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 2025-07-14 12:27:51	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 520 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 12:25:52	card stringlengths 11 1.01M
SakataHalmi/q-Taxi-v3y	SakataHalmi	2023-09-20T15:46:11Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T15:46:08Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3y results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage model = load_from_hub(repo_id="SakataHalmi/q-Taxi-v3y", 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"])
MohanaPriyaa/image_classification	MohanaPriyaa	2023-09-20T15:43:27Z	63	1	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-20T14:06:18Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: MohanaPriyaa/image_classifier 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. --> # MohanaPriyaa/food_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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2925 - Validation Loss: 0.2284 - Train Accuracy: 0.909 - 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2925 \| 0.2284 \| 0.909 \| 0 \| ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
tonystark0/bert-finetuned-ner	tonystark0	2023-09-20T15:42:25Z	120	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-09-20T15:30:39Z	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9302325581395349 - name: Recall type: recall value: 0.9491753618310333 - name: F1 type: f1 value: 0.9396084964598085 - name: Accuracy type: accuracy value: 0.9858715488314593 --- <!-- 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-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0609 - Precision: 0.9302 - Recall: 0.9492 - F1: 0.9396 - Accuracy: 0.9859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0788 \| 1.0 \| 1756 \| 0.0763 \| 0.9123 \| 0.9337 \| 0.9229 \| 0.9800 \| \| 0.0393 \| 2.0 \| 3512 \| 0.0605 \| 0.9262 \| 0.9480 \| 0.9370 \| 0.9855 \| \| 0.0255 \| 3.0 \| 5268 \| 0.0609 \| 0.9302 \| 0.9492 \| 0.9396 \| 0.9859 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
signon-project/mbart-large-cc25-ft-amr30-es	signon-project	2023-09-20T15:34:44Z	106	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:facebook/mbart-large-cc25", "base_model:finetune:facebook/mbart-large-cc25", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-01-19T11:50:36Z	--- base_model: facebook/mbart-large-cc25 tags: - generated_from_trainer model-index: - name: es+no_processing 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. --> # es+no_processing This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5874 - Smatch Precision: 74.08 - Smatch Recall: 76.84 - Smatch Fscore: 75.44 - Smatch Unparsable: 0 - Percent Not Recoverable: 0.2323 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Smatch Precision \| Smatch Recall \| Smatch Fscore \| Smatch Unparsable \| Percent Not Recoverable \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:-------------:\|:-----------------:\|:-----------------------:\| \| 0.3908 \| 1.0 \| 3477 \| 1.4300 \| 19.74 \| 68.95 \| 30.7 \| 0 \| 0.0 \| \| 0.256 \| 2.0 \| 6954 \| 0.8998 \| 27.75 \| 70.61 \| 39.85 \| 1 \| 0.0581 \| \| 0.0704 \| 3.0 \| 10431 \| 0.8727 \| 30.09 \| 72.2 \| 42.47 \| 0 \| 0.1161 \| \| 0.0586 \| 4.0 \| 13908 \| 0.7774 \| 37.1 \| 74.93 \| 49.62 \| 0 \| 0.1161 \| \| 0.1059 \| 5.0 \| 17385 \| 0.6322 \| 42.52 \| 74.54 \| 54.15 \| 1 \| 0.1161 \| \| 0.0424 \| 6.0 \| 20862 \| 0.6090 \| 47.13 \| 76.21 \| 58.25 \| 0 \| 0.0 \| \| 0.0139 \| 7.0 \| 24339 \| 0.5768 \| 48.3 \| 77.31 \| 59.46 \| 0 \| 0.0581 \| \| 0.08 \| 8.0 \| 27817 \| 0.5608 \| 55.74 \| 77.16 \| 64.72 \| 0 \| 0.1161 \| \| 0.0224 \| 9.0 \| 31294 \| 0.5937 \| 54.91 \| 77.02 \| 64.11 \| 0 \| 0.0581 \| \| 0.0757 \| 10.0 \| 34771 \| 0.5588 \| 59.53 \| 77.47 \| 67.32 \| 0 \| 0.0581 \| \| 0.0613 \| 11.0 \| 38248 \| 0.5894 \| 60.83 \| 77.82 \| 68.28 \| 0 \| 0.0581 \| \| 0.1045 \| 12.0 \| 41725 \| 0.5847 \| 61.23 \| 77.17 \| 68.28 \| 0 \| 0.1742 \| \| 0.012 \| 13.0 \| 45202 \| 0.5588 \| 65.61 \| 77.47 \| 71.05 \| 0 \| 0.0 \| \| 0.0591 \| 14.0 \| 48679 \| 0.5609 \| 66.51 \| 77.86 \| 71.74 \| 0 \| 0.0581 \| \| 0.0252 \| 15.0 \| 52156 \| 0.5653 \| 67.48 \| 77.75 \| 72.25 \| 0 \| 0.0 \| \| 0.0129 \| 16.0 \| 55634 \| 0.5602 \| 68.92 \| 77.57 \| 72.99 \| 0 \| 0.0 \| \| 0.0006 \| 17.0 \| 59111 \| 0.5876 \| 68.57 \| 77.81 \| 72.9 \| 0 \| 0.1742 \| \| 0.0182 \| 18.0 \| 62588 \| 0.5951 \| 68.97 \| 77.96 \| 73.19 \| 0 \| 0.1161 \| \| 0.018 \| 19.0 \| 66065 \| 0.5865 \| 70.63 \| 77.68 \| 73.98 \| 0 \| 0.0581 \| \| 0.0097 \| 20.0 \| 69542 \| 0.6073 \| 71.68 \| 77.38 \| 74.42 \| 0 \| 0.1161 \| \| 0.0021 \| 21.0 \| 73019 \| 0.5984 \| 72.25 \| 77.92 \| 74.98 \| 0 \| 0.0581 \| \| 0.0371 \| 22.0 \| 76496 \| 0.5907 \| 72.92 \| 77.59 \| 75.18 \| 0 \| 0.1742 \| \| 0.0382 \| 23.0 \| 79973 \| 0.5928 \| 73.06 \| 77.49 \| 75.21 \| 0 \| 0.1742 \| \| 0.0148 \| 24.0 \| 83451 \| 0.5903 \| 73.98 \| 77.15 \| 75.53 \| 0 \| 0.0581 \| \| 0.1326 \| 25.0 \| 86925 \| 0.5874 \| 74.08 \| 76.84 \| 75.44 \| 0 \| 0.2323 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
Sudhee1997/Llama-2-7b-Custom-Recruit	Sudhee1997	2023-09-20T15:33:14Z	5	0	peft	[ "peft", "region:us" ]	null	2023-09-15T11:25:00Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
willyninja30/llama-2-7b-chat-hf-fr-en-python	willyninja30	2023-09-20T15:33:02Z	5	0	peft	[ "peft", "text-generation", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	text-generation	2023-08-25T10:16:03Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf inference: true pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
CyberHarem/perusepone2shi_jashinchandropkick	CyberHarem	2023-09-20T15:32:55Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/perusepone2shi_jashinchandropkick", "license:mit", "region:us" ]	text-to-image	2023-09-20T15:01:25Z	--- license: mit datasets: - CyberHarem/perusepone2shi_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of perusepone2shi_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/perusepone2shi_jashinchandropkick.pt` as the embedding and `4080/perusepone2shi_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4080, with the score of 0.851. The trigger words are: 1. `perusepone2shi_jashinchandropkick` 2. `short_hair, pointy_ears, hair_over_one_eye, grey_hair, red_eyes, white_hair, ribbon, smile` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:-----------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.840 \| [Download](5100/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![pattern_8-5100](5100/previews/pattern_8.png) \| ![pattern_9-5100](5100/previews/pattern_9.png) \| ![pattern_10-5100](5100/previews/pattern_10.png) \| ![pattern_11-5100](5100/previews/pattern_11.png) \| ![pattern_12-5100](5100/previews/pattern_12.png) \| ![pattern_13-5100](5100/previews/pattern_13.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.844 \| [Download](4760/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![pattern_8-4760](4760/previews/pattern_8.png) \| ![pattern_9-4760](4760/previews/pattern_9.png) \| ![pattern_10-4760](4760/previews/pattern_10.png) \| ![pattern_11-4760](4760/previews/pattern_11.png) \| ![pattern_12-4760](4760/previews/pattern_12.png) \| ![pattern_13-4760](4760/previews/pattern_13.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.786 \| [Download](4420/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![pattern_8-4420](4420/previews/pattern_8.png) \| ![pattern_9-4420](4420/previews/pattern_9.png) \| ![pattern_10-4420](4420/previews/pattern_10.png) \| ![pattern_11-4420](4420/previews/pattern_11.png) \| ![pattern_12-4420](4420/previews/pattern_12.png) \| ![pattern_13-4420](4420/previews/pattern_13.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.851 \| [Download](4080/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![pattern_8-4080](4080/previews/pattern_8.png) \| ![pattern_9-4080](4080/previews/pattern_9.png) \| ![pattern_10-4080](4080/previews/pattern_10.png) \| ![pattern_11-4080](4080/previews/pattern_11.png) \| ![pattern_12-4080](4080/previews/pattern_12.png) \| ![pattern_13-4080](4080/previews/pattern_13.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.652 \| [Download](3740/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![pattern_8-3740](3740/previews/pattern_8.png) \| ![pattern_9-3740](3740/previews/pattern_9.png) \| ![pattern_10-3740](3740/previews/pattern_10.png) \| ![pattern_11-3740](3740/previews/pattern_11.png) \| ![pattern_12-3740](3740/previews/pattern_12.png) \| ![pattern_13-3740](3740/previews/pattern_13.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.654 \| [Download](3400/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![pattern_8-3400](3400/previews/pattern_8.png) \| ![pattern_9-3400](3400/previews/pattern_9.png) \| ![pattern_10-3400](3400/previews/pattern_10.png) \| ![pattern_11-3400](3400/previews/pattern_11.png) \| ![pattern_12-3400](3400/previews/pattern_12.png) \| ![pattern_13-3400](3400/previews/pattern_13.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.645 \| [Download](3060/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![pattern_8-3060](3060/previews/pattern_8.png) \| ![pattern_9-3060](3060/previews/pattern_9.png) \| ![pattern_10-3060](3060/previews/pattern_10.png) \| ![pattern_11-3060](3060/previews/pattern_11.png) \| ![pattern_12-3060](3060/previews/pattern_12.png) \| ![pattern_13-3060](3060/previews/pattern_13.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.806 \| [Download](2720/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![pattern_8-2720](2720/previews/pattern_8.png) \| ![pattern_9-2720](2720/previews/pattern_9.png) \| ![pattern_10-2720](2720/previews/pattern_10.png) \| ![pattern_11-2720](2720/previews/pattern_11.png) \| ![pattern_12-2720](2720/previews/pattern_12.png) \| ![pattern_13-2720](2720/previews/pattern_13.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.756 \| [Download](2380/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![pattern_8-2380](2380/previews/pattern_8.png) \| ![pattern_9-2380](2380/previews/pattern_9.png) \| ![pattern_10-2380](2380/previews/pattern_10.png) \| ![pattern_11-2380](2380/previews/pattern_11.png) \| ![pattern_12-2380](2380/previews/pattern_12.png) \| ![pattern_13-2380](2380/previews/pattern_13.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.674 \| [Download](2040/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![pattern_8-2040](2040/previews/pattern_8.png) \| ![pattern_9-2040](2040/previews/pattern_9.png) \| ![pattern_10-2040](2040/previews/pattern_10.png) \| ![pattern_11-2040](2040/previews/pattern_11.png) \| ![pattern_12-2040](2040/previews/pattern_12.png) \| ![pattern_13-2040](2040/previews/pattern_13.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.688 \| [Download](1700/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![pattern_8-1700](1700/previews/pattern_8.png) \| ![pattern_9-1700](1700/previews/pattern_9.png) \| ![pattern_10-1700](1700/previews/pattern_10.png) \| ![pattern_11-1700](1700/previews/pattern_11.png) \| ![pattern_12-1700](1700/previews/pattern_12.png) \| ![pattern_13-1700](1700/previews/pattern_13.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.629 \| [Download](1360/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| 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see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.097 \| [Download](340/perusepone2shi_jashinchandropkick.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![pattern_8-340](340/previews/pattern_8.png) \| ![pattern_9-340](340/previews/pattern_9.png) \| ![pattern_10-340](340/previews/pattern_10.png) \| ![pattern_11-340](340/previews/pattern_11.png) \| ![pattern_12-340](340/previews/pattern_12.png) \| ![pattern_13-340](340/previews/pattern_13.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
Faradaylab/Aria_7b_v2	Faradaylab	2023-09-20T15:30:57Z	7	3	peft	[ "peft", "llama7b", "LLAMA2", "opensource", "culture", "code", "text-generation", "fr", "en", "es", "dataset:Snit/french-conversation", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	text-generation	2023-08-25T12:59:18Z	--- language: - fr - en - es license: llama2 library_name: peft tags: - llama7b - LLAMA2 - peft - opensource - culture - code datasets: - Snit/french-conversation inference: true pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-chat-hf --- ARIA 7B V2 is a model created by Faraday 🇫🇷 🇧🇪 The growing need of artificial intelligence tools around the world has created a run for GPU power. We decided to create an affordable model with better skills in French which can run on single GPU and reduce data bias observed in models trained mostly on english only datasets.. ARIA 7B has been trained on over 20.000 tokens of a high quality french dataset. ARIA 7B is one of the best open source models in the world avaible for this size of parameters. ## Training procedure : NVIDIA A100. Thanks to NVIDIA GPU and Inception program,we have been able to train our model within less than 24 hours. ## Base model : LLAMA_2-7B-CHAT-HF We strongly believe that training models in more languages datasets can not only increase their knowledge base but also give more open analysis perspectives ,less focused visions and opinions from only one part of the world. ## Contact [email protected] ## Number of Epoch : 2 ## Timing : Less than 24 hours The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
willyninja30/aria7Beta	willyninja30	2023-09-20T15:30:47Z	8	1	peft	[ "peft", "llama2", "pytorch", "french", "text-generation", "fr", "en", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:apache-2.0", "region:us" ]	text-generation	2023-08-23T14:37:35Z	--- language: - fr - en license: apache-2.0 library_name: peft tags: - llama2 - pytorch - french inference: false pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-chat-hf --- ## ARIA 7B is a model created by Faraday The growing need of artificial intelligence tools around the world has created a run for GPU power. We decided to create an affordable model with better skills in French which can run on single GPU and reduce data bias observed in models trained mostly on english only datasets.. ARIA 7B has been trained on over 20.000 tokens of a high quality french dataset. ARIA 7B is one of the best open source models in the world avaible for this size of parameters. GPU used for training : NVIDIA V100. Thanks to NVIDIA GPU and Inception program,we have been able to train our model within less than 24 hours. Base model : LLAMA_2-7B-CHAT-HF We strongly believe that training models in more languages datasets can not only increase their knowledge base but also give more open analysis perspectives ,less focused visions and opinions from only one part of the world. ## ARIA 7B est un modèle créé par Faraday Le besoin croissant en intelligence artificiele dans le monde a créé une course vers la puissance de calcul des cartes graphiques. Nous avons décidé de créer un modèle accessible capable de tourner sur une seule carte graphique et réduisant les biais d'algorithmes observés sur les modèles entrainés uniquement sur des bases de données en anglais. ARIA 7B a été entrainé sur un dataset de grande qualité avec plus de 20.000 tokens en Français. GPU(Carte graphique) utilisée pour le finetuning: NVIDIA V100. Merci à NVIDIA et au programme Nvidia Inception qui nous a orienté pendant tout le processus et nous a permis d'entrainer le modèle en moins de 24h. Modèle de base : LLAMA_2-7B-CHAT-HF Nous pensons que le fait d'entraîner des modèles sur des langues différentes permet non seulement d'élargir la base de connaissance mais aussi de donner d'autres perspectives d'analyses plus ouvertes,et moins centrées sur la vision et les opinions exprimées par une seule partie du monde. Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
SakataHalmi/q-FrozenLake-v1-4x4-noSlippery	SakataHalmi	2023-09-20T15:30:22Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T15:30:19Z	--- 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 model = load_from_hub(repo_id="SakataHalmi/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"])
BramVanroy/mbart-large-cc25-ft-amr30-en	BramVanroy	2023-09-20T15:28:41Z	130	0	transformers	[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:facebook/mbart-large-cc25", "base_model:finetune:facebook/mbart-large-cc25", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-20T15:26:13Z	--- base_model: facebook/mbart-large-cc25 tags: - generated_from_trainer model-index: - name: en+no_processing results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # en+no_processing This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4481 - Smatch Precision: 80.57 - Smatch Recall: 83.81 - Smatch Fscore: 82.16 - Smatch Unparsable: 0 - Percent Not Recoverable: 0.3484 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Smatch Precision \| Smatch Recall \| Smatch Fscore \| Smatch Unparsable \| Percent Not Recoverable \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:----------------:\|:-------------:\|:-------------:\|:-----------------:\|:-----------------------:\| \| 0.3471 \| 1.0 \| 3477 \| 1.4889 \| 22.35 \| 73.05 \| 34.23 \| 0 \| 0.1161 \| \| 0.1741 \| 2.0 \| 6954 \| 0.8681 \| 30.1 \| 71.92 \| 42.44 \| 0 \| 0.1161 \| \| 0.1296 \| 3.0 \| 10431 \| 0.7081 \| 38.6 \| 78.68 \| 51.8 \| 0 \| 0.0581 \| \| 0.1308 \| 4.0 \| 13908 \| 0.9546 \| 37.49 \| 78.23 \| 50.69 \| 0 \| 0.0 \| \| 0.2213 \| 5.0 \| 17385 \| 0.5544 \| 47.63 \| 81.17 \| 60.03 \| 0 \| 0.0 \| \| 0.0317 \| 6.0 \| 20862 \| 0.4884 \| 49.3 \| 80.9 \| 61.27 \| 0 \| 0.0 \| \| 0.1007 \| 7.0 \| 24339 \| 0.4763 \| 54.88 \| 82.09 \| 65.78 \| 0 \| 0.0 \| \| 0.092 \| 8.0 \| 27817 \| 0.4444 \| 57.37 \| 83.2 \| 67.91 \| 0 \| 0.0 \| \| 0.1051 \| 9.0 \| 31294 \| 0.4192 \| 64.37 \| 83.81 \| 72.82 \| 0 \| 0.0 \| \| 0.0079 \| 10.0 \| 34771 \| 0.4685 \| 61.3 \| 83.1 \| 70.55 \| 0 \| 0.0 \| \| 0.0211 \| 11.0 \| 38248 \| 0.4389 \| 63.36 \| 84.57 \| 72.44 \| 0 \| 0.1161 \| \| 0.1122 \| 12.0 \| 41725 \| 0.4146 \| 69.39 \| 83.56 \| 75.82 \| 0 \| 0.0581 \| \| 0.0183 \| 13.0 \| 45202 \| 0.4003 \| 73.9 \| 83.71 \| 78.5 \| 0 \| 0.0 \| \| 0.0244 \| 14.0 \| 48679 \| 0.4208 \| 73.79 \| 83.92 \| 78.53 \| 0 \| 0.1161 \| \| 0.0116 \| 15.0 \| 52156 \| 0.4248 \| 73.88 \| 83.85 \| 78.55 \| 0 \| 0.1161 \| \| 0.0357 \| 16.0 \| 55634 \| 0.4235 \| 75.78 \| 84.08 \| 79.71 \| 0 \| 0.1161 \| \| 0.0006 \| 17.0 \| 59111 \| 0.4181 \| 76.15 \| 84.15 \| 79.95 \| 0 \| 0.0581 \| \| 0.0329 \| 18.0 \| 62588 \| 0.4494 \| 77.21 \| 84.12 \| 80.52 \| 0 \| 0.0 \| \| 0.0003 \| 19.0 \| 66065 \| 0.4389 \| 78.02 \| 84.13 \| 80.96 \| 0 \| 0.0 \| \| 0.04 \| 20.0 \| 69542 \| 0.4439 \| 78.78 \| 84.23 \| 81.41 \| 0 \| 0.0 \| \| 0.0182 \| 21.0 \| 73019 \| 0.4430 \| 79.82 \| 84.05 \| 81.88 \| 0 \| 0.0581 \| \| 0.0006 \| 22.0 \| 76496 \| 0.4488 \| 79.96 \| 83.74 \| 81.81 \| 0 \| 0.0581 \| \| 0.0074 \| 23.0 \| 79973 \| 0.4569 \| 79.84 \| 83.85 \| 81.79 \| 0 \| 0.0581 \| \| 0.0133 \| 24.0 \| 83451 \| 0.4469 \| 80.45 \| 83.81 \| 82.09 \| 0 \| 0.2904 \| \| 0.0055 \| 25.0 \| 86925 \| 0.4481 \| 80.57 \| 83.81 \| 82.16 \| 0 \| 0.3484 \| ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3
csakarwa/Model1cs	csakarwa	2023-09-20T15:24:47Z	1	0	peft	[ "peft", "region:us" ]	null	2023-09-20T15:24:45Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
AnatolyBelov/my_t5_small_test	AnatolyBelov	2023-09-20T15:19:33Z	4	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-18T14:00:07Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books metrics: - bleu model-index: - name: my_t5_small_test results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_books type: opus_books config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 6.372 --- <!-- 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_t5_small_test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.5026 - Bleu: 6.372 - Gen Len: 17.5713 ## Model description More information needed ## Intended uses & 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 \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 1.7108 \| 1.0 \| 6355 \| 1.5113 \| 6.3012 \| 17.5701 \| \| 1.6833 \| 2.0 \| 12710 \| 1.5026 \| 6.372 \| 17.5713 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
abhishek23HF/MARKEtING_BLOOMZ_1B	abhishek23HF	2023-09-20T15:17:28Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-20T15:17:23Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0
databio/v2v-ChIP-atlas-hg38-ATAC	databio	2023-09-20T15:15:34Z	0	0	null	[ "region:us" ]	null	2023-09-19T20:50:13Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Vec2Vec ChIP-atlas hg38 ## Model Details ### Model Description This is a Vec2Vec model that encodes embedding vectors of natural language into embedding vectors of BED files. This model was trained with hg38 ChIP-atlas ATAC-seq data. The natural language metadata came from the experiment list, their embedding vectors were encoded by [sentence-transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). The BED files were embedded by [Region2Vec](https://huggingface.co/databio/r2v-ChIP-atlas-hg38) - Developed by: Ziyang "Claude" Hu - Model type: Vec2Vec - Language(s) (NLP): hg38 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/databio/geniml - Paper [optional]: N/A ## Uses This model can be used to search BED files with natural language query strings. In the search interface, the query strings will be encoded by same sentence-transformers model, and the output vector will be encoded into the final query vector by this Vec2Vec. The K BED files whose embedding vectors (embedded by same Region2Vec) are closest to the final query vector are results. It is limited to hg38. It is not recommended to use this model for data outside ATAC-seq. ## How to Get Started with the Model Vec2Vec will allow direct importing from Hugging Face soon. [More Information Needed] ## Training Details ### Training Data TODO
ramchiluveru/MarketingCampaign	ramchiluveru	2023-09-20T15:14:23Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-20T15:14:21Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
badassbandit/taxi	badassbandit	2023-09-20T15:10:46Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T15:10:45Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="badassbandit/taxi", 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"]) ```
CyberHarem/matsuo_chizuru_idolmastercinderellagirls	CyberHarem	2023-09-20T15:09:30Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/matsuo_chizuru_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-20T14:52:03Z	--- license: mit datasets: - CyberHarem/matsuo_chizuru_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of matsuo_chizuru_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/matsuo_chizuru_idolmastercinderellagirls.pt` as the embedding and `4760/matsuo_chizuru_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.996. The trigger words are: 1. `matsuo_chizuru_idolmastercinderellagirls` 2. `short_hair, black_hair, blush, hair_ornament, hairclip, black_eyes, thick_eyebrows, purple_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:----------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.983 \| [Download](5100/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| [<NSFW, click to see>](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![pattern_8-5100](5100/previews/pattern_8.png) \| ![pattern_9-5100](5100/previews/pattern_9.png) \| ![pattern_10-5100](5100/previews/pattern_10.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.996 \| [Download](4760/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| [<NSFW, click to see>](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![pattern_8-4760](4760/previews/pattern_8.png) \| ![pattern_9-4760](4760/previews/pattern_9.png) \| ![pattern_10-4760](4760/previews/pattern_10.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.968 \| [Download](4420/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| [<NSFW, click to see>](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![pattern_8-4420](4420/previews/pattern_8.png) \| ![pattern_9-4420](4420/previews/pattern_9.png) \| ![pattern_10-4420](4420/previews/pattern_10.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.994 \| [Download](4080/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| [<NSFW, click to see>](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![pattern_8-4080](4080/previews/pattern_8.png) \| ![pattern_9-4080](4080/previews/pattern_9.png) \| ![pattern_10-4080](4080/previews/pattern_10.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.960 \| [Download](3740/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| [<NSFW, click to see>](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![pattern_8-3740](3740/previews/pattern_8.png) \| ![pattern_9-3740](3740/previews/pattern_9.png) \| ![pattern_10-3740](3740/previews/pattern_10.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.994 \| [Download](3400/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| [<NSFW, click to see>](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![pattern_8-3400](3400/previews/pattern_8.png) \| ![pattern_9-3400](3400/previews/pattern_9.png) \| ![pattern_10-3400](3400/previews/pattern_10.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.992 \| [Download](3060/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| [<NSFW, click to see>](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![pattern_8-3060](3060/previews/pattern_8.png) \| ![pattern_9-3060](3060/previews/pattern_9.png) \| ![pattern_10-3060](3060/previews/pattern_10.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.982 \| [Download](2720/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| [<NSFW, click to see>](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![pattern_8-2720](2720/previews/pattern_8.png) \| ![pattern_9-2720](2720/previews/pattern_9.png) \| ![pattern_10-2720](2720/previews/pattern_10.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.995 \| [Download](2380/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| [<NSFW, click to see>](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![pattern_8-2380](2380/previews/pattern_8.png) \| ![pattern_9-2380](2380/previews/pattern_9.png) \| ![pattern_10-2380](2380/previews/pattern_10.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.959 \| [Download](2040/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| [<NSFW, click to see>](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![pattern_8-2040](2040/previews/pattern_8.png) \| ![pattern_9-2040](2040/previews/pattern_9.png) \| ![pattern_10-2040](2040/previews/pattern_10.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.961 \| [Download](1700/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| [<NSFW, click to see>](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![pattern_8-1700](1700/previews/pattern_8.png) \| ![pattern_9-1700](1700/previews/pattern_9.png) \| ![pattern_10-1700](1700/previews/pattern_10.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.924 \| [Download](1360/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| [<NSFW, click to see>](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![pattern_8-1360](1360/previews/pattern_8.png) \| ![pattern_9-1360](1360/previews/pattern_9.png) \| ![pattern_10-1360](1360/previews/pattern_10.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.872 \| [Download](1020/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| [<NSFW, click to see>](1020/previews/pattern_5.png) \| ![pattern_6-1020](1020/previews/pattern_6.png) \| ![pattern_7-1020](1020/previews/pattern_7.png) \| ![pattern_8-1020](1020/previews/pattern_8.png) \| ![pattern_9-1020](1020/previews/pattern_9.png) \| ![pattern_10-1020](1020/previews/pattern_10.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.880 \| [Download](680/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| [<NSFW, click to see>](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![pattern_8-680](680/previews/pattern_8.png) \| ![pattern_9-680](680/previews/pattern_9.png) \| ![pattern_10-680](680/previews/pattern_10.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.313 \| [Download](340/matsuo_chizuru_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| [<NSFW, click to see>](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![pattern_8-340](340/previews/pattern_8.png) \| ![pattern_9-340](340/previews/pattern_9.png) \| ![pattern_10-340](340/previews/pattern_10.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
badassbandit/q-FrozenLake-v1-4x4-noSlippery	badassbandit	2023-09-20T15:06:16Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T15:06:14Z	--- 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="badassbandit/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"]) ```
BanUrsus/rl_course_additional_challenge_vizdoom_deathmatch_bots	BanUrsus	2023-09-20T14:56:40Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T14:56:24Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_deathmatch_bots type: doom_deathmatch_bots metrics: - type: mean_reward value: 1.70 +/- 1.42 name: mean_reward verified: false --- A(n) APPO model trained on the doom_deathmatch_bots environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r BanUrsus/rl_course_additional_challenge_vizdoom_deathmatch_bots ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=rl_course_additional_challenge_vizdoom_deathmatch_bots ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=rl_course_additional_challenge_vizdoom_deathmatch_bots --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
hosnasn/tannaz1-reza	hosnasn	2023-09-20T14:54:02Z	0	0	null	[ "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-09-20T14:47:47Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tannaz1_reza Dreambooth model trained by hosnasn 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:
csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4	csdc-atl	2023-09-20T14:50:13Z	170	2	transformers	[ "transformers", "safetensors", "baichuan", "text-generation", "custom_code", "en", "zh", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2023-09-15T18:06:52Z	--- language: - en - zh license: other tasks: - text-generation --- <!-- markdownlint-disable first-line-h1 --> <!-- markdownlint-disable html --> # Baichuan 2 13B Chat - Int4 <!-- description start --> ## 描述该repo包含[Baichuan 2 7B Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)的Int4 GPTQ模型文件。 <!-- description end --> <!-- README_GPTQ.md-provided-files start --> ## GPTQ参数该GPTQ文件都是用AutoGPTQ生成的。 - Bits: 4/8 - GS: 32/128 - Act Order: True - Damp %: 0.1 - GPTQ dataset: 中文、英文混合数据集 - Sequence Length: 4096 \| 模型版本 \| agieval \| ceval \| cmmlu \| size \| 推理速度(A100-40G) \| \|---\|---\|---\|---\|---\|---\| \| [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) \| 40.25 \| 56.33 \| 58.44 \| 27.79g \| 31.55 tokens/s \| \| [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) \| 39.01 \| 56.63 \| 57.81 \| 9.08g \| 18.45 tokens/s \| \| [GPTQ-4bit-32g](https://huggingface.co/csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4/tree/4bit-32g) \| 38.64 \| 57.18 \| 57.47 \| 9.87g \| 27.35(hf) \ 38.28(autogptq) tokens/s \| \| [GPTQ-4bit-128g](https://huggingface.co/csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4/tree/main) \| 38.78 \| 56.42 \| 57.78 \| 9.14g \| 28.74(hf) \ 39.24(autogptq) tokens/s \| <!-- README_GPTQ.md-provided-files end --> ## 如何在Python代码中使用此GPTQ模型 ### 安装必要的依赖必须： Transformers 4.32.0以上、Optimum 1.12.0以上、AutoGPTQ 0.4.2以上 ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` 如果您在使用预构建的pip包安装AutoGPTQ时遇到问题，请改为从源代码安装： ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### 然后可以使用以下代码 ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig model_name_or_path = "csdc-atl/Baichuan2-13B-Chat-Int4" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat") messages = [] messages.append({"role": "user", "content": "解释一下“温故而知新”"}) response = model.chat(tokenizer, messages) print(response) "温故而知新"是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。 ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> --- <div align="center"> <h1> Baichuan 2 </h1> </div> <div align="center"> <a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> \| <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">💬WeChat</a> </div> <div align="center"> 🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉 </div> # 目录/Table of Contents - [📖 模型介绍/Introduction](#Introduction) - [⚙️ 快速开始/Quick Start](#Start) - [📊 Benchmark评估/Benchmark Evaluation](#Benchmark) - [📜 声明与协议/Terms and Conditions](#Terms) # <span id="Introduction">模型介绍/Introduction</span> Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 2.6 万亿 Tokens 的高质量语料训练，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表： Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/). It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:[email protected]). The specific release versions and download links are listed in the table below: \| \| Base Model \| Chat Model \| 4bits Quantized Chat Model \| \|:---:\|:--------------------:\|:--------------------:\|:--------------------------:\| \| 7B \| [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) \| [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) \| [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits) \| \| 13B \| [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) \| [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) \| [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) \| # <span id="Start">快速开始/Quick Start</span> 在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。 In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-13B-Chat", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-13B-Chat", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-13B-Chat") messages = [] messages.append({"role": "user", "content": "解释一下“温故而知新”"}) response = model.chat(tokenizer, messages) print(response) "温故而知新"是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。 ``` # <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span> 我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。 We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md). ### 7B Model Results \| \| C-Eval \| MMLU \| CMMLU \| Gaokao \| AGIEval \| BBH \| \|:-----------------------:\|:----------:\|:--------:\|:---------:\|:----------:\|:-----------:\|:-------:\| \| \| 5-shot \| 5-shot \| 5-shot \| 5-shot \| 5-shot \| 3-shot \| \| GPT-4 \| 68.40 \| 83.93 \| 70.33 \| 66.15 \| 63.27 \| 75.12 \| \| GPT-3.5 Turbo \| 51.10 \| 68.54 \| 54.06 \| 47.07 \| 46.13 \| 61.59 \| \| LLaMA-7B \| 27.10 \| 35.10 \| 26.75 \| 27.81 \| 28.17 \| 32.38 \| \| LLaMA2-7B \| 28.90 \| 45.73 \| 31.38 \| 25.97 \| 26.53 \| 39.16 \| \| MPT-7B \| 27.15 \| 27.93 \| 26.00 \| 26.54 \| 24.83 \| 35.20 \| \| Falcon-7B \| 24.23 \| 26.03 \| 25.66 \| 24.24 \| 24.10 \| 28.77 \| \| ChatGLM2-6B \| 50.20 \| 45.90 \| 49.00 \| 49.44 \| 45.28 \| 31.65 \| \| [Baichuan-7B] \| 42.80 \| 42.30 \| 44.02 \| 36.34 \| 34.44 \| 32.48 \| \| [Baichuan2-7B-Base] \| 54.00 \| 54.16 \| 57.07 \| 47.47 \| 42.73 \| 41.56 \| ### 13B Model Results \| \| C-Eval \| MMLU \| CMMLU \| Gaokao \| AGIEval \| BBH \| \|:---------------------------:\|:----------:\|:--------:\|:---------:\|:----------:\|:-----------:\|:-------:\| \| \| 5-shot \| 5-shot \| 5-shot \| 5-shot \| 5-shot \| 3-shot \| \| GPT-4 \| 68.40 \| 83.93 \| 70.33 \| 66.15 \| 63.27 \| 75.12 \| \| GPT-3.5 Turbo \| 51.10 \| 68.54 \| 54.06 \| 47.07 \| 46.13 \| 61.59 \| \| LLaMA-13B \| 28.50 \| 46.30 \| 31.15 \| 28.23 \| 28.22 \| 37.89 \| \| LLaMA2-13B \| 35.80 \| 55.09 \| 37.99 \| 30.83 \| 32.29 \| 46.98 \| \| Vicuna-13B \| 32.80 \| 52.00 \| 36.28 \| 30.11 \| 31.55 \| 43.04 \| \| Chinese-Alpaca-Plus-13B \| 38.80 \| 43.90 \| 33.43 \| 34.78 \| 35.46 \| 28.94 \| \| XVERSE-13B \| 53.70 \| 55.21 \| 58.44 \| 44.69 \| 42.54 \| 38.06 \| \| [Baichuan-13B-Base] \| 52.40 \| 51.60 \| 55.30 \| 49.69 \| 43.20 \| 43.01 \| \| [Baichuan2-13B-Base] \| 58.10 \| 59.17 \| 61.97 \| 54.33 \| 48.17 \| 48.78 \| ## 训练过程模型/Training Dynamics 除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用（[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化： In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU. ![checkpoint](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/checkpoints.jpeg) # <span id="Terms">声明与协议/Terms and Conditions</span> ## 声明我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用 Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility. ## 协议 Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。Baichuan 2 支持商用。如果将 Baichuan 2 模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱 [[email protected]]。 The use of the source code in this repository follows the open-source license Apache 2.0. Community use of the Baichuan 2 model must adhere to the [Community License for Baichuan 2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). Baichuan 2 supports commercial use. If you are using the Baichuan 2 models or their derivatives for commercial purposes, please contact the licensor in the following manner for registration and to apply for written authorization: Email [email protected]. [GitHub]:https://github.com/baichuan-inc/Baichuan2 [Baichuan2]:https://github.com/baichuan-inc/Baichuan2 [Baichuan-7B]:https://huggingface.co/baichuan-inc/Baichuan-7B [Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base [Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat [Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits [Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base [Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base [Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat [Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits [通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F [法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91 [《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf [邮件申请]: mailto:[email protected] [Email]: mailto:[email protected] [[email protected]]: mailto:[email protected] [训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints [百川智能]: https://www.baichuan-ai.com
OpenDILabCommunity/Lunarlander-v2-C51	OpenDILabCommunity	2023-09-20T14:49:22Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "LunarLander-v2", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-04-15T12:48:19Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - LunarLander-v2 benchmark_name: OpenAI/Gym/Box2d task_name: LunarLander-v2 pipeline_tag: reinforcement-learning model-index: - name: C51 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Box2d-LunarLander-v2 type: OpenAI/Gym/Box2d-LunarLander-v2 metrics: - type: mean_reward value: 211.75 +/- 40.32 name: mean_reward --- # Play LunarLander-v2 with C51 Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple C51 implementation to OpenAI/Gym/Box2d LunarLander-v2 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import C51Agent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = C51Agent( env_id="LunarLander-v2", exp_name="LunarLander-v2-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import C51Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/LunarLander-v2-C51") # Instantiate the agent agent = C51Agent( env_id="LunarLander-v2", exp_name="LunarLander-v2-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import C51Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = C51Agent(env_id="LunarLander-v2", exp_name="LunarLander-v2-C51") # Train the agent return_ = agent.train(step=int(4000000), collector_env_num=8, evaluator_env_num=8, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Box2d", task_name="LunarLander-v2", algo_name="C51", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./c51/lunarlander_c51_deploy.py", usage_file_by_huggingface_ding="./c51/lunarlander_c51_download.py", train_file="./c51/lunarlander_c51.py", repo_id="OpenDILabCommunity/LunarLander-v2-C51", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 260, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'LunarLander-v2' }, 'policy': { 'model': { 'encoder_hidden_size_list': [512, 64], 'v_min': -30, 'v_max': 30, 'n_atom': 51, 'obs_shape': 8, 'action_shape': 4 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 64, 'learning_rate': 0.001, 'target_update_freq': 100, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 64, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 260, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 0.95, 'end': 0.1, 'decay': 50000 } }, 'on_policy': False, 'cuda': False, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'c51', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'C51PolicyDict' }, 'exp_name': 'LunarLander-v2-C51', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/Lunarlander-v2-C51) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-C51/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-C51/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 214.3 KB - Last Update Date: 2023-09-20 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/Box2d - Task: LunarLander-v2 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html)
napatswift/mt5-fixpdftext	napatswift	2023-09-20T14:33:34Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "th", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-14T08:23:49Z	--- language: - th pipeline_tag: text2text-generation widget: - text: "Fix the following corrupted text: \"เจาหนาที่รับผิดชอบในการเขาไปเยียวยา โดยเจาพนักงานเจาหนาที่ตามกฎหมาย\"" ---
Parkhat/llama2-qlora-finetunined-kg-sql	Parkhat	2023-09-20T14:32:52Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-20T14:32:31Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
jbrinkw/fp1.1	jbrinkw	2023-09-20T14:27:23Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-20T14:11:47Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: fp1.1 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. --> # fp1.1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3884 - Rouge1: 0.1551 - Rouge2: 0.0523 - Rougel: 0.1467 - Rougelsum: 0.1467 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 1.4836 \| 1.0 \| 4040 \| 1.3884 \| 0.1551 \| 0.0523 \| 0.1467 \| 0.1467 \| 19.0 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
ChinuNayak28/working	ChinuNayak28	2023-09-20T14:15:37Z	0	0	null	[ "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:finetune:meta-llama/Llama-2-13b-chat-hf", "region:us" ]	null	2023-09-14T10:13:49Z	--- base_model: meta-llama/Llama-2-13b-chat-hf tags: - generated_from_trainer model-index: - name: working 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. --> # working This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5525 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
linoyts/lora-xl-linoy_face-0.0001-0.0001-1000-1-32	linoyts	2023-09-20T14:14:01Z	4	1	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-09-20T13:53:42Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a TOK woman tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - LinoyTsaban/lora-xl-linoy_face-0.0001-0.0001-1000-1-32 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on photo of a TOK woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
CyberHarem/etou_misaki_idolmastercinderellagirls	CyberHarem	2023-09-20T14:13:39Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/etou_misaki_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-20T14:01:32Z	--- license: mit datasets: - CyberHarem/etou_misaki_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of etou_misaki_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/etou_misaki_idolmastercinderellagirls.pt` as the embedding and `4760/etou_misaki_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.964. The trigger words are: 1. `etou_misaki_idolmastercinderellagirls` 2. `brown_hair, long_hair, green_eyes, jewelry, smile, earrings, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:---------------------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.936 \| [Download](5100/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.964 \| [Download](4760/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.917 \| [Download](4420/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.889 \| [Download](4080/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.937 \| [Download](3740/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.919 \| [Download](3400/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.909 \| [Download](3060/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.865 \| [Download](2720/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.879 \| [Download](2380/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.848 \| [Download](2040/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.873 \| [Download](1700/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.847 \| [Download](1360/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.817 \| [Download](1020/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.815 \| [Download](680/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.751 \| [Download](340/etou_misaki_idolmastercinderellagirls.zip) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
davidramos/int8-whisper-base-v2-asr-bengali	davidramos	2023-09-20T14:11:09Z	1	0	peft	[ "peft", "base_model:openai/whisper-base", "base_model:adapter:openai/whisper-base", "region:us" ]	null	2023-09-14T15:51:50Z	--- library_name: peft base_model: openai/whisper-base --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 ### Training loss Epoch \| Training Loss \| Validation Loss 1 \| 0.573400 \| 0.595536 2 \| 0.476500 \| 0.506768 3 \| 0.421000 \| 0.472346
googcheng/lit_lima	googcheng	2023-09-20T14:00:57Z	0	0	null	[ "region:us" ]	null	2023-09-20T13:57:08Z	try lit-gpt train from https://sebastianraschka.com/blog/2023/optimizing-LLMs-dataset-perspective.html
Harshit0722/dolly-fine-tuned-on-med-data	Harshit0722	2023-09-20T14:00:38Z	7	1	peft	[ "peft", "biology", "medical", "code", "question-answering", "en", "dataset:databricks/databricks-dolly-15k", "dataset:medmcqa", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:bsd", "region:us" ]	question-answering	2023-06-17T14:06:53Z	--- language: - en license: bsd library_name: peft tags: - biology - medical - code datasets: - databricks/databricks-dolly-15k - medmcqa metrics: - code_eval pipeline_tag: question-answering base_model: databricks/dolly-v2-3b ---
Rexhaif/rubert-base-srl-seqlabeling	Rexhaif	2023-09-20T14:00:29Z	130	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model-index: - name: rubert-base-srl-seqlabeling 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. --> # rubert-base-srl-seqlabeling This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1723 - Causator Precision: 0.8539 - Causator Recall: 0.8352 - Causator F1: 0.8444 - Causator Number: 91 - Expiriencer Precision: 0.9259 - Expiriencer Recall: 0.9740 - Expiriencer F1: 0.9494 - Expiriencer Number: 77 - Instrument Precision: 0.375 - Instrument Recall: 1.0 - Instrument F1: 0.5455 - Instrument Number: 3 - Other Precision: 0.0 - Other Recall: 0.0 - Other F1: 0.0 - Other Number: 1 - Predicate Precision: 0.9352 - Predicate Recall: 0.9902 - Predicate F1: 0.9619 - Predicate Number: 102 - Overall Precision: 0.8916 - Overall Recall: 0.9307 - Overall F1: 0.9107 - Overall Accuracy: 0.9667 ## Model description More information needed ## Intended uses & 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Causator Precision \| Causator Recall \| Causator F1 \| Causator Number \| Expiriencer Precision \| Expiriencer Recall \| Expiriencer F1 \| Expiriencer Number \| Instrument Precision \| Instrument Recall \| Instrument F1 \| Instrument Number \| Other Precision \| Other Recall \| Other F1 \| Other Number \| Predicate Precision \| Predicate Recall \| Predicate F1 \| Predicate Number \| Overall Precision \| Overall Recall \| Overall F1 \| Overall Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------------:\|:---------------:\|:-----------:\|:---------------:\|:---------------------:\|:------------------:\|:--------------:\|:------------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-----------------:\|:---------------:\|:------------:\|:--------:\|:------------:\|:-------------------:\|:----------------:\|:------------:\|:----------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\| \| 0.2552 \| 1.0 \| 56 \| 0.3471 \| 0.8841 \| 0.6703 \| 0.7625 \| 91 \| 0.8421 \| 0.8312 \| 0.8366 \| 77 \| 0.0 \| 0.0 \| 0.0 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9259 \| 0.9804 \| 0.9524 \| 102 \| 0.8893 \| 0.8212 \| 0.8539 \| 0.9203 \| \| 0.2385 \| 2.0 \| 112 \| 0.1608 \| 0.9103 \| 0.7802 \| 0.8402 \| 91 \| 0.9375 \| 0.9740 \| 0.9554 \| 77 \| 0.2857 \| 0.6667 \| 0.4 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9519 \| 0.9706 \| 0.9612 \| 102 \| 0.9182 \| 0.9015 \| 0.9098 \| 0.9554 \| \| 0.0367 \| 3.0 \| 168 \| 0.1311 \| 0.8902 \| 0.8022 \| 0.8439 \| 91 \| 0.9375 \| 0.9740 \| 0.9554 \| 77 \| 0.4286 \| 1.0 \| 0.6 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9709 \| 0.9804 \| 0.9756 \| 102 \| 0.9228 \| 0.9161 \| 0.9194 \| 0.9673 \| \| 0.0494 \| 4.0 \| 224 \| 0.1507 \| 0.7812 \| 0.8242 \| 0.8021 \| 91 \| 0.9241 \| 0.9481 \| 0.9359 \| 77 \| 0.4286 \| 1.0 \| 0.6 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9524 \| 0.9804 \| 0.9662 \| 102 \| 0.8746 \| 0.9161 \| 0.8948 \| 0.9637 \| \| 0.0699 \| 5.0 \| 280 \| 0.1830 \| 0.8276 \| 0.7912 \| 0.8090 \| 91 \| 0.8941 \| 0.9870 \| 0.9383 \| 77 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9352 \| 0.9902 \| 0.9619 \| 102 \| 0.875 \| 0.9197 \| 0.8968 \| 0.9560 \| \| 0.0352 \| 6.0 \| 336 \| 0.1994 \| 0.7857 \| 0.8462 \| 0.8148 \| 91 \| 0.9048 \| 0.9870 \| 0.9441 \| 77 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9266 \| 0.9902 \| 0.9573 \| 102 \| 0.8595 \| 0.9380 \| 0.8970 \| 0.9572 \| \| 0.0186 \| 7.0 \| 392 \| 0.1657 \| 0.8652 \| 0.8462 \| 0.8556 \| 91 \| 0.9146 \| 0.9740 \| 0.9434 \| 77 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9352 \| 0.9902 \| 0.9619 \| 102 \| 0.8920 \| 0.9343 \| 0.9127 \| 0.9673 \| \| 0.0052 \| 8.0 \| 448 \| 0.1716 \| 0.8556 \| 0.8462 \| 0.8508 \| 91 \| 0.9259 \| 0.9740 \| 0.9494 \| 77 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9352 \| 0.9902 \| 0.9619 \| 102 \| 0.8920 \| 0.9343 \| 0.9127 \| 0.9673 \| \| 0.0094 \| 9.0 \| 504 \| 0.1715 \| 0.8444 \| 0.8352 \| 0.8398 \| 91 \| 0.9259 \| 0.9740 \| 0.9494 \| 77 \| 0.4286 \| 1.0 \| 0.6 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9352 \| 0.9902 \| 0.9619 \| 102 \| 0.8916 \| 0.9307 \| 0.9107 \| 0.9667 \| \| 0.0078 \| 10.0 \| 560 \| 0.1723 \| 0.8539 \| 0.8352 \| 0.8444 \| 91 \| 0.9259 \| 0.9740 \| 0.9494 \| 77 \| 0.375 \| 1.0 \| 0.5455 \| 3 \| 0.0 \| 0.0 \| 0.0 \| 1 \| 0.9352 \| 0.9902 \| 0.9619 \| 102 \| 0.8916 \| 0.9307 \| 0.9107 \| 0.9667 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
vabatista/sbert-squad-pt-br	vabatista	2023-09-20T13:59:10Z	106	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "sentence-similarity", "pt", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-08-11T13:47:38Z	--- license: apache-2.0 datasets: - squad language: - pt pipeline_tag: sentence-similarity library_name: sentence-transformers --- This is a sentence_bert based model fine tuned using portuguese SQuAD dataset with batchs of: - positive samples: question - sentence containing answer - hard negative samples question which answers is not in the sentence, but is in the context of SQuAD dataset. This model generate embeddings from text to use with semantic search
Yehoon/llama_13b	Yehoon	2023-09-20T13:58:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-20T13:58:50Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
jonas-luehrs/gbert-base-MLM-eu-or-ddr	jonas-luehrs	2023-09-20T13:55:47Z	124	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:deepset/gbert-base", "base_model:finetune:deepset/gbert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-09-20T12:55:52Z	--- license: mit base_model: deepset/gbert-base tags: - generated_from_trainer model-index: - name: gbert-base-MLM-eu 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. --> # gbert-base-MLM-eu-or-ddr This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.9428 \| 1.0 \| 403 \| 1.5950 \| \| 1.6342 \| 2.0 \| 806 \| 1.4453 \| \| 1.5324 \| 3.0 \| 1209 \| 1.3751 \| \| 1.4729 \| 4.0 \| 1612 \| 1.3533 \| \| 1.4536 \| 5.0 \| 2015 \| 1.3389 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
mayorov-s/Taxi-v3-QTable	mayorov-s	2023-09-20T13:50:21Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T13:50:17Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-QTable results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.81 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="mayorov-s/Taxi-v3-QTable", 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"]) ```
FedeBerto/Griffith	FedeBerto	2023-09-20T13:49:16Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2023-09-07T10:48:17Z	--- library_name: keras --- ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| AdamW \| \| weight_decay \| 0.01 \| \| clipnorm \| 1.0 \| \| 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 \| 1.9999999494757503e-05 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-08 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
Tanor/SRGPTSENTNEG2	Tanor	2023-09-20T13:47:44Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-classification", "generated_from_trainer", "base_model:Tanor/SRGPTSENTNEG2", "base_model:finetune:Tanor/SRGPTSENTNEG2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-07-10T23:17:47Z	--- base_model: Tanor/SRGPTSENTNEG2 tags: - generated_from_trainer metrics: - f1 model-index: - name: SRGPTSENTNEG2 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. --> # SRGPTSENTNEG2 This model is a fine-tuned version of [Tanor/SRGPTSENTNEG2](https://huggingface.co/Tanor/SRGPTSENTNEG2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1820 - F1: 0.3235 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - 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 \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 0.1297 \| 1.0 \| 2697 \| 0.1564 \| 0.0 \| \| 0.0867 \| 2.0 \| 5394 \| 0.1468 \| 0.2903 \| \| 0.0467 \| 3.0 \| 8091 \| 0.1674 \| 0.3288 \| \| 0.052 \| 4.0 \| 10788 \| 0.2066 \| 0.3514 \| \| 0.0403 \| 5.0 \| 13485 \| 0.1820 \| 0.3235 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0.dev20230801 - Datasets 2.14.2 - Tokenizers 0.13.3
mayorov-s/q-FrozenLake-v1-4x4-noSlippery	mayorov-s	2023-09-20T13:45:42Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-09-21T11:34:43Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="mayorov-s/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
Rachneet/T5-large-esnli-impli-figurative	Rachneet	2023-09-20T13:44:28Z	110	0	transformers	[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-20T15:29:12Z	# load model ``` from transformers import ( AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM ) model_path = "T5-large-esnli-impli-figurative" tokenizer = AutoTokenizer.from_pretrained(model_path) config = AutoConfig.from_pretrained(model_path) model = AutoModelForSeq2SeqLM.from_pretrained(model_path) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") premise = "I just caught a guy picking up used chewing gum and he put it in his mouth." hypothesis = "it was such a pleasant sight to see a guy picking up used chewing gum; and he put it in his mouth" prepared_input = f"figurative hypothesis: {hypothesis} premise: {premise}" features = tokenizer(prepared_input, max_length=128, padding="max_length", truncation=True, return_tensors="pt") model.eval() model.to(device) with torch.no_grad(): # https://huggingface.co/blog/how-to-generate generated_ids = model.generate( **features, max_length=128, use_cache=True, num_beams=4, length_penalty=0.6, early_stopping=True, ) dec_preds = tokenizer.decode(outputs[0], skip_special_tokens=True) print("The prediction is: ", dec_preds) print(dec_preds[1:].replace("explanation:", "").lstrip()) ``` # Example input figurative hypothesis: I was gone for only a few days and my considerate adult son just let the sink fill up with dirty dishes, making me feel really happy premise: I left my adult son home for a few days and just came back to a sink full of gross old dishes.
AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers	AmitMidday	2023-09-20T13:42:48Z	195	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "en", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-20T11:53:41Z	--- pipeline_tag: image-classification license: openrail language: - en metrics: - accuracy library_name: transformers ---
a-v-bely/bert-base-spanish-wwm-cased-finetuned-literature-pro	a-v-bely	2023-09-20T13:39:53Z	115	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-03-13T04:57:33Z	--- tags: - generated_from_trainer base_model: dccuchile/bert-base-spanish-wwm-cased model-index: - name: bert-base-spanish-wwm-cased-finetuned-literature-pro results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-spanish-wwm-cased-finetuned-literature-pro This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.2669 \| 1.0 \| 9787 \| 2.0414 \| \| 2.111 \| 2.0 \| 19574 \| 1.9735 \| \| 2.0512 \| 3.0 \| 29361 \| 1.9413 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2
santiagotoso/TaxiGame-QLearning	santiagotoso	2023-09-20T13:14:24Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T13:08:32Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: TaxiGame-QLearning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="santiagotoso/TaxiGame-QLearning", 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"]) ```
mgoin/mpt-7b-chat-quant	mgoin	2023-09-20T13:12:55Z	6	1	transformers	[ "transformers", "onnx", "mpt", "text-generation", "deepsparse", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2023-09-20T02:43:00Z	--- tags: - deepsparse --- # mpt_7b_chat-dense_quant_linearW8A8MatMul8Embeds8LMhead8 ```python import deepsparse from huggingface_hub import snapshot_download MODEL_PATH = snapshot_download(repo_id="mgoin/mpt-7b-chat-quant") model = deepsparse.Pipeline.create(task="text-generation", model_path=MODEL_PATH) model(sequences="Tell me a joke.") ```
CyberHarem/minosu_jashinchandropkick	CyberHarem	2023-09-20T13:07:15Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/minosu_jashinchandropkick", "license:mit", "region:us" ]	text-to-image	2023-09-20T12:48:47Z	--- license: mit datasets: - CyberHarem/minosu_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of minosu_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8840, you need to download `8840/minosu_jashinchandropkick.pt` as the embedding and `8840/minosu_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 8840, with the score of 0.955. The trigger words are: 1. `minosu_jashinchandropkick` 2. `blue_hair, horns, short_hair, bell, cow_horns, neck_bell, cowbell, cow_print, animal_print, cow_girl, red_eyes, hair_between_eyes, smile, closed_mouth, pink_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| pattern_14 \| pattern_15 \| pattern_16 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:---------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------------------\|:--------------------------------------------------\|:---------------------------------------------------\|:---------------------------------------\|:---------------------------------------\|:---------------------------------------\|:------------------------------------------------\|:-------------------------------------------------\|:---------------------------------------\|:-------------------------------------------\| \| 10200 \| 0.943 \| [Download](10200/minosu_jashinchandropkick.zip) \| ![pattern_1-10200](10200/previews/pattern_1.png) \| ![pattern_2-10200](10200/previews/pattern_2.png) \| ![pattern_3-10200](10200/previews/pattern_3.png) \| ![pattern_4-10200](10200/previews/pattern_4.png) \| ![pattern_5-10200](10200/previews/pattern_5.png) \| ![pattern_6-10200](10200/previews/pattern_6.png) \| ![pattern_7-10200](10200/previews/pattern_7.png) \| ![pattern_8-10200](10200/previews/pattern_8.png) \| ![pattern_9-10200](10200/previews/pattern_9.png) \| ![pattern_10-10200](10200/previews/pattern_10.png) \| ![pattern_11-10200](10200/previews/pattern_11.png) \| ![pattern_12-10200](10200/previews/pattern_12.png) \| ![pattern_13-10200](10200/previews/pattern_13.png) \| ![pattern_14-10200](10200/previews/pattern_14.png) \| ![pattern_15-10200](10200/previews/pattern_15.png) \| ![pattern_16-10200](10200/previews/pattern_16.png) \| [<NSFW, click to see>](10200/previews/bikini.png) \| [<NSFW, click to see>](10200/previews/bondage.png) \| ![free-10200](10200/previews/free.png) \| ![maid-10200](10200/previews/maid.png) \| ![miko-10200](10200/previews/miko.png) \| [<NSFW, click to see>](10200/previews/nude.png) \| [<NSFW, click to see>](10200/previews/nude2.png) \| ![suit-10200](10200/previews/suit.png) \| ![yukata-10200](10200/previews/yukata.png) \| \| 9520 \| 0.941 \| [Download](9520/minosu_jashinchandropkick.zip) \| ![pattern_1-9520](9520/previews/pattern_1.png) \| ![pattern_2-9520](9520/previews/pattern_2.png) \| ![pattern_3-9520](9520/previews/pattern_3.png) \| ![pattern_4-9520](9520/previews/pattern_4.png) \| ![pattern_5-9520](9520/previews/pattern_5.png) \| ![pattern_6-9520](9520/previews/pattern_6.png) \| ![pattern_7-9520](9520/previews/pattern_7.png) \| ![pattern_8-9520](9520/previews/pattern_8.png) \| ![pattern_9-9520](9520/previews/pattern_9.png) \| ![pattern_10-9520](9520/previews/pattern_10.png) \| ![pattern_11-9520](9520/previews/pattern_11.png) \| ![pattern_12-9520](9520/previews/pattern_12.png) \| ![pattern_13-9520](9520/previews/pattern_13.png) \| ![pattern_14-9520](9520/previews/pattern_14.png) \| ![pattern_15-9520](9520/previews/pattern_15.png) \| ![pattern_16-9520](9520/previews/pattern_16.png) \| [<NSFW, click to see>](9520/previews/bikini.png) \| [<NSFW, click to see>](9520/previews/bondage.png) \| ![free-9520](9520/previews/free.png) \| ![maid-9520](9520/previews/maid.png) \| ![miko-9520](9520/previews/miko.png) \| [<NSFW, click to see>](9520/previews/nude.png) \| [<NSFW, click to see>](9520/previews/nude2.png) \| ![suit-9520](9520/previews/suit.png) \| ![yukata-9520](9520/previews/yukata.png) \| \| 8840 \| 0.955 \| [Download](8840/minosu_jashinchandropkick.zip) \| ![pattern_1-8840](8840/previews/pattern_1.png) \| ![pattern_2-8840](8840/previews/pattern_2.png) \| ![pattern_3-8840](8840/previews/pattern_3.png) \| ![pattern_4-8840](8840/previews/pattern_4.png) \| ![pattern_5-8840](8840/previews/pattern_5.png) \| ![pattern_6-8840](8840/previews/pattern_6.png) \| ![pattern_7-8840](8840/previews/pattern_7.png) \| ![pattern_8-8840](8840/previews/pattern_8.png) \| ![pattern_9-8840](8840/previews/pattern_9.png) \| ![pattern_10-8840](8840/previews/pattern_10.png) \| ![pattern_11-8840](8840/previews/pattern_11.png) \| ![pattern_12-8840](8840/previews/pattern_12.png) \| ![pattern_13-8840](8840/previews/pattern_13.png) \| ![pattern_14-8840](8840/previews/pattern_14.png) \| ![pattern_15-8840](8840/previews/pattern_15.png) \| ![pattern_16-8840](8840/previews/pattern_16.png) \| [<NSFW, click to see>](8840/previews/bikini.png) \| [<NSFW, click to see>](8840/previews/bondage.png) \| ![free-8840](8840/previews/free.png) \| ![maid-8840](8840/previews/maid.png) \| ![miko-8840](8840/previews/miko.png) \| [<NSFW, click to see>](8840/previews/nude.png) \| [<NSFW, click to see>](8840/previews/nude2.png) \| ![suit-8840](8840/previews/suit.png) \| ![yukata-8840](8840/previews/yukata.png) \| \| 8160 \| 0.947 \| [Download](8160/minosu_jashinchandropkick.zip) \| ![pattern_1-8160](8160/previews/pattern_1.png) \| ![pattern_2-8160](8160/previews/pattern_2.png) \| ![pattern_3-8160](8160/previews/pattern_3.png) \| ![pattern_4-8160](8160/previews/pattern_4.png) \| ![pattern_5-8160](8160/previews/pattern_5.png) \| ![pattern_6-8160](8160/previews/pattern_6.png) \| ![pattern_7-8160](8160/previews/pattern_7.png) \| ![pattern_8-8160](8160/previews/pattern_8.png) \| ![pattern_9-8160](8160/previews/pattern_9.png) \| ![pattern_10-8160](8160/previews/pattern_10.png) \| ![pattern_11-8160](8160/previews/pattern_11.png) \| ![pattern_12-8160](8160/previews/pattern_12.png) \| ![pattern_13-8160](8160/previews/pattern_13.png) \| ![pattern_14-8160](8160/previews/pattern_14.png) \| ![pattern_15-8160](8160/previews/pattern_15.png) \| ![pattern_16-8160](8160/previews/pattern_16.png) \| [<NSFW, click to see>](8160/previews/bikini.png) \| [<NSFW, click to see>](8160/previews/bondage.png) \| ![free-8160](8160/previews/free.png) \| ![maid-8160](8160/previews/maid.png) \| ![miko-8160](8160/previews/miko.png) \| [<NSFW, click to see>](8160/previews/nude.png) \| [<NSFW, click to see>](8160/previews/nude2.png) \| ![suit-8160](8160/previews/suit.png) \| ![yukata-8160](8160/previews/yukata.png) \| \| 7480 \| 0.950 \| [Download](7480/minosu_jashinchandropkick.zip) \| ![pattern_1-7480](7480/previews/pattern_1.png) \| ![pattern_2-7480](7480/previews/pattern_2.png) \| ![pattern_3-7480](7480/previews/pattern_3.png) \| ![pattern_4-7480](7480/previews/pattern_4.png) \| ![pattern_5-7480](7480/previews/pattern_5.png) \| ![pattern_6-7480](7480/previews/pattern_6.png) \| ![pattern_7-7480](7480/previews/pattern_7.png) \| ![pattern_8-7480](7480/previews/pattern_8.png) \| ![pattern_9-7480](7480/previews/pattern_9.png) \| ![pattern_10-7480](7480/previews/pattern_10.png) \| ![pattern_11-7480](7480/previews/pattern_11.png) \| ![pattern_12-7480](7480/previews/pattern_12.png) \| ![pattern_13-7480](7480/previews/pattern_13.png) \| ![pattern_14-7480](7480/previews/pattern_14.png) \| ![pattern_15-7480](7480/previews/pattern_15.png) \| ![pattern_16-7480](7480/previews/pattern_16.png) \| [<NSFW, click to see>](7480/previews/bikini.png) \| [<NSFW, click to see>](7480/previews/bondage.png) \| ![free-7480](7480/previews/free.png) \| ![maid-7480](7480/previews/maid.png) \| ![miko-7480](7480/previews/miko.png) \| [<NSFW, click to see>](7480/previews/nude.png) \| [<NSFW, click to see>](7480/previews/nude2.png) \| ![suit-7480](7480/previews/suit.png) \| ![yukata-7480](7480/previews/yukata.png) \| \| 6800 \| 0.939 \| [Download](6800/minosu_jashinchandropkick.zip) \| ![pattern_1-6800](6800/previews/pattern_1.png) \| ![pattern_2-6800](6800/previews/pattern_2.png) \| ![pattern_3-6800](6800/previews/pattern_3.png) \| ![pattern_4-6800](6800/previews/pattern_4.png) \| ![pattern_5-6800](6800/previews/pattern_5.png) \| ![pattern_6-6800](6800/previews/pattern_6.png) \| ![pattern_7-6800](6800/previews/pattern_7.png) \| ![pattern_8-6800](6800/previews/pattern_8.png) \| ![pattern_9-6800](6800/previews/pattern_9.png) \| ![pattern_10-6800](6800/previews/pattern_10.png) \| ![pattern_11-6800](6800/previews/pattern_11.png) \| ![pattern_12-6800](6800/previews/pattern_12.png) \| ![pattern_13-6800](6800/previews/pattern_13.png) \| ![pattern_14-6800](6800/previews/pattern_14.png) \| ![pattern_15-6800](6800/previews/pattern_15.png) \| ![pattern_16-6800](6800/previews/pattern_16.png) \| [<NSFW, click to see>](6800/previews/bikini.png) \| [<NSFW, click to see>](6800/previews/bondage.png) \| ![free-6800](6800/previews/free.png) \| ![maid-6800](6800/previews/maid.png) \| ![miko-6800](6800/previews/miko.png) \| [<NSFW, click to see>](6800/previews/nude.png) \| [<NSFW, click to see>](6800/previews/nude2.png) \| ![suit-6800](6800/previews/suit.png) \| ![yukata-6800](6800/previews/yukata.png) \| \| 6120 \| 0.917 \| [Download](6120/minosu_jashinchandropkick.zip) \| ![pattern_1-6120](6120/previews/pattern_1.png) \| ![pattern_2-6120](6120/previews/pattern_2.png) \| ![pattern_3-6120](6120/previews/pattern_3.png) \| ![pattern_4-6120](6120/previews/pattern_4.png) \| ![pattern_5-6120](6120/previews/pattern_5.png) \| ![pattern_6-6120](6120/previews/pattern_6.png) \| ![pattern_7-6120](6120/previews/pattern_7.png) \| ![pattern_8-6120](6120/previews/pattern_8.png) \| ![pattern_9-6120](6120/previews/pattern_9.png) \| ![pattern_10-6120](6120/previews/pattern_10.png) \| ![pattern_11-6120](6120/previews/pattern_11.png) \| ![pattern_12-6120](6120/previews/pattern_12.png) \| ![pattern_13-6120](6120/previews/pattern_13.png) \| ![pattern_14-6120](6120/previews/pattern_14.png) \| ![pattern_15-6120](6120/previews/pattern_15.png) \| ![pattern_16-6120](6120/previews/pattern_16.png) \| [<NSFW, click to see>](6120/previews/bikini.png) \| [<NSFW, click to see>](6120/previews/bondage.png) \| ![free-6120](6120/previews/free.png) \| ![maid-6120](6120/previews/maid.png) \| ![miko-6120](6120/previews/miko.png) \| [<NSFW, click to see>](6120/previews/nude.png) \| [<NSFW, click to see>](6120/previews/nude2.png) \| ![suit-6120](6120/previews/suit.png) \| ![yukata-6120](6120/previews/yukata.png) \| \| 5440 \| 0.946 \| [Download](5440/minosu_jashinchandropkick.zip) \| ![pattern_1-5440](5440/previews/pattern_1.png) \| ![pattern_2-5440](5440/previews/pattern_2.png) \| ![pattern_3-5440](5440/previews/pattern_3.png) \| ![pattern_4-5440](5440/previews/pattern_4.png) \| ![pattern_5-5440](5440/previews/pattern_5.png) \| ![pattern_6-5440](5440/previews/pattern_6.png) \| ![pattern_7-5440](5440/previews/pattern_7.png) \| ![pattern_8-5440](5440/previews/pattern_8.png) \| ![pattern_9-5440](5440/previews/pattern_9.png) \| ![pattern_10-5440](5440/previews/pattern_10.png) \| ![pattern_11-5440](5440/previews/pattern_11.png) \| ![pattern_12-5440](5440/previews/pattern_12.png) \| ![pattern_13-5440](5440/previews/pattern_13.png) \| ![pattern_14-5440](5440/previews/pattern_14.png) \| ![pattern_15-5440](5440/previews/pattern_15.png) \| ![pattern_16-5440](5440/previews/pattern_16.png) \| [<NSFW, click to see>](5440/previews/bikini.png) \| [<NSFW, click to see>](5440/previews/bondage.png) \| ![free-5440](5440/previews/free.png) \| ![maid-5440](5440/previews/maid.png) \| ![miko-5440](5440/previews/miko.png) \| [<NSFW, click to see>](5440/previews/nude.png) \| [<NSFW, click to see>](5440/previews/nude2.png) \| ![suit-5440](5440/previews/suit.png) \| ![yukata-5440](5440/previews/yukata.png) \| \| 4760 \| 0.952 \| [Download](4760/minosu_jashinchandropkick.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![pattern_8-4760](4760/previews/pattern_8.png) \| ![pattern_9-4760](4760/previews/pattern_9.png) \| ![pattern_10-4760](4760/previews/pattern_10.png) \| ![pattern_11-4760](4760/previews/pattern_11.png) \| ![pattern_12-4760](4760/previews/pattern_12.png) \| ![pattern_13-4760](4760/previews/pattern_13.png) \| ![pattern_14-4760](4760/previews/pattern_14.png) \| ![pattern_15-4760](4760/previews/pattern_15.png) \| ![pattern_16-4760](4760/previews/pattern_16.png) \| [<NSFW, click to see>](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4080 \| 0.904 \| [Download](4080/minosu_jashinchandropkick.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![pattern_8-4080](4080/previews/pattern_8.png) \| ![pattern_9-4080](4080/previews/pattern_9.png) \| ![pattern_10-4080](4080/previews/pattern_10.png) \| ![pattern_11-4080](4080/previews/pattern_11.png) \| ![pattern_12-4080](4080/previews/pattern_12.png) \| ![pattern_13-4080](4080/previews/pattern_13.png) \| ![pattern_14-4080](4080/previews/pattern_14.png) \| ![pattern_15-4080](4080/previews/pattern_15.png) \| ![pattern_16-4080](4080/previews/pattern_16.png) \| [<NSFW, click to see>](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3400 \| 0.882 \| [Download](3400/minosu_jashinchandropkick.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![pattern_8-3400](3400/previews/pattern_8.png) \| ![pattern_9-3400](3400/previews/pattern_9.png) \| ![pattern_10-3400](3400/previews/pattern_10.png) \| ![pattern_11-3400](3400/previews/pattern_11.png) \| ![pattern_12-3400](3400/previews/pattern_12.png) \| ![pattern_13-3400](3400/previews/pattern_13.png) \| ![pattern_14-3400](3400/previews/pattern_14.png) \| ![pattern_15-3400](3400/previews/pattern_15.png) \| ![pattern_16-3400](3400/previews/pattern_16.png) \| [<NSFW, click to see>](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 2720 \| 0.813 \| [Download](2720/minosu_jashinchandropkick.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![pattern_8-2720](2720/previews/pattern_8.png) \| ![pattern_9-2720](2720/previews/pattern_9.png) \| ![pattern_10-2720](2720/previews/pattern_10.png) \| ![pattern_11-2720](2720/previews/pattern_11.png) \| ![pattern_12-2720](2720/previews/pattern_12.png) \| ![pattern_13-2720](2720/previews/pattern_13.png) \| ![pattern_14-2720](2720/previews/pattern_14.png) \| ![pattern_15-2720](2720/previews/pattern_15.png) \| ![pattern_16-2720](2720/previews/pattern_16.png) \| [<NSFW, click to see>](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2040 \| 0.773 \| [Download](2040/minosu_jashinchandropkick.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![pattern_8-2040](2040/previews/pattern_8.png) \| ![pattern_9-2040](2040/previews/pattern_9.png) \| ![pattern_10-2040](2040/previews/pattern_10.png) \| ![pattern_11-2040](2040/previews/pattern_11.png) \| ![pattern_12-2040](2040/previews/pattern_12.png) \| ![pattern_13-2040](2040/previews/pattern_13.png) \| ![pattern_14-2040](2040/previews/pattern_14.png) \| ![pattern_15-2040](2040/previews/pattern_15.png) \| ![pattern_16-2040](2040/previews/pattern_16.png) \| [<NSFW, click to see>](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1360 \| 0.717 \| [Download](1360/minosu_jashinchandropkick.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![pattern_8-1360](1360/previews/pattern_8.png) \| ![pattern_9-1360](1360/previews/pattern_9.png) \| ![pattern_10-1360](1360/previews/pattern_10.png) \| ![pattern_11-1360](1360/previews/pattern_11.png) \| ![pattern_12-1360](1360/previews/pattern_12.png) \| ![pattern_13-1360](1360/previews/pattern_13.png) \| ![pattern_14-1360](1360/previews/pattern_14.png) \| ![pattern_15-1360](1360/previews/pattern_15.png) \| ![pattern_16-1360](1360/previews/pattern_16.png) \| [<NSFW, click to see>](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 680 \| 0.494 \| [Download](680/minosu_jashinchandropkick.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![pattern_8-680](680/previews/pattern_8.png) \| ![pattern_9-680](680/previews/pattern_9.png) \| ![pattern_10-680](680/previews/pattern_10.png) \| ![pattern_11-680](680/previews/pattern_11.png) \| ![pattern_12-680](680/previews/pattern_12.png) \| ![pattern_13-680](680/previews/pattern_13.png) \| ![pattern_14-680](680/previews/pattern_14.png) \| ![pattern_15-680](680/previews/pattern_15.png) \| ![pattern_16-680](680/previews/pattern_16.png) \| [<NSFW, click to see>](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \|
AbdelKarim95/ppo-PyramidsRND	AbdelKarim95	2023-09-20T13:04:33Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-09-20T13:04:29Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AbdelKarim95/ppo-PyramidsRND 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
kmposkid1/Horse-Health-Outcome-6bca6390-b05b-43b6-831d-8bf9f7caf24c	kmposkid1	2023-09-20T13:01:03Z	0	0	sklearn	[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]	tabular-classification	2023-09-20T12:33:52Z	--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: catboost_without_hospital_number.pkl widget: structuredData: abdomen: - distend_small - distend_small - distend_large abdominal_distention: - none - none - moderate abdomo_appearance: - serosanguious - cloudy - serosanguious abdomo_protein: - 4.1 - 4.3 - 2.0 age: - adult - adult - adult capillary_refill_time: - less_3_sec - less_3_sec - more_3_sec cp_data: - 'yes' - 'yes' - 'no' lesion_1: - 7209 - 2112 - 5400 lesion_2: - 0 - 0 - 0 lesion_3: - 0 - 0 - 0 mucous_membrane: - bright_pink - bright_pink - dark_cyanotic nasogastric_reflux: - none - none - more_1_liter nasogastric_reflux_ph: - 7.0 - 3.5 - 2.0 nasogastric_tube: - slight - none - significant packed_cell_volume: - 37.0 - 44.0 - 65.0 pain: - depressed - mild_pain - extreme_pain peripheral_pulse: - normal - normal - reduced peristalsis: - hypermotile - hypomotile - absent pulse: - 84.0 - 66.0 - 72.0 rectal_exam_feces: - absent - decreased - absent rectal_temp: - 39.0 - 38.5 - 37.3 respiratory_rate: - 24.0 - 21.0 - 30.0 surgery: - 'yes' - 'yes' - 'yes' surgical_lesion: - 'yes' - 'yes' - 'yes' temp_of_extremities: - cool - normal - cool total_protein: - 6.5 - 7.6 - 13.0 --- # Model description This is a `Catboost` model trained on horse health outcome data from Kaggle. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure [More Information Needed] ### Hyperparameters <details> <summary> Click to expand </summary> \| Hyperparameter \| Value \| \|--------------------------------------------------\|-----------------------------------------------------------------------------------------------------------------------------\| \| memory \| \| \| steps \| [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', <catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)] \| \| verbose \| False \| \| preprocessor \| ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])]) \| \| classifier \| <catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10> \| \| preprocessor__n_jobs \| \| \| preprocessor__remainder \| passthrough \| \| preprocessor__sparse_threshold \| 0.3 \| \| preprocessor__transformer_weights \| \| \| preprocessor__transformers \| [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])] \| \| preprocessor__verbose \| False \| \| preprocessor__verbose_feature_names_out \| True \| \| preprocessor__num \| Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) \| \| preprocessor__cat \| Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]) \| \| preprocessor__num__memory \| \| \| preprocessor__num__steps \| [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] \| \| preprocessor__num__verbose \| False \| \| preprocessor__num__imputer \| SimpleImputer(strategy='median') \| \| preprocessor__num__scaler \| StandardScaler() \| \| preprocessor__num__imputer__add_indicator \| False \| \| preprocessor__num__imputer__copy \| True \| \| preprocessor__num__imputer__fill_value \| \| \| preprocessor__num__imputer__keep_empty_features \| False \| \| preprocessor__num__imputer__missing_values \| nan \| \| preprocessor__num__imputer__strategy \| median \| \| preprocessor__num__scaler__copy \| True \| \| preprocessor__num__scaler__with_mean \| True \| \| preprocessor__num__scaler__with_std \| True \| \| preprocessor__cat__memory \| \| \| preprocessor__cat__steps \| [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] \| \| preprocessor__cat__verbose \| False \| \| preprocessor__cat__imputer \| SimpleImputer(fill_value='missing', strategy='constant') \| \| preprocessor__cat__onehot \| OneHotEncoder(handle_unknown='ignore') \| \| preprocessor__cat__imputer__add_indicator \| False \| \| preprocessor__cat__imputer__copy \| True \| \| preprocessor__cat__imputer__fill_value \| missing \| \| preprocessor__cat__imputer__keep_empty_features \| False \| \| preprocessor__cat__imputer__missing_values \| nan \| \| preprocessor__cat__imputer__strategy \| constant \| \| preprocessor__cat__onehot__categories \| auto \| \| preprocessor__cat__onehot__drop \| \| \| preprocessor__cat__onehot__dtype \| <class 'numpy.float64'> \| \| preprocessor__cat__onehot__feature_name_combiner \| concat \| \| preprocessor__cat__onehot__handle_unknown \| ignore \| \| preprocessor__cat__onehot__max_categories \| \| \| preprocessor__cat__onehot__min_frequency \| \| \| preprocessor__cat__onehot__sparse \| deprecated \| \| preprocessor__cat__onehot__sparse_output \| True \| \| classifier__learning_rate \| 0.1 \| \| classifier__silent \| True \| \| classifier__max_depth \| 4 \| \| classifier__n_estimators \| 200 \| </details> ### Model Plot <style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 /display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier',<catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier',<catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['rectal_temp', 'pulse', 'respiratory_rate','nasogastric_reflux_ph', 'packed_cell_volume','total_protein', 'abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pipeline(steps=[('imputer',SimpleI...='missing',strategy='constant')),('onehot',OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age', 'temp_of_extremities','peripheral_pulse', 'mucous_membrane','capillary_refill_time', 'pain','peristalsis', 'abdominal_distention','nasogastric_tube', 'nasogastric_reflux','rectal_exam_feces', 'abdomen','abdomo_appearance', 'surgical_lesion','cp_data'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">CatBoostClassifier</label><div class="sk-toggleable__content"><pre><catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10></pre></div></div></div></div></div></div></div> ## Evaluation Results \| Metric \| Value \| \|----------\|----------\| \| accuracy \| 0.744939 \| \| f1 score \| 0.744939 \| ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) # How to Get Started with the Model [More Information Needed] # Model Card Authors kmposkid # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. BibTeX:* ``` [More Information Needed] ```
r0ll/Scammers	r0ll	2023-09-20T13:00:42Z	0	0	null	[ "ru", "license:openrail", "region:us" ]	null	2023-09-20T12:59:37Z	--- license: openrail language: - ru ---
margenai/StateBankPakistan	margenai	2023-09-20T12:41:13Z	0	0	peft	[ "peft", "base_model:EleutherAI/pythia-410m-v0", "base_model:adapter:EleutherAI/pythia-410m-v0", "region:us" ]	null	2023-09-13T04:05:48Z	--- library_name: peft base_model: EleutherAI/pythia-410m-v0 --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0
ckmfong/q-FrozenLake-v1-4x4-noSlippery	ckmfong	2023-09-20T12:34:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T12:34:05Z	--- 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="ckmfong/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"]) ```
CyberHarem/yokoyama_chika_idolmastercinderellagirls	CyberHarem	2023-09-20T12:26:30Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/yokoyama_chika_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-20T12:09:49Z	--- license: mit datasets: - CyberHarem/yokoyama_chika_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yokoyama_chika_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/yokoyama_chika_idolmastercinderellagirls.pt` as the embedding and `4760/yokoyama_chika_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4760, with the score of 0.980. The trigger words are: 1. `yokoyama_chika_idolmastercinderellagirls` 2. `twintails, long_hair, smile, open_mouth, blush, hair_ornament, brown_hair, bangs, brown_eyes, green_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.971 \| [Download](5100/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![pattern_4-5100](5100/previews/pattern_4.png) \| ![pattern_5-5100](5100/previews/pattern_5.png) \| ![pattern_6-5100](5100/previews/pattern_6.png) \| ![pattern_7-5100](5100/previews/pattern_7.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.980 \| [Download](4760/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![pattern_4-4760](4760/previews/pattern_4.png) \| ![pattern_5-4760](4760/previews/pattern_5.png) \| ![pattern_6-4760](4760/previews/pattern_6.png) \| ![pattern_7-4760](4760/previews/pattern_7.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.969 \| [Download](4420/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![pattern_4-4420](4420/previews/pattern_4.png) \| ![pattern_5-4420](4420/previews/pattern_5.png) \| ![pattern_6-4420](4420/previews/pattern_6.png) \| ![pattern_7-4420](4420/previews/pattern_7.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.971 \| [Download](4080/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![pattern_4-4080](4080/previews/pattern_4.png) \| ![pattern_5-4080](4080/previews/pattern_5.png) \| ![pattern_6-4080](4080/previews/pattern_6.png) \| ![pattern_7-4080](4080/previews/pattern_7.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.966 \| [Download](3740/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![pattern_4-3740](3740/previews/pattern_4.png) \| ![pattern_5-3740](3740/previews/pattern_5.png) \| ![pattern_6-3740](3740/previews/pattern_6.png) \| ![pattern_7-3740](3740/previews/pattern_7.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.964 \| [Download](3400/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![pattern_4-3400](3400/previews/pattern_4.png) \| ![pattern_5-3400](3400/previews/pattern_5.png) \| ![pattern_6-3400](3400/previews/pattern_6.png) \| ![pattern_7-3400](3400/previews/pattern_7.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.947 \| [Download](3060/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![pattern_4-3060](3060/previews/pattern_4.png) \| ![pattern_5-3060](3060/previews/pattern_5.png) \| ![pattern_6-3060](3060/previews/pattern_6.png) \| ![pattern_7-3060](3060/previews/pattern_7.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.958 \| [Download](2720/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![pattern_4-2720](2720/previews/pattern_4.png) \| ![pattern_5-2720](2720/previews/pattern_5.png) \| ![pattern_6-2720](2720/previews/pattern_6.png) \| ![pattern_7-2720](2720/previews/pattern_7.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.959 \| [Download](2380/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![pattern_4-2380](2380/previews/pattern_4.png) \| ![pattern_5-2380](2380/previews/pattern_5.png) \| ![pattern_6-2380](2380/previews/pattern_6.png) \| ![pattern_7-2380](2380/previews/pattern_7.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.942 \| [Download](2040/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![pattern_4-2040](2040/previews/pattern_4.png) \| ![pattern_5-2040](2040/previews/pattern_5.png) \| ![pattern_6-2040](2040/previews/pattern_6.png) \| ![pattern_7-2040](2040/previews/pattern_7.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.967 \| [Download](1700/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![pattern_4-1700](1700/previews/pattern_4.png) \| ![pattern_5-1700](1700/previews/pattern_5.png) \| ![pattern_6-1700](1700/previews/pattern_6.png) \| ![pattern_7-1700](1700/previews/pattern_7.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.944 \| [Download](1360/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![pattern_4-1360](1360/previews/pattern_4.png) \| ![pattern_5-1360](1360/previews/pattern_5.png) \| ![pattern_6-1360](1360/previews/pattern_6.png) \| ![pattern_7-1360](1360/previews/pattern_7.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.920 \| [Download](1020/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![pattern_4-1020](1020/previews/pattern_4.png) \| ![pattern_5-1020](1020/previews/pattern_5.png) \| ![pattern_6-1020](1020/previews/pattern_6.png) \| ![pattern_7-1020](1020/previews/pattern_7.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.912 \| [Download](680/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![pattern_4-680](680/previews/pattern_4.png) \| ![pattern_5-680](680/previews/pattern_5.png) \| ![pattern_6-680](680/previews/pattern_6.png) \| ![pattern_7-680](680/previews/pattern_7.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.692 \| [Download](340/yokoyama_chika_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![pattern_4-340](340/previews/pattern_4.png) \| ![pattern_5-340](340/previews/pattern_5.png) \| ![pattern_6-340](340/previews/pattern_6.png) \| ![pattern_7-340](340/previews/pattern_7.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
ldos/text_shortening_model_v44	ldos	2023-09-20T12:17:01Z	4	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-20T07:29:11Z	--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v44 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. --> # text_shortening_model_v44 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8836 - Rouge1: 0.4921 - Rouge2: 0.2719 - Rougel: 0.4429 - Rougelsum: 0.4423 - Bert precision: 0.8746 - Bert recall: 0.8761 - Average word count: 8.7063 - Max word count: 17 - Min word count: 5 - Average token count: 16.2989 - % shortened texts with length > 12: 8.7302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Bert precision \| Bert recall \| Average word count \| Max word count \| Min word count \| Average token count \| % shortened texts with length > 12 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:--------------:\|:-----------:\|:------------------:\|:--------------:\|:--------------:\|:-------------------:\|:----------------------------------:\| \| 1.0083 \| 1.0 \| 83 \| 1.4717 \| 0.4904 \| 0.2378 \| 0.426 \| 0.4266 \| 0.8725 \| 0.8732 \| 8.5794 \| 18 \| 4 \| 15.6164 \| 6.3492 \| \| 0.5702 \| 2.0 \| 166 \| 1.4852 \| 0.4722 \| 0.2421 \| 0.414 \| 0.4143 \| 0.869 \| 0.8653 \| 7.9101 \| 14 \| 4 \| 13.6455 \| 1.5873 \| \| 0.4588 \| 3.0 \| 249 \| 1.6283 \| 0.5038 \| 0.2733 \| 0.4424 \| 0.4422 \| 0.8732 \| 0.8794 \| 9.0053 \| 16 \| 4 \| 16.8386 \| 8.9947 \| \| 0.3586 \| 4.0 \| 332 \| 1.6017 \| 0.4965 \| 0.2762 \| 0.4381 \| 0.4383 \| 0.8709 \| 0.8787 \| 9.2381 \| 18 \| 4 \| 16.3042 \| 12.1693 \| \| 0.2479 \| 5.0 \| 415 \| 1.7497 \| 0.4794 \| 0.2613 \| 0.4295 \| 0.43 \| 0.872 \| 0.8702 \| 8.3228 \| 15 \| 4 \| 15.209 \| 3.1746 \| \| 0.2296 \| 6.0 \| 498 \| 1.8482 \| 0.4935 \| 0.2739 \| 0.4442 \| 0.4443 \| 0.8737 \| 0.8755 \| 8.7963 \| 17 \| 5 \| 16.2989 \| 7.1429 \| \| 0.3065 \| 7.0 \| 581 \| 1.9485 \| 0.4765 \| 0.2552 \| 0.4213 \| 0.4212 \| 0.8698 \| 0.8693 \| 8.4683 \| 17 \| 5 \| 15.6005 \| 7.9365 \| \| 0.2598 \| 8.0 \| 664 \| 2.1608 \| 0.4871 \| 0.2585 \| 0.4316 \| 0.4319 \| 0.8707 \| 0.8736 \| 8.963 \| 16 \| 5 \| 16.6481 \| 9.5238 \| \| 0.2707 \| 9.0 \| 747 \| 2.0966 \| 0.4758 \| 0.2603 \| 0.4231 \| 0.4246 \| 0.8709 \| 0.8717 \| 8.4841 \| 16 \| 4 \| 15.9312 \| 7.1429 \| \| 0.2099 \| 10.0 \| 830 \| 2.2721 \| 0.4777 \| 0.2604 \| 0.4246 \| 0.4246 \| 0.8735 \| 0.8724 \| 8.4312 \| 15 \| 4 \| 15.9471 \| 5.5556 \| \| 0.1668 \| 11.0 \| 913 \| 2.3536 \| 0.4758 \| 0.2541 \| 0.4331 \| 0.4328 \| 0.8721 \| 0.87 \| 8.2857 \| 14 \| 4 \| 15.7725 \| 3.1746 \| \| 0.1552 \| 12.0 \| 996 \| 2.4572 \| 0.484 \| 0.2562 \| 0.4313 \| 0.4304 \| 0.8726 \| 0.875 \| 8.828 \| 17 \| 4 \| 16.246 \| 7.9365 \| \| 0.2141 \| 13.0 \| 1079 \| 2.4485 \| 0.4785 \| 0.2631 \| 0.4257 \| 0.4252 \| 0.8678 \| 0.8736 \| 9.1402 \| 19 \| 4 \| 16.6561 \| 11.3757 \| \| 0.1348 \| 14.0 \| 1162 \| 2.5012 \| 0.4821 \| 0.2613 \| 0.4292 \| 0.4296 \| 0.8706 \| 0.8738 \| 8.8783 \| 17 \| 4 \| 16.5185 \| 10.0529 \| \| 0.074 \| 15.0 \| 1245 \| 2.5309 \| 0.4915 \| 0.2745 \| 0.445 \| 0.444 \| 0.8764 \| 0.8768 \| 8.6667 \| 16 \| 4 \| 16.2513 \| 9.2593 \| \| 0.1822 \| 16.0 \| 1328 \| 2.5735 \| 0.4709 \| 0.2566 \| 0.4239 \| 0.4232 \| 0.872 \| 0.8692 \| 8.2063 \| 15 \| 3 \| 15.7249 \| 4.2328 \| \| 0.086 \| 17.0 \| 1411 \| 2.8597 \| 0.4831 \| 0.2675 \| 0.4373 \| 0.4372 \| 0.8722 \| 0.8743 \| 8.754 \| 16 \| 5 \| 16.5476 \| 8.7302 \| \| 0.0872 \| 18.0 \| 1494 \| 2.7420 \| 0.4831 \| 0.2677 \| 0.4367 \| 0.4353 \| 0.8724 \| 0.873 \| 8.664 \| 17 \| 5 \| 16.3016 \| 7.672 \| \| 0.1164 \| 19.0 \| 1577 \| 2.8790 \| 0.4867 \| 0.269 \| 0.4388 \| 0.4381 \| 0.8737 \| 0.8755 \| 8.7725 \| 17 \| 5 \| 16.4418 \| 8.9947 \| \| 0.1101 \| 20.0 \| 1660 \| 2.8836 \| 0.4921 \| 0.2719 \| 0.4429 \| 0.4423 \| 0.8746 \| 0.8761 \| 8.7063 \| 17 \| 5 \| 16.2989 \| 8.7302 \| ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
AbdelKarim95/ppo-SnowballTarget	AbdelKarim95	2023-09-20T12:12:23Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-09-20T12:12:19Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AbdelKarim95/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
msy127/opt-350m-aihubqa-130-dpo-adapter	msy127	2023-09-20T11:58:17Z	3	0	peft	[ "peft", "region:us" ]	null	2023-09-20T10:26:03Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0
alexandrainst/da-ner-base	alexandrainst	2023-09-20T11:56:44Z	124	0	transformers	[ "transformers", "pytorch", "tf", "bert", "token-classification", "da", "dataset:dane", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	--- language: - da license: apache-2.0 datasets: - dane widget: - text: Jens Peter Hansen kommer fra Danmark --- # BERT fine-tuned for Named Entity Recognition in Danish The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC]. The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ner.html#bert) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForTokenClassification model = BertForTokenClassification.from_pretrained("alexandrainst/da-ner-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-ner-base") ``` ## Training Data The model has been trained on the [DaNE](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane).
alexandrainst/da-binary-emotion-classification-base	alexandrainst	2023-09-20T11:56:16Z	124	1	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "text-classification", "da", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - da license: apache-2.0 widget: - text: Der er et træ i haven. --- # Danish BERT for emotion detection The BERT Emotion model detects whether a Danish text is emotional or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-binary-emotion-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-binary-emotion-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.
alexandrainst/da-hatespeech-detection-base	alexandrainst	2023-09-20T11:56:09Z	122	1	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "text-classification", "da", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - da license: apache-2.0 widget: - text: Senile gamle idiot --- # Danish BERT for hate speech (offensive language) detection The BERT HateSpeech model detects whether a Danish text is offensive or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.
alexandrainst/da-hatespeech-classification-base	alexandrainst	2023-09-20T11:56:03Z	114	0	transformers	[ "transformers", "pytorch", "tf", "safetensors", "bert", "text-classification", "da", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: - da license: apache-2.0 widget: - text: Senile gamle idiot --- # Danish BERT for hate speech classification The BERT HateSpeech model classifies offensive Danish text into 4 categories: * `Særlig opmærksomhed` (special attention, e.g. threat) * `Personangreb` (personal attack) * `Sprogbrug` (offensive language) * `Spam & indhold` (spam) This model is intended to be used after the [BERT HateSpeech detection model](https://huggingface.co/alexandrainst/da-hatespeech-detection-base). It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.
alexandrainst/da-discourse-coherence-base	alexandrainst	2023-09-20T11:50:37Z	114	1	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "da", "dataset:ajders/ddisco", "base_model:NbAiLab/nb-bert-base", "base_model:finetune:NbAiLab/nb-bert-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-02-08T18:42:51Z	--- language: - da license: apache-2.0 tags: - generated_from_trainer datasets: - ajders/ddisco metrics: - accuracy base_model: NbAiLab/nb-bert-base model-index: - name: ddisco_classifier results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # da-discourse-coherence-base This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the [DDisco](https://huggingface.co/datasets/ajders/ddisco) dataset. It achieves the following results on the evaluation set: - Loss: 0.7487 - Accuracy: 0.6915 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 703 - gradient_accumulation_steps: 16 - 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: 0.05 - num_epochs: 6.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3422 \| 0.4 \| 5 \| 1.0166 \| 0.5721 \| \| 0.9645 \| 0.8 \| 10 \| 0.8966 \| 0.5721 \| \| 0.9854 \| 1.24 \| 15 \| 0.8499 \| 0.5721 \| \| 0.8628 \| 1.64 \| 20 \| 0.8379 \| 0.6517 \| \| 0.9046 \| 2.08 \| 25 \| 0.8228 \| 0.5721 \| \| 0.8361 \| 2.48 \| 30 \| 0.7980 \| 0.5821 \| \| 0.8158 \| 2.88 \| 35 \| 0.8095 \| 0.5821 \| \| 0.8689 \| 3.32 \| 40 \| 0.7989 \| 0.6169 \| \| 0.8125 \| 3.72 \| 45 \| 0.7730 \| 0.6965 \| \| 0.843 \| 4.16 \| 50 \| 0.7566 \| 0.6418 \| \| 0.7421 \| 4.56 \| 55 \| 0.7840 \| 0.6517 \| \| 0.7949 \| 4.96 \| 60 \| 0.7531 \| 0.6915 \| \| 0.828 \| 5.4 \| 65 \| 0.7464 \| 0.6816 \| \| 0.7438 \| 5.8 \| 70 \| 0.7487 \| 0.6915 \| ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.0a0+d0d6b1f - Datasets 2.9.0 - Tokenizers 0.13.2 ### Contributor [ajders](https://github.com/AJDERS)
alexandrainst/da-offensive-detection-base	alexandrainst	2023-09-20T11:50:26Z	105	3	transformers	[ "transformers", "pytorch", "safetensors", "xlm-roberta", "text-classification", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-08-12T10:04:35Z	--- license: apache-2.0 widget: - text: Din store idiot base_model: xlm-roberta-base --- # Danish Offensive Text Detection based on XLM-Roberta-Base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset consisting of approximately 5 million Facebook comments on [DR](https://dr.dk/)'s public Facebook pages. The labels have been automatically generated using weak supervision, based on the [Snorkel](https://www.snorkel.org/) framework. The model achieves SOTA on a test set consisting of 600 Facebook comments annotated using majority vote by three annotators, of which 35.8% were labelled as offensive: \| Model \| Precision \| Recall \| F1-score \| F2-score \| \| :-------- \| :------------ \| :--------- \| :----------- \| :----------- \| \| `alexandrainst/da-offensive-detection-base` (this) \| 74.81% \| 89.77% \| 81.61% \| 86.32% \| \| [`alexandrainst/da-offensive-detection-small`](https://huggingface.co/alexandrainst/da-offensive-detection-small) \| 74.13% \| 89.30% \| 81.01% \| 85.79% \| \| [`A&ttack`](https://github.com/ogtal/A-ttack) \| 97.32% \| 50.70% \| 66.67% \| 56.07% \| \| [`alexandrainst/da-hatespeech-detection-small`](https://huggingface.co/alexandrainst/da-hatespeech-detection-small) \| 86.43% \| 56.28% \| 68.17% \| 60.50% \| \| [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) \| 75.41% \| 42.79% \| 54.60% \| 46.84% \| ## Using the model You can use the model simply by running the following: ```python >>> from transformers import pipeline >>> offensive_text_pipeline = pipeline(model="alexandrainst/da-offensive-detection-base") >>> offensive_text_pipeline("Din store idiot") [{'label': 'Offensive', 'score': 0.9997463822364807}] ``` Processing multiple documents at the same time can be done as follows: ```python >>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"]) [{'label': 'Offensive', 'score': 0.9997463822364807}, {'label': 'Not offensive', 'score': 0.9996451139450073}] ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - gradient_accumulation_steps: 1 - total_train_batch_size: 32 - seed: 4242 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - max_steps: 500000 - fp16: True - eval_steps: 1000 - early_stopping_patience: 100 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
honzatoegel/falcon-rw-1b-gramma-corrections-de-en	honzatoegel	2023-09-20T11:42:24Z	5	1	peft	[ "peft", "dataset:honzatoegel/lola-gramma-de-en", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]	null	2023-08-30T04:26:51Z	--- library_name: peft datasets: - honzatoegel/lola-gramma-de-en base_model: tiiuae/falcon-rw-1b --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing major errors - the explanations of corrections are wrong The low quality is probably caused by very low volume of training data, and small LLM size. Interesting observation: The LLM react 100% on the learned example 1 (exactly from training data) ### Example 1 #### Input ''### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Mir es geht gut, danke#### AI:Correct version of the sentence: #### Output "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End of repair list for this sentence.#### End of topic, repair list and the sentence ### Example 2 #### Input ''### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er macht dort dorte, aber ich muss einmal dorte. Repairs: - Instead of "Er gehen dort" it should be "Er macht dort" - the correct verb form is "macht" - Instead of "ich muss" it should be "ich muss einmal" - the verb "einmal" is used in the future in German. - The verb "dorte" must be conjugated in the third person singular "dorte".#### End of Repairs: - Instead of "Er gehen dort" it should be "Er macht dort" - the correct verb form is "macht". ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
honzatoegel/Llama-2-70b-chat-hf-gramma-corrections-de-en-2-overfitt	honzatoegel	2023-09-20T11:42:12Z	2	0	peft	[ "peft", "de", "en", "dataset:honzatoegel/lola-gramma-de-en", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:adapter:meta-llama/Llama-2-70b-chat-hf", "region:us" ]	null	2023-08-30T13:40:43Z	--- language: - de - en library_name: peft datasets: - honzatoegel/lola-gramma-de-en base_model: meta-llama/Llama-2-70b-chat-hf --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing some minor errors - the explanations of corrections are far from optimal, they often refer to some additional words as mented in explanations which are not necessary - the output repeats itself - the output list is sometimes broken into two lists The low quality is probably caused by very low volume of training data - 100 rows, low amount of combinations which can be then hardly generalized. ### Example 1 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er geht dort, aber ich muss anders gemacht. Repairs: - Instead of "gehen" it should be "goes" - the verb "gehen" is in the third person singular. - Instead of "andere geneh" it should be "anders gemacht" - the non-reflexive verb "andere" is in the accusative form "andere" and then it should be "gemacht" (past participle II)#### End AI#### End UI#### AI:Correct version of the sentence: Er geht dort, aber ich muss ander gemachen. Repairs: - Instead of "gehen" it should be "goes" - the ### Example 2 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Mir es geht gut, danke#### AI:Correct version of the sentence: #### Output "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End AI:》 Mir es geht gut, danke Correct version of the sentence: "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End Mir es geht gut, danke Correct version of the sentence ### Example 3 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Habe ich das wirklich gemachen?#### AI:Correct version of the sentence: #### Output Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - the verb "gemachen" must be conjugated in the past tense, third person singular.#### End AI#### End User:Correct version of the sentence: Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - the verb "gemachen" must be conjugated in the past tense, third person singular.#### End AI####End User:Correct version of the sentence: Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
honzatoegel/Llama-2-70b-chat-hf-gramma-corrections-de-en	honzatoegel	2023-09-20T11:41:45Z	1	0	peft	[ "peft", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:adapter:meta-llama/Llama-2-70b-chat-hf", "region:us" ]	null	2023-08-27T13:40:56Z	--- library_name: peft base_model: meta-llama/Llama-2-70b-chat-hf --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing some minor errors - the explanations of corrections are far from optimal, they often refer to some additional words as mented in explanations which are not necessary - the output repeats itself - the output list is sometimes broken into two lists The low quality is probably caused by very low volume of training data - 100 rows, low amount of combinations which can be then hardly generalized. ### Example 1 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Ich mussen sehr rückwärts fahren.#### AI:Correct version of the sentence: #### Output Ich muss sehr zurückfahren. Repairs: - Instead of "rückwärts" it should be "zurück" - this is the correct form of the verb "to drive back".#### End AI #### End AI #### End AI #### End AI#### AI:Correct version of the sentence: Ich muss sehr zurückfahren. Repairs: - Instead of "mussen" it should be "muss" - this is the correct form of the verb "to must" in the third person singular.#### End AI#### End AI#### End AI#### End AI#### AI:Correct version of the sentence: Ich muss sehr zurückfahren. Repairs: ### Example 2 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er geht dort, aber ich muss anders gehen. Repairs: - Instead of "gehen" it should be "geht" - it is the third person singular of the verb "gehen". - Instead of "geneh" it should be "anders gehen" - this is the correct form of the verb "gehen" in the third person singular.#### End AI %} Repairs: - Instead of "gehen" it should be "geht" - it is the third person singular of the verb "gehen". - Instead of "geneh" it should be "anders gehen" - this is the correct form of the verb "gehen" in ### Example 3 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Habe ich das wirklich gemachen?#### AI:Correct version of the sentence: #### Output Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - this is the third person singular of the verb "machen" in the past tense.#### End AI %} Repairs: - Instead of "Habe ich das wirklich gemachen?" it should be "Habe ich das wirklich gemacht?" - This is the correct sentence with all the required information.#### End AI %} Repairs: - The verb "gemachen" is in the wrong form, it should be "gemacht" - this is the third person singular of the verb "machen" in the past tense.#### End AI %} Repairs: ## Training procedure Trained on: 1x RTX A6000 Used: 30GB Ram, 47GB GPU, 130GB Disc 8 Epochs, cca 25 minutes, Loss: 0.36 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
hosnasn/Reza_DB	hosnasn	2023-09-20T11:33:50Z	3	0	diffusers	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	text-to-image	2023-09-20T11:33:46Z	--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks Reza tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.
sianbru/product_classifier_split_url_nodigit_all	sianbru	2023-09-20T11:31:59Z	108	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-dbmdz-uncased", "base_model:finetune:google-bert/bert-base-german-dbmdz-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-19T21:30:51Z	--- license: mit base_model: bert-base-german-dbmdz-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: product_classifier_split_url_nodigit_all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # product_classifier_split_url_nodigit_all This model is a fine-tuned version of [bert-base-german-dbmdz-uncased](https://huggingface.co/bert-base-german-dbmdz-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1833 - Accuracy: 0.9734 - F1: 0.9732 - Precision: 0.9731 - Recall: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 0.0385 \| 1.0 \| 1300 \| 0.1880 \| 0.9666 \| 0.9663 \| 0.9665 \| 0.9666 \| \| 0.0198 \| 2.0 \| 2600 \| 0.1707 \| 0.9718 \| 0.9718 \| 0.9719 \| 0.9718 \| \| 0.0083 \| 3.0 \| 3900 \| 0.1833 \| 0.9734 \| 0.9732 \| 0.9731 \| 0.9734 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3
bavolesy/ppo-Huggy	bavolesy	2023-09-20T11:29:34Z	2	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-09-20T11:29:23Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: bavolesy/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
mychen76/donut-receipt_v2	mychen76	2023-09-20T11:19:38Z	46	0	transformers	[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-09-20T09:51:58Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-receipt_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # donut-receipt_v2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3
OpenDILabCommunity/HalfCheetah-v3-TD3	OpenDILabCommunity	2023-09-20T11:17:26Z	0	0	pytorch	[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "HalfCheetah-v3", "en", "license:apache-2.0", "region:us" ]	reinforcement-learning	2023-04-15T21:02:47Z	--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - HalfCheetah-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: HalfCheetah-v3 pipeline_tag: reinforcement-learning model-index: - name: TD3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-HalfCheetah-v3 type: OpenAI/Gym/MuJoCo-HalfCheetah-v3 metrics: - type: mean_reward value: 11100.89 +/- 77.51 name: mean_reward --- # Play HalfCheetah-v3 with TD3 Policy ## Model Description <!-- Provide a longer summary of what this model is. --> This is a simple TD3 implementation to OpenAI/Gym/MuJoCo HalfCheetah-v3 using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). DI-engine is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import SACAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = SACAgent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` run.py ```python from ding.bonus import TD3Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/HalfCheetah-v3-TD3") # Instantiate the agent agent = TD3Agent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` train.py ```python from ding.bonus import TD3Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = TD3Agent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3") # Train the agent return_ = agent.train(step=int(5000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="HalfCheetah-v3", algo_name="TD3", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./td3/halfcheetah_td3_deploy.py", usage_file_by_huggingface_ding="./td3/halfcheetah_td3_download.py", train_file="./td3/halfcheetah_td3.py", repo_id="OpenDILabCommunity/HalfCheetah-v3-TD3", create_repo=False ) ``` </details> Configuration <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 11000, 'n_evaluator_episode': 8, 'env_id': 'HalfCheetah-v3', 'collector_env_num': 1, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'twin_critic': True, 'obs_shape': 17, 'action_shape': 6, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256, 'action_space': 'regression' }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_actor': 0.001, 'learning_rate_critic': 0.001, 'ignore_done': True, 'target_theta': 0.005, 'discount_factor': 0.99, 'actor_update_freq': 2, 'noise': True, 'noise_sigma': 0.2, 'noise_range': { 'min': -0.5, 'max': 0.5 } }, 'collect': { 'collector': {}, 'unroll_len': 1, 'noise_sigma': 0.1, 'n_sample': 1 }, 'eval': { 'evaluator': { 'eval_freq': 5000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 11000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'td3', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 25000, 'transition_with_policy_data': False, 'action_space': 'continuous', 'reward_batch_norm': False, 'multi_agent': False, 'cfg_type': 'TD3PolicyDict' }, 'exp_name': 'HalfCheetah-v3-TD3', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Weights & Biases (wandb): [monitor link](https://wandb.ai/zjowowen/HalfCheetah-v3-TD3) ## Model Information <!-- Provide the basic links for the model. --> - Github Repository: [repo link](https://github.com/opendilab/DI-engine) - Doc: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html) - Configuration: [config link](https://huggingface.co/OpenDILabCommunity/HalfCheetah-v3-TD3/blob/main/policy_config.py) - Demo: [video](https://huggingface.co/OpenDILabCommunity/HalfCheetah-v3-TD3/blob/main/replay.mp4) <!-- Provide the size information for the model. --> - Parameters total size: 1690.06 KB - Last Update Date: 2023-09-20 ## Environments <!-- Address questions around what environment the model is intended to be trained and deployed at, including the necessary information needed to be provided for future users. --> - Benchmark: OpenAI/Gym/MuJoCo - Task: HalfCheetah-v3 - Gym version: 0.25.1 - DI-engine version: v0.4.9 - PyTorch version: 2.0.1+cu117 - Doc: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)
bangtai/path-to-save-model	bangtai	2023-09-20T11:16:04Z	30	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "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	2023-09-19T03:17:31Z	--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - bangtai/path-to-save-model This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
tianzhidao/marian-finetuned-kde4-en-to-fr	tianzhidao	2023-09-20T11:14:44Z	59	0	transformers	[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-20T08:05:48Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_keras_callback model-index: - name: tianzhidao/marian-finetuned-kde4-en-to-fr 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. --> # tianzhidao/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6857 - Validation Loss: 0.8029 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 1.0612 \| 0.8783 \| 0 \| \| 0.7974 \| 0.8213 \| 1 \| \| 0.6857 \| 0.8029 \| 2 \| ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3
amitonHFace/ppo-LunarLander-v2	amitonHFace	2023-09-20T11:13:33Z	7	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T09:59:28Z	--- 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: 215.91 +/- 20.01 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 ... ```
dim/ruGPT-13b_dolly_oasst1_chip2	dim	2023-09-20T11:03:21Z	3	1	peft	[ "peft", "region:us" ]	null	2023-09-20T11:00:43Z	--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/ruGPT-13b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=2, bot_token_id=46787, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) # Гавайские острова - это рай для любителей природы, культуры и приключений. Это место, где можно исследовать тропические леса, плавать с дельфинами, кататься на волнах прибоя или просто наслаждаться солнцем и пляжем. В дополнение к этому, здесь есть множество достопримечательностей, которые стоит посетить, таких как вулканы Мауна-Лоа и Килауэа, водопады Ваймеа, пещеры Халеакала и многое другое. Если вы ищете что-то более расслабляющее, то посещение гавайских ресторанов и баров может быть отличным способом провести время. ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
amiiin/methods100	amiiin	2023-09-20T10:56:44Z	0	0	null	[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]	null	2023-09-20T10:40:45Z	--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: methods100 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. --> # methods100 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3
tvganesh/test_trainer	tvganesh	2023-09-20T10:52:48Z	103	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:tvganesh/test_trainer", "base_model:finetune:tvganesh/test_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-14T07:05:08Z	--- license: apache-2.0 base_model: tvganesh/test_trainer tags: - generated_from_trainer metrics: - rouge model-index: - name: test_trainer 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. --> # test_trainer This model is a fine-tuned version of [tvganesh/test_trainer](https://huggingface.co/tvganesh/test_trainer) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge1: 0.8325 - Rouge2: 0.8187 - Rougel: 0.8294 - Rougelsum: 0.8312 - Gen Len: 18.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0056 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 0.2345 \| 0.7001 \| 0.6536 \| 0.6998 \| 0.6957 \| 16.3 \| \| No log \| 2.0 \| 10 \| 0.1472 \| 0.7958 \| 0.7695 \| 0.7929 \| 0.7965 \| 18.3 \| \| No log \| 3.0 \| 15 \| 0.1174 \| 0.7196 \| 0.6705 \| 0.7187 \| 0.7118 \| 16.3 \| \| No log \| 4.0 \| 20 \| 0.0554 \| 0.7977 \| 0.774 \| 0.7907 \| 0.7958 \| 18.6 \| \| No log \| 5.0 \| 25 \| 0.0725 \| 0.8205 \| 0.8074 \| 0.8188 \| 0.8212 \| 18.6 \| \| No log \| 6.0 \| 30 \| 0.0281 \| 0.8114 \| 0.7929 \| 0.8098 \| 0.8123 \| 18.6 \| \| No log \| 7.0 \| 35 \| 0.0451 \| 0.7959 \| 0.7678 \| 0.7908 \| 0.7945 \| 18.6 \| \| No log \| 8.0 \| 40 \| 0.0438 \| 0.8285 \| 0.8061 \| 0.8205 \| 0.8227 \| 18.5 \| \| No log \| 9.0 \| 45 \| 0.0178 \| 0.8249 \| 0.8109 \| 0.8225 \| 0.8243 \| 18.6 \| \| No log \| 10.0 \| 50 \| 0.0072 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 11.0 \| 55 \| 0.0119 \| 0.8336 \| 0.8217 \| 0.8315 \| 0.833 \| 18.6 \| \| No log \| 12.0 \| 60 \| 0.0104 \| 0.8336 \| 0.8217 \| 0.8315 \| 0.833 \| 18.6 \| \| No log \| 13.0 \| 65 \| 0.0031 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 14.0 \| 70 \| 0.0099 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 15.0 \| 75 \| 0.0067 \| 0.8284 \| 0.8053 \| 0.8213 \| 0.8226 \| 18.6 \| \| No log \| 16.0 \| 80 \| 0.0019 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 17.0 \| 85 \| 0.0173 \| 0.8143 \| 0.798 \| 0.8111 \| 0.8102 \| 18.2 \| \| No log \| 18.0 \| 90 \| 0.0007 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 19.0 \| 95 \| 0.0004 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 20.0 \| 100 \| 0.0195 \| 0.8325 \| 0.813 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 21.0 \| 105 \| 0.0057 \| 0.8325 \| 0.813 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 22.0 \| 110 \| 0.0005 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 23.0 \| 115 \| 0.0010 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 24.0 \| 120 \| 0.0003 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 25.0 \| 125 \| 0.0004 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 26.0 \| 130 \| 0.0005 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 27.0 \| 135 \| 0.0002 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 28.0 \| 140 \| 0.0001 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 29.0 \| 145 \| 0.0010 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 30.0 \| 150 \| 0.0003 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 31.0 \| 155 \| 0.0001 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 32.0 \| 160 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 33.0 \| 165 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 34.0 \| 170 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 35.0 \| 175 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 36.0 \| 180 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 37.0 \| 185 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 38.0 \| 190 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 39.0 \| 195 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| \| No log \| 40.0 \| 200 \| 0.0000 \| 0.8325 \| 0.8187 \| 0.8294 \| 0.8312 \| 18.6 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3
DataBindu/swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged	DataBindu	2023-09-20T10:43:19Z	149	0	transformers	[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "base_model:finetune:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-19T19:58:12Z	--- license: apache-2.0 base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7268518518518519 --- <!-- 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. --> # swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8626 - Accuracy: 0.7269 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.8355 \| 0.98 \| 15 \| 2.5831 \| 0.3333 \| \| 1.9292 \| 1.97 \| 30 \| 1.6850 \| 0.5046 \| \| 1.4121 \| 2.95 \| 45 \| 1.2324 \| 0.5972 \| \| 1.0121 \| 4.0 \| 61 \| 1.0345 \| 0.6852 \| \| 0.854 \| 4.98 \| 76 \| 0.9663 \| 0.6806 \| \| 0.701 \| 5.97 \| 91 \| 0.9587 \| 0.6991 \| \| 0.5956 \| 6.95 \| 106 \| 0.8626 \| 0.7269 \| \| 0.5713 \| 7.87 \| 120 \| 0.8645 \| 0.7222 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3
CyberHarem/pekora_jashinchandropkick	CyberHarem	2023-09-20T10:41:30Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/pekora_jashinchandropkick", "license:mit", "region:us" ]	text-to-image	2023-09-20T10:23:18Z	--- license: mit datasets: - CyberHarem/pekora_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of pekora_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8960, you need to download `8960/pekora_jashinchandropkick.pt` as the embedding and `8960/pekora_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 8960, with the score of 0.891. The trigger words are: 1. `pekora_jashinchandropkick` 2. `short_hair, hairband, blonde_hair, black_hairband, capelet, shaded_face, shiny_hair, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| pattern_11 \| pattern_12 \| pattern_13 \| pattern_14 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:---------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 9600 \| 0.883 \| [Download](9600/pekora_jashinchandropkick.zip) \| ![pattern_1-9600](9600/previews/pattern_1.png) \| ![pattern_2-9600](9600/previews/pattern_2.png) \| ![pattern_3-9600](9600/previews/pattern_3.png) \| ![pattern_4-9600](9600/previews/pattern_4.png) \| ![pattern_5-9600](9600/previews/pattern_5.png) \| ![pattern_6-9600](9600/previews/pattern_6.png) \| ![pattern_7-9600](9600/previews/pattern_7.png) \| ![pattern_8-9600](9600/previews/pattern_8.png) \| ![pattern_9-9600](9600/previews/pattern_9.png) \| ![pattern_10-9600](9600/previews/pattern_10.png) \| ![pattern_11-9600](9600/previews/pattern_11.png) \| ![pattern_12-9600](9600/previews/pattern_12.png) \| ![pattern_13-9600](9600/previews/pattern_13.png) \| ![pattern_14-9600](9600/previews/pattern_14.png) \| ![bikini-9600](9600/previews/bikini.png) \| [<NSFW, click to see>](9600/previews/bondage.png) \| ![free-9600](9600/previews/free.png) \| ![maid-9600](9600/previews/maid.png) \| ![miko-9600](9600/previews/miko.png) \| [<NSFW, click to see>](9600/previews/nude.png) \| [<NSFW, click to see>](9600/previews/nude2.png) \| ![suit-9600](9600/previews/suit.png) \| ![yukata-9600](9600/previews/yukata.png) \| \| 8960 \| 0.891 \| [Download](8960/pekora_jashinchandropkick.zip) \| ![pattern_1-8960](8960/previews/pattern_1.png) \| ![pattern_2-8960](8960/previews/pattern_2.png) \| ![pattern_3-8960](8960/previews/pattern_3.png) \| ![pattern_4-8960](8960/previews/pattern_4.png) \| ![pattern_5-8960](8960/previews/pattern_5.png) \| ![pattern_6-8960](8960/previews/pattern_6.png) \| ![pattern_7-8960](8960/previews/pattern_7.png) \| ![pattern_8-8960](8960/previews/pattern_8.png) \| ![pattern_9-8960](8960/previews/pattern_9.png) \| ![pattern_10-8960](8960/previews/pattern_10.png) \| ![pattern_11-8960](8960/previews/pattern_11.png) \| ![pattern_12-8960](8960/previews/pattern_12.png) \| ![pattern_13-8960](8960/previews/pattern_13.png) \| ![pattern_14-8960](8960/previews/pattern_14.png) \| ![bikini-8960](8960/previews/bikini.png) \| [<NSFW, click to see>](8960/previews/bondage.png) \| ![free-8960](8960/previews/free.png) \| ![maid-8960](8960/previews/maid.png) \| ![miko-8960](8960/previews/miko.png) \| [<NSFW, click to see>](8960/previews/nude.png) \| [<NSFW, click to see>](8960/previews/nude2.png) \| ![suit-8960](8960/previews/suit.png) \| ![yukata-8960](8960/previews/yukata.png) \| \| 8320 \| 0.861 \| [Download](8320/pekora_jashinchandropkick.zip) \| ![pattern_1-8320](8320/previews/pattern_1.png) \| ![pattern_2-8320](8320/previews/pattern_2.png) \| ![pattern_3-8320](8320/previews/pattern_3.png) \| ![pattern_4-8320](8320/previews/pattern_4.png) \| ![pattern_5-8320](8320/previews/pattern_5.png) \| ![pattern_6-8320](8320/previews/pattern_6.png) \| ![pattern_7-8320](8320/previews/pattern_7.png) \| ![pattern_8-8320](8320/previews/pattern_8.png) \| ![pattern_9-8320](8320/previews/pattern_9.png) \| ![pattern_10-8320](8320/previews/pattern_10.png) \| ![pattern_11-8320](8320/previews/pattern_11.png) \| ![pattern_12-8320](8320/previews/pattern_12.png) \| ![pattern_13-8320](8320/previews/pattern_13.png) \| ![pattern_14-8320](8320/previews/pattern_14.png) \| ![bikini-8320](8320/previews/bikini.png) \| [<NSFW, click to see>](8320/previews/bondage.png) \| ![free-8320](8320/previews/free.png) \| ![maid-8320](8320/previews/maid.png) \| ![miko-8320](8320/previews/miko.png) \| [<NSFW, click to see>](8320/previews/nude.png) \| [<NSFW, click to see>](8320/previews/nude2.png) \| ![suit-8320](8320/previews/suit.png) \| ![yukata-8320](8320/previews/yukata.png) \| \| 7680 \| 0.877 \| [Download](7680/pekora_jashinchandropkick.zip) \| ![pattern_1-7680](7680/previews/pattern_1.png) \| ![pattern_2-7680](7680/previews/pattern_2.png) \| ![pattern_3-7680](7680/previews/pattern_3.png) \| ![pattern_4-7680](7680/previews/pattern_4.png) \| ![pattern_5-7680](7680/previews/pattern_5.png) \| ![pattern_6-7680](7680/previews/pattern_6.png) \| ![pattern_7-7680](7680/previews/pattern_7.png) \| ![pattern_8-7680](7680/previews/pattern_8.png) \| ![pattern_9-7680](7680/previews/pattern_9.png) \| ![pattern_10-7680](7680/previews/pattern_10.png) \| ![pattern_11-7680](7680/previews/pattern_11.png) \| ![pattern_12-7680](7680/previews/pattern_12.png) \| ![pattern_13-7680](7680/previews/pattern_13.png) \| ![pattern_14-7680](7680/previews/pattern_14.png) \| ![bikini-7680](7680/previews/bikini.png) \| [<NSFW, click to see>](7680/previews/bondage.png) \| ![free-7680](7680/previews/free.png) \| ![maid-7680](7680/previews/maid.png) \| ![miko-7680](7680/previews/miko.png) \| [<NSFW, click to see>](7680/previews/nude.png) \| [<NSFW, click to see>](7680/previews/nude2.png) \| ![suit-7680](7680/previews/suit.png) \| ![yukata-7680](7680/previews/yukata.png) \| \| 7040 \| 0.875 \| [Download](7040/pekora_jashinchandropkick.zip) \| ![pattern_1-7040](7040/previews/pattern_1.png) \| ![pattern_2-7040](7040/previews/pattern_2.png) \| ![pattern_3-7040](7040/previews/pattern_3.png) \| ![pattern_4-7040](7040/previews/pattern_4.png) \| ![pattern_5-7040](7040/previews/pattern_5.png) \| ![pattern_6-7040](7040/previews/pattern_6.png) \| ![pattern_7-7040](7040/previews/pattern_7.png) \| ![pattern_8-7040](7040/previews/pattern_8.png) \| ![pattern_9-7040](7040/previews/pattern_9.png) \| ![pattern_10-7040](7040/previews/pattern_10.png) \| ![pattern_11-7040](7040/previews/pattern_11.png) \| ![pattern_12-7040](7040/previews/pattern_12.png) \| ![pattern_13-7040](7040/previews/pattern_13.png) \| ![pattern_14-7040](7040/previews/pattern_14.png) \| ![bikini-7040](7040/previews/bikini.png) \| [<NSFW, click to see>](7040/previews/bondage.png) \| ![free-7040](7040/previews/free.png) \| ![maid-7040](7040/previews/maid.png) \| ![miko-7040](7040/previews/miko.png) \| [<NSFW, click to see>](7040/previews/nude.png) \| [<NSFW, click to see>](7040/previews/nude2.png) \| ![suit-7040](7040/previews/suit.png) \| ![yukata-7040](7040/previews/yukata.png) \| \| 6400 \| 0.865 \| [Download](6400/pekora_jashinchandropkick.zip) \| ![pattern_1-6400](6400/previews/pattern_1.png) \| ![pattern_2-6400](6400/previews/pattern_2.png) \| ![pattern_3-6400](6400/previews/pattern_3.png) \| ![pattern_4-6400](6400/previews/pattern_4.png) \| ![pattern_5-6400](6400/previews/pattern_5.png) \| ![pattern_6-6400](6400/previews/pattern_6.png) \| ![pattern_7-6400](6400/previews/pattern_7.png) \| ![pattern_8-6400](6400/previews/pattern_8.png) \| ![pattern_9-6400](6400/previews/pattern_9.png) \| ![pattern_10-6400](6400/previews/pattern_10.png) \| ![pattern_11-6400](6400/previews/pattern_11.png) \| ![pattern_12-6400](6400/previews/pattern_12.png) \| ![pattern_13-6400](6400/previews/pattern_13.png) \| ![pattern_14-6400](6400/previews/pattern_14.png) \| ![bikini-6400](6400/previews/bikini.png) \| [<NSFW, click to see>](6400/previews/bondage.png) \| ![free-6400](6400/previews/free.png) \| ![maid-6400](6400/previews/maid.png) \| ![miko-6400](6400/previews/miko.png) \| [<NSFW, click to see>](6400/previews/nude.png) \| [<NSFW, click to see>](6400/previews/nude2.png) \| ![suit-6400](6400/previews/suit.png) \| ![yukata-6400](6400/previews/yukata.png) \| \| 5760 \| 0.841 \| [Download](5760/pekora_jashinchandropkick.zip) \| ![pattern_1-5760](5760/previews/pattern_1.png) \| ![pattern_2-5760](5760/previews/pattern_2.png) \| ![pattern_3-5760](5760/previews/pattern_3.png) \| ![pattern_4-5760](5760/previews/pattern_4.png) \| ![pattern_5-5760](5760/previews/pattern_5.png) \| ![pattern_6-5760](5760/previews/pattern_6.png) \| ![pattern_7-5760](5760/previews/pattern_7.png) \| ![pattern_8-5760](5760/previews/pattern_8.png) \| ![pattern_9-5760](5760/previews/pattern_9.png) \| ![pattern_10-5760](5760/previews/pattern_10.png) \| ![pattern_11-5760](5760/previews/pattern_11.png) \| ![pattern_12-5760](5760/previews/pattern_12.png) \| ![pattern_13-5760](5760/previews/pattern_13.png) \| ![pattern_14-5760](5760/previews/pattern_14.png) \| ![bikini-5760](5760/previews/bikini.png) \| [<NSFW, click to see>](5760/previews/bondage.png) \| ![free-5760](5760/previews/free.png) \| ![maid-5760](5760/previews/maid.png) \| ![miko-5760](5760/previews/miko.png) \| [<NSFW, click to see>](5760/previews/nude.png) \| [<NSFW, click to see>](5760/previews/nude2.png) \| ![suit-5760](5760/previews/suit.png) \| ![yukata-5760](5760/previews/yukata.png) \| \| 5120 \| 0.773 \| [Download](5120/pekora_jashinchandropkick.zip) \| ![pattern_1-5120](5120/previews/pattern_1.png) \| ![pattern_2-5120](5120/previews/pattern_2.png) \| ![pattern_3-5120](5120/previews/pattern_3.png) \| ![pattern_4-5120](5120/previews/pattern_4.png) \| ![pattern_5-5120](5120/previews/pattern_5.png) \| ![pattern_6-5120](5120/previews/pattern_6.png) \| ![pattern_7-5120](5120/previews/pattern_7.png) \| ![pattern_8-5120](5120/previews/pattern_8.png) \| ![pattern_9-5120](5120/previews/pattern_9.png) \| ![pattern_10-5120](5120/previews/pattern_10.png) \| ![pattern_11-5120](5120/previews/pattern_11.png) \| ![pattern_12-5120](5120/previews/pattern_12.png) \| ![pattern_13-5120](5120/previews/pattern_13.png) \| ![pattern_14-5120](5120/previews/pattern_14.png) \| ![bikini-5120](5120/previews/bikini.png) \| [<NSFW, click to see>](5120/previews/bondage.png) \| ![free-5120](5120/previews/free.png) \| ![maid-5120](5120/previews/maid.png) \| ![miko-5120](5120/previews/miko.png) \| [<NSFW, click to see>](5120/previews/nude.png) \| [<NSFW, click to see>](5120/previews/nude2.png) \| ![suit-5120](5120/previews/suit.png) \| ![yukata-5120](5120/previews/yukata.png) \| \| 4480 \| 0.767 \| [Download](4480/pekora_jashinchandropkick.zip) \| ![pattern_1-4480](4480/previews/pattern_1.png) \| ![pattern_2-4480](4480/previews/pattern_2.png) \| ![pattern_3-4480](4480/previews/pattern_3.png) \| ![pattern_4-4480](4480/previews/pattern_4.png) \| ![pattern_5-4480](4480/previews/pattern_5.png) \| ![pattern_6-4480](4480/previews/pattern_6.png) \| ![pattern_7-4480](4480/previews/pattern_7.png) \| ![pattern_8-4480](4480/previews/pattern_8.png) \| ![pattern_9-4480](4480/previews/pattern_9.png) \| ![pattern_10-4480](4480/previews/pattern_10.png) \| ![pattern_11-4480](4480/previews/pattern_11.png) \| ![pattern_12-4480](4480/previews/pattern_12.png) \| ![pattern_13-4480](4480/previews/pattern_13.png) \| ![pattern_14-4480](4480/previews/pattern_14.png) \| ![bikini-4480](4480/previews/bikini.png) \| [<NSFW, click to see>](4480/previews/bondage.png) \| ![free-4480](4480/previews/free.png) \| ![maid-4480](4480/previews/maid.png) \| ![miko-4480](4480/previews/miko.png) \| [<NSFW, click to see>](4480/previews/nude.png) \| [<NSFW, click to see>](4480/previews/nude2.png) \| ![suit-4480](4480/previews/suit.png) \| ![yukata-4480](4480/previews/yukata.png) \| \| 3840 \| 0.798 \| [Download](3840/pekora_jashinchandropkick.zip) \| ![pattern_1-3840](3840/previews/pattern_1.png) \| ![pattern_2-3840](3840/previews/pattern_2.png) \| ![pattern_3-3840](3840/previews/pattern_3.png) \| ![pattern_4-3840](3840/previews/pattern_4.png) \| ![pattern_5-3840](3840/previews/pattern_5.png) \| ![pattern_6-3840](3840/previews/pattern_6.png) \| ![pattern_7-3840](3840/previews/pattern_7.png) \| ![pattern_8-3840](3840/previews/pattern_8.png) \| ![pattern_9-3840](3840/previews/pattern_9.png) \| ![pattern_10-3840](3840/previews/pattern_10.png) \| ![pattern_11-3840](3840/previews/pattern_11.png) \| ![pattern_12-3840](3840/previews/pattern_12.png) \| ![pattern_13-3840](3840/previews/pattern_13.png) \| ![pattern_14-3840](3840/previews/pattern_14.png) \| ![bikini-3840](3840/previews/bikini.png) \| [<NSFW, click to see>](3840/previews/bondage.png) \| ![free-3840](3840/previews/free.png) \| ![maid-3840](3840/previews/maid.png) \| ![miko-3840](3840/previews/miko.png) \| [<NSFW, click to see>](3840/previews/nude.png) \| [<NSFW, click to see>](3840/previews/nude2.png) \| ![suit-3840](3840/previews/suit.png) \| ![yukata-3840](3840/previews/yukata.png) \| \| 3200 \| 0.714 \| [Download](3200/pekora_jashinchandropkick.zip) \| ![pattern_1-3200](3200/previews/pattern_1.png) \| ![pattern_2-3200](3200/previews/pattern_2.png) \| ![pattern_3-3200](3200/previews/pattern_3.png) \| ![pattern_4-3200](3200/previews/pattern_4.png) \| ![pattern_5-3200](3200/previews/pattern_5.png) \| ![pattern_6-3200](3200/previews/pattern_6.png) \| ![pattern_7-3200](3200/previews/pattern_7.png) \| ![pattern_8-3200](3200/previews/pattern_8.png) \| ![pattern_9-3200](3200/previews/pattern_9.png) \| ![pattern_10-3200](3200/previews/pattern_10.png) \| ![pattern_11-3200](3200/previews/pattern_11.png) \| ![pattern_12-3200](3200/previews/pattern_12.png) \| ![pattern_13-3200](3200/previews/pattern_13.png) \| ![pattern_14-3200](3200/previews/pattern_14.png) \| ![bikini-3200](3200/previews/bikini.png) \| [<NSFW, click to see>](3200/previews/bondage.png) \| ![free-3200](3200/previews/free.png) \| ![maid-3200](3200/previews/maid.png) \| ![miko-3200](3200/previews/miko.png) \| [<NSFW, click to see>](3200/previews/nude.png) \| [<NSFW, click to see>](3200/previews/nude2.png) \| ![suit-3200](3200/previews/suit.png) \| ![yukata-3200](3200/previews/yukata.png) \| \| 2560 \| 0.701 \| [Download](2560/pekora_jashinchandropkick.zip) \| ![pattern_1-2560](2560/previews/pattern_1.png) \| ![pattern_2-2560](2560/previews/pattern_2.png) \| ![pattern_3-2560](2560/previews/pattern_3.png) \| ![pattern_4-2560](2560/previews/pattern_4.png) \| ![pattern_5-2560](2560/previews/pattern_5.png) \| ![pattern_6-2560](2560/previews/pattern_6.png) \| ![pattern_7-2560](2560/previews/pattern_7.png) \| ![pattern_8-2560](2560/previews/pattern_8.png) \| ![pattern_9-2560](2560/previews/pattern_9.png) \| ![pattern_10-2560](2560/previews/pattern_10.png) \| ![pattern_11-2560](2560/previews/pattern_11.png) \| ![pattern_12-2560](2560/previews/pattern_12.png) \| ![pattern_13-2560](2560/previews/pattern_13.png) \| ![pattern_14-2560](2560/previews/pattern_14.png) \| ![bikini-2560](2560/previews/bikini.png) \| [<NSFW, click to see>](2560/previews/bondage.png) \| ![free-2560](2560/previews/free.png) \| ![maid-2560](2560/previews/maid.png) \| ![miko-2560](2560/previews/miko.png) \| [<NSFW, click to see>](2560/previews/nude.png) \| [<NSFW, click to see>](2560/previews/nude2.png) \| ![suit-2560](2560/previews/suit.png) \| ![yukata-2560](2560/previews/yukata.png) \| \| 1920 \| 0.717 \| [Download](1920/pekora_jashinchandropkick.zip) \| ![pattern_1-1920](1920/previews/pattern_1.png) \| ![pattern_2-1920](1920/previews/pattern_2.png) \| ![pattern_3-1920](1920/previews/pattern_3.png) \| ![pattern_4-1920](1920/previews/pattern_4.png) \| ![pattern_5-1920](1920/previews/pattern_5.png) \| ![pattern_6-1920](1920/previews/pattern_6.png) \| ![pattern_7-1920](1920/previews/pattern_7.png) \| ![pattern_8-1920](1920/previews/pattern_8.png) \| ![pattern_9-1920](1920/previews/pattern_9.png) \| ![pattern_10-1920](1920/previews/pattern_10.png) \| ![pattern_11-1920](1920/previews/pattern_11.png) \| ![pattern_12-1920](1920/previews/pattern_12.png) \| ![pattern_13-1920](1920/previews/pattern_13.png) \| ![pattern_14-1920](1920/previews/pattern_14.png) \| ![bikini-1920](1920/previews/bikini.png) \| [<NSFW, click to see>](1920/previews/bondage.png) \| ![free-1920](1920/previews/free.png) \| ![maid-1920](1920/previews/maid.png) \| ![miko-1920](1920/previews/miko.png) \| [<NSFW, click to see>](1920/previews/nude.png) \| [<NSFW, click to see>](1920/previews/nude2.png) \| ![suit-1920](1920/previews/suit.png) \| ![yukata-1920](1920/previews/yukata.png) \| \| 1280 \| 0.614 \| [Download](1280/pekora_jashinchandropkick.zip) \| ![pattern_1-1280](1280/previews/pattern_1.png) \| ![pattern_2-1280](1280/previews/pattern_2.png) \| ![pattern_3-1280](1280/previews/pattern_3.png) \| ![pattern_4-1280](1280/previews/pattern_4.png) \| ![pattern_5-1280](1280/previews/pattern_5.png) \| ![pattern_6-1280](1280/previews/pattern_6.png) \| ![pattern_7-1280](1280/previews/pattern_7.png) \| ![pattern_8-1280](1280/previews/pattern_8.png) \| ![pattern_9-1280](1280/previews/pattern_9.png) \| ![pattern_10-1280](1280/previews/pattern_10.png) \| ![pattern_11-1280](1280/previews/pattern_11.png) \| ![pattern_12-1280](1280/previews/pattern_12.png) \| ![pattern_13-1280](1280/previews/pattern_13.png) \| ![pattern_14-1280](1280/previews/pattern_14.png) \| ![bikini-1280](1280/previews/bikini.png) \| [<NSFW, click to see>](1280/previews/bondage.png) \| ![free-1280](1280/previews/free.png) \| ![maid-1280](1280/previews/maid.png) \| ![miko-1280](1280/previews/miko.png) \| [<NSFW, click to see>](1280/previews/nude.png) \| [<NSFW, click to see>](1280/previews/nude2.png) \| ![suit-1280](1280/previews/suit.png) \| ![yukata-1280](1280/previews/yukata.png) \| \| 640 \| 0.485 \| [Download](640/pekora_jashinchandropkick.zip) \| ![pattern_1-640](640/previews/pattern_1.png) \| ![pattern_2-640](640/previews/pattern_2.png) \| ![pattern_3-640](640/previews/pattern_3.png) \| ![pattern_4-640](640/previews/pattern_4.png) \| ![pattern_5-640](640/previews/pattern_5.png) \| ![pattern_6-640](640/previews/pattern_6.png) \| ![pattern_7-640](640/previews/pattern_7.png) \| ![pattern_8-640](640/previews/pattern_8.png) \| ![pattern_9-640](640/previews/pattern_9.png) \| ![pattern_10-640](640/previews/pattern_10.png) \| ![pattern_11-640](640/previews/pattern_11.png) \| ![pattern_12-640](640/previews/pattern_12.png) \| ![pattern_13-640](640/previews/pattern_13.png) \| ![pattern_14-640](640/previews/pattern_14.png) \| ![bikini-640](640/previews/bikini.png) \| [<NSFW, click to see>](640/previews/bondage.png) \| ![free-640](640/previews/free.png) \| ![maid-640](640/previews/maid.png) \| ![miko-640](640/previews/miko.png) \| [<NSFW, click to see>](640/previews/nude.png) \| [<NSFW, click to see>](640/previews/nude2.png) \| ![suit-640](640/previews/suit.png) \| ![yukata-640](640/previews/yukata.png) \|
dim/llama2_7b_dolly_oasst1_chip2	dim	2023-09-20T10:39:00Z	2	0	peft	[ "peft", "region:us" ]	null	2023-09-20T10:21:13Z	--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/llama2_7b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=1, bot_token_id=9225, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
dim/xglm-4.5b_dolly_oasst1_chip2	dim	2023-09-20T10:38:18Z	0	0	peft	[ "peft", "region:us" ]	null	2023-09-20T10:37:14Z	--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/xglm-4.5b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=0, bot_token_id=7425, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) # Я был там! Это было незабываемое путешествие, которое я никогда не забуду. Мы посетили все основные достопримечательности острова, включая пляжи, вулканы, пещеры, национальные парки и многое другое. Впечатления от посещения были потрясающими, а культура - уникальной. Поездка была отличным способом исследовать остров и узнать больше об истории его жителей. Надеюсь, что вы также захотите посетить это место! ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
DuyTa/Vietnamese_ASR	DuyTa	2023-09-20T10:36:20Z	4	1	peft	[ "peft", "tensorboard", "base_model:openai/whisper-medium", "base_model:adapter:openai/whisper-medium", "region:us" ]	null	2023-08-17T14:41:26Z	--- library_name: peft base_model: openai/whisper-medium --- ## Training procedure The following ` bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
bavolesy/ppo-LunarLander-v2	bavolesy	2023-09-20T10:21:44Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-09-20T10:21:22Z	--- 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: 258.69 +/- 23.10 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 ... ```
Carve/isnet	Carve	2023-09-20T10:17:12Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2023-01-29T22:51:13Z	--- license: apache-2.0 --- `isnet.pth` - ISNet model. Trained on DIS v1.0 dataset. `isnet-97-carveset.pth` - ISNet model. Trained on CarveSet. Achieves 98% on test set.
Kendong/bc_flowers	Kendong	2023-09-20T10:12:39Z	2	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2023-09-19T13:18:57Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photograph of a BSC vase of ASK flowers on a wooden table tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Kendong/bc_flowers These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photograph of a BSC vase of ASK flowers on a wooden table using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
Asheyy/dogbooth	Asheyy	2023-09-20T10:01:40Z	0	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-09-17T09:20:41Z	--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of [v]dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Asheyy/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
swaroopajit/git-base-fashion	swaroopajit	2023-09-20T09:58:46Z	170	1	transformers	[ "transformers", "pytorch", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "image-to-text", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]	image-to-text	2023-09-01T08:19:38Z	--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-fashion results: [] pipeline_tag: image-to-text --- <!-- 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. --> # git-base-fashion This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3
Technotech/sd-prompt-instruct-3b-epoch-0.4-lora	Technotech	2023-09-20T09:54:02Z	2	0	peft	[ "peft", "stable-diffusion", "instruct", "magic-prompt", "en", "dataset:Technotech/sd-prompt-instruct", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "region:us" ]	null	2023-07-29T15:14:28Z	--- language: - en license: apache-2.0 library_name: peft tags: - stable-diffusion - instruct - magic-prompt datasets: - Technotech/sd-prompt-instruct base_model: openlm-research/open_llama_3b_v2 --- # Stable Diffusion Prompt Instruct 3B (OpenLlama v2 3B) Trained for 0.4 epochs (test) on [Technotech/sd-prompt-instruct](https://huggingface.co/datasets/Technotech/sd-prompt-instruct). ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
SHENMU007/neunit_BASE_V9.5.14	SHENMU007	2023-09-20T09:53:21Z	76	0	transformers	[ "transformers", "pytorch", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2023-09-20T08:19:51Z	--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit 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. --> # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
phamhungd/GuoZovya	phamhungd	2023-09-20T09:52:42Z	0	0	null	[ "onnx", "vi", "en", "license:unknown", "region:us" ]	null	2023-02-28T05:44:19Z	--- license: unknown language: - vi - en --- About this version Model Merge : A to Zovya RPG Artist's Tools GuoFeng2 #Note: Đây là bản mix 5-5 từ 2 model yêu thích nhất của mình, hiệu quả tạo ra thực sự tuyệt vời, các gương mặt mẫu gần như là hoàn hảo với thông số mặc định.Nó gần như hoàn hảo với đầy đủ các phong cách khác.Với model này, gần như mình đã không cần phải suy nghĩ tới model khac để thực hiện nhiều bức hình với các thể loại khác nhau. Mong mọi người có thể ủng hộ và khám phá hết tiềm năng của model này. #Note : This is a 5-5 mix from my 2 favorite models, the effect is really great, the model faces are almost perfect with default settings. It's almost perfect with a full range of styles. another way. With this model, I almost didn't need to think about other models to take many pictures with different genres. Hope everyone can support and explore the full potential of this model.
mitch7w/falcon-7b-midjourney-example	mitch7w	2023-09-20T09:41:16Z	0	0	peft	[ "peft", "text-generation", "dataset:tiiuae/falcon-refinedweb", "license:openrail", "region:us" ]	text-generation	2023-09-20T07:26:15Z	--- license: openrail library_name: peft pipeline_tag: text-generation datasets: - tiiuae/falcon-refinedweb model_parameters: base_model_name_or_path: vilsonrodrigues/falcon-7b-instruct-sharded bias: none fan_in_fan_out: false inference_mode: true init_lora_weights: true lora_alpha: 32 lora_dropout: 0.05 peft_type: LORA r: 16 target_modules: - query_key_value task_type: CAUSAL_LM ---
hidude562/OpenMusenet-LContext-2.11	hidude562	2023-09-20T09:39:39Z	148	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-05T09:26:35Z	--- pipeline_tag: text-generation ---
ShivamMangale/XLM-Roberta-base-finetuned-squad-syn-first-now-squad-ent-gen-syn	ShivamMangale	2023-09-20T09:34:55Z	3	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]	question-answering	2023-09-20T09:00:27Z	--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-syn-first-now-squad-ent-gen-syn 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-squad-syn-first-now-squad-ent-gen-syn This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 9 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
haseong8012/wav2vec2-large-xlsr-53_ko	haseong8012	2023-09-20T09:33:28Z	105	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-09-20T03:43:03Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - zeroth_korean metrics: - wer model-index: - name: wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: zeroth_korean type: zeroth_korean config: clean split: test args: clean metrics: - name: Wer type: wer value: 0.8577021532901672 --- <!-- 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. --> # wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the zeroth_korean dataset. It achieves the following results on the evaluation set: - Loss: 1.4406 - Wer: 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 0.99 \| 38 \| 54.3133 \| 1.0 \| \| No log \| 2.0 \| 77 \| 33.5397 \| 1.0 \| \| No log \| 2.99 \| 115 \| 19.6459 \| 1.0 \| \| No log \| 4.0 \| 154 \| 11.1346 \| 1.0 \| \| No log \| 4.99 \| 192 \| 5.8854 \| 1.0 \| \| No log \| 6.0 \| 231 \| 4.8784 \| 1.0 \| \| No log \| 6.99 \| 269 \| 4.8369 \| 1.0 \| \| No log \| 8.0 \| 308 \| 4.8535 \| 1.0 \| \| No log \| 8.99 \| 346 \| 4.8388 \| 1.0 \| \| No log \| 10.0 \| 385 \| 4.8360 \| 1.0 \| \| 15.1801 \| 10.99 \| 423 \| 4.7653 \| 1.0 \| \| 15.1801 \| 12.0 \| 462 \| 4.7385 \| 1.0 \| \| 15.1801 \| 12.99 \| 500 \| 4.6927 \| 0.9989 \| \| 15.1801 \| 14.0 \| 539 \| 4.6673 \| 0.9991 \| \| 15.1801 \| 14.99 \| 577 \| 4.6948 \| 0.9991 \| \| 15.1801 \| 16.0 \| 616 \| 4.6713 \| 0.9991 \| \| 15.1801 \| 16.99 \| 654 \| 4.6603 \| 1.0 \| \| 15.1801 \| 18.0 \| 693 \| 4.6428 \| 0.9995 \| \| 15.1801 \| 18.99 \| 731 \| 4.6520 \| 0.9994 \| \| 15.1801 \| 20.0 \| 770 \| 4.6554 \| 0.9967 \| \| 4.3888 \| 20.99 \| 808 \| 4.6054 \| 0.9998 \| \| 4.3888 \| 22.0 \| 847 \| 4.5723 \| 0.9976 \| \| 4.3888 \| 22.99 \| 885 \| 4.4586 \| 0.9967 \| \| 4.3888 \| 24.0 \| 924 \| 4.2547 \| 0.9934 \| \| 4.3888 \| 24.99 \| 962 \| 3.6554 \| 0.9931 \| \| 4.3888 \| 26.0 \| 1001 \| 2.8387 \| 1.0084 \| \| 4.3888 \| 26.99 \| 1039 \| 2.4191 \| 1.0551 \| \| 4.3888 \| 28.0 \| 1078 \| 2.0997 \| 1.0197 \| \| 4.3888 \| 28.99 \| 1116 \| 2.0103 \| 1.0176 \| \| 4.3888 \| 30.0 \| 1155 \| 1.8189 \| 0.9461 \| \| 4.3888 \| 30.99 \| 1193 \| 1.7623 \| 0.9726 \| \| 2.7217 \| 32.0 \| 1232 \| 1.7383 \| 0.9976 \| \| 2.7217 \| 32.99 \| 1270 \| 1.6522 \| 0.9584 \| \| 2.7217 \| 34.0 \| 1309 \| 1.5558 \| 0.9193 \| \| 2.7217 \| 34.99 \| 1347 \| 1.5811 \| 0.9440 \| \| 2.7217 \| 36.0 \| 1386 \| 1.5208 \| 0.9158 \| \| 2.7217 \| 36.99 \| 1424 \| 1.5088 \| 0.9038 \| \| 2.7217 \| 38.0 \| 1463 \| 1.5039 \| 0.9086 \| \| 2.7217 \| 38.99 \| 1501 \| 1.4853 \| 0.8987 \| \| 2.7217 \| 40.0 \| 1540 \| 1.4799 \| 0.8847 \| \| 2.7217 \| 40.99 \| 1578 \| 1.4259 \| 0.8694 \| \| 0.7635 \| 42.0 \| 1617 \| 1.4878 \| 0.8883 \| \| 0.7635 \| 42.99 \| 1655 \| 1.4394 \| 0.8693 \| \| 0.7635 \| 44.0 \| 1694 \| 1.4623 \| 0.8743 \| \| 0.7635 \| 44.99 \| 1732 \| 1.4495 \| 0.8710 \| \| 0.7635 \| 46.0 \| 1771 \| 1.4463 \| 0.8655 \| \| 0.7635 \| 46.99 \| 1809 \| 1.4553 \| 0.8704 \| \| 0.7635 \| 48.0 \| 1848 \| 1.4500 \| 0.8646 \| \| 0.7635 \| 48.99 \| 1886 \| 1.4387 \| 0.8566 \| \| 0.7635 \| 49.35 \| 1900 \| 1.4406 \| 0.8577 \| ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1 - Datasets 2.14.5 - Tokenizers 0.13.3
CyberHarem/makihara_shiho_idolmastercinderellagirls	CyberHarem	2023-09-20T09:27:49Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/makihara_shiho_idolmastercinderellagirls", "license:mit", "region:us" ]	text-to-image	2023-09-20T09:15:47Z	--- license: mit datasets: - CyberHarem/makihara_shiho_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of makihara_shiho_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/makihara_shiho_idolmastercinderellagirls.pt` as the embedding and `4420/makihara_shiho_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 4420, with the score of 0.882. The trigger words are: 1. `makihara_shiho_idolmastercinderellagirls` 2. `brown_hair, long_hair, green_eyes, smile, open_mouth, blush, bow, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 5100 \| 0.859 \| [Download](5100/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-5100](5100/previews/pattern_1.png) \| ![pattern_2-5100](5100/previews/pattern_2.png) \| ![pattern_3-5100](5100/previews/pattern_3.png) \| ![bikini-5100](5100/previews/bikini.png) \| [<NSFW, click to see>](5100/previews/bondage.png) \| ![free-5100](5100/previews/free.png) \| ![maid-5100](5100/previews/maid.png) \| ![miko-5100](5100/previews/miko.png) \| [<NSFW, click to see>](5100/previews/nude.png) \| [<NSFW, click to see>](5100/previews/nude2.png) \| ![suit-5100](5100/previews/suit.png) \| ![yukata-5100](5100/previews/yukata.png) \| \| 4760 \| 0.861 \| [Download](4760/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-4760](4760/previews/pattern_1.png) \| ![pattern_2-4760](4760/previews/pattern_2.png) \| ![pattern_3-4760](4760/previews/pattern_3.png) \| ![bikini-4760](4760/previews/bikini.png) \| [<NSFW, click to see>](4760/previews/bondage.png) \| ![free-4760](4760/previews/free.png) \| ![maid-4760](4760/previews/maid.png) \| ![miko-4760](4760/previews/miko.png) \| [<NSFW, click to see>](4760/previews/nude.png) \| [<NSFW, click to see>](4760/previews/nude2.png) \| ![suit-4760](4760/previews/suit.png) \| ![yukata-4760](4760/previews/yukata.png) \| \| 4420 \| 0.882 \| [Download](4420/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-4420](4420/previews/pattern_1.png) \| ![pattern_2-4420](4420/previews/pattern_2.png) \| ![pattern_3-4420](4420/previews/pattern_3.png) \| ![bikini-4420](4420/previews/bikini.png) \| [<NSFW, click to see>](4420/previews/bondage.png) \| ![free-4420](4420/previews/free.png) \| ![maid-4420](4420/previews/maid.png) \| ![miko-4420](4420/previews/miko.png) \| [<NSFW, click to see>](4420/previews/nude.png) \| [<NSFW, click to see>](4420/previews/nude2.png) \| ![suit-4420](4420/previews/suit.png) \| ![yukata-4420](4420/previews/yukata.png) \| \| 4080 \| 0.856 \| [Download](4080/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-4080](4080/previews/pattern_1.png) \| ![pattern_2-4080](4080/previews/pattern_2.png) \| ![pattern_3-4080](4080/previews/pattern_3.png) \| ![bikini-4080](4080/previews/bikini.png) \| [<NSFW, click to see>](4080/previews/bondage.png) \| ![free-4080](4080/previews/free.png) \| ![maid-4080](4080/previews/maid.png) \| ![miko-4080](4080/previews/miko.png) \| [<NSFW, click to see>](4080/previews/nude.png) \| [<NSFW, click to see>](4080/previews/nude2.png) \| ![suit-4080](4080/previews/suit.png) \| ![yukata-4080](4080/previews/yukata.png) \| \| 3740 \| 0.858 \| [Download](3740/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-3740](3740/previews/pattern_1.png) \| ![pattern_2-3740](3740/previews/pattern_2.png) \| ![pattern_3-3740](3740/previews/pattern_3.png) \| ![bikini-3740](3740/previews/bikini.png) \| [<NSFW, click to see>](3740/previews/bondage.png) \| ![free-3740](3740/previews/free.png) \| ![maid-3740](3740/previews/maid.png) \| ![miko-3740](3740/previews/miko.png) \| [<NSFW, click to see>](3740/previews/nude.png) \| [<NSFW, click to see>](3740/previews/nude2.png) \| ![suit-3740](3740/previews/suit.png) \| ![yukata-3740](3740/previews/yukata.png) \| \| 3400 \| 0.847 \| [Download](3400/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-3400](3400/previews/pattern_1.png) \| ![pattern_2-3400](3400/previews/pattern_2.png) \| ![pattern_3-3400](3400/previews/pattern_3.png) \| ![bikini-3400](3400/previews/bikini.png) \| [<NSFW, click to see>](3400/previews/bondage.png) \| ![free-3400](3400/previews/free.png) \| ![maid-3400](3400/previews/maid.png) \| ![miko-3400](3400/previews/miko.png) \| [<NSFW, click to see>](3400/previews/nude.png) \| [<NSFW, click to see>](3400/previews/nude2.png) \| ![suit-3400](3400/previews/suit.png) \| ![yukata-3400](3400/previews/yukata.png) \| \| 3060 \| 0.795 \| [Download](3060/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-3060](3060/previews/pattern_1.png) \| ![pattern_2-3060](3060/previews/pattern_2.png) \| ![pattern_3-3060](3060/previews/pattern_3.png) \| ![bikini-3060](3060/previews/bikini.png) \| [<NSFW, click to see>](3060/previews/bondage.png) \| ![free-3060](3060/previews/free.png) \| ![maid-3060](3060/previews/maid.png) \| ![miko-3060](3060/previews/miko.png) \| [<NSFW, click to see>](3060/previews/nude.png) \| [<NSFW, click to see>](3060/previews/nude2.png) \| ![suit-3060](3060/previews/suit.png) \| ![yukata-3060](3060/previews/yukata.png) \| \| 2720 \| 0.735 \| [Download](2720/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-2720](2720/previews/pattern_1.png) \| ![pattern_2-2720](2720/previews/pattern_2.png) \| ![pattern_3-2720](2720/previews/pattern_3.png) \| ![bikini-2720](2720/previews/bikini.png) \| [<NSFW, click to see>](2720/previews/bondage.png) \| ![free-2720](2720/previews/free.png) \| ![maid-2720](2720/previews/maid.png) \| ![miko-2720](2720/previews/miko.png) \| [<NSFW, click to see>](2720/previews/nude.png) \| [<NSFW, click to see>](2720/previews/nude2.png) \| ![suit-2720](2720/previews/suit.png) \| ![yukata-2720](2720/previews/yukata.png) \| \| 2380 \| 0.781 \| [Download](2380/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-2380](2380/previews/pattern_1.png) \| ![pattern_2-2380](2380/previews/pattern_2.png) \| ![pattern_3-2380](2380/previews/pattern_3.png) \| ![bikini-2380](2380/previews/bikini.png) \| [<NSFW, click to see>](2380/previews/bondage.png) \| ![free-2380](2380/previews/free.png) \| ![maid-2380](2380/previews/maid.png) \| ![miko-2380](2380/previews/miko.png) \| [<NSFW, click to see>](2380/previews/nude.png) \| [<NSFW, click to see>](2380/previews/nude2.png) \| ![suit-2380](2380/previews/suit.png) \| ![yukata-2380](2380/previews/yukata.png) \| \| 2040 \| 0.816 \| [Download](2040/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-2040](2040/previews/pattern_1.png) \| ![pattern_2-2040](2040/previews/pattern_2.png) \| ![pattern_3-2040](2040/previews/pattern_3.png) \| ![bikini-2040](2040/previews/bikini.png) \| [<NSFW, click to see>](2040/previews/bondage.png) \| ![free-2040](2040/previews/free.png) \| ![maid-2040](2040/previews/maid.png) \| ![miko-2040](2040/previews/miko.png) \| [<NSFW, click to see>](2040/previews/nude.png) \| [<NSFW, click to see>](2040/previews/nude2.png) \| ![suit-2040](2040/previews/suit.png) \| ![yukata-2040](2040/previews/yukata.png) \| \| 1700 \| 0.798 \| [Download](1700/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-1700](1700/previews/pattern_1.png) \| ![pattern_2-1700](1700/previews/pattern_2.png) \| ![pattern_3-1700](1700/previews/pattern_3.png) \| ![bikini-1700](1700/previews/bikini.png) \| [<NSFW, click to see>](1700/previews/bondage.png) \| ![free-1700](1700/previews/free.png) \| ![maid-1700](1700/previews/maid.png) \| ![miko-1700](1700/previews/miko.png) \| [<NSFW, click to see>](1700/previews/nude.png) \| [<NSFW, click to see>](1700/previews/nude2.png) \| ![suit-1700](1700/previews/suit.png) \| ![yukata-1700](1700/previews/yukata.png) \| \| 1360 \| 0.797 \| [Download](1360/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-1360](1360/previews/pattern_1.png) \| ![pattern_2-1360](1360/previews/pattern_2.png) \| ![pattern_3-1360](1360/previews/pattern_3.png) \| ![bikini-1360](1360/previews/bikini.png) \| [<NSFW, click to see>](1360/previews/bondage.png) \| ![free-1360](1360/previews/free.png) \| ![maid-1360](1360/previews/maid.png) \| ![miko-1360](1360/previews/miko.png) \| [<NSFW, click to see>](1360/previews/nude.png) \| [<NSFW, click to see>](1360/previews/nude2.png) \| ![suit-1360](1360/previews/suit.png) \| ![yukata-1360](1360/previews/yukata.png) \| \| 1020 \| 0.670 \| [Download](1020/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-1020](1020/previews/pattern_1.png) \| ![pattern_2-1020](1020/previews/pattern_2.png) \| ![pattern_3-1020](1020/previews/pattern_3.png) \| ![bikini-1020](1020/previews/bikini.png) \| [<NSFW, click to see>](1020/previews/bondage.png) \| ![free-1020](1020/previews/free.png) \| ![maid-1020](1020/previews/maid.png) \| ![miko-1020](1020/previews/miko.png) \| [<NSFW, click to see>](1020/previews/nude.png) \| [<NSFW, click to see>](1020/previews/nude2.png) \| ![suit-1020](1020/previews/suit.png) \| ![yukata-1020](1020/previews/yukata.png) \| \| 680 \| 0.649 \| [Download](680/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-680](680/previews/pattern_1.png) \| ![pattern_2-680](680/previews/pattern_2.png) \| ![pattern_3-680](680/previews/pattern_3.png) \| ![bikini-680](680/previews/bikini.png) \| [<NSFW, click to see>](680/previews/bondage.png) \| ![free-680](680/previews/free.png) \| ![maid-680](680/previews/maid.png) \| ![miko-680](680/previews/miko.png) \| [<NSFW, click to see>](680/previews/nude.png) \| [<NSFW, click to see>](680/previews/nude2.png) \| ![suit-680](680/previews/suit.png) \| ![yukata-680](680/previews/yukata.png) \| \| 340 \| 0.455 \| [Download](340/makihara_shiho_idolmastercinderellagirls.zip) \| ![pattern_1-340](340/previews/pattern_1.png) \| ![pattern_2-340](340/previews/pattern_2.png) \| ![pattern_3-340](340/previews/pattern_3.png) \| ![bikini-340](340/previews/bikini.png) \| [<NSFW, click to see>](340/previews/bondage.png) \| ![free-340](340/previews/free.png) \| ![maid-340](340/previews/maid.png) \| ![miko-340](340/previews/miko.png) \| [<NSFW, click to see>](340/previews/nude.png) \| [<NSFW, click to see>](340/previews/nude2.png) \| ![suit-340](340/previews/suit.png) \| ![yukata-340](340/previews/yukata.png) \|
CyberHarem/hua_yuan_yurine_jashinchandropkick	CyberHarem	2023-09-20T09:25:31Z	0	0	null	[ "art", "text-to-image", "dataset:CyberHarem/hua_yuan_yurine_jashinchandropkick", "license:mit", "region:us" ]	text-to-image	2023-09-20T09:09:33Z	--- license: mit datasets: - CyberHarem/hua_yuan_yurine_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of hua_yuan_yurine_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 9600, you need to download `9600/hua_yuan_yurine_jashinchandropkick.pt` as the embedding and `9600/hua_yuan_yurine_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. The best step we recommend is 9600, with the score of 0.950. The trigger words are: 1. `hua_yuan_yurine_jashinchandropkick` 2. `long_hair, twintails, eyepatch, medical_eyepatch, bangs, mini_hat, hat, lolita_fashion, gothic_lolita, red_hair, closed_mouth, brown_eyes, ribbon, brown_hair, black_headwear` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: \| Steps \| Score \| Download \| pattern_1 \| pattern_2 \| pattern_3 \| pattern_4 \| pattern_5 \| pattern_6 \| pattern_7 \| pattern_8 \| pattern_9 \| pattern_10 \| bikini \| bondage \| free \| maid \| miko \| nude \| nude2 \| suit \| yukata \| \|:---------\|:----------\|:------------------------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-----------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------\|:--------------------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------------\|:------------------------------------------------\|:-------------------------------------\|:-----------------------------------------\| \| 9600 \| 0.950 \| [Download](9600/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-9600](9600/previews/pattern_1.png) \| ![pattern_2-9600](9600/previews/pattern_2.png) \| ![pattern_3-9600](9600/previews/pattern_3.png) \| ![pattern_4-9600](9600/previews/pattern_4.png) \| ![pattern_5-9600](9600/previews/pattern_5.png) \| ![pattern_6-9600](9600/previews/pattern_6.png) \| ![pattern_7-9600](9600/previews/pattern_7.png) \| ![pattern_8-9600](9600/previews/pattern_8.png) \| ![pattern_9-9600](9600/previews/pattern_9.png) \| ![pattern_10-9600](9600/previews/pattern_10.png) \| ![bikini-9600](9600/previews/bikini.png) \| [<NSFW, click to see>](9600/previews/bondage.png) \| ![free-9600](9600/previews/free.png) \| ![maid-9600](9600/previews/maid.png) \| ![miko-9600](9600/previews/miko.png) \| [<NSFW, click to see>](9600/previews/nude.png) \| [<NSFW, click to see>](9600/previews/nude2.png) \| ![suit-9600](9600/previews/suit.png) \| ![yukata-9600](9600/previews/yukata.png) \| \| 8960 \| 0.925 \| [Download](8960/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-8960](8960/previews/pattern_1.png) \| ![pattern_2-8960](8960/previews/pattern_2.png) \| ![pattern_3-8960](8960/previews/pattern_3.png) \| ![pattern_4-8960](8960/previews/pattern_4.png) \| ![pattern_5-8960](8960/previews/pattern_5.png) \| ![pattern_6-8960](8960/previews/pattern_6.png) \| ![pattern_7-8960](8960/previews/pattern_7.png) \| ![pattern_8-8960](8960/previews/pattern_8.png) \| ![pattern_9-8960](8960/previews/pattern_9.png) \| ![pattern_10-8960](8960/previews/pattern_10.png) \| ![bikini-8960](8960/previews/bikini.png) \| [<NSFW, click to see>](8960/previews/bondage.png) \| ![free-8960](8960/previews/free.png) \| ![maid-8960](8960/previews/maid.png) \| ![miko-8960](8960/previews/miko.png) \| [<NSFW, click to see>](8960/previews/nude.png) \| [<NSFW, click to see>](8960/previews/nude2.png) \| ![suit-8960](8960/previews/suit.png) \| ![yukata-8960](8960/previews/yukata.png) \| \| 8320 \| 0.947 \| [Download](8320/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-8320](8320/previews/pattern_1.png) \| ![pattern_2-8320](8320/previews/pattern_2.png) \| ![pattern_3-8320](8320/previews/pattern_3.png) \| ![pattern_4-8320](8320/previews/pattern_4.png) \| ![pattern_5-8320](8320/previews/pattern_5.png) \| ![pattern_6-8320](8320/previews/pattern_6.png) \| ![pattern_7-8320](8320/previews/pattern_7.png) \| ![pattern_8-8320](8320/previews/pattern_8.png) \| ![pattern_9-8320](8320/previews/pattern_9.png) \| ![pattern_10-8320](8320/previews/pattern_10.png) \| ![bikini-8320](8320/previews/bikini.png) \| [<NSFW, click to see>](8320/previews/bondage.png) \| ![free-8320](8320/previews/free.png) \| ![maid-8320](8320/previews/maid.png) \| ![miko-8320](8320/previews/miko.png) \| [<NSFW, click to see>](8320/previews/nude.png) \| [<NSFW, click to see>](8320/previews/nude2.png) \| ![suit-8320](8320/previews/suit.png) \| ![yukata-8320](8320/previews/yukata.png) \| \| 7680 \| 0.940 \| [Download](7680/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-7680](7680/previews/pattern_1.png) \| ![pattern_2-7680](7680/previews/pattern_2.png) \| ![pattern_3-7680](7680/previews/pattern_3.png) \| ![pattern_4-7680](7680/previews/pattern_4.png) \| ![pattern_5-7680](7680/previews/pattern_5.png) \| ![pattern_6-7680](7680/previews/pattern_6.png) \| ![pattern_7-7680](7680/previews/pattern_7.png) \| ![pattern_8-7680](7680/previews/pattern_8.png) \| ![pattern_9-7680](7680/previews/pattern_9.png) \| ![pattern_10-7680](7680/previews/pattern_10.png) \| ![bikini-7680](7680/previews/bikini.png) \| [<NSFW, click to see>](7680/previews/bondage.png) \| ![free-7680](7680/previews/free.png) \| ![maid-7680](7680/previews/maid.png) \| ![miko-7680](7680/previews/miko.png) \| [<NSFW, click to see>](7680/previews/nude.png) \| [<NSFW, click to see>](7680/previews/nude2.png) \| ![suit-7680](7680/previews/suit.png) \| ![yukata-7680](7680/previews/yukata.png) \| \| 7040 \| 0.933 \| [Download](7040/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-7040](7040/previews/pattern_1.png) \| ![pattern_2-7040](7040/previews/pattern_2.png) \| ![pattern_3-7040](7040/previews/pattern_3.png) \| ![pattern_4-7040](7040/previews/pattern_4.png) \| ![pattern_5-7040](7040/previews/pattern_5.png) \| ![pattern_6-7040](7040/previews/pattern_6.png) \| ![pattern_7-7040](7040/previews/pattern_7.png) \| ![pattern_8-7040](7040/previews/pattern_8.png) \| ![pattern_9-7040](7040/previews/pattern_9.png) \| ![pattern_10-7040](7040/previews/pattern_10.png) \| ![bikini-7040](7040/previews/bikini.png) \| [<NSFW, click to see>](7040/previews/bondage.png) \| ![free-7040](7040/previews/free.png) \| ![maid-7040](7040/previews/maid.png) \| ![miko-7040](7040/previews/miko.png) \| [<NSFW, click to see>](7040/previews/nude.png) \| [<NSFW, click to see>](7040/previews/nude2.png) \| ![suit-7040](7040/previews/suit.png) \| ![yukata-7040](7040/previews/yukata.png) \| \| 6400 \| 0.940 \| [Download](6400/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-6400](6400/previews/pattern_1.png) \| ![pattern_2-6400](6400/previews/pattern_2.png) \| ![pattern_3-6400](6400/previews/pattern_3.png) \| ![pattern_4-6400](6400/previews/pattern_4.png) \| ![pattern_5-6400](6400/previews/pattern_5.png) \| ![pattern_6-6400](6400/previews/pattern_6.png) \| ![pattern_7-6400](6400/previews/pattern_7.png) \| ![pattern_8-6400](6400/previews/pattern_8.png) \| ![pattern_9-6400](6400/previews/pattern_9.png) \| ![pattern_10-6400](6400/previews/pattern_10.png) \| ![bikini-6400](6400/previews/bikini.png) \| [<NSFW, click to see>](6400/previews/bondage.png) \| ![free-6400](6400/previews/free.png) \| ![maid-6400](6400/previews/maid.png) \| ![miko-6400](6400/previews/miko.png) \| [<NSFW, click to see>](6400/previews/nude.png) \| [<NSFW, click to see>](6400/previews/nude2.png) \| ![suit-6400](6400/previews/suit.png) \| ![yukata-6400](6400/previews/yukata.png) \| \| 5760 \| 0.922 \| [Download](5760/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-5760](5760/previews/pattern_1.png) \| ![pattern_2-5760](5760/previews/pattern_2.png) \| ![pattern_3-5760](5760/previews/pattern_3.png) \| ![pattern_4-5760](5760/previews/pattern_4.png) \| ![pattern_5-5760](5760/previews/pattern_5.png) \| ![pattern_6-5760](5760/previews/pattern_6.png) \| ![pattern_7-5760](5760/previews/pattern_7.png) \| ![pattern_8-5760](5760/previews/pattern_8.png) \| ![pattern_9-5760](5760/previews/pattern_9.png) \| ![pattern_10-5760](5760/previews/pattern_10.png) \| ![bikini-5760](5760/previews/bikini.png) \| [<NSFW, click to see>](5760/previews/bondage.png) \| ![free-5760](5760/previews/free.png) \| ![maid-5760](5760/previews/maid.png) \| ![miko-5760](5760/previews/miko.png) \| [<NSFW, click to see>](5760/previews/nude.png) \| [<NSFW, click to see>](5760/previews/nude2.png) \| ![suit-5760](5760/previews/suit.png) \| ![yukata-5760](5760/previews/yukata.png) \| \| 5120 \| 0.931 \| [Download](5120/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-5120](5120/previews/pattern_1.png) \| ![pattern_2-5120](5120/previews/pattern_2.png) \| ![pattern_3-5120](5120/previews/pattern_3.png) \| ![pattern_4-5120](5120/previews/pattern_4.png) \| ![pattern_5-5120](5120/previews/pattern_5.png) \| ![pattern_6-5120](5120/previews/pattern_6.png) \| ![pattern_7-5120](5120/previews/pattern_7.png) \| ![pattern_8-5120](5120/previews/pattern_8.png) \| ![pattern_9-5120](5120/previews/pattern_9.png) \| ![pattern_10-5120](5120/previews/pattern_10.png) \| ![bikini-5120](5120/previews/bikini.png) \| [<NSFW, click to see>](5120/previews/bondage.png) \| ![free-5120](5120/previews/free.png) \| ![maid-5120](5120/previews/maid.png) \| ![miko-5120](5120/previews/miko.png) \| [<NSFW, click to see>](5120/previews/nude.png) \| [<NSFW, click to see>](5120/previews/nude2.png) \| ![suit-5120](5120/previews/suit.png) \| ![yukata-5120](5120/previews/yukata.png) \| \| 4480 \| 0.891 \| [Download](4480/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-4480](4480/previews/pattern_1.png) \| ![pattern_2-4480](4480/previews/pattern_2.png) \| ![pattern_3-4480](4480/previews/pattern_3.png) \| ![pattern_4-4480](4480/previews/pattern_4.png) \| ![pattern_5-4480](4480/previews/pattern_5.png) \| ![pattern_6-4480](4480/previews/pattern_6.png) \| ![pattern_7-4480](4480/previews/pattern_7.png) \| ![pattern_8-4480](4480/previews/pattern_8.png) \| ![pattern_9-4480](4480/previews/pattern_9.png) \| ![pattern_10-4480](4480/previews/pattern_10.png) \| ![bikini-4480](4480/previews/bikini.png) \| [<NSFW, click to see>](4480/previews/bondage.png) \| ![free-4480](4480/previews/free.png) \| ![maid-4480](4480/previews/maid.png) \| ![miko-4480](4480/previews/miko.png) \| [<NSFW, click to see>](4480/previews/nude.png) \| [<NSFW, click to see>](4480/previews/nude2.png) \| ![suit-4480](4480/previews/suit.png) \| ![yukata-4480](4480/previews/yukata.png) \| \| 3840 \| 0.896 \| [Download](3840/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-3840](3840/previews/pattern_1.png) \| ![pattern_2-3840](3840/previews/pattern_2.png) \| ![pattern_3-3840](3840/previews/pattern_3.png) \| ![pattern_4-3840](3840/previews/pattern_4.png) \| ![pattern_5-3840](3840/previews/pattern_5.png) \| ![pattern_6-3840](3840/previews/pattern_6.png) \| ![pattern_7-3840](3840/previews/pattern_7.png) \| ![pattern_8-3840](3840/previews/pattern_8.png) \| ![pattern_9-3840](3840/previews/pattern_9.png) \| ![pattern_10-3840](3840/previews/pattern_10.png) \| ![bikini-3840](3840/previews/bikini.png) \| [<NSFW, click to see>](3840/previews/bondage.png) \| ![free-3840](3840/previews/free.png) \| ![maid-3840](3840/previews/maid.png) \| ![miko-3840](3840/previews/miko.png) \| [<NSFW, click to see>](3840/previews/nude.png) \| [<NSFW, click to see>](3840/previews/nude2.png) \| ![suit-3840](3840/previews/suit.png) \| ![yukata-3840](3840/previews/yukata.png) \| \| 3200 \| 0.893 \| [Download](3200/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-3200](3200/previews/pattern_1.png) \| ![pattern_2-3200](3200/previews/pattern_2.png) \| ![pattern_3-3200](3200/previews/pattern_3.png) \| ![pattern_4-3200](3200/previews/pattern_4.png) \| ![pattern_5-3200](3200/previews/pattern_5.png) \| ![pattern_6-3200](3200/previews/pattern_6.png) \| ![pattern_7-3200](3200/previews/pattern_7.png) \| ![pattern_8-3200](3200/previews/pattern_8.png) \| ![pattern_9-3200](3200/previews/pattern_9.png) \| ![pattern_10-3200](3200/previews/pattern_10.png) \| ![bikini-3200](3200/previews/bikini.png) \| [<NSFW, click to see>](3200/previews/bondage.png) \| ![free-3200](3200/previews/free.png) \| ![maid-3200](3200/previews/maid.png) \| ![miko-3200](3200/previews/miko.png) \| [<NSFW, click to see>](3200/previews/nude.png) \| [<NSFW, click to see>](3200/previews/nude2.png) \| ![suit-3200](3200/previews/suit.png) \| ![yukata-3200](3200/previews/yukata.png) \| \| 2560 \| 0.861 \| [Download](2560/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-2560](2560/previews/pattern_1.png) \| ![pattern_2-2560](2560/previews/pattern_2.png) \| ![pattern_3-2560](2560/previews/pattern_3.png) \| ![pattern_4-2560](2560/previews/pattern_4.png) \| ![pattern_5-2560](2560/previews/pattern_5.png) \| ![pattern_6-2560](2560/previews/pattern_6.png) \| ![pattern_7-2560](2560/previews/pattern_7.png) \| ![pattern_8-2560](2560/previews/pattern_8.png) \| ![pattern_9-2560](2560/previews/pattern_9.png) \| ![pattern_10-2560](2560/previews/pattern_10.png) \| ![bikini-2560](2560/previews/bikini.png) \| [<NSFW, click to see>](2560/previews/bondage.png) \| ![free-2560](2560/previews/free.png) \| ![maid-2560](2560/previews/maid.png) \| ![miko-2560](2560/previews/miko.png) \| [<NSFW, click to see>](2560/previews/nude.png) \| [<NSFW, click to see>](2560/previews/nude2.png) \| ![suit-2560](2560/previews/suit.png) \| ![yukata-2560](2560/previews/yukata.png) \| \| 1920 \| 0.837 \| [Download](1920/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-1920](1920/previews/pattern_1.png) \| ![pattern_2-1920](1920/previews/pattern_2.png) \| ![pattern_3-1920](1920/previews/pattern_3.png) \| ![pattern_4-1920](1920/previews/pattern_4.png) \| ![pattern_5-1920](1920/previews/pattern_5.png) \| ![pattern_6-1920](1920/previews/pattern_6.png) \| ![pattern_7-1920](1920/previews/pattern_7.png) \| ![pattern_8-1920](1920/previews/pattern_8.png) \| ![pattern_9-1920](1920/previews/pattern_9.png) \| ![pattern_10-1920](1920/previews/pattern_10.png) \| ![bikini-1920](1920/previews/bikini.png) \| [<NSFW, click to see>](1920/previews/bondage.png) \| ![free-1920](1920/previews/free.png) \| ![maid-1920](1920/previews/maid.png) \| ![miko-1920](1920/previews/miko.png) \| [<NSFW, click to see>](1920/previews/nude.png) \| [<NSFW, click to see>](1920/previews/nude2.png) \| ![suit-1920](1920/previews/suit.png) \| ![yukata-1920](1920/previews/yukata.png) \| \| 1280 \| 0.733 \| [Download](1280/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-1280](1280/previews/pattern_1.png) \| ![pattern_2-1280](1280/previews/pattern_2.png) \| ![pattern_3-1280](1280/previews/pattern_3.png) \| ![pattern_4-1280](1280/previews/pattern_4.png) \| ![pattern_5-1280](1280/previews/pattern_5.png) \| ![pattern_6-1280](1280/previews/pattern_6.png) \| ![pattern_7-1280](1280/previews/pattern_7.png) \| ![pattern_8-1280](1280/previews/pattern_8.png) \| ![pattern_9-1280](1280/previews/pattern_9.png) \| ![pattern_10-1280](1280/previews/pattern_10.png) \| ![bikini-1280](1280/previews/bikini.png) \| [<NSFW, click to see>](1280/previews/bondage.png) \| ![free-1280](1280/previews/free.png) \| ![maid-1280](1280/previews/maid.png) \| ![miko-1280](1280/previews/miko.png) \| [<NSFW, click to see>](1280/previews/nude.png) \| [<NSFW, click to see>](1280/previews/nude2.png) \| ![suit-1280](1280/previews/suit.png) \| ![yukata-1280](1280/previews/yukata.png) \| \| 640 \| 0.635 \| [Download](640/hua_yuan_yurine_jashinchandropkick.zip) \| ![pattern_1-640](640/previews/pattern_1.png) \| ![pattern_2-640](640/previews/pattern_2.png) \| ![pattern_3-640](640/previews/pattern_3.png) \| ![pattern_4-640](640/previews/pattern_4.png) \| ![pattern_5-640](640/previews/pattern_5.png) \| ![pattern_6-640](640/previews/pattern_6.png) \| ![pattern_7-640](640/previews/pattern_7.png) \| ![pattern_8-640](640/previews/pattern_8.png) \| ![pattern_9-640](640/previews/pattern_9.png) \| ![pattern_10-640](640/previews/pattern_10.png) \| ![bikini-640](640/previews/bikini.png) \| [<NSFW, click to see>](640/previews/bondage.png) \| ![free-640](640/previews/free.png) \| ![maid-640](640/previews/maid.png) \| ![miko-640](640/previews/miko.png) \| [<NSFW, click to see>](640/previews/nude.png) \| [<NSFW, click to see>](640/previews/nude2.png) \| ![suit-640](640/previews/suit.png) \| ![yukata-640](640/previews/yukata.png) \|
hongyin/chat-awareness-0.8b	hongyin	2023-09-20T09:24:23Z	149	1	transformers	[ "transformers", "pytorch", "opt", "text-generation", "en", "zh", "arxiv:2302.13173", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-07T12:00:00Z	--- language: - en - zh pipeline_tag: text-generation --- ## hongyin/chat-awareness-0.8b This is a lightweight English-Chinese bilingual chat assistant with a parameter size of 0.8b, dedicated to reducing the computational cost of inference. The training process consists of two parts: (1) NTP task. (2) Instruction tuning. ```python ### Human: Paraphrasing the text I love you. ### Assistant: I have a crash on you. ``` ## Bibtex entry and citation info Please cite if you find it helpful. ``` @article{zhu2023metaaid, title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models}, author={Zhu, Hongyin}, journal={arXiv preprint arXiv:2302.13173}, year={2023} } ``` --- license: other ---
ktadzjibov/opus-mt-en-ru-finetuned-en-to-ru-amls	ktadzjibov	2023-09-20T09:19:34Z	12	0	transformers	[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "base_model:Helsinki-NLP/opus-mt-en-ru", "base_model:finetune:Helsinki-NLP/opus-mt-en-ru", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-09-16T21:24:50Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ru tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ru-finetuned-en-to-ru-amls results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 config: ru-en split: validation args: ru-en metrics: - name: Bleu type: bleu value: 28.7642 --- <!-- 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. --> # opus-mt-en-ru-finetuned-en-to-ru-amls This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2832 - Bleu: 28.7642 - Gen Len: 29.495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.9806 \| 1.0 \| 94761 \| 1.2832 \| 28.7642 \| 29.495 \| ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3
kligvasser/ffhq-vqvae-clip-landmark-arcface	kligvasser	2023-09-20T09:18:41Z	2	0	diffusers	[ "diffusers", "safetensors", "diffusers:ConditionalDDPMPipeline", "region:us" ]	null	2023-09-14T07:41:15Z	Please refer [facial-diffusion](https://github.com/kligvasser/facial-diffusion) for evaluation and training examples. Condition: ![Screenshot](condition.png) Sample: ![Screenshot](sample.png)
MattyB95/pre_trained_DF_RawNet2	MattyB95	2023-09-20T09:09:36Z	0	0	null	[ "license:mit", "region:us" ]	null	2023-09-17T22:03:34Z	--- license: mit --- ## Citation If you use this code in your research please use the following citation: ```bibtex @INPROCEEDINGS{9414234, author={Tak, Hemlata and Patino, Jose and Todisco, Massimiliano and Nautsch, Andreas and Evans, Nicholas and Larcher, Anthony}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={End-to-End anti-spoofing with RawNet2}, year={2021}, pages={6369-6373} } ```
TheBloke/LLaMA-7b-GGUF	TheBloke	2023-09-20T09:03:53Z	580	7	transformers	[ "transformers", "gguf", "llama", "license:other", "region:us" ]	null	2023-09-20T02:27:21Z	--- base_model: https://ai.meta.com/blog/large-language-model-llama-meta-ai inference: false license: other model_creator: Meta model_name: LLaMA 7B model_type: llama prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # LLaMA 7B - GGUF - Model creator: [Meta](https://huggingface.co/none) - Original model: [LLaMA 7B](https://ai.meta.com/blog/large-language-model-llama-meta-ai) <!-- description start --> ## Description This repo contains GGUF format model files for [Meta's LLaMA 7b](https://ai.meta.com/blog/large-language-model-llama-meta-ai). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA-7b-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA-7b-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA-7b-GGUF) * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/huggyllama/llama-7b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [llama-7b.Q2_K.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q2_K.gguf) \| Q2_K \| 2 \| 2.83 GB\| 5.33 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [llama-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| 5.45 GB \| very small, high quality loss \| \| [llama-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| 5.80 GB \| very small, high quality loss \| \| [llama-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| 6.10 GB \| small, substantial quality loss \| \| [llama-7b.Q4_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| 6.33 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [llama-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| 6.36 GB \| small, greater quality loss \| \| [llama-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| 6.58 GB \| medium, balanced quality - recommended \| \| [llama-7b.Q5_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| 7.15 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [llama-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| 7.15 GB \| large, low quality loss - recommended \| \| [llama-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| 7.28 GB \| large, very low quality loss - recommended \| \| [llama-7b.Q6_K.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| 8.03 GB \| very large, extremely low quality loss \| \| [llama-7b.Q8_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| 9.66 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/LLaMA-7b-GGUF and below it, a specific filename to download, such as: llama-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/LLaMA-7b-GGUF llama-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/LLaMA-7b-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LLaMA-7b-GGUF llama-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m llama-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/LLaMA-7b-GGUF", model_file="llama-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Meta's LLaMA 7b This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format. <!-- original-model-card end -->
jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates	jordyvl	2023-09-20T08:54:28Z	103	0	transformers	[ "transformers", "pytorch", "layoutlmv3", "text-classification", "generated_from_trainer", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-09-19T18:46:12Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8358 - Accuracy: 0.6925 - Exit 0 Accuracy: 0.065 - Exit 1 Accuracy: 0.0625 - Exit 2 Accuracy: 0.0625 - Exit 3 Accuracy: 0.0625 - Exit 4 Accuracy: 0.0625 ## Model description More information needed ## Intended uses & 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: 24 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 288 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Exit 0 Accuracy \| Exit 1 Accuracy \| Exit 2 Accuracy \| Exit 3 Accuracy \| Exit 4 Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------:\| \| No log \| 0.71 \| 2 \| 6.0791 \| 0.0675 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 1.71 \| 4 \| 5.8341 \| 0.1125 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 2.71 \| 6 \| 5.6298 \| 0.1425 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 3.71 \| 8 \| 5.4519 \| 0.1875 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 4.71 \| 10 \| 5.2664 \| 0.1925 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 5.71 \| 12 \| 5.0830 \| 0.2075 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 6.71 \| 14 \| 4.8990 \| 0.215 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 7.71 \| 16 \| 4.7073 \| 0.215 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 8.71 \| 18 \| 4.5002 \| 0.1925 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 9.71 \| 20 \| 4.4009 \| 0.2225 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 10.71 \| 22 \| 4.2853 \| 0.23 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 11.71 \| 24 \| 4.0933 \| 0.27 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 12.71 \| 26 \| 3.9761 \| 0.27 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 13.71 \| 28 \| 3.9351 \| 0.3025 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 14.71 \| 30 \| 3.8232 \| 0.32 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 15.71 \| 32 \| 3.7217 \| 0.345 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 16.71 \| 34 \| 3.6380 \| 0.39 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 17.71 \| 36 \| 3.6336 \| 0.3925 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 18.71 \| 38 \| 3.5816 \| 0.415 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 19.71 \| 40 \| 3.4517 \| 0.445 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 20.71 \| 42 \| 3.4066 \| 0.475 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 21.71 \| 44 \| 3.3252 \| 0.4825 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 22.71 \| 46 \| 3.3034 \| 0.5075 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 23.71 \| 48 \| 3.2461 \| 0.5275 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 24.71 \| 50 \| 3.2623 \| 0.54 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 25.71 \| 52 \| 3.1701 \| 0.545 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 26.71 \| 54 \| 3.2183 \| 0.545 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 27.71 \| 56 \| 3.1410 \| 0.5625 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 28.71 \| 58 \| 3.1476 \| 0.56 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 29.71 \| 60 \| 3.0950 \| 0.585 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 30.71 \| 62 \| 3.0338 \| 0.605 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 31.71 \| 64 \| 3.0406 \| 0.6 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 32.71 \| 66 \| 3.0577 \| 0.615 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 33.71 \| 68 \| 3.0389 \| 0.6125 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 34.71 \| 70 \| 3.0199 \| 0.62 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 35.71 \| 72 \| 3.0103 \| 0.645 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 36.71 \| 74 \| 2.9517 \| 0.645 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 37.71 \| 76 \| 2.9726 \| 0.645 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 38.71 \| 78 \| 2.9587 \| 0.66 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 39.71 \| 80 \| 2.9024 \| 0.6725 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 40.71 \| 82 \| 2.9354 \| 0.655 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 41.71 \| 84 \| 2.8976 \| 0.6775 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 42.71 \| 86 \| 2.8988 \| 0.6875 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 43.71 \| 88 \| 2.8944 \| 0.6825 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 44.71 \| 90 \| 2.8853 \| 0.6825 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 45.71 \| 92 \| 2.8685 \| 0.6825 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 46.71 \| 94 \| 2.8623 \| 0.665 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 47.71 \| 96 \| 2.8534 \| 0.675 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 48.71 \| 98 \| 2.8601 \| 0.685 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 49.71 \| 100 \| 2.8378 \| 0.6875 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 50.71 \| 102 \| 2.8203 \| 0.6875 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 51.71 \| 104 \| 2.8153 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 52.71 \| 106 \| 2.8077 \| 0.6925 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 53.71 \| 108 \| 2.8061 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 54.71 \| 110 \| 2.8099 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 55.71 \| 112 \| 2.8061 \| 0.6875 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 56.71 \| 114 \| 2.8268 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 57.71 \| 116 \| 2.8331 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 58.71 \| 118 \| 2.8371 \| 0.69 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| \| No log \| 59.71 \| 120 \| 2.8358 \| 0.6925 \| 0.065 \| 0.0625 \| 0.0625 \| 0.0625 \| 0.0625 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

SakataHalmi/q-Taxi-v3y

SakataHalmi

2023-09-20T15:46:11Z

0

null

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

reinforcement-learning

2023-09-20T15:46:08Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3y results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage model = load_from_hub(repo_id="SakataHalmi/q-Taxi-v3y", 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"])

MohanaPriyaa/image_classification

MohanaPriyaa

2023-09-20T15:43:27Z

63

1

transformers

[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-20T14:06:18Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: MohanaPriyaa/image_classifier results: [] ---  # MohanaPriyaa/food_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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2925 - Validation Loss: 0.2284 - Train Accuracy: 0.909 - 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2925 | 0.2284 | 0.909 | 0 | ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

tonystark0/bert-finetuned-ner

tonystark0

2023-09-20T15:42:25Z

120

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2023-09-20T15:30:39Z

--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9302325581395349 - name: Recall type: recall value: 0.9491753618310333 - name: F1 type: f1 value: 0.9396084964598085 - name: Accuracy type: accuracy value: 0.9858715488314593 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0609 - Precision: 0.9302 - Recall: 0.9492 - F1: 0.9396 - Accuracy: 0.9859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0788 | 1.0 | 1756 | 0.0763 | 0.9123 | 0.9337 | 0.9229 | 0.9800 | | 0.0393 | 2.0 | 3512 | 0.0605 | 0.9262 | 0.9480 | 0.9370 | 0.9855 | | 0.0255 | 3.0 | 5268 | 0.0609 | 0.9302 | 0.9492 | 0.9396 | 0.9859 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

signon-project/mbart-large-cc25-ft-amr30-es

signon-project

2023-09-20T15:34:44Z

106

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:facebook/mbart-large-cc25", "base_model:finetune:facebook/mbart-large-cc25", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-01-19T11:50:36Z

--- base_model: facebook/mbart-large-cc25 tags: - generated_from_trainer model-index: - name: es+no_processing results: [] ---  # es+no_processing This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5874 - Smatch Precision: 74.08 - Smatch Recall: 76.84 - Smatch Fscore: 75.44 - Smatch Unparsable: 0 - Percent Not Recoverable: 0.2323 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Smatch Precision | Smatch Recall | Smatch Fscore | Smatch Unparsable | Percent Not Recoverable | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:-------------:|:-----------------:|:-----------------------:| | 0.3908 | 1.0 | 3477 | 1.4300 | 19.74 | 68.95 | 30.7 | 0 | 0.0 | | 0.256 | 2.0 | 6954 | 0.8998 | 27.75 | 70.61 | 39.85 | 1 | 0.0581 | | 0.0704 | 3.0 | 10431 | 0.8727 | 30.09 | 72.2 | 42.47 | 0 | 0.1161 | | 0.0586 | 4.0 | 13908 | 0.7774 | 37.1 | 74.93 | 49.62 | 0 | 0.1161 | | 0.1059 | 5.0 | 17385 | 0.6322 | 42.52 | 74.54 | 54.15 | 1 | 0.1161 | | 0.0424 | 6.0 | 20862 | 0.6090 | 47.13 | 76.21 | 58.25 | 0 | 0.0 | | 0.0139 | 7.0 | 24339 | 0.5768 | 48.3 | 77.31 | 59.46 | 0 | 0.0581 | | 0.08 | 8.0 | 27817 | 0.5608 | 55.74 | 77.16 | 64.72 | 0 | 0.1161 | | 0.0224 | 9.0 | 31294 | 0.5937 | 54.91 | 77.02 | 64.11 | 0 | 0.0581 | | 0.0757 | 10.0 | 34771 | 0.5588 | 59.53 | 77.47 | 67.32 | 0 | 0.0581 | | 0.0613 | 11.0 | 38248 | 0.5894 | 60.83 | 77.82 | 68.28 | 0 | 0.0581 | | 0.1045 | 12.0 | 41725 | 0.5847 | 61.23 | 77.17 | 68.28 | 0 | 0.1742 | | 0.012 | 13.0 | 45202 | 0.5588 | 65.61 | 77.47 | 71.05 | 0 | 0.0 | | 0.0591 | 14.0 | 48679 | 0.5609 | 66.51 | 77.86 | 71.74 | 0 | 0.0581 | | 0.0252 | 15.0 | 52156 | 0.5653 | 67.48 | 77.75 | 72.25 | 0 | 0.0 | | 0.0129 | 16.0 | 55634 | 0.5602 | 68.92 | 77.57 | 72.99 | 0 | 0.0 | | 0.0006 | 17.0 | 59111 | 0.5876 | 68.57 | 77.81 | 72.9 | 0 | 0.1742 | | 0.0182 | 18.0 | 62588 | 0.5951 | 68.97 | 77.96 | 73.19 | 0 | 0.1161 | | 0.018 | 19.0 | 66065 | 0.5865 | 70.63 | 77.68 | 73.98 | 0 | 0.0581 | | 0.0097 | 20.0 | 69542 | 0.6073 | 71.68 | 77.38 | 74.42 | 0 | 0.1161 | | 0.0021 | 21.0 | 73019 | 0.5984 | 72.25 | 77.92 | 74.98 | 0 | 0.0581 | | 0.0371 | 22.0 | 76496 | 0.5907 | 72.92 | 77.59 | 75.18 | 0 | 0.1742 | | 0.0382 | 23.0 | 79973 | 0.5928 | 73.06 | 77.49 | 75.21 | 0 | 0.1742 | | 0.0148 | 24.0 | 83451 | 0.5903 | 73.98 | 77.15 | 75.53 | 0 | 0.0581 | | 0.1326 | 25.0 | 86925 | 0.5874 | 74.08 | 76.84 | 75.44 | 0 | 0.2323 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

Sudhee1997/Llama-2-7b-Custom-Recruit

Sudhee1997

2023-09-20T15:33:14Z

5

0

peft

[ "peft", "region:us" ]

null

2023-09-15T11:25:00Z

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

willyninja30/llama-2-7b-chat-hf-fr-en-python

willyninja30

2023-09-20T15:33:02Z

5

0

peft

[ "peft", "text-generation", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "region:us" ]

text-generation

2023-08-25T10:16:03Z

--- library_name: peft base_model: meta-llama/Llama-2-7b-chat-hf inference: true pipeline_tag: text-generation --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

CyberHarem/perusepone2shi_jashinchandropkick

CyberHarem

2023-09-20T15:32:55Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/perusepone2shi_jashinchandropkick", "license:mit", "region:us" ]

text-to-image

2023-09-20T15:01:25Z

--- license: mit datasets: - CyberHarem/perusepone2shi_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of perusepone2shi_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4080, you need to download `4080/perusepone2shi_jashinchandropkick.pt` as the embedding and `4080/perusepone2shi_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4080**, with the score of 0.851. The trigger words are: 1. `perusepone2shi_jashinchandropkick` 2. `short_hair, pointy_ears, hair_over_one_eye, grey_hair, red_eyes, white_hair, ribbon, smile` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:-----------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.840 | [Download](5100/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![pattern_8-5100](5100/previews/pattern_8.png) | ![pattern_9-5100](5100/previews/pattern_9.png) | ![pattern_10-5100](5100/previews/pattern_10.png) | ![pattern_11-5100](5100/previews/pattern_11.png) | ![pattern_12-5100](5100/previews/pattern_12.png) | ![pattern_13-5100](5100/previews/pattern_13.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.844 | [Download](4760/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![pattern_9-4760](4760/previews/pattern_9.png) | ![pattern_10-4760](4760/previews/pattern_10.png) | ![pattern_11-4760](4760/previews/pattern_11.png) | ![pattern_12-4760](4760/previews/pattern_12.png) | ![pattern_13-4760](4760/previews/pattern_13.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.786 | [Download](4420/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![pattern_8-4420](4420/previews/pattern_8.png) | ![pattern_9-4420](4420/previews/pattern_9.png) | ![pattern_10-4420](4420/previews/pattern_10.png) | ![pattern_11-4420](4420/previews/pattern_11.png) | ![pattern_12-4420](4420/previews/pattern_12.png) | ![pattern_13-4420](4420/previews/pattern_13.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | **4080** | **0.851** | [**Download**](4080/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![pattern_9-4080](4080/previews/pattern_9.png) | ![pattern_10-4080](4080/previews/pattern_10.png) | ![pattern_11-4080](4080/previews/pattern_11.png) | ![pattern_12-4080](4080/previews/pattern_12.png) | ![pattern_13-4080](4080/previews/pattern_13.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.652 | [Download](3740/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![pattern_8-3740](3740/previews/pattern_8.png) | ![pattern_9-3740](3740/previews/pattern_9.png) | ![pattern_10-3740](3740/previews/pattern_10.png) | ![pattern_11-3740](3740/previews/pattern_11.png) | ![pattern_12-3740](3740/previews/pattern_12.png) | ![pattern_13-3740](3740/previews/pattern_13.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.654 | [Download](3400/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![pattern_9-3400](3400/previews/pattern_9.png) | ![pattern_10-3400](3400/previews/pattern_10.png) | ![pattern_11-3400](3400/previews/pattern_11.png) | ![pattern_12-3400](3400/previews/pattern_12.png) | ![pattern_13-3400](3400/previews/pattern_13.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.645 | [Download](3060/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![pattern_8-3060](3060/previews/pattern_8.png) | ![pattern_9-3060](3060/previews/pattern_9.png) | ![pattern_10-3060](3060/previews/pattern_10.png) | ![pattern_11-3060](3060/previews/pattern_11.png) | ![pattern_12-3060](3060/previews/pattern_12.png) | ![pattern_13-3060](3060/previews/pattern_13.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.806 | [Download](2720/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![pattern_9-2720](2720/previews/pattern_9.png) | ![pattern_10-2720](2720/previews/pattern_10.png) | ![pattern_11-2720](2720/previews/pattern_11.png) | ![pattern_12-2720](2720/previews/pattern_12.png) | ![pattern_13-2720](2720/previews/pattern_13.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.756 | [Download](2380/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![pattern_8-2380](2380/previews/pattern_8.png) | ![pattern_9-2380](2380/previews/pattern_9.png) | ![pattern_10-2380](2380/previews/pattern_10.png) | ![pattern_11-2380](2380/previews/pattern_11.png) | ![pattern_12-2380](2380/previews/pattern_12.png) | ![pattern_13-2380](2380/previews/pattern_13.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.674 | [Download](2040/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![pattern_9-2040](2040/previews/pattern_9.png) | ![pattern_10-2040](2040/previews/pattern_10.png) | ![pattern_11-2040](2040/previews/pattern_11.png) | ![pattern_12-2040](2040/previews/pattern_12.png) | ![pattern_13-2040](2040/previews/pattern_13.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.688 | [Download](1700/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![pattern_8-1700](1700/previews/pattern_8.png) | ![pattern_9-1700](1700/previews/pattern_9.png) | ![pattern_10-1700](1700/previews/pattern_10.png) | ![pattern_11-1700](1700/previews/pattern_11.png) | ![pattern_12-1700](1700/previews/pattern_12.png) | ![pattern_13-1700](1700/previews/pattern_13.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.629 | [Download](1360/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![pattern_9-1360](1360/previews/pattern_9.png) | ![pattern_10-1360](1360/previews/pattern_10.png) | ![pattern_11-1360](1360/previews/pattern_11.png) | ![pattern_12-1360](1360/previews/pattern_12.png) | ![pattern_13-1360](1360/previews/pattern_13.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.470 | [Download](1020/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![pattern_8-1020](1020/previews/pattern_8.png) | ![pattern_9-1020](1020/previews/pattern_9.png) | ![pattern_10-1020](1020/previews/pattern_10.png) | ![pattern_11-1020](1020/previews/pattern_11.png) | ![pattern_12-1020](1020/previews/pattern_12.png) | ![pattern_13-1020](1020/previews/pattern_13.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.382 | [Download](680/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![pattern_9-680](680/previews/pattern_9.png) | ![pattern_10-680](680/previews/pattern_10.png) | ![pattern_11-680](680/previews/pattern_11.png) | ![pattern_12-680](680/previews/pattern_12.png) | ![pattern_13-680](680/previews/pattern_13.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.097 | [Download](340/perusepone2shi_jashinchandropkick.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![pattern_8-340](340/previews/pattern_8.png) | ![pattern_9-340](340/previews/pattern_9.png) | ![pattern_10-340](340/previews/pattern_10.png) | ![pattern_11-340](340/previews/pattern_11.png) | ![pattern_12-340](340/previews/pattern_12.png) | ![pattern_13-340](340/previews/pattern_13.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

Faradaylab/Aria_7b_v2

Faradaylab

2023-09-20T15:30:57Z

7

3

peft

[ "peft", "llama7b", "LLAMA2", "opensource", "culture", "code", "text-generation", "fr", "en", "es", "dataset:Snit/french-conversation", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]

text-generation

2023-08-25T12:59:18Z

--- language: - fr - en - es license: llama2 library_name: peft tags: - llama7b - LLAMA2 - peft - opensource - culture - code datasets: - Snit/french-conversation inference: true pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-chat-hf --- ARIA 7B V2 is a model created by Faraday 🇫🇷 🇧🇪 The growing need of artificial intelligence tools around the world has created a run for GPU power. We decided to create an affordable model with better skills in French which can run on single GPU and reduce data bias observed in models trained mostly on english only datasets.. ARIA 7B has been trained on over 20.000 tokens of a high quality french dataset. ARIA 7B is one of the best open source models in the world avaible for this size of parameters. ## Training procedure : NVIDIA A100. Thanks to NVIDIA GPU and Inception program,we have been able to train our model within less than 24 hours. ## Base model : LLAMA_2-7B-CHAT-HF We strongly believe that training models in more languages datasets can not only increase their knowledge base but also give more open analysis perspectives ,less focused visions and opinions from only one part of the world. ## Contact [email protected] ## Number of Epoch : 2 ## Timing : Less than 24 hours The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

willyninja30/aria7Beta

willyninja30

2023-09-20T15:30:47Z

8

1

peft

[ "peft", "llama2", "pytorch", "french", "text-generation", "fr", "en", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:adapter:meta-llama/Llama-2-7b-chat-hf", "license:apache-2.0", "region:us" ]

text-generation

2023-08-23T14:37:35Z

--- language: - fr - en license: apache-2.0 library_name: peft tags: - llama2 - pytorch - french inference: false pipeline_tag: text-generation base_model: meta-llama/Llama-2-7b-chat-hf --- ## ARIA 7B is a model created by Faraday The growing need of artificial intelligence tools around the world has created a run for GPU power. We decided to create an affordable model with better skills in French which can run on single GPU and reduce data bias observed in models trained mostly on english only datasets.. ARIA 7B has been trained on over 20.000 tokens of a high quality french dataset. ARIA 7B is one of the best open source models in the world avaible for this size of parameters. GPU used for training : NVIDIA V100. Thanks to NVIDIA GPU and Inception program,we have been able to train our model within less than 24 hours. Base model : LLAMA_2-7B-CHAT-HF We strongly believe that training models in more languages datasets can not only increase their knowledge base but also give more open analysis perspectives ,less focused visions and opinions from only one part of the world. ## ARIA 7B est un modèle créé par Faraday Le besoin croissant en intelligence artificiele dans le monde a créé une course vers la puissance de calcul des cartes graphiques. Nous avons décidé de créer un modèle accessible capable de tourner sur une seule carte graphique et réduisant les biais d'algorithmes observés sur les modèles entrainés uniquement sur des bases de données en anglais. ARIA 7B a été entrainé sur un dataset de grande qualité avec plus de 20.000 tokens en Français. GPU(Carte graphique) utilisée pour le finetuning: NVIDIA V100. Merci à NVIDIA et au programme Nvidia Inception qui nous a orienté pendant tout le processus et nous a permis d'entrainer le modèle en moins de 24h. Modèle de base : LLAMA_2-7B-CHAT-HF Nous pensons que le fait d'entraîner des modèles sur des langues différentes permet non seulement d'élargir la base de connaissance mais aussi de donner d'autres perspectives d'analyses plus ouvertes,et moins centrées sur la vision et les opinions exprimées par une seule partie du monde. Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

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

SakataHalmi

2023-09-20T15:30:22Z

0

null

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

reinforcement-learning

2023-09-20T15:30:19Z

--- 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 model = load_from_hub(repo_id="SakataHalmi/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"])

BramVanroy/mbart-large-cc25-ft-amr30-en

BramVanroy

2023-09-20T15:28:41Z

130

0

transformers

[ "transformers", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:facebook/mbart-large-cc25", "base_model:finetune:facebook/mbart-large-cc25", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-20T15:26:13Z

--- base_model: facebook/mbart-large-cc25 tags: - generated_from_trainer model-index: - name: en+no_processing results: [] ---  # en+no_processing This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4481 - Smatch Precision: 80.57 - Smatch Recall: 83.81 - Smatch Fscore: 82.16 - Smatch Unparsable: 0 - Percent Not Recoverable: 0.3484 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results | Training Loss | Epoch | Step | Validation Loss | Smatch Precision | Smatch Recall | Smatch Fscore | Smatch Unparsable | Percent Not Recoverable | |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:-------------:|:-----------------:|:-----------------------:| | 0.3471 | 1.0 | 3477 | 1.4889 | 22.35 | 73.05 | 34.23 | 0 | 0.1161 | | 0.1741 | 2.0 | 6954 | 0.8681 | 30.1 | 71.92 | 42.44 | 0 | 0.1161 | | 0.1296 | 3.0 | 10431 | 0.7081 | 38.6 | 78.68 | 51.8 | 0 | 0.0581 | | 0.1308 | 4.0 | 13908 | 0.9546 | 37.49 | 78.23 | 50.69 | 0 | 0.0 | | 0.2213 | 5.0 | 17385 | 0.5544 | 47.63 | 81.17 | 60.03 | 0 | 0.0 | | 0.0317 | 6.0 | 20862 | 0.4884 | 49.3 | 80.9 | 61.27 | 0 | 0.0 | | 0.1007 | 7.0 | 24339 | 0.4763 | 54.88 | 82.09 | 65.78 | 0 | 0.0 | | 0.092 | 8.0 | 27817 | 0.4444 | 57.37 | 83.2 | 67.91 | 0 | 0.0 | | 0.1051 | 9.0 | 31294 | 0.4192 | 64.37 | 83.81 | 72.82 | 0 | 0.0 | | 0.0079 | 10.0 | 34771 | 0.4685 | 61.3 | 83.1 | 70.55 | 0 | 0.0 | | 0.0211 | 11.0 | 38248 | 0.4389 | 63.36 | 84.57 | 72.44 | 0 | 0.1161 | | 0.1122 | 12.0 | 41725 | 0.4146 | 69.39 | 83.56 | 75.82 | 0 | 0.0581 | | 0.0183 | 13.0 | 45202 | 0.4003 | 73.9 | 83.71 | 78.5 | 0 | 0.0 | | 0.0244 | 14.0 | 48679 | 0.4208 | 73.79 | 83.92 | 78.53 | 0 | 0.1161 | | 0.0116 | 15.0 | 52156 | 0.4248 | 73.88 | 83.85 | 78.55 | 0 | 0.1161 | | 0.0357 | 16.0 | 55634 | 0.4235 | 75.78 | 84.08 | 79.71 | 0 | 0.1161 | | 0.0006 | 17.0 | 59111 | 0.4181 | 76.15 | 84.15 | 79.95 | 0 | 0.0581 | | 0.0329 | 18.0 | 62588 | 0.4494 | 77.21 | 84.12 | 80.52 | 0 | 0.0 | | 0.0003 | 19.0 | 66065 | 0.4389 | 78.02 | 84.13 | 80.96 | 0 | 0.0 | | 0.04 | 20.0 | 69542 | 0.4439 | 78.78 | 84.23 | 81.41 | 0 | 0.0 | | 0.0182 | 21.0 | 73019 | 0.4430 | 79.82 | 84.05 | 81.88 | 0 | 0.0581 | | 0.0006 | 22.0 | 76496 | 0.4488 | 79.96 | 83.74 | 81.81 | 0 | 0.0581 | | 0.0074 | 23.0 | 79973 | 0.4569 | 79.84 | 83.85 | 81.79 | 0 | 0.0581 | | 0.0133 | 24.0 | 83451 | 0.4469 | 80.45 | 83.81 | 82.09 | 0 | 0.2904 | | 0.0055 | 25.0 | 86925 | 0.4481 | 80.57 | 83.81 | 82.16 | 0 | 0.3484 | ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.2 - Tokenizers 0.13.3

csakarwa/Model1cs

csakarwa

2023-09-20T15:24:47Z

1

0

peft

[ "peft", "region:us" ]

null

2023-09-20T15:24:45Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

AnatolyBelov/my_t5_small_test

AnatolyBelov

2023-09-20T15:19:33Z

4

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-18T14:00:07Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - opus_books metrics: - bleu model-index: - name: my_t5_small_test results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: opus_books type: opus_books config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 6.372 ---  # my_t5_small_test This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 1.5026 - Bleu: 6.372 - Gen Len: 17.5713 ## Model description More information needed ## Intended uses & 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 | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | 1.7108 | 1.0 | 6355 | 1.5113 | 6.3012 | 17.5701 | | 1.6833 | 2.0 | 12710 | 1.5026 | 6.372 | 17.5713 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

abhishek23HF/MARKEtING_BLOOMZ_1B

abhishek23HF

2023-09-20T15:17:28Z

0

peft

[ "peft", "region:us" ]

null

2023-09-20T15:17:23Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0.dev0

databio/v2v-ChIP-atlas-hg38-ATAC

databio

2023-09-20T15:15:34Z

0

null

[ "region:us" ]

null

2023-09-19T20:50:13Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Vec2Vec ChIP-atlas hg38 ## Model Details ### Model Description This is a Vec2Vec model that encodes embedding vectors of natural language into embedding vectors of BED files. This model was trained with hg38 ChIP-atlas ATAC-seq data. The natural language metadata came from the experiment list, their embedding vectors were encoded by [sentence-transformers](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). The BED files were embedded by [Region2Vec](https://huggingface.co/databio/r2v-ChIP-atlas-hg38) - **Developed by:** Ziyang "Claude" Hu - **Model type:** Vec2Vec - **Language(s) (NLP):** hg38 ### Model Sources [optional]  - **Repository:** https://github.com/databio/geniml - **Paper [optional]:** N/A ## Uses This model can be used to search BED files with natural language query strings. In the search interface, the query strings will be encoded by same sentence-transformers model, and the output vector will be encoded into the final query vector by this Vec2Vec. The K BED files whose embedding vectors (embedded by same Region2Vec) are closest to the final query vector are results. It is limited to hg38. It is not recommended to use this model for data outside ATAC-seq. ## How to Get Started with the Model Vec2Vec will allow direct importing from Hugging Face soon. [More Information Needed] ## Training Details ### Training Data TODO

ramchiluveru/MarketingCampaign

ramchiluveru

2023-09-20T15:14:23Z

0

peft

[ "peft", "region:us" ]

null

2023-09-20T15:14:21Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

badassbandit/taxi

badassbandit

2023-09-20T15:10:46Z

0

null

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

reinforcement-learning

2023-09-20T15:10:45Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="badassbandit/taxi", 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"]) ```

CyberHarem/matsuo_chizuru_idolmastercinderellagirls

CyberHarem

2023-09-20T15:09:30Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/matsuo_chizuru_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-20T14:52:03Z

--- license: mit datasets: - CyberHarem/matsuo_chizuru_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of matsuo_chizuru_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/matsuo_chizuru_idolmastercinderellagirls.pt` as the embedding and `4760/matsuo_chizuru_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.996. The trigger words are: 1. `matsuo_chizuru_idolmastercinderellagirls` 2. `short_hair, black_hair, blush, hair_ornament, hairclip, black_eyes, thick_eyebrows, purple_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.983 | [Download](5100/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | [<NSFW, click to see>](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![pattern_8-5100](5100/previews/pattern_8.png) | ![pattern_9-5100](5100/previews/pattern_9.png) | ![pattern_10-5100](5100/previews/pattern_10.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.996** | [**Download**](4760/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | [<NSFW, click to see>](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![pattern_9-4760](4760/previews/pattern_9.png) | ![pattern_10-4760](4760/previews/pattern_10.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.968 | [Download](4420/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | [<NSFW, click to see>](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![pattern_8-4420](4420/previews/pattern_8.png) | ![pattern_9-4420](4420/previews/pattern_9.png) | ![pattern_10-4420](4420/previews/pattern_10.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.994 | [Download](4080/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | [<NSFW, click to see>](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![pattern_9-4080](4080/previews/pattern_9.png) | ![pattern_10-4080](4080/previews/pattern_10.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.960 | [Download](3740/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | [<NSFW, click to see>](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![pattern_8-3740](3740/previews/pattern_8.png) | ![pattern_9-3740](3740/previews/pattern_9.png) | ![pattern_10-3740](3740/previews/pattern_10.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.994 | [Download](3400/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | [<NSFW, click to see>](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![pattern_9-3400](3400/previews/pattern_9.png) | ![pattern_10-3400](3400/previews/pattern_10.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.992 | [Download](3060/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | [<NSFW, click to see>](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![pattern_8-3060](3060/previews/pattern_8.png) | ![pattern_9-3060](3060/previews/pattern_9.png) | ![pattern_10-3060](3060/previews/pattern_10.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.982 | [Download](2720/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | [<NSFW, click to see>](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![pattern_9-2720](2720/previews/pattern_9.png) | ![pattern_10-2720](2720/previews/pattern_10.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.995 | [Download](2380/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | [<NSFW, click to see>](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![pattern_8-2380](2380/previews/pattern_8.png) | ![pattern_9-2380](2380/previews/pattern_9.png) | ![pattern_10-2380](2380/previews/pattern_10.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.959 | [Download](2040/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | [<NSFW, click to see>](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![pattern_9-2040](2040/previews/pattern_9.png) | ![pattern_10-2040](2040/previews/pattern_10.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.961 | [Download](1700/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | [<NSFW, click to see>](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![pattern_8-1700](1700/previews/pattern_8.png) | ![pattern_9-1700](1700/previews/pattern_9.png) | ![pattern_10-1700](1700/previews/pattern_10.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.924 | [Download](1360/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | [<NSFW, click to see>](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![pattern_9-1360](1360/previews/pattern_9.png) | ![pattern_10-1360](1360/previews/pattern_10.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.872 | [Download](1020/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | [<NSFW, click to see>](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![pattern_8-1020](1020/previews/pattern_8.png) | ![pattern_9-1020](1020/previews/pattern_9.png) | ![pattern_10-1020](1020/previews/pattern_10.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.880 | [Download](680/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | [<NSFW, click to see>](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![pattern_9-680](680/previews/pattern_9.png) | ![pattern_10-680](680/previews/pattern_10.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.313 | [Download](340/matsuo_chizuru_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | [<NSFW, click to see>](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![pattern_8-340](340/previews/pattern_8.png) | ![pattern_9-340](340/previews/pattern_9.png) | ![pattern_10-340](340/previews/pattern_10.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

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

badassbandit

2023-09-20T15:06:16Z

0

null

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

reinforcement-learning

2023-09-20T15:06:14Z

--- 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="badassbandit/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"]) ```

BanUrsus/rl_course_additional_challenge_vizdoom_deathmatch_bots

BanUrsus

2023-09-20T14:56:40Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-09-20T14:56:24Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_deathmatch_bots type: doom_deathmatch_bots metrics: - type: mean_reward value: 1.70 +/- 1.42 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_deathmatch_bots** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r BanUrsus/rl_course_additional_challenge_vizdoom_deathmatch_bots ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=rl_course_additional_challenge_vizdoom_deathmatch_bots ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_deathmatch_bots --train_dir=./train_dir --experiment=rl_course_additional_challenge_vizdoom_deathmatch_bots --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

hosnasn/tannaz1-reza

hosnasn

2023-09-20T14:54:02Z

0

null

[ "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]

text-to-image

2023-09-20T14:47:47Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### tannaz1_reza Dreambooth model trained by hosnasn 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:

csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4

csdc-atl

2023-09-20T14:50:13Z

170

2

transformers

[ "transformers", "safetensors", "baichuan", "text-generation", "custom_code", "en", "zh", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]

text-generation

2023-09-15T18:06:52Z

--- language: - en - zh license: other tasks: - text-generation ---   # Baichuan 2 13B Chat - Int4  ## 描述该repo包含[Baichuan 2 7B Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat)的Int4 GPTQ模型文件。   ## GPTQ参数该GPTQ文件都是用AutoGPTQ生成的。 - Bits: 4/8 - GS: 32/128 - Act Order: True - Damp %: 0.1 - GPTQ dataset: 中文、英文混合数据集 - Sequence Length: 4096 | 模型版本 | agieval | ceval | cmmlu | size | 推理速度(A100-40G) | |---|---|---|---|---|---| | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | 40.25 | 56.33 | 58.44 | 27.79g | 31.55 tokens/s | | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) | 39.01 | 56.63 | 57.81 | 9.08g | 18.45 tokens/s | | [GPTQ-4bit-32g](https://huggingface.co/csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4/tree/4bit-32g) | 38.64 | 57.18 | 57.47 | 9.87g | 27.35(hf) \ 38.28(autogptq) tokens/s | | [GPTQ-4bit-128g](https://huggingface.co/csdc-atl/Baichuan2-13B-Chat-GPTQ-Int4/tree/main) | 38.78 | 56.42 | 57.78 | 9.14g | 28.74(hf) \ 39.24(autogptq) tokens/s |  ## 如何在Python代码中使用此GPTQ模型 ### 安装必要的依赖必须： Transformers 4.32.0以上、Optimum 1.12.0以上、AutoGPTQ 0.4.2以上 ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` 如果您在使用预构建的pip包安装AutoGPTQ时遇到问题，请改为从源代码安装： ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### 然后可以使用以下代码 ```python from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig model_name_or_path = "csdc-atl/Baichuan2-13B-Chat-Int4" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-7B-Chat") messages = [] messages.append({"role": "user", "content": "解释一下“温故而知新”"}) response = model.chat(tokenizer, messages) print(response) "温故而知新"是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。 ```   --- <div align="center"> <h1> Baichuan 2 </h1> </div> <div align="center"> <a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> | <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">💬WeChat</a> </div> <div align="center"> 🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉 </div> # 目录/Table of Contents - [📖 模型介绍/Introduction](#Introduction) - [⚙️ 快速开始/Quick Start](#Start) - [📊 Benchmark评估/Benchmark Evaluation](#Benchmark) - [📜 声明与协议/Terms and Conditions](#Terms) # <span id="Introduction">模型介绍/Introduction</span> Baichuan 2 是[百川智能]推出的新一代开源大语言模型，采用 **2.6 万亿** Tokens 的高质量语料训练，在权威的中文和英文 benchmark 上均取得同尺寸最好的效果。本次发布包含有 7B、13B 的 Base 和 Chat 版本，并提供了 Chat 版本的 4bits 量化，所有版本不仅对学术研究完全开放，开发者也仅需[邮件申请]并获得官方商用许可后，即可以免费商用。具体发布版本和下载见下表： Baichuan 2 is the new generation of large-scale open-source language models launched by [Baichuan Intelligence inc.](https://www.baichuan-ai.com/). It is trained on a high-quality corpus with 2.6 trillion tokens and has achieved the best performance in authoritative Chinese and English benchmarks of the same size. This release includes 7B and 13B versions for both Base and Chat models, along with a 4bits quantized version for the Chat model. All versions are fully open to academic research, and developers can also use them for free in commercial applications after obtaining an official commercial license through [email request](mailto:[email protected]). The specific release versions and download links are listed in the table below: | | Base Model | Chat Model | 4bits Quantized Chat Model | |:---:|:--------------------:|:--------------------:|:--------------------------:| | 7B | [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) | [Baichuan2-7B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat) | [Baichuan2-7B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base-4bits) | | 13B | [Baichuan2-13B-Base](https://huggingface.co/baichuan-inc/Baichuan2-13B-Base) | [Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | [Baichuan2-13B-Chat-4bits](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits) | # <span id="Start">快速开始/Quick Start</span> 在Baichuan2系列模型中，我们为了加快推理速度使用了Pytorch2.0加入的新功能F.scaled_dot_product_attention，因此模型需要在Pytorch2.0环境下运行。 In the Baichuan 2 series models, we have utilized the new feature `F.scaled_dot_product_attention` introduced in PyTorch 2.0 to accelerate inference speed. Therefore, the model needs to be run in a PyTorch 2.0 environment. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.generation.utils import GenerationConfig tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan2-13B-Chat", use_fast=False, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-13B-Chat", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True) model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan2-13B-Chat") messages = [] messages.append({"role": "user", "content": "解释一下“温故而知新”"}) response = model.chat(tokenizer, messages) print(response) "温故而知新"是一句中国古代的成语，出自《论语·为政》篇。这句话的意思是：通过回顾过去，我们可以发现新的知识和理解。换句话说，学习历史和经验可以让我们更好地理解现在和未来。这句话鼓励我们在学习和生活中不断地回顾和反思过去的经验，从而获得新的启示和成长。通过重温旧的知识和经历，我们可以发现新的观点和理解，从而更好地应对不断变化的世界和挑战。 ``` # <span id="Benchmark">Benchmark 结果/Benchmark Evaluation</span> 我们在[通用]、[法律]、[医疗]、[数学]、[代码]和[多语言翻译]六个领域的中英文权威数据集上对模型进行了广泛测试，更多详细测评结果可查看[GitHub]。 We have extensively tested the model on authoritative Chinese-English datasets across six domains: [General](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#general-domain), [Legal](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Medical](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#law-and-medicine), [Mathematics](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), [Code](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#mathematics-and-code), and [Multilingual Translation](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md#multilingual-translation). For more detailed evaluation results, please refer to [GitHub](https://github.com/baichuan-inc/Baichuan2/blob/main/README_EN.md). ### 7B Model Results | | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | |:-----------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| | | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | | **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | | **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | | **LLaMA-7B** | 27.10 | 35.10 | 26.75 | 27.81 | 28.17 | 32.38 | | **LLaMA2-7B** | 28.90 | 45.73 | 31.38 | 25.97 | 26.53 | 39.16 | | **MPT-7B** | 27.15 | 27.93 | 26.00 | 26.54 | 24.83 | 35.20 | | **Falcon-7B** | 24.23 | 26.03 | 25.66 | 24.24 | 24.10 | 28.77 | | **ChatGLM2-6B** | 50.20 | 45.90 | 49.00 | 49.44 | 45.28 | 31.65 | | **[Baichuan-7B]** | 42.80 | 42.30 | 44.02 | 36.34 | 34.44 | 32.48 | | **[Baichuan2-7B-Base]** | 54.00 | 54.16 | 57.07 | 47.47 | 42.73 | 41.56 | ### 13B Model Results | | **C-Eval** | **MMLU** | **CMMLU** | **Gaokao** | **AGIEval** | **BBH** | |:---------------------------:|:----------:|:--------:|:---------:|:----------:|:-----------:|:-------:| | | 5-shot | 5-shot | 5-shot | 5-shot | 5-shot | 3-shot | | **GPT-4** | 68.40 | 83.93 | 70.33 | 66.15 | 63.27 | 75.12 | | **GPT-3.5 Turbo** | 51.10 | 68.54 | 54.06 | 47.07 | 46.13 | 61.59 | | **LLaMA-13B** | 28.50 | 46.30 | 31.15 | 28.23 | 28.22 | 37.89 | | **LLaMA2-13B** | 35.80 | 55.09 | 37.99 | 30.83 | 32.29 | 46.98 | | **Vicuna-13B** | 32.80 | 52.00 | 36.28 | 30.11 | 31.55 | 43.04 | | **Chinese-Alpaca-Plus-13B** | 38.80 | 43.90 | 33.43 | 34.78 | 35.46 | 28.94 | | **XVERSE-13B** | 53.70 | 55.21 | 58.44 | 44.69 | 42.54 | 38.06 | | **[Baichuan-13B-Base]** | 52.40 | 51.60 | 55.30 | 49.69 | 43.20 | 43.01 | | **[Baichuan2-13B-Base]** | 58.10 | 59.17 | 61.97 | 54.33 | 48.17 | 48.78 | ## 训练过程模型/Training Dynamics 除了训练了 2.6 万亿 Tokens 的 [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) 模型，我们还提供了在此之前的另外 11 个中间过程的模型（分别对应训练了约 0.2 ~ 2.4 万亿 Tokens）供社区研究使用（[训练过程checkpoint下载](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)）。下图给出了这些 checkpoints 在 C-Eval、MMLU、CMMLU 三个 benchmark 上的效果变化： In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base) model trained on 2.6 trillion tokens, we also offer 11 additional intermediate-stage models for community research, corresponding to training on approximately 0.2 to 2.4 trillion tokens each ([Intermediate Checkpoints Download](https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints)). The graph below shows the performance changes of these checkpoints on three benchmarks: C-Eval, MMLU, and CMMLU. ![checkpoint](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/checkpoints.jpeg) # <span id="Terms">声明与协议/Terms and Conditions</span> ## 声明我们在此声明，我们的开发团队并未基于 Baichuan 2 模型开发任何应用，无论是在 iOS、Android、网页或任何其他平台。我们强烈呼吁所有使用者，不要利用 Baichuan 2 模型进行任何危害国家社会安全或违法的活动。另外，我们也要求使用者不要将 Baichuan 2 模型用于未经适当安全审查和备案的互联网服务。我们希望所有的使用者都能遵守这个原则，确保科技的发展能在规范和合法的环境下进行。我们已经尽我们所能，来确保模型训练过程中使用的数据的合规性。然而，尽管我们已经做出了巨大的努力，但由于模型和数据的复杂性，仍有可能存在一些无法预见的问题。因此，如果由于使用 Baichuan 2 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 We hereby declare that our team has not developed any applications based on Baichuan 2 models, not on iOS, Android, the web, or any other platform. We strongly call on all users not to use Baichuan 2 models for any activities that harm national / social security or violate the law. Also, we ask users not to use Baichuan 2 models for Internet services that have not undergone appropriate security reviews and filings. We hope that all users can abide by this principle and ensure that the development of technology proceeds in a regulated and legal environment. We have done our best to ensure the compliance of the data used in the model training process. However, despite our considerable efforts, there may still be some unforeseeable issues due to the complexity of the model and data. Therefore, if any problems arise due to the use of Baichuan 2 open-source models, including but not limited to data security issues, public opinion risks, or any risks and problems brought about by the model being misled, abused, spread or improperly exploited, we will not assume any responsibility. ## 协议 Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。Baichuan 2 支持商用。如果将 Baichuan 2 模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱 [[email protected]]。 The use of the source code in this repository follows the open-source license Apache 2.0. Community use of the Baichuan 2 model must adhere to the [Community License for Baichuan 2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). Baichuan 2 supports commercial use. If you are using the Baichuan 2 models or their derivatives for commercial purposes, please contact the licensor in the following manner for registration and to apply for written authorization: Email [email protected]. [GitHub]:https://github.com/baichuan-inc/Baichuan2 [Baichuan2]:https://github.com/baichuan-inc/Baichuan2 [Baichuan-7B]:https://huggingface.co/baichuan-inc/Baichuan-7B [Baichuan2-7B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base [Baichuan2-7B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat [Baichuan2-7B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat-4bits [Baichuan-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan-13B-Base [Baichuan2-13B-Base]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Base [Baichuan2-13B-Chat]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat [Baichuan2-13B-Chat-4bits]:https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat-4bits [通用]:https://github.com/baichuan-inc/Baichuan2#%E9%80%9A%E7%94%A8%E9%A2%86%E5%9F%9F [法律]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [医疗]:https://github.com/baichuan-inc/Baichuan2#%E6%B3%95%E5%BE%8B%E5%8C%BB%E7%96%97 [数学]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [代码]:https://github.com/baichuan-inc/Baichuan2#%E6%95%B0%E5%AD%A6%E4%BB%A3%E7%A0%81 [多语言翻译]:https://github.com/baichuan-inc/Baichuan2#%E5%A4%9A%E8%AF%AD%E8%A8%80%E7%BF%BB%E8%AF%91 [《Baichuan 2 模型社区许可协议》]:https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf [邮件申请]: mailto:[email protected] [Email]: mailto:[email protected] [[email protected]]: mailto:[email protected] [训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints [百川智能]: https://www.baichuan-ai.com

OpenDILabCommunity/Lunarlander-v2-C51

OpenDILabCommunity

2023-09-20T14:49:22Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "LunarLander-v2", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-04-15T12:48:19Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - LunarLander-v2 benchmark_name: OpenAI/Gym/Box2d task_name: LunarLander-v2 pipeline_tag: reinforcement-learning model-index: - name: C51 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/Box2d-LunarLander-v2 type: OpenAI/Gym/Box2d-LunarLander-v2 metrics: - type: mean_reward value: 211.75 +/- 40.32 name: mean_reward --- # Play **LunarLander-v2** with **C51** Policy ## Model Description  This is a simple **C51** implementation to OpenAI/Gym/Box2d **LunarLander-v2** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = C51Agent( env_id="LunarLander-v2", exp_name="LunarLander-v2-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import C51Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/LunarLander-v2-C51") # Instantiate the agent agent = C51Agent( env_id="LunarLander-v2", exp_name="LunarLander-v2-C51", cfg=cfg.exp_config, policy_state_dict=policy_state_dict ) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import C51Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = C51Agent(env_id="LunarLander-v2", exp_name="LunarLander-v2-C51") # Train the agent return_ = agent.train(step=int(4000000), collector_env_num=8, evaluator_env_num=8, debug=False) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/Box2d", task_name="LunarLander-v2", algo_name="C51", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html", installation_guide="pip3 install DI-engine[common_env]", usage_file_by_git_clone="./c51/lunarlander_c51_deploy.py", usage_file_by_huggingface_ding="./c51/lunarlander_c51_download.py", train_file="./c51/lunarlander_c51.py", repo_id="OpenDILabCommunity/LunarLander-v2-C51", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 260, 'n_evaluator_episode': 8, 'collector_env_num': 8, 'evaluator_env_num': 8, 'env_id': 'LunarLander-v2' }, 'policy': { 'model': { 'encoder_hidden_size_list': [512, 64], 'v_min': -30, 'v_max': 30, 'n_atom': 51, 'obs_shape': 8, 'action_shape': 4 }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 10, 'batch_size': 64, 'learning_rate': 0.001, 'target_update_freq': 100, 'target_theta': 0.005, 'ignore_done': False }, 'collect': { 'collector': {}, 'n_sample': 64, 'unroll_len': 1 }, 'eval': { 'evaluator': { 'eval_freq': 1000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 260, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 100000 }, 'eps': { 'type': 'exp', 'start': 0.95, 'end': 0.1, 'decay': 50000 } }, 'on_policy': False, 'cuda': False, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'c51', 'priority': False, 'priority_IS_weight': False, 'discount_factor': 0.99, 'nstep': 3, 'cfg_type': 'C51PolicyDict' }, 'exp_name': 'LunarLander-v2-C51', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/Lunarlander-v2-C51) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/c51.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-C51/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/LunarLander-v2-C51/blob/main/replay.mp4)  - **Parameters total size:** 214.3 KB - **Last Update Date:** 2023-09-20 ## Environments  - **Benchmark:** OpenAI/Gym/Box2d - **Task:** LunarLander-v2 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/lunarlander.html)

napatswift/mt5-fixpdftext

napatswift

2023-09-20T14:33:34Z

103

0

transformers

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

text2text-generation

2023-09-14T08:23:49Z

--- language: - th pipeline_tag: text2text-generation widget: - text: "Fix the following corrupted text: \"เจาหนาที่รับผิดชอบในการเขาไปเยียวยา โดยเจาพนักงานเจาหนาที่ตามกฎหมาย\"" ---

Parkhat/llama2-qlora-finetunined-kg-sql

Parkhat

2023-09-20T14:32:52Z

0

peft

[ "peft", "region:us" ]

null

2023-09-20T14:32:31Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0

jbrinkw/fp1.1

jbrinkw

2023-09-20T14:27:23Z

103

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-20T14:11:47Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: fp1.1 results: [] ---  # fp1.1 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3884 - Rouge1: 0.1551 - Rouge2: 0.0523 - Rougel: 0.1467 - Rougelsum: 0.1467 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 1.4836 | 1.0 | 4040 | 1.3884 | 0.1551 | 0.0523 | 0.1467 | 0.1467 | 19.0 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3

ChinuNayak28/working

ChinuNayak28

2023-09-20T14:15:37Z

0

null

[ "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:finetune:meta-llama/Llama-2-13b-chat-hf", "region:us" ]

null

2023-09-14T10:13:49Z

--- base_model: meta-llama/Llama-2-13b-chat-hf tags: - generated_from_trainer model-index: - name: working results: [] ---  # working This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5525 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3

linoyts/lora-xl-linoy_face-0.0001-0.0001-1000-1-32

linoyts

2023-09-20T14:14:01Z

4

1

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-09-20T13:53:42Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a TOK woman tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - LinoyTsaban/lora-xl-linoy_face-0.0001-0.0001-1000-1-32 These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on photo of a TOK woman using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: True. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

CyberHarem/etou_misaki_idolmastercinderellagirls

CyberHarem

2023-09-20T14:13:39Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/etou_misaki_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-20T14:01:32Z

--- license: mit datasets: - CyberHarem/etou_misaki_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of etou_misaki_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/etou_misaki_idolmastercinderellagirls.pt` as the embedding and `4760/etou_misaki_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.964. The trigger words are: 1. `etou_misaki_idolmastercinderellagirls` 2. `brown_hair, long_hair, green_eyes, jewelry, smile, earrings, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.936 | [Download](5100/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.964** | [**Download**](4760/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.917 | [Download](4420/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.889 | [Download](4080/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.937 | [Download](3740/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.919 | [Download](3400/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.909 | [Download](3060/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.865 | [Download](2720/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.879 | [Download](2380/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.848 | [Download](2040/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.873 | [Download](1700/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.847 | [Download](1360/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.817 | [Download](1020/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.815 | [Download](680/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.751 | [Download](340/etou_misaki_idolmastercinderellagirls.zip) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

davidramos/int8-whisper-base-v2-asr-bengali

davidramos

2023-09-20T14:11:09Z

1

0

peft

[ "peft", "base_model:openai/whisper-base", "base_model:adapter:openai/whisper-base", "region:us" ]

null

2023-09-14T15:51:50Z

--- library_name: peft base_model: openai/whisper-base --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0 ### Training loss Epoch | Training Loss | Validation Loss 1 | 0.573400 | 0.595536 2 | 0.476500 | 0.506768 3 | 0.421000 | 0.472346

googcheng/lit_lima

googcheng

2023-09-20T14:00:57Z

0

null

[ "region:us" ]

null

2023-09-20T13:57:08Z

try lit-gpt train from https://sebastianraschka.com/blog/2023/optimizing-LLMs-dataset-perspective.html

Harshit0722/dolly-fine-tuned-on-med-data

Harshit0722

2023-09-20T14:00:38Z

7

1

peft

[ "peft", "biology", "medical", "code", "question-answering", "en", "dataset:databricks/databricks-dolly-15k", "dataset:medmcqa", "base_model:databricks/dolly-v2-3b", "base_model:adapter:databricks/dolly-v2-3b", "license:bsd", "region:us" ]

question-answering

2023-06-17T14:06:53Z

--- language: - en license: bsd library_name: peft tags: - biology - medical - code datasets: - databricks/databricks-dolly-15k - medmcqa metrics: - code_eval pipeline_tag: question-answering base_model: databricks/dolly-v2-3b ---

Rexhaif/rubert-base-srl-seqlabeling

Rexhaif

2023-09-20T14:00:29Z

130

0

transformers

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

token-classification

2022-03-02T23:29:04Z

--- tags: - generated_from_trainer model-index: - name: rubert-base-srl-seqlabeling results: [] ---  # rubert-base-srl-seqlabeling This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1723 - Causator Precision: 0.8539 - Causator Recall: 0.8352 - Causator F1: 0.8444 - Causator Number: 91 - Expiriencer Precision: 0.9259 - Expiriencer Recall: 0.9740 - Expiriencer F1: 0.9494 - Expiriencer Number: 77 - Instrument Precision: 0.375 - Instrument Recall: 1.0 - Instrument F1: 0.5455 - Instrument Number: 3 - Other Precision: 0.0 - Other Recall: 0.0 - Other F1: 0.0 - Other Number: 1 - Predicate Precision: 0.9352 - Predicate Recall: 0.9902 - Predicate F1: 0.9619 - Predicate Number: 102 - Overall Precision: 0.8916 - Overall Recall: 0.9307 - Overall F1: 0.9107 - Overall Accuracy: 0.9667 ## Model description More information needed ## Intended uses & 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Causator Precision | Causator Recall | Causator F1 | Causator Number | Expiriencer Precision | Expiriencer Recall | Expiriencer F1 | Expiriencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Other Precision | Other Recall | Other F1 | Other Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.2552 | 1.0 | 56 | 0.3471 | 0.8841 | 0.6703 | 0.7625 | 91 | 0.8421 | 0.8312 | 0.8366 | 77 | 0.0 | 0.0 | 0.0 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9259 | 0.9804 | 0.9524 | 102 | 0.8893 | 0.8212 | 0.8539 | 0.9203 | | 0.2385 | 2.0 | 112 | 0.1608 | 0.9103 | 0.7802 | 0.8402 | 91 | 0.9375 | 0.9740 | 0.9554 | 77 | 0.2857 | 0.6667 | 0.4 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9519 | 0.9706 | 0.9612 | 102 | 0.9182 | 0.9015 | 0.9098 | 0.9554 | | 0.0367 | 3.0 | 168 | 0.1311 | 0.8902 | 0.8022 | 0.8439 | 91 | 0.9375 | 0.9740 | 0.9554 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9709 | 0.9804 | 0.9756 | 102 | 0.9228 | 0.9161 | 0.9194 | 0.9673 | | 0.0494 | 4.0 | 224 | 0.1507 | 0.7812 | 0.8242 | 0.8021 | 91 | 0.9241 | 0.9481 | 0.9359 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9524 | 0.9804 | 0.9662 | 102 | 0.8746 | 0.9161 | 0.8948 | 0.9637 | | 0.0699 | 5.0 | 280 | 0.1830 | 0.8276 | 0.7912 | 0.8090 | 91 | 0.8941 | 0.9870 | 0.9383 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.875 | 0.9197 | 0.8968 | 0.9560 | | 0.0352 | 6.0 | 336 | 0.1994 | 0.7857 | 0.8462 | 0.8148 | 91 | 0.9048 | 0.9870 | 0.9441 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9266 | 0.9902 | 0.9573 | 102 | 0.8595 | 0.9380 | 0.8970 | 0.9572 | | 0.0186 | 7.0 | 392 | 0.1657 | 0.8652 | 0.8462 | 0.8556 | 91 | 0.9146 | 0.9740 | 0.9434 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8920 | 0.9343 | 0.9127 | 0.9673 | | 0.0052 | 8.0 | 448 | 0.1716 | 0.8556 | 0.8462 | 0.8508 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8920 | 0.9343 | 0.9127 | 0.9673 | | 0.0094 | 9.0 | 504 | 0.1715 | 0.8444 | 0.8352 | 0.8398 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.4286 | 1.0 | 0.6 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8916 | 0.9307 | 0.9107 | 0.9667 | | 0.0078 | 10.0 | 560 | 0.1723 | 0.8539 | 0.8352 | 0.8444 | 91 | 0.9259 | 0.9740 | 0.9494 | 77 | 0.375 | 1.0 | 0.5455 | 3 | 0.0 | 0.0 | 0.0 | 1 | 0.9352 | 0.9902 | 0.9619 | 102 | 0.8916 | 0.9307 | 0.9107 | 0.9667 | ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3

vabatista/sbert-squad-pt-br

vabatista

2023-09-20T13:59:10Z

106

0

sentence-transformers

[ "sentence-transformers", "pytorch", "xlm-roberta", "sentence-similarity", "pt", "dataset:squad", "license:apache-2.0", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2023-08-11T13:47:38Z

--- license: apache-2.0 datasets: - squad language: - pt pipeline_tag: sentence-similarity library_name: sentence-transformers --- This is a sentence_bert based model fine tuned using portuguese SQuAD dataset with batchs of: - positive samples: question - sentence containing answer - hard negative samples question which answers is not in the sentence, but is in the context of SQuAD dataset. This model generate embeddings from text to use with semantic search

Yehoon/llama_13b

Yehoon

2023-09-20T13:58:58Z

0

peft

[ "peft", "region:us" ]

null

2023-09-20T13:58:50Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0

jonas-luehrs/gbert-base-MLM-eu-or-ddr

jonas-luehrs

2023-09-20T13:55:47Z

124

0

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "base_model:deepset/gbert-base", "base_model:finetune:deepset/gbert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-09-20T12:55:52Z

--- license: mit base_model: deepset/gbert-base tags: - generated_from_trainer model-index: - name: gbert-base-MLM-eu results: [] ---  # gbert-base-MLM-eu-or-ddr This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3578 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9428 | 1.0 | 403 | 1.5950 | | 1.6342 | 2.0 | 806 | 1.4453 | | 1.5324 | 3.0 | 1209 | 1.3751 | | 1.4729 | 4.0 | 1612 | 1.3533 | | 1.4536 | 5.0 | 2015 | 1.3389 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

mayorov-s/Taxi-v3-QTable

mayorov-s

2023-09-20T13:50:21Z

0

null

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

reinforcement-learning

2023-09-20T13:50:17Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-QTable results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.81 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="mayorov-s/Taxi-v3-QTable", 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"]) ```

FedeBerto/Griffith

FedeBerto

2023-09-20T13:49:16Z

0

keras

[ "keras", "tf-keras", "region:us" ]

null

2023-09-07T10:48:17Z

Tanor/SRGPTSENTNEG2

Tanor

2023-09-20T13:47:44Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-classification", "generated_from_trainer", "base_model:Tanor/SRGPTSENTNEG2", "base_model:finetune:Tanor/SRGPTSENTNEG2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2023-07-10T23:17:47Z

--- base_model: Tanor/SRGPTSENTNEG2 tags: - generated_from_trainer metrics: - f1 model-index: - name: SRGPTSENTNEG2 results: [] ---  # SRGPTSENTNEG2 This model is a fine-tuned version of [Tanor/SRGPTSENTNEG2](https://huggingface.co/Tanor/SRGPTSENTNEG2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1820 - F1: 0.3235 ## Model description More information needed ## Intended uses & 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - 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 | F1 | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.1297 | 1.0 | 2697 | 0.1564 | 0.0 | | 0.0867 | 2.0 | 5394 | 0.1468 | 0.2903 | | 0.0467 | 3.0 | 8091 | 0.1674 | 0.3288 | | 0.052 | 4.0 | 10788 | 0.2066 | 0.3514 | | 0.0403 | 5.0 | 13485 | 0.1820 | 0.3235 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.1.0.dev20230801 - Datasets 2.14.2 - Tokenizers 0.13.3

mayorov-s/q-FrozenLake-v1-4x4-noSlippery

mayorov-s

2023-09-20T13:45:42Z

0

null

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

reinforcement-learning

2022-09-21T11:34:43Z

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

Rachneet/T5-large-esnli-impli-figurative

Rachneet

2023-09-20T13:44:28Z

110

0

transformers

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

text2text-generation

2022-10-20T15:29:12Z

# load model ``` from transformers import ( AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM ) model_path = "T5-large-esnli-impli-figurative" tokenizer = AutoTokenizer.from_pretrained(model_path) config = AutoConfig.from_pretrained(model_path) model = AutoModelForSeq2SeqLM.from_pretrained(model_path) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") premise = "I just caught a guy picking up used chewing gum and he put it in his mouth." hypothesis = "it was such a pleasant sight to see a guy picking up used chewing gum; and he put it in his mouth" prepared_input = f"figurative hypothesis: {hypothesis} premise: {premise}" features = tokenizer(prepared_input, max_length=128, padding="max_length", truncation=True, return_tensors="pt") model.eval() model.to(device) with torch.no_grad(): # https://huggingface.co/blog/how-to-generate generated_ids = model.generate( **features, max_length=128, use_cache=True, num_beams=4, length_penalty=0.6, early_stopping=True, ) dec_preds = tokenizer.decode(outputs[0], skip_special_tokens=True) print("The prediction is: ", dec_preds) print(dec_preds[1:].replace("explanation:", "").lstrip()) ``` # Example input figurative hypothesis: I was gone for only a few days and my considerate adult son just let the sink fill up with dirty dishes, making me feel really happy premise: I left my adult son home for a few days and just came back to a sink full of gross old dishes.

AmitMidday/Dogs-Breed-Classification-Using-Vision-Transformers

AmitMidday

2023-09-20T13:42:48Z

195

0

transformers

[ "transformers", "pytorch", "vit", "image-classification", "en", "license:openrail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-20T11:53:41Z

--- pipeline_tag: image-classification license: openrail language: - en metrics: - accuracy library_name: transformers ---

a-v-bely/bert-base-spanish-wwm-cased-finetuned-literature-pro

a-v-bely

2023-09-20T13:39:53Z

115

0

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-03-13T04:57:33Z

--- tags: - generated_from_trainer base_model: dccuchile/bert-base-spanish-wwm-cased model-index: - name: bert-base-spanish-wwm-cased-finetuned-literature-pro results: [] ---  # bert-base-spanish-wwm-cased-finetuned-literature-pro This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9425 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.2669 | 1.0 | 9787 | 2.0414 | | 2.111 | 2.0 | 19574 | 1.9735 | | 2.0512 | 3.0 | 29361 | 1.9413 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2

santiagotoso/TaxiGame-QLearning

santiagotoso

2023-09-20T13:14:24Z

0

null

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

reinforcement-learning

2023-09-20T13:08:32Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: TaxiGame-QLearning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.54 +/- 2.73 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="santiagotoso/TaxiGame-QLearning", 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"]) ```

mgoin/mpt-7b-chat-quant

mgoin

2023-09-20T13:12:55Z

6

1

transformers

[ "transformers", "onnx", "mpt", "text-generation", "deepsparse", "custom_code", "autotrain_compatible", "region:us" ]

text-generation

2023-09-20T02:43:00Z

--- tags: - deepsparse --- # mpt_7b_chat-dense_quant_linearW8A8MatMul8Embeds8LMhead8 ```python import deepsparse from huggingface_hub import snapshot_download MODEL_PATH = snapshot_download(repo_id="mgoin/mpt-7b-chat-quant") model = deepsparse.Pipeline.create(task="text-generation", model_path=MODEL_PATH) model(sequences="Tell me a joke.") ```

CyberHarem/minosu_jashinchandropkick

CyberHarem

2023-09-20T13:07:15Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/minosu_jashinchandropkick", "license:mit", "region:us" ]

text-to-image

2023-09-20T12:48:47Z

--- license: mit datasets: - CyberHarem/minosu_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of minosu_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8840, you need to download `8840/minosu_jashinchandropkick.pt` as the embedding and `8840/minosu_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 8840**, with the score of 0.955. The trigger words are: 1. `minosu_jashinchandropkick` 2. `blue_hair, horns, short_hair, bell, cow_horns, neck_bell, cowbell, cow_print, animal_print, cow_girl, red_eyes, hair_between_eyes, smile, closed_mouth, pink_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:--------------------------------------------------|:---------------------------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:------------------------------------------------|:-------------------------------------------------|:---------------------------------------|:-------------------------------------------| | 10200 | 0.943 | [Download](10200/minosu_jashinchandropkick.zip) | ![pattern_1-10200](10200/previews/pattern_1.png) | ![pattern_2-10200](10200/previews/pattern_2.png) | ![pattern_3-10200](10200/previews/pattern_3.png) | ![pattern_4-10200](10200/previews/pattern_4.png) | ![pattern_5-10200](10200/previews/pattern_5.png) | ![pattern_6-10200](10200/previews/pattern_6.png) | ![pattern_7-10200](10200/previews/pattern_7.png) | ![pattern_8-10200](10200/previews/pattern_8.png) | ![pattern_9-10200](10200/previews/pattern_9.png) | ![pattern_10-10200](10200/previews/pattern_10.png) | ![pattern_11-10200](10200/previews/pattern_11.png) | ![pattern_12-10200](10200/previews/pattern_12.png) | ![pattern_13-10200](10200/previews/pattern_13.png) | ![pattern_14-10200](10200/previews/pattern_14.png) | ![pattern_15-10200](10200/previews/pattern_15.png) | ![pattern_16-10200](10200/previews/pattern_16.png) | [<NSFW, click to see>](10200/previews/bikini.png) | [<NSFW, click to see>](10200/previews/bondage.png) | ![free-10200](10200/previews/free.png) | ![maid-10200](10200/previews/maid.png) | ![miko-10200](10200/previews/miko.png) | [<NSFW, click to see>](10200/previews/nude.png) | [<NSFW, click to see>](10200/previews/nude2.png) | ![suit-10200](10200/previews/suit.png) | ![yukata-10200](10200/previews/yukata.png) | | 9520 | 0.941 | [Download](9520/minosu_jashinchandropkick.zip) | ![pattern_1-9520](9520/previews/pattern_1.png) | ![pattern_2-9520](9520/previews/pattern_2.png) | ![pattern_3-9520](9520/previews/pattern_3.png) | ![pattern_4-9520](9520/previews/pattern_4.png) | ![pattern_5-9520](9520/previews/pattern_5.png) | ![pattern_6-9520](9520/previews/pattern_6.png) | ![pattern_7-9520](9520/previews/pattern_7.png) | ![pattern_8-9520](9520/previews/pattern_8.png) | ![pattern_9-9520](9520/previews/pattern_9.png) | ![pattern_10-9520](9520/previews/pattern_10.png) | ![pattern_11-9520](9520/previews/pattern_11.png) | ![pattern_12-9520](9520/previews/pattern_12.png) | ![pattern_13-9520](9520/previews/pattern_13.png) | ![pattern_14-9520](9520/previews/pattern_14.png) | ![pattern_15-9520](9520/previews/pattern_15.png) | ![pattern_16-9520](9520/previews/pattern_16.png) | [<NSFW, click to see>](9520/previews/bikini.png) | [<NSFW, click to see>](9520/previews/bondage.png) | ![free-9520](9520/previews/free.png) | ![maid-9520](9520/previews/maid.png) | ![miko-9520](9520/previews/miko.png) | [<NSFW, click to see>](9520/previews/nude.png) | [<NSFW, click to see>](9520/previews/nude2.png) | ![suit-9520](9520/previews/suit.png) | ![yukata-9520](9520/previews/yukata.png) | | **8840** | **0.955** | [**Download**](8840/minosu_jashinchandropkick.zip) | ![pattern_1-8840](8840/previews/pattern_1.png) | ![pattern_2-8840](8840/previews/pattern_2.png) | ![pattern_3-8840](8840/previews/pattern_3.png) | ![pattern_4-8840](8840/previews/pattern_4.png) | ![pattern_5-8840](8840/previews/pattern_5.png) | ![pattern_6-8840](8840/previews/pattern_6.png) | ![pattern_7-8840](8840/previews/pattern_7.png) | ![pattern_8-8840](8840/previews/pattern_8.png) | ![pattern_9-8840](8840/previews/pattern_9.png) | ![pattern_10-8840](8840/previews/pattern_10.png) | ![pattern_11-8840](8840/previews/pattern_11.png) | ![pattern_12-8840](8840/previews/pattern_12.png) | ![pattern_13-8840](8840/previews/pattern_13.png) | ![pattern_14-8840](8840/previews/pattern_14.png) | ![pattern_15-8840](8840/previews/pattern_15.png) | ![pattern_16-8840](8840/previews/pattern_16.png) | [<NSFW, click to see>](8840/previews/bikini.png) | [<NSFW, click to see>](8840/previews/bondage.png) | ![free-8840](8840/previews/free.png) | ![maid-8840](8840/previews/maid.png) | ![miko-8840](8840/previews/miko.png) | [<NSFW, click to see>](8840/previews/nude.png) | [<NSFW, click to see>](8840/previews/nude2.png) | ![suit-8840](8840/previews/suit.png) | ![yukata-8840](8840/previews/yukata.png) | | 8160 | 0.947 | [Download](8160/minosu_jashinchandropkick.zip) | ![pattern_1-8160](8160/previews/pattern_1.png) | ![pattern_2-8160](8160/previews/pattern_2.png) | ![pattern_3-8160](8160/previews/pattern_3.png) | ![pattern_4-8160](8160/previews/pattern_4.png) | ![pattern_5-8160](8160/previews/pattern_5.png) | ![pattern_6-8160](8160/previews/pattern_6.png) | ![pattern_7-8160](8160/previews/pattern_7.png) | ![pattern_8-8160](8160/previews/pattern_8.png) | ![pattern_9-8160](8160/previews/pattern_9.png) | ![pattern_10-8160](8160/previews/pattern_10.png) | ![pattern_11-8160](8160/previews/pattern_11.png) | ![pattern_12-8160](8160/previews/pattern_12.png) | ![pattern_13-8160](8160/previews/pattern_13.png) | ![pattern_14-8160](8160/previews/pattern_14.png) | ![pattern_15-8160](8160/previews/pattern_15.png) | ![pattern_16-8160](8160/previews/pattern_16.png) | [<NSFW, click to see>](8160/previews/bikini.png) | [<NSFW, click to see>](8160/previews/bondage.png) | ![free-8160](8160/previews/free.png) | ![maid-8160](8160/previews/maid.png) | ![miko-8160](8160/previews/miko.png) | [<NSFW, click to see>](8160/previews/nude.png) | [<NSFW, click to see>](8160/previews/nude2.png) | ![suit-8160](8160/previews/suit.png) | ![yukata-8160](8160/previews/yukata.png) | | 7480 | 0.950 | [Download](7480/minosu_jashinchandropkick.zip) | ![pattern_1-7480](7480/previews/pattern_1.png) | ![pattern_2-7480](7480/previews/pattern_2.png) | ![pattern_3-7480](7480/previews/pattern_3.png) | ![pattern_4-7480](7480/previews/pattern_4.png) | ![pattern_5-7480](7480/previews/pattern_5.png) | ![pattern_6-7480](7480/previews/pattern_6.png) | ![pattern_7-7480](7480/previews/pattern_7.png) | ![pattern_8-7480](7480/previews/pattern_8.png) | ![pattern_9-7480](7480/previews/pattern_9.png) | ![pattern_10-7480](7480/previews/pattern_10.png) | ![pattern_11-7480](7480/previews/pattern_11.png) | ![pattern_12-7480](7480/previews/pattern_12.png) | ![pattern_13-7480](7480/previews/pattern_13.png) | ![pattern_14-7480](7480/previews/pattern_14.png) | ![pattern_15-7480](7480/previews/pattern_15.png) | ![pattern_16-7480](7480/previews/pattern_16.png) | [<NSFW, click to see>](7480/previews/bikini.png) | [<NSFW, click to see>](7480/previews/bondage.png) | ![free-7480](7480/previews/free.png) | ![maid-7480](7480/previews/maid.png) | ![miko-7480](7480/previews/miko.png) | [<NSFW, click to see>](7480/previews/nude.png) | [<NSFW, click to see>](7480/previews/nude2.png) | ![suit-7480](7480/previews/suit.png) | ![yukata-7480](7480/previews/yukata.png) | | 6800 | 0.939 | [Download](6800/minosu_jashinchandropkick.zip) | ![pattern_1-6800](6800/previews/pattern_1.png) | ![pattern_2-6800](6800/previews/pattern_2.png) | ![pattern_3-6800](6800/previews/pattern_3.png) | ![pattern_4-6800](6800/previews/pattern_4.png) | ![pattern_5-6800](6800/previews/pattern_5.png) | ![pattern_6-6800](6800/previews/pattern_6.png) | ![pattern_7-6800](6800/previews/pattern_7.png) | ![pattern_8-6800](6800/previews/pattern_8.png) | ![pattern_9-6800](6800/previews/pattern_9.png) | ![pattern_10-6800](6800/previews/pattern_10.png) | ![pattern_11-6800](6800/previews/pattern_11.png) | ![pattern_12-6800](6800/previews/pattern_12.png) | ![pattern_13-6800](6800/previews/pattern_13.png) | ![pattern_14-6800](6800/previews/pattern_14.png) | ![pattern_15-6800](6800/previews/pattern_15.png) | ![pattern_16-6800](6800/previews/pattern_16.png) | [<NSFW, click to see>](6800/previews/bikini.png) | [<NSFW, click to see>](6800/previews/bondage.png) | ![free-6800](6800/previews/free.png) | ![maid-6800](6800/previews/maid.png) | ![miko-6800](6800/previews/miko.png) | [<NSFW, click to see>](6800/previews/nude.png) | [<NSFW, click to see>](6800/previews/nude2.png) | ![suit-6800](6800/previews/suit.png) | ![yukata-6800](6800/previews/yukata.png) | | 6120 | 0.917 | [Download](6120/minosu_jashinchandropkick.zip) | ![pattern_1-6120](6120/previews/pattern_1.png) | ![pattern_2-6120](6120/previews/pattern_2.png) | ![pattern_3-6120](6120/previews/pattern_3.png) | ![pattern_4-6120](6120/previews/pattern_4.png) | ![pattern_5-6120](6120/previews/pattern_5.png) | ![pattern_6-6120](6120/previews/pattern_6.png) | ![pattern_7-6120](6120/previews/pattern_7.png) | ![pattern_8-6120](6120/previews/pattern_8.png) | ![pattern_9-6120](6120/previews/pattern_9.png) | ![pattern_10-6120](6120/previews/pattern_10.png) | ![pattern_11-6120](6120/previews/pattern_11.png) | ![pattern_12-6120](6120/previews/pattern_12.png) | ![pattern_13-6120](6120/previews/pattern_13.png) | ![pattern_14-6120](6120/previews/pattern_14.png) | ![pattern_15-6120](6120/previews/pattern_15.png) | ![pattern_16-6120](6120/previews/pattern_16.png) | [<NSFW, click to see>](6120/previews/bikini.png) | [<NSFW, click to see>](6120/previews/bondage.png) | ![free-6120](6120/previews/free.png) | ![maid-6120](6120/previews/maid.png) | ![miko-6120](6120/previews/miko.png) | [<NSFW, click to see>](6120/previews/nude.png) | [<NSFW, click to see>](6120/previews/nude2.png) | ![suit-6120](6120/previews/suit.png) | ![yukata-6120](6120/previews/yukata.png) | | 5440 | 0.946 | [Download](5440/minosu_jashinchandropkick.zip) | ![pattern_1-5440](5440/previews/pattern_1.png) | ![pattern_2-5440](5440/previews/pattern_2.png) | ![pattern_3-5440](5440/previews/pattern_3.png) | ![pattern_4-5440](5440/previews/pattern_4.png) | ![pattern_5-5440](5440/previews/pattern_5.png) | ![pattern_6-5440](5440/previews/pattern_6.png) | ![pattern_7-5440](5440/previews/pattern_7.png) | ![pattern_8-5440](5440/previews/pattern_8.png) | ![pattern_9-5440](5440/previews/pattern_9.png) | ![pattern_10-5440](5440/previews/pattern_10.png) | ![pattern_11-5440](5440/previews/pattern_11.png) | ![pattern_12-5440](5440/previews/pattern_12.png) | ![pattern_13-5440](5440/previews/pattern_13.png) | ![pattern_14-5440](5440/previews/pattern_14.png) | ![pattern_15-5440](5440/previews/pattern_15.png) | ![pattern_16-5440](5440/previews/pattern_16.png) | [<NSFW, click to see>](5440/previews/bikini.png) | [<NSFW, click to see>](5440/previews/bondage.png) | ![free-5440](5440/previews/free.png) | ![maid-5440](5440/previews/maid.png) | ![miko-5440](5440/previews/miko.png) | [<NSFW, click to see>](5440/previews/nude.png) | [<NSFW, click to see>](5440/previews/nude2.png) | ![suit-5440](5440/previews/suit.png) | ![yukata-5440](5440/previews/yukata.png) | | 4760 | 0.952 | [Download](4760/minosu_jashinchandropkick.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![pattern_8-4760](4760/previews/pattern_8.png) | ![pattern_9-4760](4760/previews/pattern_9.png) | ![pattern_10-4760](4760/previews/pattern_10.png) | ![pattern_11-4760](4760/previews/pattern_11.png) | ![pattern_12-4760](4760/previews/pattern_12.png) | ![pattern_13-4760](4760/previews/pattern_13.png) | ![pattern_14-4760](4760/previews/pattern_14.png) | ![pattern_15-4760](4760/previews/pattern_15.png) | ![pattern_16-4760](4760/previews/pattern_16.png) | [<NSFW, click to see>](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4080 | 0.904 | [Download](4080/minosu_jashinchandropkick.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![pattern_8-4080](4080/previews/pattern_8.png) | ![pattern_9-4080](4080/previews/pattern_9.png) | ![pattern_10-4080](4080/previews/pattern_10.png) | ![pattern_11-4080](4080/previews/pattern_11.png) | ![pattern_12-4080](4080/previews/pattern_12.png) | ![pattern_13-4080](4080/previews/pattern_13.png) | ![pattern_14-4080](4080/previews/pattern_14.png) | ![pattern_15-4080](4080/previews/pattern_15.png) | ![pattern_16-4080](4080/previews/pattern_16.png) | [<NSFW, click to see>](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3400 | 0.882 | [Download](3400/minosu_jashinchandropkick.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![pattern_8-3400](3400/previews/pattern_8.png) | ![pattern_9-3400](3400/previews/pattern_9.png) | ![pattern_10-3400](3400/previews/pattern_10.png) | ![pattern_11-3400](3400/previews/pattern_11.png) | ![pattern_12-3400](3400/previews/pattern_12.png) | ![pattern_13-3400](3400/previews/pattern_13.png) | ![pattern_14-3400](3400/previews/pattern_14.png) | ![pattern_15-3400](3400/previews/pattern_15.png) | ![pattern_16-3400](3400/previews/pattern_16.png) | [<NSFW, click to see>](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 2720 | 0.813 | [Download](2720/minosu_jashinchandropkick.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![pattern_8-2720](2720/previews/pattern_8.png) | ![pattern_9-2720](2720/previews/pattern_9.png) | ![pattern_10-2720](2720/previews/pattern_10.png) | ![pattern_11-2720](2720/previews/pattern_11.png) | ![pattern_12-2720](2720/previews/pattern_12.png) | ![pattern_13-2720](2720/previews/pattern_13.png) | ![pattern_14-2720](2720/previews/pattern_14.png) | ![pattern_15-2720](2720/previews/pattern_15.png) | ![pattern_16-2720](2720/previews/pattern_16.png) | [<NSFW, click to see>](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2040 | 0.773 | [Download](2040/minosu_jashinchandropkick.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![pattern_8-2040](2040/previews/pattern_8.png) | ![pattern_9-2040](2040/previews/pattern_9.png) | ![pattern_10-2040](2040/previews/pattern_10.png) | ![pattern_11-2040](2040/previews/pattern_11.png) | ![pattern_12-2040](2040/previews/pattern_12.png) | ![pattern_13-2040](2040/previews/pattern_13.png) | ![pattern_14-2040](2040/previews/pattern_14.png) | ![pattern_15-2040](2040/previews/pattern_15.png) | ![pattern_16-2040](2040/previews/pattern_16.png) | [<NSFW, click to see>](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1360 | 0.717 | [Download](1360/minosu_jashinchandropkick.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![pattern_8-1360](1360/previews/pattern_8.png) | ![pattern_9-1360](1360/previews/pattern_9.png) | ![pattern_10-1360](1360/previews/pattern_10.png) | ![pattern_11-1360](1360/previews/pattern_11.png) | ![pattern_12-1360](1360/previews/pattern_12.png) | ![pattern_13-1360](1360/previews/pattern_13.png) | ![pattern_14-1360](1360/previews/pattern_14.png) | ![pattern_15-1360](1360/previews/pattern_15.png) | ![pattern_16-1360](1360/previews/pattern_16.png) | [<NSFW, click to see>](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 680 | 0.494 | [Download](680/minosu_jashinchandropkick.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![pattern_8-680](680/previews/pattern_8.png) | ![pattern_9-680](680/previews/pattern_9.png) | ![pattern_10-680](680/previews/pattern_10.png) | ![pattern_11-680](680/previews/pattern_11.png) | ![pattern_12-680](680/previews/pattern_12.png) | ![pattern_13-680](680/previews/pattern_13.png) | ![pattern_14-680](680/previews/pattern_14.png) | ![pattern_15-680](680/previews/pattern_15.png) | ![pattern_16-680](680/previews/pattern_16.png) | 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AbdelKarim95/ppo-PyramidsRND

AbdelKarim95

2023-09-20T13:04:33Z

5

0

ml-agents

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

reinforcement-learning

2023-09-20T13:04:29Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AbdelKarim95/ppo-PyramidsRND 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

kmposkid1/Horse-Health-Outcome-6bca6390-b05b-43b6-831d-8bf9f7caf24c

kmposkid1

2023-09-20T13:01:03Z

0

sklearn

[ "sklearn", "skops", "tabular-classification", "license:mit", "region:us" ]

tabular-classification

2023-09-20T12:33:52Z

--- license: mit library_name: sklearn tags: - sklearn - skops - tabular-classification model_format: pickle model_file: catboost_without_hospital_number.pkl widget: structuredData: abdomen: - distend_small - distend_small - distend_large abdominal_distention: - none - none - moderate abdomo_appearance: - serosanguious - cloudy - serosanguious abdomo_protein: - 4.1 - 4.3 - 2.0 age: - adult - adult - adult capillary_refill_time: - less_3_sec - less_3_sec - more_3_sec cp_data: - 'yes' - 'yes' - 'no' lesion_1: - 7209 - 2112 - 5400 lesion_2: - 0 - 0 - 0 lesion_3: - 0 - 0 - 0 mucous_membrane: - bright_pink - bright_pink - dark_cyanotic nasogastric_reflux: - none - none - more_1_liter nasogastric_reflux_ph: - 7.0 - 3.5 - 2.0 nasogastric_tube: - slight - none - significant packed_cell_volume: - 37.0 - 44.0 - 65.0 pain: - depressed - mild_pain - extreme_pain peripheral_pulse: - normal - normal - reduced peristalsis: - hypermotile - hypomotile - absent pulse: - 84.0 - 66.0 - 72.0 rectal_exam_feces: - absent - decreased - absent rectal_temp: - 39.0 - 38.5 - 37.3 respiratory_rate: - 24.0 - 21.0 - 30.0 surgery: - 'yes' - 'yes' - 'yes' surgical_lesion: - 'yes' - 'yes' - 'yes' temp_of_extremities: - cool - normal - cool total_protein: - 6.5 - 7.6 - 13.0 --- # Model description This is a `Catboost` model trained on horse health outcome data from Kaggle. ## Intended uses & limitations This model is not ready to be used in production. ## Training Procedure [More Information Needed] ### Hyperparameters <details> <summary> Click to expand </summary> | Hyperparameter | Value | |--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------| | memory | | | steps | [('preprocessor', ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])])), ('classifier', <catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)] | | verbose | False | | preprocessor | ColumnTransformer(remainder='passthrough',<br /> transformers=[('num',<br /> Pipeline(steps=[('imputer',<br /> SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]),<br /> ['rectal_temp', 'pulse', 'respiratory_rate',<br /> 'nasogastric_reflux_ph', 'packed_cell_volume',<br /> 'total_protein', 'abdomo_protein', 'lesion_1',<br /> 'lesion_2', 'lesion_3']),<br /> ('cat',<br /> Pipeline(steps=[('imputer',<br /> SimpleI...='missing',<br /> strategy='constant')),<br /> ('onehot',<br /> OneHotEncoder(handle_unknown='ignore'))]),<br /> ['surgery', 'age', 'temp_of_extremities',<br /> 'peripheral_pulse', 'mucous_membrane',<br /> 'capillary_refill_time', 'pain',<br /> 'peristalsis', 'abdominal_distention',<br /> 'nasogastric_tube', 'nasogastric_reflux',<br /> 'rectal_exam_feces', 'abdomen',<br /> 'abdomo_appearance', 'surgical_lesion',<br /> 'cp_data'])]) | | classifier | <catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10> | | preprocessor__n_jobs | | | preprocessor__remainder | passthrough | | preprocessor__sparse_threshold | 0.3 | | preprocessor__transformer_weights | | | preprocessor__transformers | [('num', Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]), ['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']), ('cat', Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data'])] | | preprocessor__verbose | False | | preprocessor__verbose_feature_names_out | True | | preprocessor__num | Pipeline(steps=[('imputer', SimpleImputer(strategy='median')),<br /> ('scaler', StandardScaler())]) | | preprocessor__cat | Pipeline(steps=[('imputer',<br /> SimpleImputer(fill_value='missing', strategy='constant')),<br /> ('onehot', OneHotEncoder(handle_unknown='ignore'))]) | | preprocessor__num__memory | | | preprocessor__num__steps | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())] | | preprocessor__num__verbose | False | | preprocessor__num__imputer | SimpleImputer(strategy='median') | | preprocessor__num__scaler | StandardScaler() | | preprocessor__num__imputer__add_indicator | False | | preprocessor__num__imputer__copy | True | | preprocessor__num__imputer__fill_value | | | preprocessor__num__imputer__keep_empty_features | False | | preprocessor__num__imputer__missing_values | nan | | preprocessor__num__imputer__strategy | median | | preprocessor__num__scaler__copy | True | | preprocessor__num__scaler__with_mean | True | | preprocessor__num__scaler__with_std | True | | preprocessor__cat__memory | | | preprocessor__cat__steps | [('imputer', SimpleImputer(fill_value='missing', strategy='constant')), ('onehot', OneHotEncoder(handle_unknown='ignore'))] | | preprocessor__cat__verbose | False | | preprocessor__cat__imputer | SimpleImputer(fill_value='missing', strategy='constant') | | preprocessor__cat__onehot | OneHotEncoder(handle_unknown='ignore') | | preprocessor__cat__imputer__add_indicator | False | | preprocessor__cat__imputer__copy | True | | preprocessor__cat__imputer__fill_value | missing | | preprocessor__cat__imputer__keep_empty_features | False | | preprocessor__cat__imputer__missing_values | nan | | preprocessor__cat__imputer__strategy | constant | | preprocessor__cat__onehot__categories | auto | | preprocessor__cat__onehot__drop | | | preprocessor__cat__onehot__dtype | <class 'numpy.float64'> | | preprocessor__cat__onehot__feature_name_combiner | concat | | preprocessor__cat__onehot__handle_unknown | ignore | | preprocessor__cat__onehot__max_categories | | | preprocessor__cat__onehot__min_frequency | | | preprocessor__cat__onehot__sparse | deprecated | | preprocessor__cat__onehot__sparse_output | True | | classifier__learning_rate | 0.1 | | classifier__silent | True | | classifier__max_depth | 4 | | classifier__n_estimators | 200 | </details> ### Model Plot <style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier',<catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('preprocessor',ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler',StandardScaler())]),['rectal_temp', 'pulse','respiratory_rate','nasogastric_reflux_ph','packed_cell_volume','total_protein','abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pi...OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age','temp_of_extremities','peripheral_pulse','mucous_membrane','capillary_refill_time','pain', 'peristalsis','abdominal_distention','nasogastric_tube','nasogastric_reflux','rectal_exam_feces','abdomen','abdomo_appearance','surgical_lesion','cp_data'])])),('classifier',<catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10>)])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">preprocessor: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('num',Pipeline(steps=[('imputer',SimpleImputer(strategy='median')),('scaler', StandardScaler())]),['rectal_temp', 'pulse', 'respiratory_rate','nasogastric_reflux_ph', 'packed_cell_volume','total_protein', 'abdomo_protein', 'lesion_1','lesion_2', 'lesion_3']),('cat',Pipeline(steps=[('imputer',SimpleI...='missing',strategy='constant')),('onehot',OneHotEncoder(handle_unknown='ignore'))]),['surgery', 'age', 'temp_of_extremities','peripheral_pulse', 'mucous_membrane','capillary_refill_time', 'pain','peristalsis', 'abdominal_distention','nasogastric_tube', 'nasogastric_reflux','rectal_exam_feces', 'abdomen','abdomo_appearance', 'surgical_lesion','cp_data'])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">num</label><div class="sk-toggleable__content"><pre>['rectal_temp', 'pulse', 'respiratory_rate', 'nasogastric_reflux_ph', 'packed_cell_volume', 'total_protein', 'abdomo_protein', 'lesion_1', 'lesion_2', 'lesion_3']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(strategy='median')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">cat</label><div class="sk-toggleable__content"><pre>['surgery', 'age', 'temp_of_extremities', 'peripheral_pulse', 'mucous_membrane', 'capillary_refill_time', 'pain', 'peristalsis', 'abdominal_distention', 'nasogastric_tube', 'nasogastric_reflux', 'rectal_exam_feces', 'abdomen', 'abdomo_appearance', 'surgical_lesion', 'cp_data']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">SimpleImputer</label><div class="sk-toggleable__content"><pre>SimpleImputer(fill_value='missing', strategy='constant')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>[]</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-10" type="checkbox" ><label for="sk-estimator-id-10" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-11" type="checkbox" ><label for="sk-estimator-id-11" class="sk-toggleable__label sk-toggleable__label-arrow">CatBoostClassifier</label><div class="sk-toggleable__content"><pre><catboost.core.CatBoostClassifier object at 0x000001C4CE4ABF10></pre></div></div></div></div></div></div></div> ## Evaluation Results | Metric | Value | |----------|----------| | accuracy | 0.744939 | | f1 score | 0.744939 | ### Confusion Matrix ![Confusion Matrix](confusion_matrix.png) # How to Get Started with the Model [More Information Needed] # Model Card Authors kmposkid # Model Card Contact You can contact the model card authors through following channels: [More Information Needed] # Citation Below you can find information related to citation. **BibTeX:** ``` [More Information Needed] ```

r0ll/Scammers

r0ll

2023-09-20T13:00:42Z

0

null

[ "ru", "license:openrail", "region:us" ]

null

2023-09-20T12:59:37Z

--- license: openrail language: - ru ---

margenai/StateBankPakistan

margenai

2023-09-20T12:41:13Z

0

peft

[ "peft", "base_model:EleutherAI/pythia-410m-v0", "base_model:adapter:EleutherAI/pythia-410m-v0", "region:us" ]

null

2023-09-13T04:05:48Z

--- library_name: peft base_model: EleutherAI/pythia-410m-v0 --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0

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

ckmfong

2023-09-20T12:34:08Z

0

null

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

reinforcement-learning

2023-09-20T12:34:05Z

--- 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="ckmfong/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"]) ```

CyberHarem/yokoyama_chika_idolmastercinderellagirls

CyberHarem

2023-09-20T12:26:30Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/yokoyama_chika_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-20T12:09:49Z

--- license: mit datasets: - CyberHarem/yokoyama_chika_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of yokoyama_chika_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4760, you need to download `4760/yokoyama_chika_idolmastercinderellagirls.pt` as the embedding and `4760/yokoyama_chika_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4760**, with the score of 0.980. The trigger words are: 1. `yokoyama_chika_idolmastercinderellagirls` 2. `twintails, long_hair, smile, open_mouth, blush, hair_ornament, brown_hair, bangs, brown_eyes, green_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.971 | [Download](5100/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![pattern_4-5100](5100/previews/pattern_4.png) | ![pattern_5-5100](5100/previews/pattern_5.png) | ![pattern_6-5100](5100/previews/pattern_6.png) | ![pattern_7-5100](5100/previews/pattern_7.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | **4760** | **0.980** | [**Download**](4760/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![pattern_4-4760](4760/previews/pattern_4.png) | ![pattern_5-4760](4760/previews/pattern_5.png) | ![pattern_6-4760](4760/previews/pattern_6.png) | ![pattern_7-4760](4760/previews/pattern_7.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | 4420 | 0.969 | [Download](4420/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![pattern_4-4420](4420/previews/pattern_4.png) | ![pattern_5-4420](4420/previews/pattern_5.png) | ![pattern_6-4420](4420/previews/pattern_6.png) | ![pattern_7-4420](4420/previews/pattern_7.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.971 | [Download](4080/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![pattern_4-4080](4080/previews/pattern_4.png) | ![pattern_5-4080](4080/previews/pattern_5.png) | ![pattern_6-4080](4080/previews/pattern_6.png) | ![pattern_7-4080](4080/previews/pattern_7.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.966 | [Download](3740/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![pattern_4-3740](3740/previews/pattern_4.png) | ![pattern_5-3740](3740/previews/pattern_5.png) | ![pattern_6-3740](3740/previews/pattern_6.png) | ![pattern_7-3740](3740/previews/pattern_7.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.964 | [Download](3400/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![pattern_4-3400](3400/previews/pattern_4.png) | ![pattern_5-3400](3400/previews/pattern_5.png) | ![pattern_6-3400](3400/previews/pattern_6.png) | ![pattern_7-3400](3400/previews/pattern_7.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.947 | [Download](3060/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![pattern_4-3060](3060/previews/pattern_4.png) | ![pattern_5-3060](3060/previews/pattern_5.png) | ![pattern_6-3060](3060/previews/pattern_6.png) | ![pattern_7-3060](3060/previews/pattern_7.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.958 | [Download](2720/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![pattern_4-2720](2720/previews/pattern_4.png) | ![pattern_5-2720](2720/previews/pattern_5.png) | ![pattern_6-2720](2720/previews/pattern_6.png) | ![pattern_7-2720](2720/previews/pattern_7.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.959 | [Download](2380/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![pattern_4-2380](2380/previews/pattern_4.png) | ![pattern_5-2380](2380/previews/pattern_5.png) | ![pattern_6-2380](2380/previews/pattern_6.png) | ![pattern_7-2380](2380/previews/pattern_7.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.942 | [Download](2040/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![pattern_4-2040](2040/previews/pattern_4.png) | ![pattern_5-2040](2040/previews/pattern_5.png) | ![pattern_6-2040](2040/previews/pattern_6.png) | ![pattern_7-2040](2040/previews/pattern_7.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.967 | [Download](1700/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![pattern_4-1700](1700/previews/pattern_4.png) | ![pattern_5-1700](1700/previews/pattern_5.png) | ![pattern_6-1700](1700/previews/pattern_6.png) | ![pattern_7-1700](1700/previews/pattern_7.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.944 | [Download](1360/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![pattern_4-1360](1360/previews/pattern_4.png) | ![pattern_5-1360](1360/previews/pattern_5.png) | ![pattern_6-1360](1360/previews/pattern_6.png) | ![pattern_7-1360](1360/previews/pattern_7.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.920 | [Download](1020/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![pattern_4-1020](1020/previews/pattern_4.png) | ![pattern_5-1020](1020/previews/pattern_5.png) | ![pattern_6-1020](1020/previews/pattern_6.png) | ![pattern_7-1020](1020/previews/pattern_7.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.912 | [Download](680/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![pattern_4-680](680/previews/pattern_4.png) | ![pattern_5-680](680/previews/pattern_5.png) | ![pattern_6-680](680/previews/pattern_6.png) | ![pattern_7-680](680/previews/pattern_7.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.692 | [Download](340/yokoyama_chika_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![pattern_4-340](340/previews/pattern_4.png) | ![pattern_5-340](340/previews/pattern_5.png) | ![pattern_6-340](340/previews/pattern_6.png) | ![pattern_7-340](340/previews/pattern_7.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

ldos/text_shortening_model_v44

ldos

2023-09-20T12:17:01Z

4

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-xsum", "base_model:finetune:facebook/bart-large-xsum", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-20T07:29:11Z

--- license: mit base_model: facebook/bart-large-xsum tags: - generated_from_trainer metrics: - rouge model-index: - name: text_shortening_model_v44 results: [] ---  # text_shortening_model_v44 This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8836 - Rouge1: 0.4921 - Rouge2: 0.2719 - Rougel: 0.4429 - Rougelsum: 0.4423 - Bert precision: 0.8746 - Bert recall: 0.8761 - Average word count: 8.7063 - Max word count: 17 - Min word count: 5 - Average token count: 16.2989 - % shortened texts with length > 12: 8.7302 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:| | 1.0083 | 1.0 | 83 | 1.4717 | 0.4904 | 0.2378 | 0.426 | 0.4266 | 0.8725 | 0.8732 | 8.5794 | 18 | 4 | 15.6164 | 6.3492 | | 0.5702 | 2.0 | 166 | 1.4852 | 0.4722 | 0.2421 | 0.414 | 0.4143 | 0.869 | 0.8653 | 7.9101 | 14 | 4 | 13.6455 | 1.5873 | | 0.4588 | 3.0 | 249 | 1.6283 | 0.5038 | 0.2733 | 0.4424 | 0.4422 | 0.8732 | 0.8794 | 9.0053 | 16 | 4 | 16.8386 | 8.9947 | | 0.3586 | 4.0 | 332 | 1.6017 | 0.4965 | 0.2762 | 0.4381 | 0.4383 | 0.8709 | 0.8787 | 9.2381 | 18 | 4 | 16.3042 | 12.1693 | | 0.2479 | 5.0 | 415 | 1.7497 | 0.4794 | 0.2613 | 0.4295 | 0.43 | 0.872 | 0.8702 | 8.3228 | 15 | 4 | 15.209 | 3.1746 | | 0.2296 | 6.0 | 498 | 1.8482 | 0.4935 | 0.2739 | 0.4442 | 0.4443 | 0.8737 | 0.8755 | 8.7963 | 17 | 5 | 16.2989 | 7.1429 | | 0.3065 | 7.0 | 581 | 1.9485 | 0.4765 | 0.2552 | 0.4213 | 0.4212 | 0.8698 | 0.8693 | 8.4683 | 17 | 5 | 15.6005 | 7.9365 | | 0.2598 | 8.0 | 664 | 2.1608 | 0.4871 | 0.2585 | 0.4316 | 0.4319 | 0.8707 | 0.8736 | 8.963 | 16 | 5 | 16.6481 | 9.5238 | | 0.2707 | 9.0 | 747 | 2.0966 | 0.4758 | 0.2603 | 0.4231 | 0.4246 | 0.8709 | 0.8717 | 8.4841 | 16 | 4 | 15.9312 | 7.1429 | | 0.2099 | 10.0 | 830 | 2.2721 | 0.4777 | 0.2604 | 0.4246 | 0.4246 | 0.8735 | 0.8724 | 8.4312 | 15 | 4 | 15.9471 | 5.5556 | | 0.1668 | 11.0 | 913 | 2.3536 | 0.4758 | 0.2541 | 0.4331 | 0.4328 | 0.8721 | 0.87 | 8.2857 | 14 | 4 | 15.7725 | 3.1746 | | 0.1552 | 12.0 | 996 | 2.4572 | 0.484 | 0.2562 | 0.4313 | 0.4304 | 0.8726 | 0.875 | 8.828 | 17 | 4 | 16.246 | 7.9365 | | 0.2141 | 13.0 | 1079 | 2.4485 | 0.4785 | 0.2631 | 0.4257 | 0.4252 | 0.8678 | 0.8736 | 9.1402 | 19 | 4 | 16.6561 | 11.3757 | | 0.1348 | 14.0 | 1162 | 2.5012 | 0.4821 | 0.2613 | 0.4292 | 0.4296 | 0.8706 | 0.8738 | 8.8783 | 17 | 4 | 16.5185 | 10.0529 | | 0.074 | 15.0 | 1245 | 2.5309 | 0.4915 | 0.2745 | 0.445 | 0.444 | 0.8764 | 0.8768 | 8.6667 | 16 | 4 | 16.2513 | 9.2593 | | 0.1822 | 16.0 | 1328 | 2.5735 | 0.4709 | 0.2566 | 0.4239 | 0.4232 | 0.872 | 0.8692 | 8.2063 | 15 | 3 | 15.7249 | 4.2328 | | 0.086 | 17.0 | 1411 | 2.8597 | 0.4831 | 0.2675 | 0.4373 | 0.4372 | 0.8722 | 0.8743 | 8.754 | 16 | 5 | 16.5476 | 8.7302 | | 0.0872 | 18.0 | 1494 | 2.7420 | 0.4831 | 0.2677 | 0.4367 | 0.4353 | 0.8724 | 0.873 | 8.664 | 17 | 5 | 16.3016 | 7.672 | | 0.1164 | 19.0 | 1577 | 2.8790 | 0.4867 | 0.269 | 0.4388 | 0.4381 | 0.8737 | 0.8755 | 8.7725 | 17 | 5 | 16.4418 | 8.9947 | | 0.1101 | 20.0 | 1660 | 2.8836 | 0.4921 | 0.2719 | 0.4429 | 0.4423 | 0.8746 | 0.8761 | 8.7063 | 17 | 5 | 16.2989 | 8.7302 | ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

AbdelKarim95/ppo-SnowballTarget

AbdelKarim95

2023-09-20T12:12:23Z

0

ml-agents

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

reinforcement-learning

2023-09-20T12:12:19Z

--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # **ppo** Agent playing **SnowballTarget** This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: AbdelKarim95/ppo-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

msy127/opt-350m-aihubqa-130-dpo-adapter

msy127

2023-09-20T11:58:17Z

3

0

peft

[ "peft", "region:us" ]

null

2023-09-20T10:26:03Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.6.0.dev0

alexandrainst/da-ner-base

alexandrainst

2023-09-20T11:56:44Z

124

0

transformers

[ "transformers", "pytorch", "tf", "bert", "token-classification", "da", "dataset:dane", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:04Z

--- language: - da license: apache-2.0 datasets: - dane widget: - text: Jens Peter Hansen kommer fra Danmark --- # BERT fine-tuned for Named Entity Recognition in Danish The model tags tokens (in Danish sentences) with named entity tags (BIO format) [PER, ORG, LOC, MISC]. The pretrained language model used for fine-tuning is the [Danish BERT](https://github.com/certainlyio/nordic_bert) by BotXO. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/ner.html#bert) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForTokenClassification model = BertForTokenClassification.from_pretrained("alexandrainst/da-ner-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-ner-base") ``` ## Training Data The model has been trained on the [DaNE](https://danlp-alexandra.readthedocs.io/en/latest/docs/datasets.html#dane).

alexandrainst/da-binary-emotion-classification-base

alexandrainst

2023-09-20T11:56:16Z

124

1

transformers

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

text-classification

2022-03-02T23:29:04Z

--- language: - da license: apache-2.0 widget: - text: Der er et træ i haven. --- # Danish BERT for emotion detection The BERT Emotion model detects whether a Danish text is emotional or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/sentiment_analysis.html#bert-emotion) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-binary-emotion-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-binary-emotion-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

alexandrainst/da-hatespeech-detection-base

alexandrainst

2023-09-20T11:56:09Z

122

1

transformers

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

text-classification

2022-03-02T23:29:04Z

--- language: - da license: apache-2.0 widget: - text: Senile gamle idiot --- # Danish BERT for hate speech (offensive language) detection The BERT HateSpeech model detects whether a Danish text is offensive or not. It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-detection-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-detection-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

alexandrainst/da-hatespeech-classification-base

alexandrainst

2023-09-20T11:56:03Z

114

0

transformers

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

text-classification

2022-03-02T23:29:04Z

--- language: - da license: apache-2.0 widget: - text: Senile gamle idiot --- # Danish BERT for hate speech classification The BERT HateSpeech model classifies offensive Danish text into 4 categories: * `Særlig opmærksomhed` (special attention, e.g. threat) * `Personangreb` (personal attack) * `Sprogbrug` (offensive language) * `Spam & indhold` (spam) This model is intended to be used after the [BERT HateSpeech detection model](https://huggingface.co/alexandrainst/da-hatespeech-detection-base). It is based on the pretrained [Danish BERT](https://github.com/certainlyio/nordic_bert) model by BotXO which has been fine-tuned on social media data. See the [DaNLP documentation](https://danlp-alexandra.readthedocs.io/en/latest/docs/tasks/hatespeech.html#bertdr) for more details. Here is how to use the model: ```python from transformers import BertTokenizer, BertForSequenceClassification model = BertForSequenceClassification.from_pretrained("alexandrainst/da-hatespeech-classification-base") tokenizer = BertTokenizer.from_pretrained("alexandrainst/da-hatespeech-classification-base") ``` ## Training data The data used for training has not been made publicly available. It consists of social media data manually annotated in collaboration with Danmarks Radio.

alexandrainst/da-discourse-coherence-base

alexandrainst

2023-09-20T11:50:37Z

114

1

transformers

[ "transformers", "pytorch", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "da", "dataset:ajders/ddisco", "base_model:NbAiLab/nb-bert-base", "base_model:finetune:NbAiLab/nb-bert-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-02-08T18:42:51Z

--- language: - da license: apache-2.0 tags: - generated_from_trainer datasets: - ajders/ddisco metrics: - accuracy base_model: NbAiLab/nb-bert-base model-index: - name: ddisco_classifier results: [] ---  # da-discourse-coherence-base This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) on the [DDisco](https://huggingface.co/datasets/ajders/ddisco) dataset. It achieves the following results on the evaluation set: - Loss: 0.7487 - Accuracy: 0.6915 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 703 - gradient_accumulation_steps: 16 - 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: 0.05 - num_epochs: 6.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.3422 | 0.4 | 5 | 1.0166 | 0.5721 | | 0.9645 | 0.8 | 10 | 0.8966 | 0.5721 | | 0.9854 | 1.24 | 15 | 0.8499 | 0.5721 | | 0.8628 | 1.64 | 20 | 0.8379 | 0.6517 | | 0.9046 | 2.08 | 25 | 0.8228 | 0.5721 | | 0.8361 | 2.48 | 30 | 0.7980 | 0.5821 | | 0.8158 | 2.88 | 35 | 0.8095 | 0.5821 | | 0.8689 | 3.32 | 40 | 0.7989 | 0.6169 | | 0.8125 | 3.72 | 45 | 0.7730 | 0.6965 | | 0.843 | 4.16 | 50 | 0.7566 | 0.6418 | | 0.7421 | 4.56 | 55 | 0.7840 | 0.6517 | | 0.7949 | 4.96 | 60 | 0.7531 | 0.6915 | | 0.828 | 5.4 | 65 | 0.7464 | 0.6816 | | 0.7438 | 5.8 | 70 | 0.7487 | 0.6915 | ### Framework versions - Transformers 4.26.0 - Pytorch 1.13.0a0+d0d6b1f - Datasets 2.9.0 - Tokenizers 0.13.2 ### Contributor [ajders](https://github.com/AJDERS)

alexandrainst/da-offensive-detection-base

alexandrainst

2023-09-20T11:50:26Z

105

3

transformers

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "text-classification", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-08-12T10:04:35Z

--- license: apache-2.0 widget: - text: Din store idiot base_model: xlm-roberta-base --- # Danish Offensive Text Detection based on XLM-Roberta-Base This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a dataset consisting of approximately 5 million Facebook comments on [DR](https://dr.dk/)'s public Facebook pages. The labels have been automatically generated using weak supervision, based on the [Snorkel](https://www.snorkel.org/) framework. The model achieves SOTA on a test set consisting of 600 Facebook comments annotated using majority vote by three annotators, of which 35.8% were labelled as offensive: | **Model** | **Precision** | **Recall** | **F1-score** | **F2-score** | | :-------- | :------------ | :--------- | :----------- | :----------- | | `alexandrainst/da-offensive-detection-base` (this) | 74.81% | **89.77%** | **81.61%** | **86.32%** | | [`alexandrainst/da-offensive-detection-small`](https://huggingface.co/alexandrainst/da-offensive-detection-small) | 74.13% | 89.30% | 81.01% | 85.79% | | [`A&ttack`](https://github.com/ogtal/A-ttack) | **97.32%** | 50.70% | 66.67% | 56.07% | | [`alexandrainst/da-hatespeech-detection-small`](https://huggingface.co/alexandrainst/da-hatespeech-detection-small) | 86.43% | 56.28% | 68.17% | 60.50% | | [`Guscode/DKbert-hatespeech-detection`](https://huggingface.co/Guscode/DKbert-hatespeech-detection) | 75.41% | 42.79% | 54.60% | 46.84% | ## Using the model You can use the model simply by running the following: ```python >>> from transformers import pipeline >>> offensive_text_pipeline = pipeline(model="alexandrainst/da-offensive-detection-base") >>> offensive_text_pipeline("Din store idiot") [{'label': 'Offensive', 'score': 0.9997463822364807}] ``` Processing multiple documents at the same time can be done as follows: ```python >>> offensive_text_pipeline(["Din store idiot", "ej hvor godt :)"]) [{'label': 'Offensive', 'score': 0.9997463822364807}, {'label': 'Not offensive', 'score': 0.9996451139450073}] ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - gradient_accumulation_steps: 1 - total_train_batch_size: 32 - seed: 4242 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - max_steps: 500000 - fp16: True - eval_steps: 1000 - early_stopping_patience: 100 ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1

honzatoegel/falcon-rw-1b-gramma-corrections-de-en

honzatoegel

2023-09-20T11:42:24Z

5

1

peft

[ "peft", "dataset:honzatoegel/lola-gramma-de-en", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]

null

2023-08-30T04:26:51Z

--- library_name: peft datasets: - honzatoegel/lola-gramma-de-en base_model: tiiuae/falcon-rw-1b --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing major errors - the explanations of corrections are wrong The low quality is probably caused by very low volume of training data, and small LLM size. Interesting observation: The LLM react 100% on the learned example 1 (exactly from training data) ### Example 1 #### Input ''### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Mir es geht gut, danke#### AI:Correct version of the sentence: #### Output "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End of repair list for this sentence.#### End of topic, repair list and the sentence ### Example 2 #### Input ''### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er macht dort dorte, aber ich muss einmal dorte. Repairs: - Instead of "Er gehen dort" it should be "Er macht dort" - the correct verb form is "macht" - Instead of "ich muss" it should be "ich muss einmal" - the verb "einmal" is used in the future in German. - The verb "dorte" must be conjugated in the third person singular "dorte".#### End of Repairs: - Instead of "Er gehen dort" it should be "Er macht dort" - the correct verb form is "macht". ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

honzatoegel/Llama-2-70b-chat-hf-gramma-corrections-de-en-2-overfitt

honzatoegel

2023-09-20T11:42:12Z

2

0

peft

[ "peft", "de", "en", "dataset:honzatoegel/lola-gramma-de-en", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:adapter:meta-llama/Llama-2-70b-chat-hf", "region:us" ]

null

2023-08-30T13:40:43Z

--- language: - de - en library_name: peft datasets: - honzatoegel/lola-gramma-de-en base_model: meta-llama/Llama-2-70b-chat-hf --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing some minor errors - the explanations of corrections are far from optimal, they often refer to some additional words as mented in explanations which are not necessary - the output repeats itself - the output list is sometimes broken into two lists The low quality is probably caused by very low volume of training data - 100 rows, low amount of combinations which can be then hardly generalized. ### Example 1 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er geht dort, aber ich muss anders gemacht. Repairs: - Instead of "gehen" it should be "goes" - the verb "gehen" is in the third person singular. - Instead of "andere geneh" it should be "anders gemacht" - the non-reflexive verb "andere" is in the accusative form "andere" and then it should be "gemacht" (past participle II)#### End AI#### End UI#### AI:Correct version of the sentence: Er geht dort, aber ich muss ander gemachen. Repairs: - Instead of "gehen" it should be "goes" - the ### Example 2 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Mir es geht gut, danke#### AI:Correct version of the sentence: #### Output "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End AI:》 Mir es geht gut, danke Correct version of the sentence: "Mir geht es gut, danke." Repairs: - "Mir es geht" the verb should be placed in the second position "Mir geht es" - because of the correct word order. - The sentence lacks a period at the end.#### End Mir es geht gut, danke Correct version of the sentence ### Example 3 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Habe ich das wirklich gemachen?#### AI:Correct version of the sentence: #### Output Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - the verb "gemachen" must be conjugated in the past tense, third person singular.#### End AI#### End User:Correct version of the sentence: Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - the verb "gemachen" must be conjugated in the past tense, third person singular.#### End AI####End User:Correct version of the sentence: Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

honzatoegel/Llama-2-70b-chat-hf-gramma-corrections-de-en

honzatoegel

2023-09-20T11:41:45Z

1

0

peft

[ "peft", "base_model:meta-llama/Llama-2-70b-chat-hf", "base_model:adapter:meta-llama/Llama-2-70b-chat-hf", "region:us" ]

null

2023-08-27T13:40:56Z

--- library_name: peft base_model: meta-llama/Llama-2-70b-chat-hf --- ## Examples As you can see from examples bellow the output is far from ideal, and far from simple GPT/LLama2 prompt without finetuning. Quality issues: - the corrected sentences are containing some minor errors - the explanations of corrections are far from optimal, they often refer to some additional words as mented in explanations which are not necessary - the output repeats itself - the output list is sometimes broken into two lists The low quality is probably caused by very low volume of training data - 100 rows, low amount of combinations which can be then hardly generalized. ### Example 1 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Ich mussen sehr rückwärts fahren.#### AI:Correct version of the sentence: #### Output Ich muss sehr zurückfahren. Repairs: - Instead of "rückwärts" it should be "zurück" - this is the correct form of the verb "to drive back".#### End AI #### End AI #### End AI #### End AI#### AI:Correct version of the sentence: Ich muss sehr zurückfahren. Repairs: - Instead of "mussen" it should be "muss" - this is the correct form of the verb "to must" in the third person singular.#### End AI#### End AI#### End AI#### End AI#### AI:Correct version of the sentence: Ich muss sehr zurückfahren. Repairs: ### Example 2 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Er gehen dort, aber ich muss ander geneh.#### AI:Correct version of the sentence: #### Output Er geht dort, aber ich muss anders gehen. Repairs: - Instead of "gehen" it should be "geht" - it is the third person singular of the verb "gehen". - Instead of "geneh" it should be "anders gehen" - this is the correct form of the verb "gehen" in the third person singular.#### End AI %} Repairs: - Instead of "gehen" it should be "geht" - it is the third person singular of the verb "gehen". - Instead of "geneh" it should be "anders gehen" - this is the correct form of the verb "gehen" in ### Example 3 #### Input '### User: Check the gramma of the following sentence and list all error and relevant corrections. Sentence:Habe ich das wirklich gemachen?#### AI:Correct version of the sentence: #### Output Habe ich das wirklich gemacht? Repairs: - Instead of "gemachen" it should be "gemacht" - this is the third person singular of the verb "machen" in the past tense.#### End AI %} Repairs: - Instead of "Habe ich das wirklich gemachen?" it should be "Habe ich das wirklich gemacht?" - This is the correct sentence with all the required information.#### End AI %} Repairs: - The verb "gemachen" is in the wrong form, it should be "gemacht" - this is the third person singular of the verb "machen" in the past tense.#### End AI %} Repairs: ## Training procedure Trained on: 1x RTX A6000 Used: 30GB Ram, 47GB GPU, 130GB Disc 8 Epochs, cca 25 minutes, Loss: 0.36 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0

hosnasn/Reza_DB

hosnasn

2023-09-20T11:33:50Z

3

0

diffusers

[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

text-to-image

2023-09-20T11:33:46Z

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: photo of a sks Reza tags: - text-to-image - diffusers - autotrain inference: true --- # DreamBooth trained by AutoTrain Text encoder was not trained.

sianbru/product_classifier_split_url_nodigit_all

sianbru

2023-09-20T11:31:59Z

108

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-dbmdz-uncased", "base_model:finetune:google-bert/bert-base-german-dbmdz-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-09-19T21:30:51Z

--- license: mit base_model: bert-base-german-dbmdz-uncased tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: product_classifier_split_url_nodigit_all results: [] ---  # product_classifier_split_url_nodigit_all This model is a fine-tuned version of [bert-base-german-dbmdz-uncased](https://huggingface.co/bert-base-german-dbmdz-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1833 - Accuracy: 0.9734 - F1: 0.9732 - Precision: 0.9731 - Recall: 0.9734 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.0385 | 1.0 | 1300 | 0.1880 | 0.9666 | 0.9663 | 0.9665 | 0.9666 | | 0.0198 | 2.0 | 2600 | 0.1707 | 0.9718 | 0.9718 | 0.9719 | 0.9718 | | 0.0083 | 3.0 | 3900 | 0.1833 | 0.9734 | 0.9732 | 0.9731 | 0.9734 | ### Framework versions - Transformers 4.32.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.13.3

bavolesy/ppo-Huggy

bavolesy

2023-09-20T11:29:34Z

2

0

ml-agents

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

reinforcement-learning

2023-09-20T11:29:23Z

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

mychen76/donut-receipt_v2

mychen76

2023-09-20T11:19:38Z

46

0

transformers

[ "transformers", "pytorch", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-09-20T09:51:58Z

--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer model-index: - name: donut-receipt_v2 results: [] ---  # donut-receipt_v2 This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - 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 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.34.0.dev0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3

OpenDILabCommunity/HalfCheetah-v3-TD3

OpenDILabCommunity

2023-09-20T11:17:26Z

0

pytorch

[ "pytorch", "deep-reinforcement-learning", "reinforcement-learning", "DI-engine", "HalfCheetah-v3", "en", "license:apache-2.0", "region:us" ]

reinforcement-learning

2023-04-15T21:02:47Z

--- language: en license: apache-2.0 library_name: pytorch tags: - deep-reinforcement-learning - reinforcement-learning - DI-engine - HalfCheetah-v3 benchmark_name: OpenAI/Gym/MuJoCo task_name: HalfCheetah-v3 pipeline_tag: reinforcement-learning model-index: - name: TD3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: OpenAI/Gym/MuJoCo-HalfCheetah-v3 type: OpenAI/Gym/MuJoCo-HalfCheetah-v3 metrics: - type: mean_reward value: 11100.89 +/- 77.51 name: mean_reward --- # Play **HalfCheetah-v3** with **TD3** Policy ## Model Description  This is a simple **TD3** implementation to OpenAI/Gym/MuJoCo **HalfCheetah-v3** using the [DI-engine library](https://github.com/opendilab/di-engine) and the [DI-zoo](https://github.com/opendilab/DI-engine/tree/main/dizoo). **DI-engine** is a python library for solving general decision intelligence problems, which is based on implementations of reinforcement learning framework using PyTorch or JAX. This library aims to standardize the reinforcement learning framework across different algorithms, benchmarks, environments, and to support both academic researches and prototype applications. Besides, self-customized training pipelines and applications are supported by reusing different abstraction levels of DI-engine reinforcement learning framework. ## Model Usage ### Install the Dependencies <details close> <summary>(Click for Details)</summary> ```shell # install huggingface_ding git clone https://github.com/opendilab/huggingface_ding.git pip3 install -e ./huggingface_ding/ # install environment dependencies if needed sudo apt update -y && sudo apt install -y build-essential libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev libglfw3 libglfw3-dev libsdl2-dev libsdl2-image-dev libglm-dev libfreetype6-dev patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ``` </details> ### Git Clone from Huggingface and Run the Model <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import SACAgent from ding.config import Config from easydict import EasyDict import torch # Pull model from files which are git cloned from huggingface policy_state_dict = torch.load("pytorch_model.bin", map_location=torch.device("cpu")) cfg = EasyDict(Config.file_to_dict("policy_config.py").cfg_dict) # Instantiate the agent agent = SACAgent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ### Run Model by Using Huggingface_ding <details close> <summary>(Click for Details)</summary> ```shell # running with trained model python3 -u run.py ``` **run.py** ```python from ding.bonus import TD3Agent from huggingface_ding import pull_model_from_hub # Pull model from Hugggingface hub policy_state_dict, cfg = pull_model_from_hub(repo_id="OpenDILabCommunity/HalfCheetah-v3-TD3") # Instantiate the agent agent = TD3Agent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3", cfg=cfg.exp_config, policy_state_dict=policy_state_dict) # Continue training agent.train(step=5000) # Render the new agent performance agent.deploy(enable_save_replay=True) ``` </details> ## Model Training ### Train the Model and Push to Huggingface_hub <details close> <summary>(Click for Details)</summary> ```shell #Training Your Own Agent python3 -u train.py ``` **train.py** ```python from ding.bonus import TD3Agent from huggingface_ding import push_model_to_hub # Instantiate the agent agent = TD3Agent(env_id="HalfCheetah-v3", exp_name="HalfCheetah-v3-TD3") # Train the agent return_ = agent.train(step=int(5000000)) # Push model to huggingface hub push_model_to_hub( agent=agent.best, env_name="OpenAI/Gym/MuJoCo", task_name="HalfCheetah-v3", algo_name="TD3", wandb_url=return_.wandb_url, github_repo_url="https://github.com/opendilab/DI-engine", github_doc_model_url="https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html", github_doc_env_url="https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html", installation_guide=''' sudo apt update -y \ && sudo apt install -y \ build-essential \ libgl1-mesa-dev \ libgl1-mesa-glx \ libglew-dev \ libosmesa6-dev \ libglfw3 \ libglfw3-dev \ libsdl2-dev \ libsdl2-image-dev \ libglm-dev \ libfreetype6-dev \ patchelf mkdir -p ~/.mujoco wget https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz -O mujoco.tar.gz tar -xf mujoco.tar.gz -C ~/.mujoco echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin" >> ~/.bashrc export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mjpro210/bin:~/.mujoco/mujoco210/bin pip3 install "cython<3" pip3 install DI-engine[common_env] ''', usage_file_by_git_clone="./td3/halfcheetah_td3_deploy.py", usage_file_by_huggingface_ding="./td3/halfcheetah_td3_download.py", train_file="./td3/halfcheetah_td3.py", repo_id="OpenDILabCommunity/HalfCheetah-v3-TD3", create_repo=False ) ``` </details> **Configuration** <details close> <summary>(Click for Details)</summary> ```python exp_config = { 'env': { 'manager': { 'episode_num': float("inf"), 'max_retry': 1, 'retry_type': 'reset', 'auto_reset': True, 'step_timeout': None, 'reset_timeout': None, 'retry_waiting_time': 0.1, 'cfg_type': 'BaseEnvManagerDict' }, 'stop_value': 11000, 'n_evaluator_episode': 8, 'env_id': 'HalfCheetah-v3', 'collector_env_num': 1, 'evaluator_env_num': 8, 'env_wrapper': 'mujoco_default' }, 'policy': { 'model': { 'twin_critic': True, 'obs_shape': 17, 'action_shape': 6, 'actor_head_hidden_size': 256, 'critic_head_hidden_size': 256, 'action_space': 'regression' }, 'learn': { 'learner': { 'train_iterations': 1000000000, 'dataloader': { 'num_workers': 0 }, 'log_policy': True, 'hook': { 'load_ckpt_before_run': '', 'log_show_after_iter': 100, 'save_ckpt_after_iter': 10000, 'save_ckpt_after_run': True }, 'cfg_type': 'BaseLearnerDict' }, 'update_per_collect': 1, 'batch_size': 256, 'learning_rate_actor': 0.001, 'learning_rate_critic': 0.001, 'ignore_done': True, 'target_theta': 0.005, 'discount_factor': 0.99, 'actor_update_freq': 2, 'noise': True, 'noise_sigma': 0.2, 'noise_range': { 'min': -0.5, 'max': 0.5 } }, 'collect': { 'collector': {}, 'unroll_len': 1, 'noise_sigma': 0.1, 'n_sample': 1 }, 'eval': { 'evaluator': { 'eval_freq': 5000, 'render': { 'render_freq': -1, 'mode': 'train_iter' }, 'figure_path': None, 'cfg_type': 'InteractionSerialEvaluatorDict', 'stop_value': 11000, 'n_episode': 8 } }, 'other': { 'replay_buffer': { 'replay_buffer_size': 1000000 } }, 'on_policy': False, 'cuda': True, 'multi_gpu': False, 'bp_update_sync': True, 'traj_len_inf': False, 'type': 'td3', 'priority': False, 'priority_IS_weight': False, 'random_collect_size': 25000, 'transition_with_policy_data': False, 'action_space': 'continuous', 'reward_batch_norm': False, 'multi_agent': False, 'cfg_type': 'TD3PolicyDict' }, 'exp_name': 'HalfCheetah-v3-TD3', 'seed': 0, 'wandb_logger': { 'gradient_logger': True, 'video_logger': True, 'plot_logger': True, 'action_logger': True, 'return_logger': False } } ``` </details> **Training Procedure**  - **Weights & Biases (wandb):** [monitor link](https://wandb.ai/zjowowen/HalfCheetah-v3-TD3) ## Model Information  - **Github Repository:** [repo link](https://github.com/opendilab/DI-engine) - **Doc**: [DI-engine-docs Algorithm link](https://di-engine-docs.readthedocs.io/en/latest/12_policies/td3.html) - **Configuration:** [config link](https://huggingface.co/OpenDILabCommunity/HalfCheetah-v3-TD3/blob/main/policy_config.py) - **Demo:** [video](https://huggingface.co/OpenDILabCommunity/HalfCheetah-v3-TD3/blob/main/replay.mp4)  - **Parameters total size:** 1690.06 KB - **Last Update Date:** 2023-09-20 ## Environments  - **Benchmark:** OpenAI/Gym/MuJoCo - **Task:** HalfCheetah-v3 - **Gym version:** 0.25.1 - **DI-engine version:** v0.4.9 - **PyTorch version:** 2.0.1+cu117 - **Doc**: [DI-engine-docs Environments link](https://di-engine-docs.readthedocs.io/en/latest/13_envs/mujoco.html)

bangtai/path-to-save-model

bangtai

2023-09-20T11:16:04Z

30

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "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

2023-09-19T03:17:31Z

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

tianzhidao/marian-finetuned-kde4-en-to-fr

tianzhidao

2023-09-20T11:14:44Z

59

0

transformers

[ "transformers", "tf", "marian", "text2text-generation", "generated_from_keras_callback", "base_model:Helsinki-NLP/opus-mt-en-fr", "base_model:finetune:Helsinki-NLP/opus-mt-en-fr", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-20T08:05:48Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-fr tags: - generated_from_keras_callback model-index: - name: tianzhidao/marian-finetuned-kde4-en-to-fr results: [] ---  # tianzhidao/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6857 - Validation Loss: 0.8029 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 17733, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0612 | 0.8783 | 0 | | 0.7974 | 0.8213 | 1 | | 0.6857 | 0.8029 | 2 | ### Framework versions - Transformers 4.33.2 - TensorFlow 2.13.0 - Datasets 2.14.5 - Tokenizers 0.13.3

amitonHFace/ppo-LunarLander-v2

amitonHFace

2023-09-20T11:13:33Z

7

0

stable-baselines3

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

reinforcement-learning

2023-09-20T09:59:28Z

--- 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: 215.91 +/- 20.01 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 ... ```

dim/ruGPT-13b_dolly_oasst1_chip2

dim

2023-09-20T11:03:21Z

3

1

peft

[ "peft", "region:us" ]

null

2023-09-20T11:00:43Z

--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(**message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(**data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/ruGPT-13b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=2, bot_token_id=46787, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) # Гавайские острова - это рай для любителей природы, культуры и приключений. Это место, где можно исследовать тропические леса, плавать с дельфинами, кататься на волнах прибоя или просто наслаждаться солнцем и пляжем. В дополнение к этому, здесь есть множество достопримечательностей, которые стоит посетить, таких как вулканы Мауна-Лоа и Килауэа, водопады Ваймеа, пещеры Халеакала и многое другое. Если вы ищете что-то более расслабляющее, то посещение гавайских ресторанов и баров может быть отличным способом провести время. ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

amiiin/methods100

amiiin

2023-09-20T10:56:44Z

0

null

[ "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "region:us" ]

null

2023-09-20T10:40:45Z

--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: methods100 results: [] ---  # methods100 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu118 - Datasets 2.14.5 - Tokenizers 0.13.3

tvganesh/test_trainer

tvganesh

2023-09-20T10:52:48Z

103

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "base_model:tvganesh/test_trainer", "base_model:finetune:tvganesh/test_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-14T07:05:08Z

--- license: apache-2.0 base_model: tvganesh/test_trainer tags: - generated_from_trainer metrics: - rouge model-index: - name: test_trainer results: [] ---  # test_trainer This model is a fine-tuned version of [tvganesh/test_trainer](https://huggingface.co/tvganesh/test_trainer) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Rouge1: 0.8325 - Rouge2: 0.8187 - Rougel: 0.8294 - Rougelsum: 0.8312 - Gen Len: 18.6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0056 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 5 | 0.2345 | 0.7001 | 0.6536 | 0.6998 | 0.6957 | 16.3 | | No log | 2.0 | 10 | 0.1472 | 0.7958 | 0.7695 | 0.7929 | 0.7965 | 18.3 | | No log | 3.0 | 15 | 0.1174 | 0.7196 | 0.6705 | 0.7187 | 0.7118 | 16.3 | | No log | 4.0 | 20 | 0.0554 | 0.7977 | 0.774 | 0.7907 | 0.7958 | 18.6 | | No log | 5.0 | 25 | 0.0725 | 0.8205 | 0.8074 | 0.8188 | 0.8212 | 18.6 | | No log | 6.0 | 30 | 0.0281 | 0.8114 | 0.7929 | 0.8098 | 0.8123 | 18.6 | | No log | 7.0 | 35 | 0.0451 | 0.7959 | 0.7678 | 0.7908 | 0.7945 | 18.6 | | No log | 8.0 | 40 | 0.0438 | 0.8285 | 0.8061 | 0.8205 | 0.8227 | 18.5 | | No log | 9.0 | 45 | 0.0178 | 0.8249 | 0.8109 | 0.8225 | 0.8243 | 18.6 | | No log | 10.0 | 50 | 0.0072 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 11.0 | 55 | 0.0119 | 0.8336 | 0.8217 | 0.8315 | 0.833 | 18.6 | | No log | 12.0 | 60 | 0.0104 | 0.8336 | 0.8217 | 0.8315 | 0.833 | 18.6 | | No log | 13.0 | 65 | 0.0031 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 14.0 | 70 | 0.0099 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 15.0 | 75 | 0.0067 | 0.8284 | 0.8053 | 0.8213 | 0.8226 | 18.6 | | No log | 16.0 | 80 | 0.0019 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 17.0 | 85 | 0.0173 | 0.8143 | 0.798 | 0.8111 | 0.8102 | 18.2 | | No log | 18.0 | 90 | 0.0007 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 19.0 | 95 | 0.0004 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 20.0 | 100 | 0.0195 | 0.8325 | 0.813 | 0.8294 | 0.8312 | 18.6 | | No log | 21.0 | 105 | 0.0057 | 0.8325 | 0.813 | 0.8294 | 0.8312 | 18.6 | | No log | 22.0 | 110 | 0.0005 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 23.0 | 115 | 0.0010 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 24.0 | 120 | 0.0003 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 25.0 | 125 | 0.0004 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 26.0 | 130 | 0.0005 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 27.0 | 135 | 0.0002 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 28.0 | 140 | 0.0001 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 29.0 | 145 | 0.0010 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 30.0 | 150 | 0.0003 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 31.0 | 155 | 0.0001 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 32.0 | 160 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 33.0 | 165 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 34.0 | 170 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 35.0 | 175 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 36.0 | 180 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 37.0 | 185 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 38.0 | 190 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 39.0 | 195 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | | No log | 40.0 | 200 | 0.0000 | 0.8325 | 0.8187 | 0.8294 | 0.8312 | 18.6 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.0 - Datasets 2.14.5 - Tokenizers 0.13.3

DataBindu/swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged

DataBindu

2023-09-20T10:43:19Z

149

0

transformers

[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "base_model:finetune:microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-09-19T19:58:12Z

--- license: apache-2.0 base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.7268518518518519 ---  # swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes-merged This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8626 - Accuracy: 0.7269 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 3.8355 | 0.98 | 15 | 2.5831 | 0.3333 | | 1.9292 | 1.97 | 30 | 1.6850 | 0.5046 | | 1.4121 | 2.95 | 45 | 1.2324 | 0.5972 | | 1.0121 | 4.0 | 61 | 1.0345 | 0.6852 | | 0.854 | 4.98 | 76 | 0.9663 | 0.6806 | | 0.701 | 5.97 | 91 | 0.9587 | 0.6991 | | 0.5956 | 6.95 | 106 | 0.8626 | 0.7269 | | 0.5713 | 7.87 | 120 | 0.8645 | 0.7222 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cpu - Datasets 2.14.4 - Tokenizers 0.13.3

CyberHarem/pekora_jashinchandropkick

CyberHarem

2023-09-20T10:41:30Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/pekora_jashinchandropkick", "license:mit", "region:us" ]

text-to-image

2023-09-20T10:23:18Z

--- license: mit datasets: - CyberHarem/pekora_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of pekora_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8960, you need to download `8960/pekora_jashinchandropkick.pt` as the embedding and `8960/pekora_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 8960**, with the score of 0.891. The trigger words are: 1. `pekora_jashinchandropkick` 2. `short_hair, hairband, blonde_hair, black_hairband, capelet, shaded_face, shiny_hair, hair_between_eyes` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:---------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 9600 | 0.883 | [Download](9600/pekora_jashinchandropkick.zip) | ![pattern_1-9600](9600/previews/pattern_1.png) | ![pattern_2-9600](9600/previews/pattern_2.png) | ![pattern_3-9600](9600/previews/pattern_3.png) | ![pattern_4-9600](9600/previews/pattern_4.png) | ![pattern_5-9600](9600/previews/pattern_5.png) | ![pattern_6-9600](9600/previews/pattern_6.png) | ![pattern_7-9600](9600/previews/pattern_7.png) | ![pattern_8-9600](9600/previews/pattern_8.png) | ![pattern_9-9600](9600/previews/pattern_9.png) | ![pattern_10-9600](9600/previews/pattern_10.png) | ![pattern_11-9600](9600/previews/pattern_11.png) | ![pattern_12-9600](9600/previews/pattern_12.png) | ![pattern_13-9600](9600/previews/pattern_13.png) | ![pattern_14-9600](9600/previews/pattern_14.png) | ![bikini-9600](9600/previews/bikini.png) | [<NSFW, click to see>](9600/previews/bondage.png) | ![free-9600](9600/previews/free.png) | ![maid-9600](9600/previews/maid.png) | ![miko-9600](9600/previews/miko.png) | [<NSFW, click to see>](9600/previews/nude.png) | [<NSFW, click to see>](9600/previews/nude2.png) | ![suit-9600](9600/previews/suit.png) | ![yukata-9600](9600/previews/yukata.png) | | **8960** | **0.891** | [**Download**](8960/pekora_jashinchandropkick.zip) | ![pattern_1-8960](8960/previews/pattern_1.png) | ![pattern_2-8960](8960/previews/pattern_2.png) | ![pattern_3-8960](8960/previews/pattern_3.png) | ![pattern_4-8960](8960/previews/pattern_4.png) | ![pattern_5-8960](8960/previews/pattern_5.png) | ![pattern_6-8960](8960/previews/pattern_6.png) | ![pattern_7-8960](8960/previews/pattern_7.png) | ![pattern_8-8960](8960/previews/pattern_8.png) | ![pattern_9-8960](8960/previews/pattern_9.png) | ![pattern_10-8960](8960/previews/pattern_10.png) | ![pattern_11-8960](8960/previews/pattern_11.png) | ![pattern_12-8960](8960/previews/pattern_12.png) | ![pattern_13-8960](8960/previews/pattern_13.png) | ![pattern_14-8960](8960/previews/pattern_14.png) | ![bikini-8960](8960/previews/bikini.png) | [<NSFW, click to see>](8960/previews/bondage.png) | ![free-8960](8960/previews/free.png) | ![maid-8960](8960/previews/maid.png) | ![miko-8960](8960/previews/miko.png) | [<NSFW, click to see>](8960/previews/nude.png) | [<NSFW, click to see>](8960/previews/nude2.png) | ![suit-8960](8960/previews/suit.png) | ![yukata-8960](8960/previews/yukata.png) | | 8320 | 0.861 | [Download](8320/pekora_jashinchandropkick.zip) | ![pattern_1-8320](8320/previews/pattern_1.png) | ![pattern_2-8320](8320/previews/pattern_2.png) | ![pattern_3-8320](8320/previews/pattern_3.png) | ![pattern_4-8320](8320/previews/pattern_4.png) | ![pattern_5-8320](8320/previews/pattern_5.png) | ![pattern_6-8320](8320/previews/pattern_6.png) | ![pattern_7-8320](8320/previews/pattern_7.png) | ![pattern_8-8320](8320/previews/pattern_8.png) | ![pattern_9-8320](8320/previews/pattern_9.png) | ![pattern_10-8320](8320/previews/pattern_10.png) | ![pattern_11-8320](8320/previews/pattern_11.png) | ![pattern_12-8320](8320/previews/pattern_12.png) | ![pattern_13-8320](8320/previews/pattern_13.png) | ![pattern_14-8320](8320/previews/pattern_14.png) | ![bikini-8320](8320/previews/bikini.png) | [<NSFW, click to see>](8320/previews/bondage.png) | ![free-8320](8320/previews/free.png) | ![maid-8320](8320/previews/maid.png) | ![miko-8320](8320/previews/miko.png) | [<NSFW, click to see>](8320/previews/nude.png) | [<NSFW, click to see>](8320/previews/nude2.png) | ![suit-8320](8320/previews/suit.png) | ![yukata-8320](8320/previews/yukata.png) | | 7680 | 0.877 | [Download](7680/pekora_jashinchandropkick.zip) | ![pattern_1-7680](7680/previews/pattern_1.png) | ![pattern_2-7680](7680/previews/pattern_2.png) | ![pattern_3-7680](7680/previews/pattern_3.png) | ![pattern_4-7680](7680/previews/pattern_4.png) | ![pattern_5-7680](7680/previews/pattern_5.png) | ![pattern_6-7680](7680/previews/pattern_6.png) | ![pattern_7-7680](7680/previews/pattern_7.png) | ![pattern_8-7680](7680/previews/pattern_8.png) | ![pattern_9-7680](7680/previews/pattern_9.png) | ![pattern_10-7680](7680/previews/pattern_10.png) | ![pattern_11-7680](7680/previews/pattern_11.png) | ![pattern_12-7680](7680/previews/pattern_12.png) | ![pattern_13-7680](7680/previews/pattern_13.png) | ![pattern_14-7680](7680/previews/pattern_14.png) | ![bikini-7680](7680/previews/bikini.png) | [<NSFW, click to see>](7680/previews/bondage.png) | ![free-7680](7680/previews/free.png) | ![maid-7680](7680/previews/maid.png) | ![miko-7680](7680/previews/miko.png) | [<NSFW, click to see>](7680/previews/nude.png) | [<NSFW, click to see>](7680/previews/nude2.png) | ![suit-7680](7680/previews/suit.png) | ![yukata-7680](7680/previews/yukata.png) | | 7040 | 0.875 | [Download](7040/pekora_jashinchandropkick.zip) | ![pattern_1-7040](7040/previews/pattern_1.png) | ![pattern_2-7040](7040/previews/pattern_2.png) | ![pattern_3-7040](7040/previews/pattern_3.png) | ![pattern_4-7040](7040/previews/pattern_4.png) | ![pattern_5-7040](7040/previews/pattern_5.png) | ![pattern_6-7040](7040/previews/pattern_6.png) | ![pattern_7-7040](7040/previews/pattern_7.png) | ![pattern_8-7040](7040/previews/pattern_8.png) | ![pattern_9-7040](7040/previews/pattern_9.png) | ![pattern_10-7040](7040/previews/pattern_10.png) | ![pattern_11-7040](7040/previews/pattern_11.png) | ![pattern_12-7040](7040/previews/pattern_12.png) | ![pattern_13-7040](7040/previews/pattern_13.png) | ![pattern_14-7040](7040/previews/pattern_14.png) | ![bikini-7040](7040/previews/bikini.png) | [<NSFW, click to see>](7040/previews/bondage.png) | ![free-7040](7040/previews/free.png) | ![maid-7040](7040/previews/maid.png) | ![miko-7040](7040/previews/miko.png) | [<NSFW, click to see>](7040/previews/nude.png) | [<NSFW, click to see>](7040/previews/nude2.png) | ![suit-7040](7040/previews/suit.png) | ![yukata-7040](7040/previews/yukata.png) | | 6400 | 0.865 | [Download](6400/pekora_jashinchandropkick.zip) | ![pattern_1-6400](6400/previews/pattern_1.png) | ![pattern_2-6400](6400/previews/pattern_2.png) | ![pattern_3-6400](6400/previews/pattern_3.png) | ![pattern_4-6400](6400/previews/pattern_4.png) | ![pattern_5-6400](6400/previews/pattern_5.png) | ![pattern_6-6400](6400/previews/pattern_6.png) | ![pattern_7-6400](6400/previews/pattern_7.png) | ![pattern_8-6400](6400/previews/pattern_8.png) | ![pattern_9-6400](6400/previews/pattern_9.png) | ![pattern_10-6400](6400/previews/pattern_10.png) | ![pattern_11-6400](6400/previews/pattern_11.png) | ![pattern_12-6400](6400/previews/pattern_12.png) | ![pattern_13-6400](6400/previews/pattern_13.png) | ![pattern_14-6400](6400/previews/pattern_14.png) | ![bikini-6400](6400/previews/bikini.png) | [<NSFW, click to see>](6400/previews/bondage.png) | ![free-6400](6400/previews/free.png) | ![maid-6400](6400/previews/maid.png) | ![miko-6400](6400/previews/miko.png) | [<NSFW, click to see>](6400/previews/nude.png) | [<NSFW, click to see>](6400/previews/nude2.png) | ![suit-6400](6400/previews/suit.png) | ![yukata-6400](6400/previews/yukata.png) | | 5760 | 0.841 | [Download](5760/pekora_jashinchandropkick.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![pattern_11-5760](5760/previews/pattern_11.png) | ![pattern_12-5760](5760/previews/pattern_12.png) | ![pattern_13-5760](5760/previews/pattern_13.png) | ![pattern_14-5760](5760/previews/pattern_14.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5120 | 0.773 | [Download](5120/pekora_jashinchandropkick.zip) | ![pattern_1-5120](5120/previews/pattern_1.png) | ![pattern_2-5120](5120/previews/pattern_2.png) | ![pattern_3-5120](5120/previews/pattern_3.png) | ![pattern_4-5120](5120/previews/pattern_4.png) | ![pattern_5-5120](5120/previews/pattern_5.png) | ![pattern_6-5120](5120/previews/pattern_6.png) | ![pattern_7-5120](5120/previews/pattern_7.png) | ![pattern_8-5120](5120/previews/pattern_8.png) | ![pattern_9-5120](5120/previews/pattern_9.png) | ![pattern_10-5120](5120/previews/pattern_10.png) | ![pattern_11-5120](5120/previews/pattern_11.png) | ![pattern_12-5120](5120/previews/pattern_12.png) | ![pattern_13-5120](5120/previews/pattern_13.png) | ![pattern_14-5120](5120/previews/pattern_14.png) | ![bikini-5120](5120/previews/bikini.png) | [<NSFW, click to see>](5120/previews/bondage.png) | ![free-5120](5120/previews/free.png) | ![maid-5120](5120/previews/maid.png) | ![miko-5120](5120/previews/miko.png) | [<NSFW, click to see>](5120/previews/nude.png) | [<NSFW, click to see>](5120/previews/nude2.png) | ![suit-5120](5120/previews/suit.png) | ![yukata-5120](5120/previews/yukata.png) | | 4480 | 0.767 | [Download](4480/pekora_jashinchandropkick.zip) | ![pattern_1-4480](4480/previews/pattern_1.png) | ![pattern_2-4480](4480/previews/pattern_2.png) | ![pattern_3-4480](4480/previews/pattern_3.png) | ![pattern_4-4480](4480/previews/pattern_4.png) | ![pattern_5-4480](4480/previews/pattern_5.png) | ![pattern_6-4480](4480/previews/pattern_6.png) | ![pattern_7-4480](4480/previews/pattern_7.png) | ![pattern_8-4480](4480/previews/pattern_8.png) | ![pattern_9-4480](4480/previews/pattern_9.png) | ![pattern_10-4480](4480/previews/pattern_10.png) | ![pattern_11-4480](4480/previews/pattern_11.png) | ![pattern_12-4480](4480/previews/pattern_12.png) | ![pattern_13-4480](4480/previews/pattern_13.png) | ![pattern_14-4480](4480/previews/pattern_14.png) | ![bikini-4480](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) | ![free-4480](4480/previews/free.png) | ![maid-4480](4480/previews/maid.png) | ![miko-4480](4480/previews/miko.png) | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) | ![suit-4480](4480/previews/suit.png) | ![yukata-4480](4480/previews/yukata.png) | | 3840 | 0.798 | [Download](3840/pekora_jashinchandropkick.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![pattern_11-3840](3840/previews/pattern_11.png) | ![pattern_12-3840](3840/previews/pattern_12.png) | ![pattern_13-3840](3840/previews/pattern_13.png) | ![pattern_14-3840](3840/previews/pattern_14.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3200 | 0.714 | [Download](3200/pekora_jashinchandropkick.zip) | ![pattern_1-3200](3200/previews/pattern_1.png) | ![pattern_2-3200](3200/previews/pattern_2.png) | ![pattern_3-3200](3200/previews/pattern_3.png) | ![pattern_4-3200](3200/previews/pattern_4.png) | ![pattern_5-3200](3200/previews/pattern_5.png) | ![pattern_6-3200](3200/previews/pattern_6.png) | ![pattern_7-3200](3200/previews/pattern_7.png) | ![pattern_8-3200](3200/previews/pattern_8.png) | ![pattern_9-3200](3200/previews/pattern_9.png) | ![pattern_10-3200](3200/previews/pattern_10.png) | ![pattern_11-3200](3200/previews/pattern_11.png) | ![pattern_12-3200](3200/previews/pattern_12.png) | ![pattern_13-3200](3200/previews/pattern_13.png) | ![pattern_14-3200](3200/previews/pattern_14.png) | ![bikini-3200](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | ![free-3200](3200/previews/free.png) | ![maid-3200](3200/previews/maid.png) | ![miko-3200](3200/previews/miko.png) | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) | ![suit-3200](3200/previews/suit.png) | ![yukata-3200](3200/previews/yukata.png) | | 2560 | 0.701 | [Download](2560/pekora_jashinchandropkick.zip) | ![pattern_1-2560](2560/previews/pattern_1.png) | ![pattern_2-2560](2560/previews/pattern_2.png) | ![pattern_3-2560](2560/previews/pattern_3.png) | ![pattern_4-2560](2560/previews/pattern_4.png) | ![pattern_5-2560](2560/previews/pattern_5.png) | ![pattern_6-2560](2560/previews/pattern_6.png) | ![pattern_7-2560](2560/previews/pattern_7.png) | ![pattern_8-2560](2560/previews/pattern_8.png) | ![pattern_9-2560](2560/previews/pattern_9.png) | ![pattern_10-2560](2560/previews/pattern_10.png) | ![pattern_11-2560](2560/previews/pattern_11.png) | ![pattern_12-2560](2560/previews/pattern_12.png) | ![pattern_13-2560](2560/previews/pattern_13.png) | ![pattern_14-2560](2560/previews/pattern_14.png) | ![bikini-2560](2560/previews/bikini.png) | [<NSFW, click to see>](2560/previews/bondage.png) | ![free-2560](2560/previews/free.png) | ![maid-2560](2560/previews/maid.png) | ![miko-2560](2560/previews/miko.png) | [<NSFW, click to see>](2560/previews/nude.png) | [<NSFW, click to see>](2560/previews/nude2.png) | ![suit-2560](2560/previews/suit.png) | ![yukata-2560](2560/previews/yukata.png) | | 1920 | 0.717 | [Download](1920/pekora_jashinchandropkick.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![pattern_11-1920](1920/previews/pattern_11.png) | ![pattern_12-1920](1920/previews/pattern_12.png) | ![pattern_13-1920](1920/previews/pattern_13.png) | ![pattern_14-1920](1920/previews/pattern_14.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1280 | 0.614 | [Download](1280/pekora_jashinchandropkick.zip) | ![pattern_1-1280](1280/previews/pattern_1.png) | ![pattern_2-1280](1280/previews/pattern_2.png) | ![pattern_3-1280](1280/previews/pattern_3.png) | ![pattern_4-1280](1280/previews/pattern_4.png) | ![pattern_5-1280](1280/previews/pattern_5.png) | ![pattern_6-1280](1280/previews/pattern_6.png) | ![pattern_7-1280](1280/previews/pattern_7.png) | ![pattern_8-1280](1280/previews/pattern_8.png) | ![pattern_9-1280](1280/previews/pattern_9.png) | ![pattern_10-1280](1280/previews/pattern_10.png) | ![pattern_11-1280](1280/previews/pattern_11.png) | ![pattern_12-1280](1280/previews/pattern_12.png) | ![pattern_13-1280](1280/previews/pattern_13.png) | ![pattern_14-1280](1280/previews/pattern_14.png) | ![bikini-1280](1280/previews/bikini.png) | [<NSFW, click to see>](1280/previews/bondage.png) | ![free-1280](1280/previews/free.png) | ![maid-1280](1280/previews/maid.png) | ![miko-1280](1280/previews/miko.png) | [<NSFW, click to see>](1280/previews/nude.png) | [<NSFW, click to see>](1280/previews/nude2.png) | ![suit-1280](1280/previews/suit.png) | ![yukata-1280](1280/previews/yukata.png) | | 640 | 0.485 | [Download](640/pekora_jashinchandropkick.zip) | ![pattern_1-640](640/previews/pattern_1.png) | ![pattern_2-640](640/previews/pattern_2.png) | ![pattern_3-640](640/previews/pattern_3.png) | ![pattern_4-640](640/previews/pattern_4.png) | ![pattern_5-640](640/previews/pattern_5.png) | ![pattern_6-640](640/previews/pattern_6.png) | ![pattern_7-640](640/previews/pattern_7.png) | ![pattern_8-640](640/previews/pattern_8.png) | ![pattern_9-640](640/previews/pattern_9.png) | ![pattern_10-640](640/previews/pattern_10.png) | ![pattern_11-640](640/previews/pattern_11.png) | ![pattern_12-640](640/previews/pattern_12.png) | ![pattern_13-640](640/previews/pattern_13.png) | ![pattern_14-640](640/previews/pattern_14.png) | ![bikini-640](640/previews/bikini.png) | [<NSFW, click to see>](640/previews/bondage.png) | ![free-640](640/previews/free.png) | ![maid-640](640/previews/maid.png) | ![miko-640](640/previews/miko.png) | [<NSFW, click to see>](640/previews/nude.png) | [<NSFW, click to see>](640/previews/nude2.png) | ![suit-640](640/previews/suit.png) | ![yukata-640](640/previews/yukata.png) |

dim/llama2_7b_dolly_oasst1_chip2

dim

2023-09-20T10:39:00Z

2

0

peft

[ "peft", "region:us" ]

null

2023-09-20T10:21:13Z

--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(**message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(**data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/llama2_7b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=1, bot_token_id=9225, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

dim/xglm-4.5b_dolly_oasst1_chip2

dim

2023-09-20T10:38:18Z

0

peft

[ "peft", "region:us" ]

null

2023-09-20T10:37:14Z

--- library_name: peft --- ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig import torch from peft import PeftModel, PeftConfig class GoralConversation: def __init__( self, message_template=" <s> {role}\n{content} </s>\n", system_prompt="Ты — Горал, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.", start_token_id=1, bot_token_id=9225, ): self.message_template = message_template self.start_token_id = start_token_id self.bot_token_id = bot_token_id self.messages = [{"role": "system", "content": system_prompt}] def get_start_token_id(self): return self.start_token_id def get_bot_token_id(self): return self.bot_token_id def add_user_message(self, message): self.messages.append({"role": "user", "content": message}) def add_bot_message(self, message): self.messages.append({"role": "bot", "content": message}) def get_prompt(self, tokenizer): final_text = "" for message in self.messages: message_text = self.message_template.format(**message) final_text += message_text final_text += tokenizer.decode( [ self.start_token_id, ] ) final_text += " " final_text += tokenizer.decode([self.bot_token_id]) return final_text.strip() def generate(model, tokenizer, prompt, generation_config): data = tokenizer( prompt, return_tensors="pt", truncation=True, max_length=2048, ) data = {k: v.to(model.device) for k, v in data.items()} output_ids = model.generate(**data, generation_config=generation_config)[0] output_ids = output_ids[len(data["input_ids"][0]) :] output = tokenizer.decode(output_ids, skip_special_tokens=True) return output.strip() weights_path = "dim/xglm-4.5b_dolly_oasst1_chip2" access_token = "" config = PeftConfig.from_pretrained(weights_path) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, load_in_8bit=True, torch_dtype=torch.float16, device_map={"": 0}, token=access_token, ) model = PeftModel.from_pretrained( model, weights_path, torch_dtype=torch.float16, ) model.eval() tokenizer = AutoTokenizer.from_pretrained(weights_path) generation_config = GenerationConfig.from_pretrained(weights_path) generation_config.do_sample = False inp = "Напишите интересный пост в блоге о недавней поездке на Гавайи, рассказывая о культурном опыте и достопримечательностях, которые обязательно нужно увидеть." conversation = GoralConversation( start_token_id=0, bot_token_id=7425, ) conversation.add_user_message(inp) prompt = conversation.get_prompt(tokenizer) output = generate(model, tokenizer, prompt, generation_config) print(inp) print(output) # Я был там! Это было незабываемое путешествие, которое я никогда не забуду. Мы посетили все основные достопримечательности острова, включая пляжи, вулканы, пещеры, национальные парки и многое другое. Впечатления от посещения были потрясающими, а культура - уникальной. Поездка была отличным способом исследовать остров и узнать больше об истории его жителей. Надеюсь, что вы также захотите посетить это место! ``` ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0

DuyTa/Vietnamese_ASR

DuyTa

2023-09-20T10:36:20Z

4

1

peft

[ "peft", "tensorboard", "base_model:openai/whisper-medium", "base_model:adapter:openai/whisper-medium", "region:us" ]

null

2023-08-17T14:41:26Z

--- library_name: peft base_model: openai/whisper-medium --- ## Training procedure The following ` bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

bavolesy/ppo-LunarLander-v2

bavolesy

2023-09-20T10:21:44Z

0

stable-baselines3

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

reinforcement-learning

2023-09-20T10:21:22Z

--- 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: 258.69 +/- 23.10 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 ... ```

Carve/isnet

Carve

2023-09-20T10:17:12Z

0

1

null

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

null

2023-01-29T22:51:13Z

--- license: apache-2.0 --- `isnet.pth` - ISNet model. Trained on DIS v1.0 dataset. `isnet-97-carveset.pth` - ISNet model. Trained on CarveSet. Achieves 98% on test set.

Kendong/bc_flowers

Kendong

2023-09-20T10:12:39Z

2

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

text-to-image

2023-09-19T13:18:57Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photograph of a BSC vase of ASK flowers on a wooden table tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Kendong/bc_flowers These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photograph of a BSC vase of ASK flowers on a wooden table using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

Asheyy/dogbooth

Asheyy

2023-09-20T10:01:40Z

0

diffusers

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2023-09-17T09:20:41Z

--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of [v]dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - Asheyy/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.

swaroopajit/git-base-fashion

swaroopajit

2023-09-20T09:58:46Z

170

1

transformers

[ "transformers", "pytorch", "safetensors", "git", "image-text-to-text", "generated_from_trainer", "image-to-text", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]

image-to-text

2023-09-01T08:19:38Z

--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer model-index: - name: git-base-fashion results: [] pipeline_tag: image-to-text ---  # git-base-fashion This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1+cu118 - Datasets 2.14.4 - Tokenizers 0.13.3

Technotech/sd-prompt-instruct-3b-epoch-0.4-lora

Technotech

2023-09-20T09:54:02Z

2

0

peft

[ "peft", "stable-diffusion", "instruct", "magic-prompt", "en", "dataset:Technotech/sd-prompt-instruct", "base_model:openlm-research/open_llama_3b_v2", "base_model:adapter:openlm-research/open_llama_3b_v2", "license:apache-2.0", "region:us" ]

null

2023-07-29T15:14:28Z

--- language: - en license: apache-2.0 library_name: peft tags: - stable-diffusion - instruct - magic-prompt datasets: - Technotech/sd-prompt-instruct base_model: openlm-research/open_llama_3b_v2 --- # Stable Diffusion Prompt Instruct 3B (OpenLlama v2 3B) Trained for 0.4 epochs (test) on [Technotech/sd-prompt-instruct](https://huggingface.co/datasets/Technotech/sd-prompt-instruct). ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0

SHENMU007/neunit_BASE_V9.5.14

SHENMU007

2023-09-20T09:53:21Z

76

0

transformers

[ "transformers", "pytorch", "speecht5", "text-to-audio", "1.1.0", "generated_from_trainer", "zh", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]

text-to-audio

2023-09-20T08:19:51Z

--- language: - zh license: mit base_model: microsoft/speecht5_tts tags: - 1.1.0 - generated_from_trainer datasets: - facebook/voxpopuli model-index: - name: SpeechT5 TTS Dutch neunit results: [] ---  # SpeechT5 TTS Dutch neunit This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results ### Framework versions - Transformers 4.33.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

phamhungd/GuoZovya

phamhungd

2023-09-20T09:52:42Z

0

null

[ "onnx", "vi", "en", "license:unknown", "region:us" ]

null

2023-02-28T05:44:19Z

--- license: unknown language: - vi - en --- About this version Model Merge : A to Zovya RPG Artist's Tools GuoFeng2 #Note: Đây là bản mix 5-5 từ 2 model yêu thích nhất của mình, hiệu quả tạo ra thực sự tuyệt vời, các gương mặt mẫu gần như là hoàn hảo với thông số mặc định.Nó gần như hoàn hảo với đầy đủ các phong cách khác.Với model này, gần như mình đã không cần phải suy nghĩ tới model khac để thực hiện nhiều bức hình với các thể loại khác nhau. Mong mọi người có thể ủng hộ và khám phá hết tiềm năng của model này. #Note : This is a 5-5 mix from my 2 favorite models, the effect is really great, the model faces are almost perfect with default settings. It's almost perfect with a full range of styles. another way. With this model, I almost didn't need to think about other models to take many pictures with different genres. Hope everyone can support and explore the full potential of this model.

mitch7w/falcon-7b-midjourney-example

mitch7w

2023-09-20T09:41:16Z

0

peft

[ "peft", "text-generation", "dataset:tiiuae/falcon-refinedweb", "license:openrail", "region:us" ]

text-generation

2023-09-20T07:26:15Z

--- license: openrail library_name: peft pipeline_tag: text-generation datasets: - tiiuae/falcon-refinedweb model_parameters: base_model_name_or_path: vilsonrodrigues/falcon-7b-instruct-sharded bias: none fan_in_fan_out: false inference_mode: true init_lora_weights: true lora_alpha: 32 lora_dropout: 0.05 peft_type: LORA r: 16 target_modules: - query_key_value task_type: CAUSAL_LM ---

hidude562/OpenMusenet-LContext-2.11

hidude562

2023-09-20T09:39:39Z

148

0

transformers

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

text-generation

2023-09-05T09:26:35Z

--- pipeline_tag: text-generation ---

ShivamMangale/XLM-Roberta-base-finetuned-squad-syn-first-now-squad-ent-gen-syn

ShivamMangale

2023-09-20T09:34:55Z

3

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

question-answering

2023-09-20T09:00:27Z

--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - squad model-index: - name: XLM-Roberta-base-finetuned-squad-syn-first-now-squad-ent-gen-syn results: [] ---  # XLM-Roberta-base-finetuned-squad-syn-first-now-squad-ent-gen-syn This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 9 ### Training results ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

haseong8012/wav2vec2-large-xlsr-53_ko

haseong8012

2023-09-20T09:33:28Z

105

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:zeroth_korean", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-09-20T03:43:03Z

--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer datasets: - zeroth_korean metrics: - wer model-index: - name: wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: zeroth_korean type: zeroth_korean config: clean split: test args: clean metrics: - name: Wer type: wer value: 0.8577021532901672 ---  # wav2vec2-large-xlsr-53-fine-tune_korean_byAILAB This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the zeroth_korean dataset. It achieves the following results on the evaluation set: - Loss: 1.4406 - Wer: 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | No log | 0.99 | 38 | 54.3133 | 1.0 | | No log | 2.0 | 77 | 33.5397 | 1.0 | | No log | 2.99 | 115 | 19.6459 | 1.0 | | No log | 4.0 | 154 | 11.1346 | 1.0 | | No log | 4.99 | 192 | 5.8854 | 1.0 | | No log | 6.0 | 231 | 4.8784 | 1.0 | | No log | 6.99 | 269 | 4.8369 | 1.0 | | No log | 8.0 | 308 | 4.8535 | 1.0 | | No log | 8.99 | 346 | 4.8388 | 1.0 | | No log | 10.0 | 385 | 4.8360 | 1.0 | | 15.1801 | 10.99 | 423 | 4.7653 | 1.0 | | 15.1801 | 12.0 | 462 | 4.7385 | 1.0 | | 15.1801 | 12.99 | 500 | 4.6927 | 0.9989 | | 15.1801 | 14.0 | 539 | 4.6673 | 0.9991 | | 15.1801 | 14.99 | 577 | 4.6948 | 0.9991 | | 15.1801 | 16.0 | 616 | 4.6713 | 0.9991 | | 15.1801 | 16.99 | 654 | 4.6603 | 1.0 | | 15.1801 | 18.0 | 693 | 4.6428 | 0.9995 | | 15.1801 | 18.99 | 731 | 4.6520 | 0.9994 | | 15.1801 | 20.0 | 770 | 4.6554 | 0.9967 | | 4.3888 | 20.99 | 808 | 4.6054 | 0.9998 | | 4.3888 | 22.0 | 847 | 4.5723 | 0.9976 | | 4.3888 | 22.99 | 885 | 4.4586 | 0.9967 | | 4.3888 | 24.0 | 924 | 4.2547 | 0.9934 | | 4.3888 | 24.99 | 962 | 3.6554 | 0.9931 | | 4.3888 | 26.0 | 1001 | 2.8387 | 1.0084 | | 4.3888 | 26.99 | 1039 | 2.4191 | 1.0551 | | 4.3888 | 28.0 | 1078 | 2.0997 | 1.0197 | | 4.3888 | 28.99 | 1116 | 2.0103 | 1.0176 | | 4.3888 | 30.0 | 1155 | 1.8189 | 0.9461 | | 4.3888 | 30.99 | 1193 | 1.7623 | 0.9726 | | 2.7217 | 32.0 | 1232 | 1.7383 | 0.9976 | | 2.7217 | 32.99 | 1270 | 1.6522 | 0.9584 | | 2.7217 | 34.0 | 1309 | 1.5558 | 0.9193 | | 2.7217 | 34.99 | 1347 | 1.5811 | 0.9440 | | 2.7217 | 36.0 | 1386 | 1.5208 | 0.9158 | | 2.7217 | 36.99 | 1424 | 1.5088 | 0.9038 | | 2.7217 | 38.0 | 1463 | 1.5039 | 0.9086 | | 2.7217 | 38.99 | 1501 | 1.4853 | 0.8987 | | 2.7217 | 40.0 | 1540 | 1.4799 | 0.8847 | | 2.7217 | 40.99 | 1578 | 1.4259 | 0.8694 | | 0.7635 | 42.0 | 1617 | 1.4878 | 0.8883 | | 0.7635 | 42.99 | 1655 | 1.4394 | 0.8693 | | 0.7635 | 44.0 | 1694 | 1.4623 | 0.8743 | | 0.7635 | 44.99 | 1732 | 1.4495 | 0.8710 | | 0.7635 | 46.0 | 1771 | 1.4463 | 0.8655 | | 0.7635 | 46.99 | 1809 | 1.4553 | 0.8704 | | 0.7635 | 48.0 | 1848 | 1.4500 | 0.8646 | | 0.7635 | 48.99 | 1886 | 1.4387 | 0.8566 | | 0.7635 | 49.35 | 1900 | 1.4406 | 0.8577 | ### Framework versions - Transformers 4.33.2 - Pytorch 1.12.1 - Datasets 2.14.5 - Tokenizers 0.13.3

CyberHarem/makihara_shiho_idolmastercinderellagirls

CyberHarem

2023-09-20T09:27:49Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/makihara_shiho_idolmastercinderellagirls", "license:mit", "region:us" ]

text-to-image

2023-09-20T09:15:47Z

--- license: mit datasets: - CyberHarem/makihara_shiho_idolmastercinderellagirls pipeline_tag: text-to-image tags: - art --- # Lora of makihara_shiho_idolmastercinderellagirls This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 4420, you need to download `4420/makihara_shiho_idolmastercinderellagirls.pt` as the embedding and `4420/makihara_shiho_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 4420**, with the score of 0.882. The trigger words are: 1. `makihara_shiho_idolmastercinderellagirls` 2. `brown_hair, long_hair, green_eyes, smile, open_mouth, blush, bow, breasts` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | 5100 | 0.859 | [Download](5100/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-5100](5100/previews/pattern_1.png) | ![pattern_2-5100](5100/previews/pattern_2.png) | ![pattern_3-5100](5100/previews/pattern_3.png) | ![bikini-5100](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) | ![free-5100](5100/previews/free.png) | ![maid-5100](5100/previews/maid.png) | ![miko-5100](5100/previews/miko.png) | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) | ![suit-5100](5100/previews/suit.png) | ![yukata-5100](5100/previews/yukata.png) | | 4760 | 0.861 | [Download](4760/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-4760](4760/previews/pattern_1.png) | ![pattern_2-4760](4760/previews/pattern_2.png) | ![pattern_3-4760](4760/previews/pattern_3.png) | ![bikini-4760](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) | ![free-4760](4760/previews/free.png) | ![maid-4760](4760/previews/maid.png) | ![miko-4760](4760/previews/miko.png) | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) | ![suit-4760](4760/previews/suit.png) | ![yukata-4760](4760/previews/yukata.png) | | **4420** | **0.882** | [**Download**](4420/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-4420](4420/previews/pattern_1.png) | ![pattern_2-4420](4420/previews/pattern_2.png) | ![pattern_3-4420](4420/previews/pattern_3.png) | ![bikini-4420](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) | ![free-4420](4420/previews/free.png) | ![maid-4420](4420/previews/maid.png) | ![miko-4420](4420/previews/miko.png) | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) | ![suit-4420](4420/previews/suit.png) | ![yukata-4420](4420/previews/yukata.png) | | 4080 | 0.856 | [Download](4080/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-4080](4080/previews/pattern_1.png) | ![pattern_2-4080](4080/previews/pattern_2.png) | ![pattern_3-4080](4080/previews/pattern_3.png) | ![bikini-4080](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) | ![free-4080](4080/previews/free.png) | ![maid-4080](4080/previews/maid.png) | ![miko-4080](4080/previews/miko.png) | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) | ![suit-4080](4080/previews/suit.png) | ![yukata-4080](4080/previews/yukata.png) | | 3740 | 0.858 | [Download](3740/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-3740](3740/previews/pattern_1.png) | ![pattern_2-3740](3740/previews/pattern_2.png) | ![pattern_3-3740](3740/previews/pattern_3.png) | ![bikini-3740](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) | ![free-3740](3740/previews/free.png) | ![maid-3740](3740/previews/maid.png) | ![miko-3740](3740/previews/miko.png) | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) | ![suit-3740](3740/previews/suit.png) | ![yukata-3740](3740/previews/yukata.png) | | 3400 | 0.847 | [Download](3400/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-3400](3400/previews/pattern_1.png) | ![pattern_2-3400](3400/previews/pattern_2.png) | ![pattern_3-3400](3400/previews/pattern_3.png) | ![bikini-3400](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) | ![free-3400](3400/previews/free.png) | ![maid-3400](3400/previews/maid.png) | ![miko-3400](3400/previews/miko.png) | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) | ![suit-3400](3400/previews/suit.png) | ![yukata-3400](3400/previews/yukata.png) | | 3060 | 0.795 | [Download](3060/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-3060](3060/previews/pattern_1.png) | ![pattern_2-3060](3060/previews/pattern_2.png) | ![pattern_3-3060](3060/previews/pattern_3.png) | ![bikini-3060](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) | ![free-3060](3060/previews/free.png) | ![maid-3060](3060/previews/maid.png) | ![miko-3060](3060/previews/miko.png) | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) | ![suit-3060](3060/previews/suit.png) | ![yukata-3060](3060/previews/yukata.png) | | 2720 | 0.735 | [Download](2720/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-2720](2720/previews/pattern_1.png) | ![pattern_2-2720](2720/previews/pattern_2.png) | ![pattern_3-2720](2720/previews/pattern_3.png) | ![bikini-2720](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) | ![free-2720](2720/previews/free.png) | ![maid-2720](2720/previews/maid.png) | ![miko-2720](2720/previews/miko.png) | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) | ![suit-2720](2720/previews/suit.png) | ![yukata-2720](2720/previews/yukata.png) | | 2380 | 0.781 | [Download](2380/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-2380](2380/previews/pattern_1.png) | ![pattern_2-2380](2380/previews/pattern_2.png) | ![pattern_3-2380](2380/previews/pattern_3.png) | ![bikini-2380](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) | ![free-2380](2380/previews/free.png) | ![maid-2380](2380/previews/maid.png) | ![miko-2380](2380/previews/miko.png) | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) | ![suit-2380](2380/previews/suit.png) | ![yukata-2380](2380/previews/yukata.png) | | 2040 | 0.816 | [Download](2040/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-2040](2040/previews/pattern_1.png) | ![pattern_2-2040](2040/previews/pattern_2.png) | ![pattern_3-2040](2040/previews/pattern_3.png) | ![bikini-2040](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) | ![free-2040](2040/previews/free.png) | ![maid-2040](2040/previews/maid.png) | ![miko-2040](2040/previews/miko.png) | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) | ![suit-2040](2040/previews/suit.png) | ![yukata-2040](2040/previews/yukata.png) | | 1700 | 0.798 | [Download](1700/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-1700](1700/previews/pattern_1.png) | ![pattern_2-1700](1700/previews/pattern_2.png) | ![pattern_3-1700](1700/previews/pattern_3.png) | ![bikini-1700](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) | ![free-1700](1700/previews/free.png) | ![maid-1700](1700/previews/maid.png) | ![miko-1700](1700/previews/miko.png) | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) | ![suit-1700](1700/previews/suit.png) | ![yukata-1700](1700/previews/yukata.png) | | 1360 | 0.797 | [Download](1360/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-1360](1360/previews/pattern_1.png) | ![pattern_2-1360](1360/previews/pattern_2.png) | ![pattern_3-1360](1360/previews/pattern_3.png) | ![bikini-1360](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) | ![free-1360](1360/previews/free.png) | ![maid-1360](1360/previews/maid.png) | ![miko-1360](1360/previews/miko.png) | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) | ![suit-1360](1360/previews/suit.png) | ![yukata-1360](1360/previews/yukata.png) | | 1020 | 0.670 | [Download](1020/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-1020](1020/previews/pattern_1.png) | ![pattern_2-1020](1020/previews/pattern_2.png) | ![pattern_3-1020](1020/previews/pattern_3.png) | ![bikini-1020](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) | ![free-1020](1020/previews/free.png) | ![maid-1020](1020/previews/maid.png) | ![miko-1020](1020/previews/miko.png) | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) | ![suit-1020](1020/previews/suit.png) | ![yukata-1020](1020/previews/yukata.png) | | 680 | 0.649 | [Download](680/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-680](680/previews/pattern_1.png) | ![pattern_2-680](680/previews/pattern_2.png) | ![pattern_3-680](680/previews/pattern_3.png) | ![bikini-680](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) | ![free-680](680/previews/free.png) | ![maid-680](680/previews/maid.png) | ![miko-680](680/previews/miko.png) | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) | ![suit-680](680/previews/suit.png) | ![yukata-680](680/previews/yukata.png) | | 340 | 0.455 | [Download](340/makihara_shiho_idolmastercinderellagirls.zip) | ![pattern_1-340](340/previews/pattern_1.png) | ![pattern_2-340](340/previews/pattern_2.png) | ![pattern_3-340](340/previews/pattern_3.png) | ![bikini-340](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) | ![free-340](340/previews/free.png) | ![maid-340](340/previews/maid.png) | ![miko-340](340/previews/miko.png) | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) | ![suit-340](340/previews/suit.png) | ![yukata-340](340/previews/yukata.png) |

CyberHarem/hua_yuan_yurine_jashinchandropkick

CyberHarem

2023-09-20T09:25:31Z

0

null

[ "art", "text-to-image", "dataset:CyberHarem/hua_yuan_yurine_jashinchandropkick", "license:mit", "region:us" ]

text-to-image

2023-09-20T09:09:33Z

--- license: mit datasets: - CyberHarem/hua_yuan_yurine_jashinchandropkick pipeline_tag: text-to-image tags: - art --- # Lora of hua_yuan_yurine_jashinchandropkick This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 9600, you need to download `9600/hua_yuan_yurine_jashinchandropkick.pt` as the embedding and `9600/hua_yuan_yurine_jashinchandropkick.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters. **The best step we recommend is 9600**, with the score of 0.950. The trigger words are: 1. `hua_yuan_yurine_jashinchandropkick` 2. `long_hair, twintails, eyepatch, medical_eyepatch, bangs, mini_hat, hat, lolita_fashion, gothic_lolita, red_hair, closed_mouth, brown_eyes, ribbon, brown_hair, black_headwear` For the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. These are available steps: | Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata | |:---------|:----------|:------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------| | **9600** | **0.950** | [**Download**](9600/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-9600](9600/previews/pattern_1.png) | ![pattern_2-9600](9600/previews/pattern_2.png) | ![pattern_3-9600](9600/previews/pattern_3.png) | ![pattern_4-9600](9600/previews/pattern_4.png) | ![pattern_5-9600](9600/previews/pattern_5.png) | ![pattern_6-9600](9600/previews/pattern_6.png) | ![pattern_7-9600](9600/previews/pattern_7.png) | ![pattern_8-9600](9600/previews/pattern_8.png) | ![pattern_9-9600](9600/previews/pattern_9.png) | ![pattern_10-9600](9600/previews/pattern_10.png) | ![bikini-9600](9600/previews/bikini.png) | [<NSFW, click to see>](9600/previews/bondage.png) | ![free-9600](9600/previews/free.png) | ![maid-9600](9600/previews/maid.png) | ![miko-9600](9600/previews/miko.png) | [<NSFW, click to see>](9600/previews/nude.png) | [<NSFW, click to see>](9600/previews/nude2.png) | ![suit-9600](9600/previews/suit.png) | ![yukata-9600](9600/previews/yukata.png) | | 8960 | 0.925 | [Download](8960/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-8960](8960/previews/pattern_1.png) | ![pattern_2-8960](8960/previews/pattern_2.png) | ![pattern_3-8960](8960/previews/pattern_3.png) | ![pattern_4-8960](8960/previews/pattern_4.png) | ![pattern_5-8960](8960/previews/pattern_5.png) | ![pattern_6-8960](8960/previews/pattern_6.png) | ![pattern_7-8960](8960/previews/pattern_7.png) | ![pattern_8-8960](8960/previews/pattern_8.png) | ![pattern_9-8960](8960/previews/pattern_9.png) | ![pattern_10-8960](8960/previews/pattern_10.png) | ![bikini-8960](8960/previews/bikini.png) | [<NSFW, click to see>](8960/previews/bondage.png) | ![free-8960](8960/previews/free.png) | ![maid-8960](8960/previews/maid.png) | ![miko-8960](8960/previews/miko.png) | [<NSFW, click to see>](8960/previews/nude.png) | [<NSFW, click to see>](8960/previews/nude2.png) | ![suit-8960](8960/previews/suit.png) | ![yukata-8960](8960/previews/yukata.png) | | 8320 | 0.947 | [Download](8320/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-8320](8320/previews/pattern_1.png) | ![pattern_2-8320](8320/previews/pattern_2.png) | ![pattern_3-8320](8320/previews/pattern_3.png) | ![pattern_4-8320](8320/previews/pattern_4.png) | ![pattern_5-8320](8320/previews/pattern_5.png) | ![pattern_6-8320](8320/previews/pattern_6.png) | ![pattern_7-8320](8320/previews/pattern_7.png) | ![pattern_8-8320](8320/previews/pattern_8.png) | ![pattern_9-8320](8320/previews/pattern_9.png) | ![pattern_10-8320](8320/previews/pattern_10.png) | ![bikini-8320](8320/previews/bikini.png) | [<NSFW, click to see>](8320/previews/bondage.png) | ![free-8320](8320/previews/free.png) | ![maid-8320](8320/previews/maid.png) | ![miko-8320](8320/previews/miko.png) | [<NSFW, click to see>](8320/previews/nude.png) | [<NSFW, click to see>](8320/previews/nude2.png) | ![suit-8320](8320/previews/suit.png) | ![yukata-8320](8320/previews/yukata.png) | | 7680 | 0.940 | [Download](7680/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-7680](7680/previews/pattern_1.png) | ![pattern_2-7680](7680/previews/pattern_2.png) | ![pattern_3-7680](7680/previews/pattern_3.png) | ![pattern_4-7680](7680/previews/pattern_4.png) | ![pattern_5-7680](7680/previews/pattern_5.png) | ![pattern_6-7680](7680/previews/pattern_6.png) | ![pattern_7-7680](7680/previews/pattern_7.png) | ![pattern_8-7680](7680/previews/pattern_8.png) | ![pattern_9-7680](7680/previews/pattern_9.png) | ![pattern_10-7680](7680/previews/pattern_10.png) | ![bikini-7680](7680/previews/bikini.png) | [<NSFW, click to see>](7680/previews/bondage.png) | ![free-7680](7680/previews/free.png) | ![maid-7680](7680/previews/maid.png) | ![miko-7680](7680/previews/miko.png) | [<NSFW, click to see>](7680/previews/nude.png) | [<NSFW, click to see>](7680/previews/nude2.png) | ![suit-7680](7680/previews/suit.png) | ![yukata-7680](7680/previews/yukata.png) | | 7040 | 0.933 | [Download](7040/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-7040](7040/previews/pattern_1.png) | ![pattern_2-7040](7040/previews/pattern_2.png) | ![pattern_3-7040](7040/previews/pattern_3.png) | ![pattern_4-7040](7040/previews/pattern_4.png) | ![pattern_5-7040](7040/previews/pattern_5.png) | ![pattern_6-7040](7040/previews/pattern_6.png) | ![pattern_7-7040](7040/previews/pattern_7.png) | ![pattern_8-7040](7040/previews/pattern_8.png) | ![pattern_9-7040](7040/previews/pattern_9.png) | ![pattern_10-7040](7040/previews/pattern_10.png) | ![bikini-7040](7040/previews/bikini.png) | [<NSFW, click to see>](7040/previews/bondage.png) | ![free-7040](7040/previews/free.png) | ![maid-7040](7040/previews/maid.png) | ![miko-7040](7040/previews/miko.png) | [<NSFW, click to see>](7040/previews/nude.png) | [<NSFW, click to see>](7040/previews/nude2.png) | ![suit-7040](7040/previews/suit.png) | ![yukata-7040](7040/previews/yukata.png) | | 6400 | 0.940 | [Download](6400/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-6400](6400/previews/pattern_1.png) | ![pattern_2-6400](6400/previews/pattern_2.png) | ![pattern_3-6400](6400/previews/pattern_3.png) | ![pattern_4-6400](6400/previews/pattern_4.png) | ![pattern_5-6400](6400/previews/pattern_5.png) | ![pattern_6-6400](6400/previews/pattern_6.png) | ![pattern_7-6400](6400/previews/pattern_7.png) | ![pattern_8-6400](6400/previews/pattern_8.png) | ![pattern_9-6400](6400/previews/pattern_9.png) | ![pattern_10-6400](6400/previews/pattern_10.png) | ![bikini-6400](6400/previews/bikini.png) | [<NSFW, click to see>](6400/previews/bondage.png) | ![free-6400](6400/previews/free.png) | ![maid-6400](6400/previews/maid.png) | ![miko-6400](6400/previews/miko.png) | [<NSFW, click to see>](6400/previews/nude.png) | [<NSFW, click to see>](6400/previews/nude2.png) | ![suit-6400](6400/previews/suit.png) | ![yukata-6400](6400/previews/yukata.png) | | 5760 | 0.922 | [Download](5760/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-5760](5760/previews/pattern_1.png) | ![pattern_2-5760](5760/previews/pattern_2.png) | ![pattern_3-5760](5760/previews/pattern_3.png) | ![pattern_4-5760](5760/previews/pattern_4.png) | ![pattern_5-5760](5760/previews/pattern_5.png) | ![pattern_6-5760](5760/previews/pattern_6.png) | ![pattern_7-5760](5760/previews/pattern_7.png) | ![pattern_8-5760](5760/previews/pattern_8.png) | ![pattern_9-5760](5760/previews/pattern_9.png) | ![pattern_10-5760](5760/previews/pattern_10.png) | ![bikini-5760](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) | ![free-5760](5760/previews/free.png) | ![maid-5760](5760/previews/maid.png) | ![miko-5760](5760/previews/miko.png) | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) | ![suit-5760](5760/previews/suit.png) | ![yukata-5760](5760/previews/yukata.png) | | 5120 | 0.931 | [Download](5120/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-5120](5120/previews/pattern_1.png) | ![pattern_2-5120](5120/previews/pattern_2.png) | ![pattern_3-5120](5120/previews/pattern_3.png) | ![pattern_4-5120](5120/previews/pattern_4.png) | ![pattern_5-5120](5120/previews/pattern_5.png) | ![pattern_6-5120](5120/previews/pattern_6.png) | ![pattern_7-5120](5120/previews/pattern_7.png) | ![pattern_8-5120](5120/previews/pattern_8.png) | ![pattern_9-5120](5120/previews/pattern_9.png) | ![pattern_10-5120](5120/previews/pattern_10.png) | ![bikini-5120](5120/previews/bikini.png) | [<NSFW, click to see>](5120/previews/bondage.png) | ![free-5120](5120/previews/free.png) | ![maid-5120](5120/previews/maid.png) | ![miko-5120](5120/previews/miko.png) | [<NSFW, click to see>](5120/previews/nude.png) | [<NSFW, click to see>](5120/previews/nude2.png) | ![suit-5120](5120/previews/suit.png) | ![yukata-5120](5120/previews/yukata.png) | | 4480 | 0.891 | [Download](4480/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-4480](4480/previews/pattern_1.png) | ![pattern_2-4480](4480/previews/pattern_2.png) | ![pattern_3-4480](4480/previews/pattern_3.png) | ![pattern_4-4480](4480/previews/pattern_4.png) | ![pattern_5-4480](4480/previews/pattern_5.png) | ![pattern_6-4480](4480/previews/pattern_6.png) | ![pattern_7-4480](4480/previews/pattern_7.png) | ![pattern_8-4480](4480/previews/pattern_8.png) | ![pattern_9-4480](4480/previews/pattern_9.png) | ![pattern_10-4480](4480/previews/pattern_10.png) | ![bikini-4480](4480/previews/bikini.png) | [<NSFW, click to see>](4480/previews/bondage.png) | ![free-4480](4480/previews/free.png) | ![maid-4480](4480/previews/maid.png) | ![miko-4480](4480/previews/miko.png) | [<NSFW, click to see>](4480/previews/nude.png) | [<NSFW, click to see>](4480/previews/nude2.png) | ![suit-4480](4480/previews/suit.png) | ![yukata-4480](4480/previews/yukata.png) | | 3840 | 0.896 | [Download](3840/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-3840](3840/previews/pattern_1.png) | ![pattern_2-3840](3840/previews/pattern_2.png) | ![pattern_3-3840](3840/previews/pattern_3.png) | ![pattern_4-3840](3840/previews/pattern_4.png) | ![pattern_5-3840](3840/previews/pattern_5.png) | ![pattern_6-3840](3840/previews/pattern_6.png) | ![pattern_7-3840](3840/previews/pattern_7.png) | ![pattern_8-3840](3840/previews/pattern_8.png) | ![pattern_9-3840](3840/previews/pattern_9.png) | ![pattern_10-3840](3840/previews/pattern_10.png) | ![bikini-3840](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) | ![free-3840](3840/previews/free.png) | ![maid-3840](3840/previews/maid.png) | ![miko-3840](3840/previews/miko.png) | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) | ![suit-3840](3840/previews/suit.png) | ![yukata-3840](3840/previews/yukata.png) | | 3200 | 0.893 | [Download](3200/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-3200](3200/previews/pattern_1.png) | ![pattern_2-3200](3200/previews/pattern_2.png) | ![pattern_3-3200](3200/previews/pattern_3.png) | ![pattern_4-3200](3200/previews/pattern_4.png) | ![pattern_5-3200](3200/previews/pattern_5.png) | ![pattern_6-3200](3200/previews/pattern_6.png) | ![pattern_7-3200](3200/previews/pattern_7.png) | ![pattern_8-3200](3200/previews/pattern_8.png) | ![pattern_9-3200](3200/previews/pattern_9.png) | ![pattern_10-3200](3200/previews/pattern_10.png) | ![bikini-3200](3200/previews/bikini.png) | [<NSFW, click to see>](3200/previews/bondage.png) | ![free-3200](3200/previews/free.png) | ![maid-3200](3200/previews/maid.png) | ![miko-3200](3200/previews/miko.png) | [<NSFW, click to see>](3200/previews/nude.png) | [<NSFW, click to see>](3200/previews/nude2.png) | ![suit-3200](3200/previews/suit.png) | ![yukata-3200](3200/previews/yukata.png) | | 2560 | 0.861 | [Download](2560/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-2560](2560/previews/pattern_1.png) | ![pattern_2-2560](2560/previews/pattern_2.png) | ![pattern_3-2560](2560/previews/pattern_3.png) | ![pattern_4-2560](2560/previews/pattern_4.png) | ![pattern_5-2560](2560/previews/pattern_5.png) | ![pattern_6-2560](2560/previews/pattern_6.png) | ![pattern_7-2560](2560/previews/pattern_7.png) | ![pattern_8-2560](2560/previews/pattern_8.png) | ![pattern_9-2560](2560/previews/pattern_9.png) | ![pattern_10-2560](2560/previews/pattern_10.png) | ![bikini-2560](2560/previews/bikini.png) | [<NSFW, click to see>](2560/previews/bondage.png) | ![free-2560](2560/previews/free.png) | ![maid-2560](2560/previews/maid.png) | ![miko-2560](2560/previews/miko.png) | [<NSFW, click to see>](2560/previews/nude.png) | [<NSFW, click to see>](2560/previews/nude2.png) | ![suit-2560](2560/previews/suit.png) | ![yukata-2560](2560/previews/yukata.png) | | 1920 | 0.837 | [Download](1920/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-1920](1920/previews/pattern_1.png) | ![pattern_2-1920](1920/previews/pattern_2.png) | ![pattern_3-1920](1920/previews/pattern_3.png) | ![pattern_4-1920](1920/previews/pattern_4.png) | ![pattern_5-1920](1920/previews/pattern_5.png) | ![pattern_6-1920](1920/previews/pattern_6.png) | ![pattern_7-1920](1920/previews/pattern_7.png) | ![pattern_8-1920](1920/previews/pattern_8.png) | ![pattern_9-1920](1920/previews/pattern_9.png) | ![pattern_10-1920](1920/previews/pattern_10.png) | ![bikini-1920](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) | ![free-1920](1920/previews/free.png) | ![maid-1920](1920/previews/maid.png) | ![miko-1920](1920/previews/miko.png) | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) | ![suit-1920](1920/previews/suit.png) | ![yukata-1920](1920/previews/yukata.png) | | 1280 | 0.733 | [Download](1280/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-1280](1280/previews/pattern_1.png) | ![pattern_2-1280](1280/previews/pattern_2.png) | ![pattern_3-1280](1280/previews/pattern_3.png) | ![pattern_4-1280](1280/previews/pattern_4.png) | ![pattern_5-1280](1280/previews/pattern_5.png) | ![pattern_6-1280](1280/previews/pattern_6.png) | ![pattern_7-1280](1280/previews/pattern_7.png) | ![pattern_8-1280](1280/previews/pattern_8.png) | ![pattern_9-1280](1280/previews/pattern_9.png) | ![pattern_10-1280](1280/previews/pattern_10.png) | ![bikini-1280](1280/previews/bikini.png) | [<NSFW, click to see>](1280/previews/bondage.png) | ![free-1280](1280/previews/free.png) | ![maid-1280](1280/previews/maid.png) | ![miko-1280](1280/previews/miko.png) | [<NSFW, click to see>](1280/previews/nude.png) | [<NSFW, click to see>](1280/previews/nude2.png) | ![suit-1280](1280/previews/suit.png) | ![yukata-1280](1280/previews/yukata.png) | | 640 | 0.635 | [Download](640/hua_yuan_yurine_jashinchandropkick.zip) | ![pattern_1-640](640/previews/pattern_1.png) | ![pattern_2-640](640/previews/pattern_2.png) | ![pattern_3-640](640/previews/pattern_3.png) | ![pattern_4-640](640/previews/pattern_4.png) | ![pattern_5-640](640/previews/pattern_5.png) | ![pattern_6-640](640/previews/pattern_6.png) | ![pattern_7-640](640/previews/pattern_7.png) | ![pattern_8-640](640/previews/pattern_8.png) | ![pattern_9-640](640/previews/pattern_9.png) | ![pattern_10-640](640/previews/pattern_10.png) | ![bikini-640](640/previews/bikini.png) | [<NSFW, click to see>](640/previews/bondage.png) | ![free-640](640/previews/free.png) | ![maid-640](640/previews/maid.png) | ![miko-640](640/previews/miko.png) | [<NSFW, click to see>](640/previews/nude.png) | [<NSFW, click to see>](640/previews/nude2.png) | ![suit-640](640/previews/suit.png) | ![yukata-640](640/previews/yukata.png) |

hongyin/chat-awareness-0.8b

hongyin

2023-09-20T09:24:23Z

149

1

transformers

[ "transformers", "pytorch", "opt", "text-generation", "en", "zh", "arxiv:2302.13173", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-07T12:00:00Z

--- language: - en - zh pipeline_tag: text-generation --- ## hongyin/chat-awareness-0.8b This is a lightweight English-Chinese bilingual chat assistant with a parameter size of 0.8b, dedicated to reducing the computational cost of inference. The training process consists of two parts: (1) NTP task. (2) Instruction tuning. ```python ### Human: Paraphrasing the text I love you. ### Assistant: I have a crash on you. ``` ## Bibtex entry and citation info Please cite if you find it helpful. ``` @article{zhu2023metaaid, title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models}, author={Zhu, Hongyin}, journal={arXiv preprint arXiv:2302.13173}, year={2023} } ``` --- license: other ---

ktadzjibov/opus-mt-en-ru-finetuned-en-to-ru-amls

ktadzjibov

2023-09-20T09:19:34Z

12

0

transformers

[ "transformers", "pytorch", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "base_model:Helsinki-NLP/opus-mt-en-ru", "base_model:finetune:Helsinki-NLP/opus-mt-en-ru", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-09-16T21:24:50Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-en-ru tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-en-ru-finetuned-en-to-ru-amls results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 config: ru-en split: validation args: ru-en metrics: - name: Bleu type: bleu value: 28.7642 ---  # opus-mt-en-ru-finetuned-en-to-ru-amls This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ru](https://huggingface.co/Helsinki-NLP/opus-mt-en-ru) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2832 - Bleu: 28.7642 - Gen Len: 29.495 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 1.9806 | 1.0 | 94761 | 1.2832 | 28.7642 | 29.495 | ### Framework versions - Transformers 4.33.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.3

kligvasser/ffhq-vqvae-clip-landmark-arcface

kligvasser

2023-09-20T09:18:41Z

2

0

diffusers

[ "diffusers", "safetensors", "diffusers:ConditionalDDPMPipeline", "region:us" ]

null

2023-09-14T07:41:15Z

Please refer [facial-diffusion](https://github.com/kligvasser/facial-diffusion) for evaluation and training examples. Condition: ![Screenshot](condition.png) Sample: ![Screenshot](sample.png)

MattyB95/pre_trained_DF_RawNet2

MattyB95

2023-09-20T09:09:36Z

0

null

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

null

2023-09-17T22:03:34Z

--- license: mit --- ## Citation If you use this code in your research please use the following citation: ```bibtex @INPROCEEDINGS{9414234, author={Tak, Hemlata and Patino, Jose and Todisco, Massimiliano and Nautsch, Andreas and Evans, Nicholas and Larcher, Anthony}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={End-to-End anti-spoofing with RawNet2}, year={2021}, pages={6369-6373} } ```

TheBloke/LLaMA-7b-GGUF

TheBloke

2023-09-20T09:03:53Z

580

7

transformers

[ "transformers", "gguf", "llama", "license:other", "region:us" ]

null

2023-09-20T02:27:21Z

--- base_model: https://ai.meta.com/blog/large-language-model-llama-meta-ai inference: false license: other model_creator: Meta model_name: LLaMA 7B model_type: llama prompt_template: '{prompt} ' quantized_by: TheBloke ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  # LLaMA 7B - GGUF - Model creator: [Meta](https://huggingface.co/none) - Original model: [LLaMA 7B](https://ai.meta.com/blog/large-language-model-llama-meta-ai)  ## Description This repo contains GGUF format model files for [Meta's LLaMA 7b](https://ai.meta.com/blog/large-language-model-llama-meta-ai).   ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.   ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA-7b-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA-7b-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA-7b-GGUF) * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/huggyllama/llama-7b)   ## Prompt template: None ``` {prompt} ```   ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details>   ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [llama-7b.Q2_K.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes | | [llama-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss | | [llama-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss | | [llama-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss | | [llama-7b.Q4_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [llama-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss | | [llama-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended | | [llama-7b.Q5_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [llama-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended | | [llama-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended | | [llama-7b.Q6_K.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss | | [llama-7b.Q8_0.gguf](https://huggingface.co/TheBloke/LLaMA-7b-GGUF/blob/main/llama-7b.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.   ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: - LM Studio - LoLLMS Web UI - Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/LLaMA-7b-GGUF and below it, a specific filename to download, such as: llama-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/LLaMA-7b-GGUF llama-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/LLaMA-7b-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LLaMA-7b-GGUF llama-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details>   ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m llama-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/LLaMA-7b-GGUF", model_file="llama-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)    ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant.   # Original model card: Meta's LLaMA 7b This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.

jordyvl/EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates

jordyvl

2023-09-20T08:54:28Z

103

0

transformers

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

text-classification

2023-09-19T18:46:12Z

--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates results: [] ---  # EElayoutlmv3_jordyvl_rvl_cdip_100_examples_per_class_2023-09-19_baseline_gates This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.8358 - Accuracy: 0.6925 - Exit 0 Accuracy: 0.065 - Exit 1 Accuracy: 0.0625 - Exit 2 Accuracy: 0.0625 - Exit 3 Accuracy: 0.0625 - Exit 4 Accuracy: 0.0625 ## Model description More information needed ## Intended uses & 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: 24 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 12 - total_train_batch_size: 288 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 60 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Exit 0 Accuracy | Exit 1 Accuracy | Exit 2 Accuracy | Exit 3 Accuracy | Exit 4 Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:---------------:|:---------------:|:---------------:| | No log | 0.71 | 2 | 6.0791 | 0.0675 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 1.71 | 4 | 5.8341 | 0.1125 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 2.71 | 6 | 5.6298 | 0.1425 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 3.71 | 8 | 5.4519 | 0.1875 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 4.71 | 10 | 5.2664 | 0.1925 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 5.71 | 12 | 5.0830 | 0.2075 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 6.71 | 14 | 4.8990 | 0.215 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 7.71 | 16 | 4.7073 | 0.215 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 8.71 | 18 | 4.5002 | 0.1925 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 9.71 | 20 | 4.4009 | 0.2225 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 10.71 | 22 | 4.2853 | 0.23 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 11.71 | 24 | 4.0933 | 0.27 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 12.71 | 26 | 3.9761 | 0.27 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 13.71 | 28 | 3.9351 | 0.3025 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 14.71 | 30 | 3.8232 | 0.32 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 15.71 | 32 | 3.7217 | 0.345 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 16.71 | 34 | 3.6380 | 0.39 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 17.71 | 36 | 3.6336 | 0.3925 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 18.71 | 38 | 3.5816 | 0.415 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 19.71 | 40 | 3.4517 | 0.445 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 20.71 | 42 | 3.4066 | 0.475 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 21.71 | 44 | 3.3252 | 0.4825 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 22.71 | 46 | 3.3034 | 0.5075 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 23.71 | 48 | 3.2461 | 0.5275 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 24.71 | 50 | 3.2623 | 0.54 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 25.71 | 52 | 3.1701 | 0.545 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 26.71 | 54 | 3.2183 | 0.545 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 27.71 | 56 | 3.1410 | 0.5625 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 28.71 | 58 | 3.1476 | 0.56 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 29.71 | 60 | 3.0950 | 0.585 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 30.71 | 62 | 3.0338 | 0.605 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 31.71 | 64 | 3.0406 | 0.6 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 32.71 | 66 | 3.0577 | 0.615 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 33.71 | 68 | 3.0389 | 0.6125 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 34.71 | 70 | 3.0199 | 0.62 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 35.71 | 72 | 3.0103 | 0.645 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 36.71 | 74 | 2.9517 | 0.645 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 37.71 | 76 | 2.9726 | 0.645 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 38.71 | 78 | 2.9587 | 0.66 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 39.71 | 80 | 2.9024 | 0.6725 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 40.71 | 82 | 2.9354 | 0.655 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 41.71 | 84 | 2.8976 | 0.6775 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 42.71 | 86 | 2.8988 | 0.6875 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 43.71 | 88 | 2.8944 | 0.6825 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 44.71 | 90 | 2.8853 | 0.6825 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 45.71 | 92 | 2.8685 | 0.6825 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 46.71 | 94 | 2.8623 | 0.665 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 47.71 | 96 | 2.8534 | 0.675 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 48.71 | 98 | 2.8601 | 0.685 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 49.71 | 100 | 2.8378 | 0.6875 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 50.71 | 102 | 2.8203 | 0.6875 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 51.71 | 104 | 2.8153 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 52.71 | 106 | 2.8077 | 0.6925 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 53.71 | 108 | 2.8061 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 54.71 | 110 | 2.8099 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 55.71 | 112 | 2.8061 | 0.6875 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 56.71 | 114 | 2.8268 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 57.71 | 116 | 2.8331 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 58.71 | 118 | 2.8371 | 0.69 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | | No log | 59.71 | 120 | 2.8358 | 0.6925 | 0.065 | 0.0625 | 0.0625 | 0.0625 | 0.0625 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2