64 | 320 | 0.5387 | | 0.5689 | 0.66 | 330 | 0.5407 | | 0.5721 | 0.68 | 340 | 0.5377 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

cuadron11/5

cuadron11

2023-05-05T19:16:37Z

5

0

transformers

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

token-classification

2023-05-05T17:44:27Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: '5' results: [] ---  # 5 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2255 - Precision: 0.6432 - Recall: 0.595 - F1: 0.6182 - Accuracy: 0.9709 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.5e-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: 32 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.3210 | 0.0 | 0.0 | 0.0 | 0.9324 | | No log | 2.0 | 58 | 0.2694 | 0.0 | 0.0 | 0.0 | 0.9324 | | No log | 3.0 | 87 | 0.2216 | 0.0 | 0.0 | 0.0 | 0.9316 | | No log | 4.0 | 116 | 0.2115 | 0.25 | 0.035 | 0.0614 | 0.9403 | | No log | 5.0 | 145 | 0.1740 | 0.3465 | 0.175 | 0.2326 | 0.9512 | | No log | 6.0 | 174 | 0.1589 | 0.42 | 0.315 | 0.36 | 0.9566 | | No log | 7.0 | 203 | 0.1514 | 0.4797 | 0.295 | 0.3653 | 0.9584 | | No log | 8.0 | 232 | 0.1686 | 0.4576 | 0.405 | 0.4297 | 0.9624 | | No log | 9.0 | 261 | 0.1840 | 0.5971 | 0.415 | 0.4897 | 0.9646 | | No log | 10.0 | 290 | 0.1571 | 0.5505 | 0.545 | 0.5477 | 0.9646 | | No log | 11.0 | 319 | 0.1809 | 0.6158 | 0.545 | 0.5782 | 0.9700 | | No log | 12.0 | 348 | 0.1763 | 0.6129 | 0.57 | 0.5907 | 0.9681 | | No log | 13.0 | 377 | 0.1902 | 0.5571 | 0.61 | 0.5823 | 0.9655 | | No log | 14.0 | 406 | 0.1916 | 0.5842 | 0.555 | 0.5692 | 0.9673 | | No log | 15.0 | 435 | 0.1895 | 0.6335 | 0.605 | 0.6189 | 0.9697 | | No log | 16.0 | 464 | 0.1951 | 0.5880 | 0.635 | 0.6106 | 0.9667 | | No log | 17.0 | 493 | 0.1918 | 0.6324 | 0.585 | 0.6078 | 0.9702 | | 0.0838 | 18.0 | 522 | 0.1957 | 0.6020 | 0.605 | 0.6035 | 0.9699 | | 0.0838 | 19.0 | 551 | 0.1886 | 0.6 | 0.6 | 0.6 | 0.9681 | | 0.0838 | 20.0 | 580 | 0.1992 | 0.6158 | 0.585 | 0.6 | 0.9702 | | 0.0838 | 21.0 | 609 | 0.2043 | 0.625 | 0.6 | 0.6122 | 0.9706 | | 0.0838 | 22.0 | 638 | 0.2110 | 0.6243 | 0.59 | 0.6067 | 0.9707 | | 0.0838 | 23.0 | 667 | 0.2121 | 0.6421 | 0.61 | 0.6256 | 0.9714 | | 0.0838 | 24.0 | 696 | 0.2112 | 0.6455 | 0.61 | 0.6272 | 0.9713 | | 0.0838 | 25.0 | 725 | 0.2150 | 0.6392 | 0.62 | 0.6294 | 0.9711 | | 0.0838 | 26.0 | 754 | 0.2229 | 0.6264 | 0.57 | 0.5969 | 0.9702 | | 0.0838 | 27.0 | 783 | 0.2219 | 0.6339 | 0.58 | 0.6057 | 0.9706 | | 0.0838 | 28.0 | 812 | 0.2239 | 0.6429 | 0.585 | 0.6126 | 0.9707 | | 0.0838 | 29.0 | 841 | 0.2211 | 0.6402 | 0.605 | 0.6221 | 0.9713 | | 0.0838 | 30.0 | 870 | 0.2230 | 0.6364 | 0.595 | 0.6150 | 0.9709 | | 0.0838 | 31.0 | 899 | 0.2244 | 0.6432 | 0.595 | 0.6182 | 0.9709 | | 0.0838 | 32.0 | 928 | 0.2255 | 0.6432 | 0.595 | 0.6182 | 0.9709 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

Parcurcik/code

Parcurcik

2023-05-05T19:07:26Z

147

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T19:03:52Z

--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: code results: [] ---  # code This model is a fine-tuned version of [sberbank-ai/rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1352 - Accuracy: 0.7530 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2

pcalhoun/LLaMA-7b-JonathanSwift

pcalhoun

2023-05-05T18:58:30Z

0

Transformers

[ "Transformers", "text2text-generation", "license:apache-2.0", "region:us" ]

text2text-generation

2023-05-05T15:28:54Z

--- license: apache-2.0 library_name: Transformers pipeline_tag: text2text-generation --- Uses PEFT weights: https://huggingface.co/blog/peft Finetuning on about 4500 strings of [modern translation of Jonathan Swift string] + " |SWIFTIFY| " + [Jonathan Swift text] + " |END|" Some dataset examples: > How has he been bombarded, harassed, and battered by just one smart aleck who promises never to leave him, dead or alive! |SWIFTIFY| How hath he been pelted, pestered, and pounded by one single wag, who promiseth never to forsake him living or dead! |END| > The third advantage is the convenience of secure harbors and ports for transporting their own finished goods and importing those of others that are minimally processed, as mutual trade demands. |SWIFTIFY| The third, is the conveniency of safe ports and havens, to carry out their own goods, as much manufactured, and bring in those of others, as little manufactured as the nature of mutual commerce will allow. |END| Some example output for novel input sentences: > The spectroscopy part of what you do — spectroscopes — you can use them to judge if food is rotten, if a painting is authentic, if something is an explosive. What do you use them to do? |SWIFTIFY| That branch of your art which you call the Spectra-physics; -- Spectroscope: You may apply those instruments in judging whether meat be putrid or not, whether a picture be genuine or counterfeit, and so on with other things that are called Explosives. Now, what service doth all this perform? |END| > Colorado Parks and Wildlife has a comprehensive list of animals you can own without a permit in Colorado, from dogs and cats to red kangaroos and wallaroos. If it isn’t on the list, you need a permit. Capybaras are not on the list. |SWIFTIFY| The Colorado Division of Parks & Wildlife lists all domesticated animals that may be kept within the state's borders without a license: Dogs, Cats, Red Kangaroo, Wallaroo, etc., but no capybara is listed. |END| > And I think that's the true beauty of a good film: the allegory, the moral, the true message is at the core – it's not fed to us directly – and it's up to the viewer to take what they need from it. |SWIFTIFY| And this, I believe, is the real excellence in all good pictures; for the allegorical, the moral, or the true lesson lies at the bottom -- it is not poured down our throats -- but left to be taken as we please. |END|

vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo

vg055

2023-05-05T18:37:12Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T17:06:08Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo results: [] ---  # roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-tipo This model is a fine-tuned version of [vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation](https://huggingface.co/vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0472 - F1: 0.9902 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0479 | 1.0 | 14159 | 0.0521 | 0.9878 | | 0.0154 | 2.0 | 28318 | 0.0472 | 0.9902 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

AppledaMapple/Reenatest

AppledaMapple

2023-05-05T18:21:22Z

43

0

diffusers

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

text-to-image

2023-01-14T14:30:31Z

--- language: - en tags: - stable-diffusion - text-to-image - diffusers inference: true --- ## Reena Character Model A model trained on OC character Reena ![00290-2068285911-A_drawing_of_a_robot_badass_Reena._By_greg_Rutkowski.png](https://s3.amazonaws.com/moonup/production/uploads/1673719587816-635eafb49f24f6db0a1eafd1.png)

mHossain/bangla-para-v1-350000

mHossain

2023-05-05T18:19:59Z

4

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T14:25:45Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bangla-para-v1-350000 results: [] ---  # bangla-para-v1-350000 This model is a fine-tuned version of [mHossain/bangla-para-v1-320000](https://huggingface.co/mHossain/bangla-para-v1-320000) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9279 - Rouge1: 0.0 - Rouge2: 0.0 - Rougel: 0.0 - Rougelsum: 0.0 - Gen Len: 18.3427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5000 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.1838 | 1.0 | 6750 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | 18.3427 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

danny3/codehelper-ds

danny3

2023-05-05T18:11:00Z

146

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-04T21:41:08Z

--- license: mit tags: - generated_from_trainer model-index: - name: codehelper-ds results: [] ---  # codehelper-ds 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: 2.1453 ## Model description More information needed ## Intended uses & 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 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

safwaan21/q-FrozenLake-v1-4x4-noSlippery

safwaan21

2023-05-05T17:52:51Z

0

null

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

reinforcement-learning

2023-05-05T17:52:49Z

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="safwaan21/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

thejuuuuu/torgo_b6_2

thejuuuuu

2023-05-05T17:36:46Z

106

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-05T17:35:52Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_b6_2 results: [] ---  # torgo_b6_2 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) 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: 0.0001 - train_batch_size: 6 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

pnparam/torgo_hel2

pnparam

2023-05-05T17:31:40Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-05T15:36:18Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_hel2 results: [] ---  # torgo_hel2 This model is a fine-tuned version of [pnparam/torgo_healthy_2_40](https://huggingface.co/pnparam/torgo_healthy_2_40) 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

EExe/my_ppo_LunarLand

EExe

2023-05-05T17:25:42Z

0

null

[ "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]

reinforcement-learning

2023-05-05T17:25:20Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -30.85 +/- 140.48 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'debug' 'gym_id': 'LunarLander-v2' 'hface_repo': 'EExe/my_ppo_LunarLand' 'learning_rate': 0.00025 'seed': 42 'total_timesteps': 2000000 'num_envs': 4 'num_steps': 1024 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 64 'num_epochs': 10 'clip_coeff': 0.2 'ent_coeff': 0.1 'vf_coeff': 0.1 'max_gradient_norm': 0.5 'target_kl': 0.015} ```

safwaan21/ppo-LunarLander-v2

safwaan21

2023-05-05T17:24:18Z

1

0

stable-baselines3

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

reinforcement-learning

2023-04-26T20:48:33Z

--- 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: 246.01 +/- 17.65 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

RamAnanth1/sd-1-5-ram

RamAnanth1

2023-05-05T17:17:40Z

46

1

diffusers

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

text-to-image

2023-01-08T04:42:30Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### SD-1-5-Ram Dreambooth model trained by RamAnanth1 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:

Bainbridge/gpt2-kl_01_05_hscnspecial-hs_cn

Bainbridge

2023-05-05T17:15:14Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T16:10:18Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_05_hscnspecial-hs_cn results: [] ---  # gpt2-kl_01_05_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5409 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5512 | 0.02 | 10 | 69.5664 | | 46.0783 | 0.04 | 20 | 32.9183 | | 13.5625 | 0.06 | 30 | 10.6417 | | 6.8251 | 0.08 | 40 | 4.2115 | | 3.5615 | 0.1 | 50 | 2.0291 | | 1.4471 | 0.12 | 60 | 1.0824 | | 1.1156 | 0.14 | 70 | 0.9043 | | 0.9839 | 0.16 | 80 | 0.7788 | | 0.75 | 0.18 | 90 | 0.6556 | | 0.7311 | 0.2 | 100 | 0.6302 | | 0.6331 | 0.22 | 110 | 0.6142 | | 0.6426 | 0.24 | 120 | 0.6279 | | 0.7058 | 0.26 | 130 | 0.5762 | | 0.6443 | 0.28 | 140 | 0.5702 | | 0.6437 | 0.3 | 150 | 0.5679 | | 0.6041 | 0.32 | 160 | 0.5654 | | 0.5403 | 0.34 | 170 | 0.5669 | | 0.6511 | 0.36 | 180 | 0.5710 | | 0.651 | 0.38 | 190 | 0.5604 | | 0.6074 | 0.4 | 200 | 0.5608 | | 0.7023 | 0.42 | 210 | 0.5603 | | 0.6567 | 0.44 | 220 | 0.5581 | | 0.5573 | 0.46 | 230 | 0.5606 | | 0.5748 | 0.48 | 240 | 0.5573 | | 0.5728 | 0.5 | 250 | 0.5559 | | 0.6418 | 0.52 | 260 | 0.5447 | | 0.5855 | 0.54 | 270 | 0.5406 | | 0.6127 | 0.56 | 280 | 0.5423 | | 0.6065 | 0.58 | 290 | 0.5415 | | 0.5687 | 0.6 | 300 | 0.5409 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

Norod78/sd15-fluentui-emoji

Norod78

2023-05-05T17:14:27Z

42

7

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/microsoft-fluentui-emoji-512-whitebg", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-01-02T05:31:23Z

--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd15-fluentui-emoji/resolve/main/sample_images/sd15-fluentui-emoji-Thumbnail.png" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/microsoft-fluentui-emoji-512-whitebg inference: true widget: - text: A cute dog flat - text: A cute dog high contrast - text: A cute dog color - text: A cute dog --- # SDv1.5 sd15-fluentui-emoji model ### Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/microsoft-fluentui-emoji-512-whitebg](https://huggingface.co/datasets/Norod78/microsoft-fluentui-emoji-512-whitebg) # The Emoji file names were converted to become the text descriptions. It made the model learn a few special words: "flat", "high contrast" and "color" ![thumbnail](https://huggingface.co/Norod78/sd15-fluentui-emoji/resolve/main/sample_images/sd15-fluentui-emoji-Thumbnail.png) ## A few sample pictures generated with this model are available [here](https://huggingface.co/Norod78/sd15-fluentui-emoji/tree/main/sample_images)

Norod78/SD15-VinageStyle

Norod78

2023-05-05T17:14:21Z

42

15

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/vintage-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-12-21T21:01:18Z

--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/SD15-VintageStyle-Thumbnail.jpg" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/vintage-blip-captions inference: true widget: - text: A Pulp Cover featuring Gal Gadot, very detailed, clean, high quality, sharp image, Saturno Butto - text: A photo of an astronaut riding a horse on mars, Vintage style, Pulp Cover, very detailed, clean, high quality, sharp image, Dave Dorman - text: A beatiful person, Vintage face - text: A Vintage style commercial for cat food --- # SDv1.5 SD15-VinageStyle model, trained by Norod78 in two parts. ### First Stable-Diffusion v1.5 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/vintage-blip-captions](https://huggingface.co/datasets/Norod78/vintage-blip-captions) then it underwent further fine tuning with Dreambooth using the same images as the ones in the dataset but rather then having it blip-captioned, it was split into "Vintage style", "Vintage face" and "Pulp cover" concepts. ### Dreambooth model was trained with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ## Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch # Trigger-Words are: "Vintage", "Vintage style", "Vintage face", "Pulp cover" ![thumbnail](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/SD15-VintageStyle-Thumbnail.jpg) ## A few sample pictures generated with this mode (more available [here](https://huggingface.co/Norod78/SD15-VinageStyle/tree/main/sample_images)): A photo of Gal Gadot as wonderwoman, Vintage style, very detailed, clean, high quality, sharp image.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3486356206, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 2 ![1](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/00186-3486356206-A%20photo%20of%20Gal%20Gadot%20as%20wonderwoman%2C%20Vintage%20style%2C%20very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image.jpeg) A photo of Gal Gadot as wonderwoman fighting against Cthulhu, Vintage, very detailed, clean, high quality, sharp image, ,Naoto Hattori.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3408435550, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 3 ![2](https://huggingface.co/Norod78/SD15-VinageStyle/resolve/main/sample_images/00199-3408435550-A%20photo%20of%20Gal%20Gadot%20as%20wonderwoman%20fighting%20against%20Cthulhu%2C%20Vintage%2C%20very%20detailed%2C%20clean%2C%20high%20quality%2C%20sharp%20image%2C%20%2CNaoto%20H.jpeg)

Norod78/claymationxmas

Norod78

2023-05-05T17:14:10Z

36

7

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/ChristmasClaymation-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-12-14T17:51:54Z

--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0167-3362370631-A_TARDIS_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/ChristmasClaymation-blip-captions inference: true widget: - text: A Gal Gadot in the snow with Santa ClaymationXmas, very detailed, clean, high quality, sharp image - text: A TARDIS in the snow with Santa ClaymationXmas, very detailed, clean, high quality, sharp image --- ### SDv1.5 ClaymationXmas Dreambooth model trained by Norod78 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ## Use ClaymationXmas in your prompt 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) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0167-3362370631-A_TARDIS_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg) ![1](https://huggingface.co/Norod78/claymationxmas/resolve/main/sample_images/grid-0168-2731336261-A_Gal_Gadot_in_the_snow_with_Santa_ClaymationXmas,_very_detailed,_clean,_high_quality,_sharp_image.jpg)

Norod78/sd-simpsons-model

Norod78

2023-05-05T17:14:04Z

39

17

diffusers

[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "en", "dataset:Norod78/simpsons-blip-captions", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-10T16:41:01Z

--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00496-2202810362-A%20beautiful%20hungry%20demon%20girl,%20John%20Philip%20Falter,%20Very%20detailed%20painting,%20Mark%20Ryden.jpg" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image datasets: - Norod78/simpsons-blip-captions inference: true --- # Simpsons diffusion *Stable Diffusion fine tuned on images related to "The Simpsons" If you want more details on how to generate your own blip cpationed dataset see this [colab](https://colab.research.google.com/gist/Norod/ee6ee3c4bf11c2d2be531d728ec30824/buildimagedatasetwithblipcaptionsanduploadtohf.ipynb) Training was done using a slightly modified version of Hugging-Face's text to image training [example script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) ## About Put in a text prompt and generate cartoony/simpsony images **A beautiful hungry demon girl, John Philip Falter, Very detailed painting, Mark Ryden** ![A beautiful hungry demon girl, John Philip Falter, Very detailed painting, Mark Ryden](https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00496-2202810362-A%20beautiful%20hungry%20demon%20girl,%20John%20Philip%20Falter,%20Very%20detailed%20painting,%20Mark%20Ryden.jpg) **Gal Gadot, cartoon** ![Gal Gadot, cartoon](https://huggingface.co/Norod78/sd-simpsons-model/raw/main/examples/00323-2574793241-Gal%20Gadot,%20cartoon.jpg) ## More examples The [examples](https://huggingface.co/Norod78/sd-simpsons-model/tree/main/examples) folder contains a few images generated by this model's ckpt file using [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) which means their EXIF info contain the parameter used to generate them ## Sample code ```py from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler import torch # this will substitute the default PNDM scheduler for K-LMS lms = LMSDiscreteScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear" ) guidance_scale=9 seed=7777 steps=100 model_id = "Norod78/sd-simpsons-model" pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=lms, torch_dtype=torch.float16) pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ## Dataset and Training Finetuned for 10,000 iterations upon [Runway ML's Stable-Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) on [BLIP captioned Simpsons images](https://huggingface.co/datasets/Norod78/simpsons-blip-captions) using 1xA5000 GPU on my home desktop computer Trained by [@Norod78](https://twitter.com/Norod78)

Norod78/sd21-hearthstone-cards

Norod78

2023-05-05T17:13:58Z

15

2

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "stable-diffusion-diffusers", "en", "dataset:Norod78/hearthstone-cards-512", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-01-06T12:02:06Z

--- license: creativeml-openrail-m language: - en thumbnail: "https://huggingface.co/Norod78/sd21-hearthstone-cards/resolve/main/sample_images/00005-166904889-Snoop%20Dogg%20music%20power%20Hearthstone%20card.png" tags: - text-to-image - stable-diffusion - stable-diffusion-diffusers datasets: - Norod78/hearthstone-cards-512 inference: true widget: - text: 3 Cute dog, Fluff. Hearthstone card - text: Gal Gadot Super Wonderwoman power. Hearthstone card - text: Cute Pikachu Pokemon Electricity buzzzz Hearthstone card - text: 4 Snoop Dogg music power Hearthstone card library_name: diffusers pipeline_tag: text-to-image --- # SDv2.1 sd21-hearthstone-cards model ### Stable-Diffusion v2.1 fine-tuned for 10k steps using [Huggingface Diffusers train_text_to_image script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) upon [Norod78/hearthstone-cards-512](https://huggingface.co/datasets/Norod78/hearthstone-cards-512) # Stable-Diffusion Hearthstone card generator. First digit in prompt controls the Mana-cost (pretty well) then card name, then special ability and description, then "Hearthstone card". ![thumbnail](https://huggingface.co/Norod78/sd21-hearthstone-cards/resolve/main/sample_images/sd21-hearthstone-cards-animation-GalGadot.gif) ## A few sample pictures generated with this model are available [here](https://huggingface.co/Norod78/sd21-hearthstone-cards/tree/main/sample_images) Please note that the entire training set contains actual Hearthstone card images which are copyrighted by Blizzard So it is possible that the generated images contain copyrighted elements and should only be use for your private entertainment Trained by [@Norod78](https://twitter.com/Norod78)

nolanaatama/skmchnstyl

nolanaatama

2023-05-05T17:04:24Z

0

null

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

null

2023-05-05T16:59:48Z

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

rifatul123/Primary_doctor_v1

rifatul123

2023-05-05T16:57:39Z

0

adapter-transformers

[ "adapter-transformers", "pytorch", "gpt2", "biology", "medical", "chemistry", "text-generation-inference", "text-generation", "en", "region:us" ]

text-generation

2023-05-05T08:35:44Z

--- language: - en metrics: - accuracy library_name: adapter-transformers pipeline_tag: text-generation tags: - biology - medical - chemistry - text-generation-inference --- ![Screenshot 2023-05-05 092541.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/9gMBxc270uN8agP8n6-5m.png) ![Screenshot 2023-05-05 094102.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/kqOUgU2wyxLDP1gKnCKPC.png) ![Screenshot 2023-05-05 094303.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/WpNXVBwbLCNNvWJ65dJI8.png) ![Screenshot 2023-05-05 094409.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/HZ1YdlwfZAi8CPlvrcqDr.png) ![Screenshot 2023-05-05 094542.png](https://s3.amazonaws.com/moonup/production/uploads/641ee41d863b87326f45a5f1/h9EJw9fRNMBpOwJVVw6zI.png) # Model Card for Model ID This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset. The model is intended for text generation in the medical research domain. ## Model Details This modelcard describes a fine-tuned GPT-2 language model for medical research using a personally collected dataset. The model is intended for text generation in the medical research domain. ### Model Description The model has been fine-tuned on a GPT-2 architecture and trained with a task-specific parameter for text generation. The do_sample parameter is set to true, which means that the model can generate text on its own rather than simply copying from the input. The max_length parameter is set to 50, which means that the maximum length of the generated text will be 50 tokens. - **Developed by:** [OpenAI] - **Shared by [optional]:** [More Information Needed] - **Model type:** [Language Model] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [GPT-2] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use This model can be used for text generation in the medical research domain. It can be used to generate text for a variety of purposes, such as research papers, reports, and summaries. ### Downstream Use [optional] The model can be fine-tuned for downstream tasks such as summarization, question answering, and text classification. ### Out-of-Scope Use This model may not perform as well on text outside the medical research domain. It is important to carefully evaluate the generated text to ensure that it is appropriate for the intended use. ## Bias, Risks, and Limitations This modelcard acknowledges that all language models have limitations and potential biases. The model may produce biased or inaccurate outputs if the input data contains bias or if the training data is not diverse enough. The risks of using the model include the possibility of generating misleading or harmful information. ### Recommendations To mitigate potential risks and limitations, users of the model should carefully evaluate the generated text and consider the following recommendations: 1)Evaluate the input data for potential bias and ensure that it is diverse and representative. 2)Consider fine-tuning the model on additional data to improve its accuracy and reduce the risk of bias. 3)Review and edit the generated text before use to ensure that it is appropriate for the intended purpose. 4)Provide clear and transparent documentation of the model's limitations and potential biases to users and stakeholders. ## How to Get Started with the Model To use the model, load it in your preferred programming language using the transformers library, and pass in the input text. The model will generate text based on the input, using the task-specific parameters that have been set.

4bd4774h/bert-base-uncased-finetuned-cola

4bd4774h

2023-05-05T16:54:05Z

104

0

transformers

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

text-classification

2023-05-05T15:20:24Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: bert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5815775806078913 ---  # bert-base-uncased-finetuned-cola This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 1.0375 - Matthews Correlation: 0.5816 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.999174630178768e-05 - train_batch_size: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 6 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.4594 | 1.0 | 1069 | 0.4619 | 0.5155 | | 0.3105 | 2.0 | 2138 | 0.5069 | 0.5807 | | 0.2003 | 3.0 | 3207 | 1.0033 | 0.5524 | | 0.1074 | 4.0 | 4276 | 1.0375 | 0.5816 | | 0.0715 | 5.0 | 5345 | 1.1228 | 0.5743 | | 0.0355 | 6.0 | 6414 | 1.3127 | 0.5728 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad

vg055

2023-05-05T16:52:06Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-05-05T10:27:51Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad results: [] ---  # roberta-base-bne-finetuned-TripAdvisorDomainAdaptation-finetuned-e2-RestMex2023-polaridad This model is a fine-tuned version of [vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation](https://huggingface.co/vg055/roberta-base-bne-finetuned-TripAdvisorDomainAdaptation) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5996 - F1: 0.7468 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.5823 | 1.0 | 14159 | 0.5671 | 0.7452 | | 0.4536 | 2.0 | 28318 | 0.5996 | 0.7468 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

cuadron11/modelBsc5

cuadron11

2023-05-05T16:43:35Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-05-05T15:52:41Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: modelBsc5 results: [] ---  # modelBsc5 This model is a fine-tuned version of [PlanTL-GOB-ES/bsc-bio-ehr-es](https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1546 - Precision: 0.5567 - Recall: 0.6075 - F1: 0.5810 - Accuracy: 0.9708 ## Model description More information needed ## Intended uses & 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: 32 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.2697 | 0.0 | 0.0 | 0.0 | 0.9446 | | No log | 2.0 | 58 | 0.2357 | 0.0 | 0.0 | 0.0 | 0.9446 | | No log | 3.0 | 87 | 0.2176 | 0.0 | 0.0 | 0.0 | 0.9446 | | No log | 4.0 | 116 | 0.1889 | 0.0541 | 0.0108 | 0.0179 | 0.9406 | | No log | 5.0 | 145 | 0.1800 | 0.1613 | 0.0269 | 0.0461 | 0.9477 | | No log | 6.0 | 174 | 0.1625 | 0.2745 | 0.0753 | 0.1181 | 0.9532 | | No log | 7.0 | 203 | 0.1502 | 0.4306 | 0.1667 | 0.2403 | 0.9571 | | No log | 8.0 | 232 | 0.1426 | 0.3810 | 0.2581 | 0.3077 | 0.9576 | | No log | 9.0 | 261 | 0.1459 | 0.5586 | 0.3333 | 0.4175 | 0.9638 | | No log | 10.0 | 290 | 0.1288 | 0.5732 | 0.5054 | 0.5371 | 0.9636 | | No log | 11.0 | 319 | 0.1241 | 0.4769 | 0.5538 | 0.5124 | 0.9638 | | No log | 12.0 | 348 | 0.1282 | 0.5189 | 0.5161 | 0.5175 | 0.9664 | | No log | 13.0 | 377 | 0.1335 | 0.4232 | 0.6667 | 0.5177 | 0.9595 | | No log | 14.0 | 406 | 0.1314 | 0.5854 | 0.5161 | 0.5486 | 0.9693 | | No log | 15.0 | 435 | 0.1357 | 0.4772 | 0.6183 | 0.5386 | 0.9653 | | No log | 16.0 | 464 | 0.1382 | 0.4398 | 0.6290 | 0.5177 | 0.9638 | | No log | 17.0 | 493 | 0.1362 | 0.5634 | 0.6452 | 0.6015 | 0.9705 | | 0.1141 | 18.0 | 522 | 0.1466 | 0.5 | 0.6505 | 0.5654 | 0.9669 | | 0.1141 | 19.0 | 551 | 0.1497 | 0.5441 | 0.5968 | 0.5692 | 0.9703 | | 0.1141 | 20.0 | 580 | 0.1375 | 0.5520 | 0.6559 | 0.5995 | 0.9700 | | 0.1141 | 21.0 | 609 | 0.1373 | 0.5707 | 0.6290 | 0.5985 | 0.9722 | | 0.1141 | 22.0 | 638 | 0.1490 | 0.5777 | 0.6398 | 0.6071 | 0.9710 | | 0.1141 | 23.0 | 667 | 0.1550 | 0.5314 | 0.6828 | 0.5976 | 0.9679 | | 0.1141 | 24.0 | 696 | 0.1506 | 0.5397 | 0.6935 | 0.6071 | 0.9679 | | 0.1141 | 25.0 | 725 | 0.1528 | 0.5346 | 0.6237 | 0.5757 | 0.9703 | | 0.1141 | 26.0 | 754 | 0.1503 | 0.5764 | 0.6290 | 0.6015 | 0.9715 | | 0.1141 | 27.0 | 783 | 0.1446 | 0.5767 | 0.5860 | 0.5813 | 0.9722 | | 0.1141 | 28.0 | 812 | 0.1486 | 0.5377 | 0.6129 | 0.5729 | 0.9701 | | 0.1141 | 29.0 | 841 | 0.1538 | 0.5577 | 0.6237 | 0.5888 | 0.9703 | | 0.1141 | 30.0 | 870 | 0.1543 | 0.5545 | 0.6022 | 0.5773 | 0.9701 | | 0.1141 | 31.0 | 899 | 0.1551 | 0.5517 | 0.6022 | 0.5758 | 0.9708 | | 0.1141 | 32.0 | 928 | 0.1546 | 0.5567 | 0.6075 | 0.5810 | 0.9708 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

cuadron11/modelBeto5

cuadron11

2023-05-05T16:43:23Z

3

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-05-05T15:52:22Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: modelBeto5 results: [] ---  # modelBeto5 This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1686 - Precision: 0.5990 - Recall: 0.6541 - F1: 0.6253 - Accuracy: 0.9727 ## Model description More information needed ## Intended uses & 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: 32 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 29 | 0.2706 | 0.0 | 0.0 | 0.0 | 0.9451 | | No log | 2.0 | 58 | 0.3328 | 0.0 | 0.0 | 0.0 | 0.9451 | | No log | 3.0 | 87 | 0.1872 | 0.0476 | 0.0108 | 0.0176 | 0.9320 | | No log | 4.0 | 116 | 0.1428 | 0.3971 | 0.1459 | 0.2134 | 0.9551 | | No log | 5.0 | 145 | 0.1169 | 0.4690 | 0.2865 | 0.3557 | 0.9614 | | No log | 6.0 | 174 | 0.1259 | 0.5414 | 0.5297 | 0.5355 | 0.9629 | | No log | 7.0 | 203 | 0.1166 | 0.4575 | 0.6108 | 0.5231 | 0.9604 | | No log | 8.0 | 232 | 0.1240 | 0.6149 | 0.4919 | 0.5465 | 0.9693 | | No log | 9.0 | 261 | 0.1145 | 0.5276 | 0.5676 | 0.5469 | 0.9681 | | No log | 10.0 | 290 | 0.1377 | 0.5612 | 0.5946 | 0.5774 | 0.9688 | | No log | 11.0 | 319 | 0.1321 | 0.5833 | 0.6432 | 0.6118 | 0.9700 | | No log | 12.0 | 348 | 0.1549 | 0.6581 | 0.5514 | 0.6 | 0.9717 | | No log | 13.0 | 377 | 0.1482 | 0.6080 | 0.6541 | 0.6302 | 0.9713 | | No log | 14.0 | 406 | 0.1589 | 0.5348 | 0.6649 | 0.5928 | 0.9675 | | No log | 15.0 | 435 | 0.1507 | 0.6178 | 0.6378 | 0.6277 | 0.9720 | | No log | 16.0 | 464 | 0.1554 | 0.6082 | 0.6378 | 0.6227 | 0.9720 | | No log | 17.0 | 493 | 0.1658 | 0.5918 | 0.6270 | 0.6089 | 0.9708 | | 0.0785 | 18.0 | 522 | 0.1616 | 0.5792 | 0.6919 | 0.6305 | 0.9715 | | 0.0785 | 19.0 | 551 | 0.1632 | 0.6059 | 0.6649 | 0.6340 | 0.9717 | | 0.0785 | 20.0 | 580 | 0.1638 | 0.6103 | 0.6432 | 0.6263 | 0.9726 | | 0.0785 | 21.0 | 609 | 0.1603 | 0.6010 | 0.6432 | 0.6214 | 0.9724 | | 0.0785 | 22.0 | 638 | 0.1652 | 0.6078 | 0.6703 | 0.6375 | 0.9722 | | 0.0785 | 23.0 | 667 | 0.1577 | 0.6440 | 0.6649 | 0.6543 | 0.9738 | | 0.0785 | 24.0 | 696 | 0.1600 | 0.6492 | 0.6703 | 0.6596 | 0.9743 | | 0.0785 | 25.0 | 725 | 0.1663 | 0.6256 | 0.6595 | 0.6421 | 0.9733 | | 0.0785 | 26.0 | 754 | 0.1686 | 0.6106 | 0.6865 | 0.6463 | 0.9713 | | 0.0785 | 27.0 | 783 | 0.1691 | 0.5951 | 0.6595 | 0.6256 | 0.9720 | | 0.0785 | 28.0 | 812 | 0.1668 | 0.61 | 0.6595 | 0.6338 | 0.9731 | | 0.0785 | 29.0 | 841 | 0.1679 | 0.5931 | 0.6541 | 0.6221 | 0.9724 | | 0.0785 | 30.0 | 870 | 0.1678 | 0.6162 | 0.6595 | 0.6371 | 0.9734 | | 0.0785 | 31.0 | 899 | 0.1683 | 0.6040 | 0.6595 | 0.6305 | 0.9729 | | 0.0785 | 32.0 | 928 | 0.1686 | 0.5990 | 0.6541 | 0.6253 | 0.9727 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

stanford-rl/PPO-LunarLander-v2

stanford-rl

2023-05-05T16:40:41Z

0

stable-baselines3

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

reinforcement-learning

2023-05-05T16:39:55Z

--- 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: 259.73 +/- 15.96 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

apurik-parv/abstract-nature-pattern-v1-2

apurik-parv

2023-05-05T16:25:27Z

34

1

diffusers

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

text-to-image

2022-11-26T17:16:27Z

--- license: creativeml-openrail-m tags: - text-to-image --- ### abstract_nature_pattern_v1.2 Dreambooth model trained by apurik-parv with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook ##### This model is trained with more data but unfortunately it overfits the image. I am not aware of the extent of overfitting some images are good some are really bad. Please leave suggestions in comment. Inference Prompt: **abnapa** The model is trained on 1024x1024 images for a total step of 3000.

tang11/tang

tang11

2023-05-05T16:20:35Z

0

null

[ "medical", "translation", "dataset:BAAI/COIG", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]

translation

2023-05-05T16:19:01Z

--- license: apache-2.0 datasets: - BAAI/COIG metrics: - bleurt pipeline_tag: translation tags: - medical --- # Model Card for Model ID  This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hannahh7/ppo-LunarLander-v2

hannahh7

2023-05-05T16:15:56Z

0

stable-baselines3

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

reinforcement-learning

2023-05-05T16:15:34Z

--- 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: 260.14 +/- 19.62 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

mbshaikh/my_awesome_mind_model

mbshaikh

2023-05-05T16:10:59Z

162

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "license:apache-2.0", "endpoints_compatible", "region:us" ]

audio-classification

2023-05-05T16:05:16Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - minds14 metrics: - accuracy model-index: - name: my_awesome_mind_model results: [] ---  # my_awesome_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6548 - Accuracy: 0.0619 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.8 | 3 | 2.6339 | 0.0708 | | No log | 1.87 | 7 | 2.6405 | 0.0531 | | 2.6345 | 2.93 | 11 | 2.6415 | 0.0354 | | 2.6345 | 4.0 | 15 | 2.6437 | 0.0531 | | 2.6345 | 4.8 | 18 | 2.6504 | 0.0354 | | 2.6263 | 5.87 | 22 | 2.6528 | 0.0531 | | 2.6263 | 6.93 | 26 | 2.6548 | 0.0531 | | 2.6155 | 8.0 | 30 | 2.6548 | 0.0619 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

ayan4m1/trinart_diffusers_v2

ayan4m1

2023-05-05T16:10:04Z

9

6

diffusers

[ "diffusers", "license:apache-2.0", "diffusers:LDMTextToImagePipeline", "region:us" ]

null

2022-09-14T15:40:03Z

--- license: apache-2.0 --- This is a copy of [trinart_stable_diffusion_v2](https://huggingface.co/naclbit/trinart_stable_diffusion_v2) ported for use with the (diffusers)[https://github.com/huggingface/diffusers]) library. All credit for this model goes to [naclbit](https://huggingface.co/naclbit).

Bainbridge/gpt2-kl_01_04_hscnspecial-hs_cn

Bainbridge

2023-05-05T16:09:30Z

8

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T14:39:55Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_04_hscnspecial-hs_cn results: [] ---  # gpt2-kl_01_04_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5583 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5419 | 0.02 | 10 | 69.5826 | | 46.0912 | 0.04 | 20 | 32.9478 | | 13.5577 | 0.06 | 30 | 10.6427 | | 6.8429 | 0.08 | 40 | 4.2253 | | 3.5596 | 0.1 | 50 | 2.0345 | | 1.4308 | 0.12 | 60 | 1.0810 | | 1.0569 | 0.14 | 70 | 0.8577 | | 1.0017 | 0.16 | 80 | 0.8291 | | 0.7285 | 0.18 | 90 | 0.6919 | | 0.7232 | 0.2 | 100 | 0.6110 | | 0.5979 | 0.22 | 110 | 0.6133 | | 0.6368 | 0.24 | 120 | 0.6051 | | 0.6606 | 0.26 | 130 | 0.6075 | | 0.6428 | 0.28 | 140 | 0.5920 | | 0.6106 | 0.3 | 150 | 0.5754 | | 0.6218 | 0.32 | 160 | 0.5741 | | 0.534 | 0.34 | 170 | 0.5633 | | 0.6411 | 0.36 | 180 | 0.5685 | | 0.6353 | 0.38 | 190 | 0.5805 | | 0.5922 | 0.4 | 200 | 0.5556 | | 0.6924 | 0.42 | 210 | 0.5595 | | 0.6429 | 0.44 | 220 | 0.5601 | | 0.5428 | 0.46 | 230 | 0.5583 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

crumb/eva-fusion-v2.22

crumb

2023-05-05T16:01:46Z

13

7

diffusers

[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-10-19T07:07:47Z

--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 --- # EVA-Fusion 2.22 - You are (Not) HD for an art project of mine | model | init model | lr | bs | acc | total steps | training samples | training resolution | models trained | |--- |--- | --- | --- | --- | --- | --- | --- | --- | | eva-fusion-v2 |compvis 1-4| 2.057e-06 | 1 | 1 | 8,000 | 1,143 (captioned just "picture in the style of evangelion") | 512x384 (4:3) | unet | | eva-fusion-v2.1 | eva-fusion-v2 | 1.313e-06 | 1 | 1 | 10,000 | + 560 (hand captioned) | 512x384 (4:3) | unet | | eva-fusion-v2.22 "you are (not) HD" | previous models + compvis1-4 + wd1.3 at various ratios | x | x | x | x | x | x | x | for generating let me suggest [Whatchamacallit](https://colab.research.google.com/github/aicrumb/whatchamacallit/blob/main/Whatchamacallit.ipynb), suggested in the negative prompt: "blurry jpeg, multiple views, simple background, black and white reference sheet". it wasn't trained on black and white character sheets, it was barely trained on any grayscale images, I do not know why it leans heavily towards them sometimes. link to ckpt file: https://huggingface.co/crumb/eva-model-ckpt/blob/main/test%20model%205.ckpt I also recommend "in the style of Evangelion" in the prompt

doohickey/doohickey-mega-v1.01

doohickey

2023-05-05T16:01:31Z

70

4

diffusers

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

text-to-image

2022-11-14T22:30:13Z

--- tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image --- Models better suited for High-Resolution Image Synthesis. The main model (doohickey/doohickey-mega) has been finetuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) near a resolution of 768x768 (suggested method of generating from model is with [Doohickey](https://colab.research.google.com/github/aicrumb/doohickey/blob/main/Doohickey_Diffusion.ipynb)). This is a version of Stable Diffusion finetuned to use [laion/CLIP-ViT-L-14-laion2B-s32B-b82K](https://huggingface.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K) instead of the stock openai clip model. While doohickey-mega finetuned the CLIP model as well, this ckpt was finetuned without the CLIP model being trained. In total 6000 steps. _Limitations and Biases from Stable Diffusion also apply to this model._ <div style="font-size:10px"> This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license </div>

doohickey/doohickey-mega

doohickey

2023-05-05T16:01:30Z

118

4

diffusers

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

text-to-image

2022-11-12T17:40:10Z

--- tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image --- Models better suited for High-Resolution Image Synthesis. The main model (doohickey/doohickey-mega) has been finetuned from [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) near a resolution of 768x768 (suggested method of generating from model is with [Doohickey](https://colab.research.google.com/github/aicrumb/doohickey/blob/main/Doohickey_Diffusion.ipynb)). Current models: | name | description | datasets used | | --- | --- | --- | | doohickey/doohickey-mega/v1-3000steps.ckpt | first try, rlly good hd, bad results w/ other aspect ratios than 1:1 trained at 704x704 | A-1k| | doohickey/doohickey-mega/v2-3000steps.ckpt | same as last one but worse | A-1k + ~1k samples from LAION-2b-En-Aesthetic >=768x768 | | doohickey/doohickey-mega/v3-3000.ckpt | with new CLIP model ([laion/CLIP-ViT-L-14-laion2B-s32B-b82K](https://hf.co/laion/CLIP-ViT-L-14-laion2B-s32B-b82K)) (CLIP model also finetuned the 3k steps), models past this point were trained with various aspect ratios from 640x640 min to 768x768 max resolution. (examples 768x640 or 704x768) | A-1k + E-10k | | doohickey/doohickey-mega/v3-6000.ckpt | 3k steps on top of v3-3000.ckpt, better at hands! (just UNet finetune, added a RandomHorizontalFlip operation at 50%) | A-1k | | doohickey/doohickey-mega/v3-7000.ckpt | continuation of last model, I thought Colab would crash after 3k steps but it kept going for a little while saving ckpts every 1k steps. | A-1k | | doohickey/doohickey-mega/v3-8000.ckpt | see last description, v3-6000 + 2k steps | A-1k | The currently loaded model for diffusers is doohickey/doohickey-mega/v3-8000.ckpt Datasets: | name | description | | --- | --- | | A-1K | 1k scraped images, captioned with BLIP (more refined aesthetic) | | E-10k | 10k scraped images captioned with BLIP (less refined aesthetic) | _Limitations and Biases from Stable Diffusion also apply to this model._ <div style="font-size:10px"> This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license </div>

doohickey/doodad-v1-2

doohickey

2023-05-05T16:01:22Z

33

2

diffusers

[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-09-28T17:38:09Z

--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 inference: false --- # Doodad *<p style="color:green"> <b> This is the 1st part of a 2 (3?) part project meant to be used with <a href="https://huggingface.co/doohickey">Doohickey</a> </b> </p>* The style was trained with [Dreambooth-Stable](https://www.reddit.com/r/StableDiffusion/comments/xphaiw/dreambooth_stable_diffusion_training_in_just_125/) and is used with "<doodad>". Tt mixes especially well with the <midj-strong> style included in Doohickey. It's a finetuned version of the Trinart-Waifu-diffusion-50-50 included in this organization's models and was trained on 48 images from the author's ([crumb](https://huggingface.co/crumb)'s) Pinterest feed. | Parameter | Value | |----------------------|------------| | resolution | 512 | | train_batch_size | 1 | | gradient_accumulation_steps | 2 | | learning_rate | 5e-6 | | num_class_images | 120 | | max_train_steps | 1200 | Example outputs:

doohickey/doodad-v1-1

doohickey

2023-05-05T16:01:20Z

32

3

diffusers

[ "diffusers", "stable-diffusion", "text-to-image", "en", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-09-28T09:40:37Z

--- language: - en tags: - stable-diffusion - text-to-image license: bigscience-bloom-rail-1.0 inference: false --- # Doodad *<p style="color:green"> <b> This is the 1st part of a 2 (3?) part project meant to be used with <a href="https://huggingface.co/doohickey">Doohickey</a> </b> </p>* The style was trained with [Dreambooth-Stable](https://www.reddit.com/r/StableDiffusion/comments/xphaiw/dreambooth_stable_diffusion_training_in_just_125/) and is used with "imv" (an uncommonly used token recommended by the author I think) and it mixes especially well with the <midj-strong> style included in Doohickey. It's a finetuned version of the Trinart-Waifu-diffusion-50-50 included in this organization's models. | Parameter | Value | |----------------------|------------| | resolution | 512 | | train_batch_size | 1 | | gradient_accumulation_steps | 2 | | learning_rate | 5e-6 | | num_class_images | 120 | | max_train_steps | 1200 | Example outputs: "portal into another dimension" ![](https://cdn.discordapp.com/attachments/1024588665596411944/1024618996479311892/unknown.png) "portrait of a dying god" ![](https://pbs.twimg.com/media/Fdu1c9XWQAA4v2g?format=png&name=900x900) "photograph" ![](https://pbs.twimg.com/media/Fdu6hg4X0AI5aNI?format=png&name=900x900) "The alchemist's laboratory by Greg Rutkowski and Claude Monet, oil on canvas" ![](https://pbs.twimg.com/media/FdvB2ZiWYAQHg-7?format=png&name=900x900)

krasserm/perceiver-ar-sam-giant-midi

krasserm

2023-05-05T15:56:09Z

54

12

transformers

[ "transformers", "pytorch", "perceiver-ar-symbolic-audio-model", "audio-to-audio", "arxiv:2202.07765", "arxiv:2104.09864", "arxiv:1809.04281", "license:apache-2.0", "region:us" ]

audio-to-audio

2023-05-03T08:26:15Z

--- license: apache-2.0 inference: false pipeline_tag: audio-to-audio --- # Perceiver AR symbolic audio model This model is a [Perceiver AR](https://arxiv.org/abs/2202.07765) symbolic audio model (134M parameters) pretrained on the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset for 27 epochs (157M tokens). It uses [rotary embedding](https://arxiv.org/abs/2104.09864) for relative position encoding. It is a [training example](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#giantmidi-piano) of the [perceiver-io](https://github.com/krasserm/perceiver-io) library. ## Model description Perceiver AR is a simple extension of a plain decoder-only transformer such as GPT-2, for example. A core building block of both is the *decoder layer* consisting of a self-attention layer followed by a position-wise MLP. Self-attention uses a causal attention mask. Perceiver AR additionally cross-attends to a longer prefix of the input sequence in its first attention layer. This layer is a hybrid self- and cross-attention layer. Self-attention is over the last n positions of the input sequence, with a causal attention mask, cross-attention is from the last n positions to the first m positions. The length of the input sequence is m + n. This allows a Perceiver AR to process a much larger context than decoder-only transformers which are based on self-attention only. <p align="center"> <img src="https://krasserm.github.io/img/2023-01-23/perceiver-ar.png" alt="Perceiver AR" width="600"/><br/> <i>Fig. 1</i>. Attention in Perceiver AR with m=8 prefix tokens and n=3 latent tokens. <p/> The output of the hybrid attention layer are n latent arrays corresponding to the last n tokens of the input sequence. These are further processed by a stack of L-1 decoder layers where the total number of attention layers is L. A final layer (not shown in Fig. 1) predicts the target token for each latent position. The weights of the final layer are shared with the input embedding layer. Except for the initial cross-attention to the prefix sequence, a Perceiver AR is architecturally identical to a decoder-only transformer. ## Model training The model was [trained](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md#giantmidi-piano) with the task of symbolic audio modeling on the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset for 27 epochs (157M tokens). This dataset consists of [MIDI](https://en.wikipedia.org/wiki/MIDI) files, tokenized using the approach from the [Perceiver AR paper](https://arxiv.org/pdf/2202.07765.pdf), which is described in detail in Section A.2 of [Huang et al (2019)](https://arxiv.org/abs/1809.04281). All hyperparameters are summarized in the [training script](https://github.com/krasserm/perceiver-io/blob/main/examples/training/sam/giantmidi/train.sh). The context length was set to 6144 tokens with 2048 latent positions, resulting in a maximal prefix length of 4096. The actual prefix length per example was randomly chosen between 0 and 4096. Training was done with [PyTorch Lightning](https://www.pytorchlightning.ai/index.html) and the resulting checkpoint was converted to this 🤗 model with a library-specific [conversion utility](#checkpoint-conversion). ## Intended use and limitations This model can be used for audio generation with a user-defined initial number of latent tokens. It mainly exists for demonstration purposes on how to train Perceiver AR models with the [perceiver-io library](https://github.com/krasserm/perceiver-io). To improve on the quality of the generated audio samples a much larger dataset than [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) is required for training. ## Usage examples To use this model you first need to [install](https://github.com/krasserm/perceiver-io/blob/main/README.md#installation) the `perceiver-io` library with extension `audio`. ```shell pip install perceiver-io[audio] ``` Then the model can be used with PyTorch. Either use the model directly to generate MIDI files: ```python import torch from perceiver.model.audio.symbolic import PerceiverSymbolicAudioModel from perceiver.data.audio.midi_processor import decode_midi, encode_midi from pretty_midi import PrettyMIDI repo_id = "krasserm/perceiver-ar-sam-giant-midi" model = PerceiverSymbolicAudioModel.from_pretrained(repo_id) prompt_midi = PrettyMIDI("prompt.mid") prompt = torch.tensor(encode_midi(prompt_midi)).unsqueeze(0) output = model.generate(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0) output_midi = decode_midi(output[0].cpu().numpy()) type(output_midi) ``` ``` pretty_midi.pretty_midi.PrettyMIDI ``` use a `symbolic-audio-generation` pipeline to generate a MIDI output: ```python from transformers import pipeline from pretty_midi import PrettyMIDI from perceiver.model.audio import symbolic # auto-class registration repo_id = "krasserm/perceiver-ar-sam-giant-midi" prompt = PrettyMIDI("prompt.mid") audio_generator = pipeline("symbolic-audio-generation", model=repo_id) output = audio_generator(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0) type(output["generated_audio_midi"]) ``` ``` pretty_midi.pretty_midi.PrettyMIDI ``` or generate WAV output by rendering the MIDI symbols using [fluidsynth](https://www.fluidsynth.org/) (Note: fluidsynth must be installed in order for the following example to work): ```python from transformers import pipeline from pretty_midi import PrettyMIDI from perceiver.model.audio import symbolic # auto-class registration repo_id = "krasserm/perceiver-ar-sam-giant-midi" prompt = PrettyMIDI("prompt.mid") audio_generator = pipeline("symbolic-audio-generation", model=repo_id) output = audio_generator(prompt, max_new_tokens=64, num_latents=1, do_sample=True, top_p=0.95, temperature=1.0, render=True) with open("generated_audio.wav", "wb") as f: f.write(output["generated_audio_wav"]) ``` ## Audio samples The following (hand-picked) audio samples were generated using various prompts from the validation subset of the [GiantMIDI-Piano](https://github.com/bytedance/GiantMIDI-Piano) dataset. The input prompts are not included in the audio output. <table> <tr> <th>Audio sample</th> <th>Top-K</th> <th>Top-p</th> <th>Temperature</th> <th>Prefix length</th> <th>Latents</th> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/01_nehrlich_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">1</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/02_eduardo_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">64</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/03_membree_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">1024</td> <td style="vertical-align: top;">1</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/04_membree_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">15</td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">16</td> </tr> <tr> <td> <audio controls> <source src="https://martin-krasser.com/perceiver/data/midi/05_kinscella_continuation.wav" type="audio/wav"> Your browser does not support the audio element. </audio> </td> <td style="vertical-align: top;">-</td> <td style="vertical-align: top;">0.95</td> <td style="vertical-align: top;">1.0</td> <td style="vertical-align: top;">4096</td> <td style="vertical-align: top;">1</td> </tr> </table> ## Checkpoint conversion The `krasserm/perceiver-ar-sam-giant-midi` model has been created from a training checkpoint with: ```python from perceiver.model.audio.symbolic import convert_checkpoint convert_checkpoint( save_dir="krasserm/perceiver-ar-sam-giant-midi", ckpt_url="https://martin-krasser.com/perceiver/logs-0.8.0/sam/version_1/checkpoints/epoch=027-val_loss=1.944.ckpt", push_to_hub=True, ) ``` ## Citation ```bibtex @inproceedings{hawthorne2022general, title={General-purpose, long-context autoregressive modeling with perceiver ar}, author={Hawthorne, Curtis and Jaegle, Andrew and Cangea, C{\u{a}}t{\u{a}}lina and Borgeaud, Sebastian and Nash, Charlie and Malinowski, Mateusz and Dieleman, Sander and Vinyals, Oriol and Botvinick, Matthew and Simon, Ian and others}, booktitle={International Conference on Machine Learning}, pages={8535--8558}, year={2022}, organization={PMLR} } ```

sd-dreambooth-library/magikarp-pokemon

sd-dreambooth-library

2023-05-05T15:49:09Z

34

2

diffusers

[ "diffusers", "license:mit", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-23T21:12:46Z

--- license: mit --- ### Magikarp pokemon on Stable Diffusion via Dreambooth #### model by BenjiKan This your the Stable Diffusion model fine-tuned the Magikarp pokemon concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **a photo of sks pokemon** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/1.jpeg) ![image 1](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/4.jpeg) ![image 3](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/3.jpeg) ![image 4](https://huggingface.co/sd-dreambooth-library/magikarp-pokemon/resolve/main/concept_images/2.jpeg)

jasonsurya0/BART_SIX

jasonsurya0

2023-05-05T15:49:02Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T15:28:44Z

BART MODEL #6 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM

mbshaikh/videomae-base-finetuned-ucf101-subset

mbshaikh

2023-05-05T15:33:34Z

59

0

transformers

[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

video-classification

2023-05-05T14:48:13Z

--- license: cc-by-nc-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset results: [] ---  # videomae-base-finetuned-ucf101-subset This model is a fine-tuned version of [sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0024 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 300 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0171 | 0.25 | 75 | 0.0063 | 1.0 | | 0.0391 | 1.25 | 150 | 0.0048 | 1.0 | | 0.0019 | 2.25 | 225 | 0.0032 | 1.0 | | 0.1704 | 3.25 | 300 | 0.0024 | 1.0 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

huggingtweets/mildlysomewhat

huggingtweets

2023-05-05T15:32:30Z

146

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T15:32:23Z

--- language: en thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1559746526234263558/n8RqkkaD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">☆♪sheeks☆彡</div> <div style="text-align: center; font-size: 14px;">@mildlysomewhat</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from ☆♪sheeks☆彡. | Data | ☆♪sheeks☆彡 | | --- | --- | | Tweets downloaded | 705 | | Retweets | 207 | | Short tweets | 32 | | Tweets kept | 466 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zuthsw7a/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @mildlysomewhat's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2kq2dk2t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2kq2dk2t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/mildlysomewhat') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

pcuenq/k-upscaler

pcuenq

2023-05-05T15:27:49Z

6

1

diffusers

[ "diffusers", "diffusers:StableDiffusionUpscalerPipeline", "region:us" ]

null

2022-11-14T17:47:59Z

# k-diffusion latent upscaler By Katherine Crowson. This is a temporary model repository that I created for convenience. I just copied the weights and configuration from this base URL: https://models.rivershavewings.workers.dev/

AkshatSurolia/ICD-10-Code-Prediction

AkshatSurolia

2023-05-05T15:24:14Z

977

45

transformers

[ "transformers", "pytorch", "bert", "text-classification", "license:apache-2.0", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - text-classification --- # Clinical BERT for ICD-10 Prediction The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either all MIMIC notes or only discharge summaries. --- ## How to use the model Load the model via the transformers library: from transformers import AutoTokenizer, BertForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") model = BertForSequenceClassification.from_pretrained("AkshatSurolia/ICD-10-Code-Prediction") config = model.config Run the model with clinical diagonosis text: text = "subarachnoid hemorrhage scalp laceration service: surgery major surgical or invasive" encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) Return the Top-5 predicted ICD-10 codes: results = output.logits.detach().cpu().numpy()[0].argsort()[::-1][:5] return [ config.id2label[ids] for ids in results]

AkshatSurolia/ViT-FaceMask-Finetuned

AkshatSurolia

2023-05-05T15:22:03Z

293

1

transformers

[ "transformers", "pytorch", "safetensors", "vit", "image-classification", "dataset:Face-Mask18K", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-03-02T23:29:04Z

--- license: apache-2.0 tags: - image-classification datasets: - Face-Mask18K --- # Vision Transformer (ViT) for Face Mask Detection Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not provide any fine-tuned heads, as these were zero'd by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification). By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Training Metrics epoch = 0.89 total_flos = 923776502GF train_loss = 0.057 train_runtime = 0:40:10.40 train_samples_per_second = 23.943 train_steps_per_second = 1.497 --- ## Evaluation Metrics epoch = 0.89 eval_accuracy = 0.9894 eval_loss = 0.0395 eval_runtime = 0:00:36.81 eval_samples_per_second = 97.685 eval_steps_per_second = 12.224

gsaivinay/OpenAssistant-SFT-7-Llama-30B-HF

gsaivinay

2023-05-05T15:17:18Z

5

2

transformers

[ "transformers", "pytorch", "llama", "text-generation", "arxiv:2304.07327", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T15:17:17Z

--- license: other duplicated_from: TheBloke/OpenAssistant-SFT-7-Llama-30B-HF --- # OpenAssistant LLaMA 30B SFT 7 HF This in HF format repo of [OpenAssistant's LLaMA 30B SFT 7](https://huggingface.co/OpenAssistant/oasst-sft-7-llama-30b-xor). It is the result of merging the XORs from the above repo with the original Llama 30B weights. This is epoch 7 of OpenAssistant's training of a Llama 30B model. # Original model card ``` llama-30b-sft-7: dtype: fp16 log_dir: "llama_log_30b" learning_rate: 1e-5 model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500 #model_name: OpenAssistant/llama-30b-super-pretrain output_dir: llama_model_30b deepspeed_config: configs/zero3_config_sft.json weight_decay: 0.0 residual_dropout: 0.0 max_length: 2048 use_flash_attention: true warmup_steps: 20 gradient_checkpointing: true gradient_accumulation_steps: 12 per_device_train_batch_size: 2 per_device_eval_batch_size: 3 eval_steps: 101 save_steps: 485 num_train_epochs: 4 save_total_limit: 3 use_custom_sampler: true sort_by_length: false #save_strategy: steps save_strategy: epoch datasets: - oasst_export: lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz val_split: 0.05 - vicuna: val_split: 0.05 max_val_set: 800 fraction: 1.0 - dolly15k: val_split: 0.05 max_val_set: 300 - grade_school_math_instructions: val_split: 0.05 - code_alpaca: val_split: 0.05 max_val_set: 250 ``` - **OASST dataset paper:** https://arxiv.org/abs/2304.07327

jasonsurya0/BART_FIVE

jasonsurya0

2023-05-05T15:05:15Z

105

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-05T14:36:24Z

BART MODEL #5 PRETRAINED ON XSUM AND FINETUNED ON SAMSUM

ameyachitnis/ppo-Huggy

ameyachitnis

2023-05-05T15:00:33Z

15

0

ml-agents

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

reinforcement-learning

2023-05-05T09:54:27Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` 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. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy 2. Step 1: Find your model_id: ameyachitnis/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

hsyntemiz/turcoins-classifier

hsyntemiz

2023-05-05T14:57:17Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-05-05T12:06:04Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: turcoins-classifier results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: hsyntemiz--turcoins split: test args: hsyntemiz--turcoins metrics: - name: Accuracy type: accuracy value: 0.9548611111111112 ---  # turcoins-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - Accuracy: 0.9549 ## Model description More information needed ## Intended uses & 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: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.9277 | 1.0 | 146 | 1.9660 | 0.7726 | | 1.6627 | 2.0 | 292 | 1.7154 | 0.7917 | | 1.4071 | 2.99 | 438 | 1.4120 | 0.8079 | | 1.09 | 4.0 | 585 | 1.1225 | 0.8362 | | 0.8086 | 5.0 | 731 | 0.8917 | 0.8675 | | 0.7636 | 6.0 | 877 | 0.7596 | 0.8709 | | 0.611 | 6.99 | 1023 | 0.6493 | 0.8883 | | 0.4605 | 8.0 | 1170 | 0.5899 | 0.8872 | | 0.37 | 9.0 | 1316 | 0.4978 | 0.9045 | | 0.3882 | 10.0 | 1462 | 0.4424 | 0.9132 | | 0.3139 | 10.99 | 1608 | 0.3969 | 0.9115 | | 0.3178 | 12.0 | 1755 | 0.3525 | 0.9294 | | 0.2796 | 13.0 | 1901 | 0.3552 | 0.9161 | | 0.2571 | 14.0 | 2047 | 0.3189 | 0.9265 | | 0.2481 | 14.99 | 2193 | 0.2945 | 0.9358 | | 0.1875 | 16.0 | 2340 | 0.2647 | 0.9392 | | 0.1861 | 17.0 | 2486 | 0.2404 | 0.9410 | | 0.1839 | 18.0 | 2632 | 0.2556 | 0.9421 | | 0.173 | 18.99 | 2778 | 0.2387 | 0.9462 | | 0.1837 | 20.0 | 2925 | 0.2049 | 0.9485 | | 0.1724 | 21.0 | 3071 | 0.2065 | 0.9525 | | 0.1399 | 22.0 | 3217 | 0.2089 | 0.9404 | | 0.1696 | 22.99 | 3363 | 0.1957 | 0.9497 | | 0.1405 | 24.0 | 3510 | 0.1848 | 0.9554 | | 0.1009 | 25.0 | 3656 | 0.1912 | 0.9520 | | 0.1126 | 26.0 | 3802 | 0.1717 | 0.9560 | | 0.1336 | 26.99 | 3948 | 0.1699 | 0.9589 | | 0.1046 | 28.0 | 4095 | 0.1600 | 0.9601 | | 0.126 | 29.0 | 4241 | 0.1839 | 0.9520 | | 0.0882 | 29.95 | 4380 | 0.1763 | 0.9549 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

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

SharKRippeR

2023-05-05T14:56:02Z

103

0

transformers

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

token-classification

2023-05-05T14:52:11Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme config: PAN-X.it split: validation args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8149959250203749 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2579 - F1: 0.8150 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.9299 | 1.0 | 53 | 0.3701 | 0.7122 | | 0.313 | 2.0 | 106 | 0.2874 | 0.7881 | | 0.2174 | 3.0 | 159 | 0.2579 | 0.8150 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

SanketJadhav/PlantDiseaseClassifier

SanketJadhav

2023-05-05T14:47:43Z

0

fastai

[ "fastai", "region:us" ]

null

2023-03-22T14:59:49Z

--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed

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

SharKRippeR

2023-05-05T14:40:45Z

136

0

transformers

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

token-classification

2023-05-05T14:27:26Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1624 - F1: 0.8577 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2988 | 1.0 | 537 | 0.1743 | 0.8300 | | 0.1472 | 2.0 | 1074 | 0.1616 | 0.8470 | | 0.0987 | 3.0 | 1611 | 0.1624 | 0.8577 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

marianodo/MegaBatchMarginLoss-light-model-10

marianodo

2023-05-05T14:36:13Z

4

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-05-05T14:35:49Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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.  ## 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 16 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` 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": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, '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}) (2): Normalize() ) ``` ## Citing & Authors

thejuuuuu/torgo_b4_2

thejuuuuu

2023-05-05T14:33:34Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-05-05T14:32:15Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: torgo_b4_2 results: [] ---  # torgo_b4_2 This model is a fine-tuned version of [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 80 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3

marianodo/MegaBatchMarginLoss-10

marianodo

2023-05-05T14:28:48Z

9

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-05-05T14:27:55Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} 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.  ## 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('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 16 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MegaBatchMarginLoss.MegaBatchMarginLoss` 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": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 16, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel (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}) (2): Normalize() ) ``` ## Citing & Authors

GeneralAwareness/MirrorMask

GeneralAwareness

2023-05-05T14:25:59Z

16

5

diffusers

[ "diffusers", "stable-diffusion", "text-to-image", "image-to-image", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-23T08:48:54Z

--- license: cc-by-nc-sa-4.0 language: - en thumbnail: "https://huggingface.co/GeneralAwareness/Mirrormask/resolve/main/mm1.png" tags: - stable-diffusion - text-to-image - image-to-image - diffusers --- ### Mirror Mask Welcome to MirrorMask - This is a fine-tuned Stable Diffusion model trained from the 2005 dark fantasy film MirrorMask. Use the token mirrormask in your prompts for the style to show. Examples down below: ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm1.png)![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm2.png)![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm3.png) #### Prompt An example of calling mirrormask before the prompt (mirrormask a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mm.png) An example of calling mirrormask with a comma before the prompt (mirrormask, a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmcomma.png) An example of calling mirrormask with the additional word of "by" before the prompt (mirrormask by a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmby.png) An example of calling mirrormask with the additional word of "in" before the prompt (mirrormask in a woman) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/mmin.png) An example of calling the prompt then mirrormask (a woman mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/_mm.png) An example of calling the prompt then adding a comma before mirrormask (a woman, mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/commamm.png) An example of calling the prompt then adding the word "in" before mirrormask (a woman in mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/inmm.png) An example of calling the prompt then adding the word "by" before mirrormask (a woman by mirrormask) ![Single Samples](https://huggingface.co/GeneralAwareness/MirrorMask/resolve/main/bymm.png) For more variety try adding the word "style" after the token word mirrormask (no examples given to save space). ### 🧨 Diffusers This model can be used just as you would any other Stable Diffusion style model. For more information, please have a look at the [Stable Diffusion](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion). You can also export the model to [ONNX](https://huggingface.co/docs/diffusers/optimization/onnx), [MPS](https://huggingface.co/docs/diffusers/optimization/mps) and/or [FLAX/JAX](). ```python from diffusers import StableDiffusionPipeline import torch model_id = "GeneralAwareness/MirrorMask" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a woman by mirrormask" image = pipe(prompt).images[0] image.save("./awoman.png") ``` ## License This model is under a creative commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). To see what rights you have under this licence follow this link - https://creativecommons.org/licenses/by-nc-sa/4.0/

cornut/a2c-PandaReachDense-v2

cornut

2023-05-05T14:24:58Z

0

stable-baselines3

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

reinforcement-learning

2023-05-05T14:21:56Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.66 +/- 0.47 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-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 ... ```

rozek/OpenLLaMA_7B_300BT_q4

rozek

2023-05-05T14:23:02Z

0

1

null

[ "open-llama", "ggml", "7b", "quantized", "text-generation", "dataset:togethercomputer/RedPajama-Data-1T", "license:apache-2.0", "region:us" ]

text-generation

2023-05-05T13:17:46Z

--- license: apache-2.0 datasets: - togethercomputer/RedPajama-Data-1T tags: - open-llama - ggml - 7b - quantized - text-generation --- This repository contains the [300BT preview](https://huggingface.co/openlm-research/open_llama_7b_preview_300bt) of the [Open LLaMA](https://github.com/openlm-research/open_llama) model based on the [RedPajama 1T dataset](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T), converted to GGML and quantized to 4 bit, ready to be used with [llama.cpp](https://github.com/rozek/llama.cpp) ## Usage ## In order to use this model with llama.cpp * install llama.cpp as [described in the docs](https://github.com/ggerganov/llama.cpp#usage) * download this model * move it into the `models` subfolder of llama.cpp * run inferences with the additional parameter `-m ./models/7B/ggml-openllama-7b-300bt-q4_0.bin` For example, ``` ./main -m ./models/7B/ggml-openllama-7b-300bt-q4_0.bin -n 128 -p "who was Joseph Weizenbaum?" ``` ## License ## Since the underlying model is itself Apache 2.0 licensed, this repo is as well. --- license: apache-2.0 ---

Bainbridge/gpt2-kl_01_03_hscnspecial-hs_cn

Bainbridge

2023-05-05T14:11:46Z

5

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-05-05T13:20:34Z

--- license: mit tags: - generated_from_trainer model-index: - name: gpt2-kl_01_03_hscnspecial-hs_cn results: [] ---  # gpt2-kl_01_03_hscnspecial-hs_cn This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5608 ## Model description More information needed ## Intended uses & 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: 4 - seed: 21 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 73.5326 | 0.02 | 10 | 69.5739 | | 46.0984 | 0.04 | 20 | 32.9644 | | 13.5448 | 0.06 | 30 | 10.6411 | | 6.7693 | 0.08 | 40 | 4.2565 | | 2.6755 | 0.1 | 50 | 2.0724 | | 1.4407 | 0.12 | 60 | 1.1299 | | 1.082 | 0.14 | 70 | 0.9249 | | 0.8847 | 0.16 | 80 | 0.7332 | | 0.7248 | 0.18 | 90 | 0.6492 | | 0.7073 | 0.2 | 100 | 0.6516 | | 0.6199 | 0.22 | 110 | 0.6608 | | 0.6244 | 0.24 | 120 | 0.6069 | | 0.6289 | 0.26 | 130 | 0.6602 | | 0.6198 | 0.28 | 140 | 0.5919 | | 0.5988 | 0.3 | 150 | 0.6068 | | 0.6156 | 0.32 | 160 | 0.5664 | | 0.5252 | 0.34 | 170 | 0.5626 | | 0.6351 | 0.36 | 180 | 0.5663 | | 0.6246 | 0.38 | 190 | 0.5599 | | 0.5791 | 0.4 | 200 | 0.5609 | | 0.6755 | 0.42 | 210 | 0.5627 | | 0.6367 | 0.44 | 220 | 0.5608 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 1.12.0a0+bd13bc6 - Datasets 2.12.0 - Tokenizers 0.13.3

tomofi/trocr-captcha

tomofi

2023-05-05T13:59:12Z

613

5

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2022-03-11T12:04:59Z

--- license: mit --- CER: 0.0019 training code https://colab.research.google.com/drive/14MfFkhgPS63RJcP7rpBOK6OII_y34jx_?usp=sharing