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 2025-07-15 18:28:48	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-15 18:28:34	card stringlengths 11 1.01M
zjunlp/mt5-ie	zjunlp	2023-07-28T11:46:33Z	110	1	transformers	[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-05-17T11:39:03Z	--- license: mit --- We trained the MT5-base model for the CCKS2023 Instruction-based KGC task using 27W weakly supervised data without employing any additional techniques. To learn more about the training process and how to utilize the model, please consult the following GitHub repository: https://github.com/zjunlp/DeepKE/tree/main/example/triple/mt5. There, you will find detailed information on how to train the model and leverage its capabilities for the given task.
jcy204/heat	jcy204	2023-07-28T11:32:20Z	62	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-28T10:57:46Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/heat 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. --> # jcy204/heat This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2735 - Validation Loss: 0.5758 - Train Accuracy: 0.7927 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6362 \| 0.5353 \| 0.7838 \| 0 \| \| 0.4101 \| 0.5194 \| 0.7927 \| 1 \| \| 0.2735 \| 0.5758 \| 0.7927 \| 2 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
mw00/yolov7-lego	mw00	2023-07-28T11:09:08Z	0	1	null	[ "lego", "brick", "object-detection", "license:cc0-1.0", "region:us" ]	object-detection	2023-07-26T16:52:21Z	--- license: cc0-1.0 pipeline_tag: object-detection tags: - lego - brick --- # Overview The model(s) in this repository are trained with the [dreamfactor/biggest-lego-dataset-600-parts](https://www.kaggle.com/datasets/dreamfactor/biggest-lego-dataset-600-parts) from Kaggle and the [Yolov7](https://github.com/WongKinYiu/yolov7) training script. ## Limitations The `zero-shot-1000-single-class.pt` was trained in the `training-zero-shot-1000-single-class.ipynb` notebook with 1000 images and does not differentiate lego classes but only tries to predict Lego objects. This can be easily reconfigured and retrained in the notebook, but the current implementation leads to many false positives on non-Lego objects and therefore can be improved upon. Also, it could be worth investigating if the metrics improve with a bigger training dataset, as currently only 1000 images are being used (approx. 0.6% of the full dataset).
hruslen/LunarLander-v2-ppo-self	hruslen	2023-07-28T11:04:54Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T11:04:47Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -126.43 +/- 74.98 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'f': None 'exp_name': 'ppo-selfmade2' 'seed': 1 'repo_id': 'hruslen/LunarLander-v2-ppo-self' 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'batch_size': 512 'minibatch_size': 128} ```
manuu01/ppo-Pyramids	manuu01	2023-07-28T10:35:59Z	25	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T10:35:58Z	--- 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: manuu01/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Imxxn/RLCourseU4-Pixelcopter-v0	Imxxn	2023-07-28T10:29:13Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T06:18:26Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RLCourseU4-Pixelcopter-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 54.30 +/- 41.99 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Kexa/Kx_01	Kexa	2023-07-28T10:16:31Z	0	0	allennlp	[ "allennlp", "chemistry", "question-answering", "es", "dataset:Open-Orca/OpenOrca", "arxiv:1910.09700", "license:unknown", "region:us" ]	question-answering	2023-07-28T10:14:03Z	--- license: unknown datasets: - Open-Orca/OpenOrca language: - es metrics: - accuracy library_name: allennlp pipeline_tag: question-answering tags: - chemistry --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HaziqRazali/Reinforce-pixelcopter	HaziqRazali	2023-07-28T10:13:26Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T10:11:15Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
RTT/q-FrozenLake-v1-4x4-noSlippery	RTT	2023-07-28T10:09:43Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T10:09:42Z	--- 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="RTT/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"]) ```
Ding-Qiang/q-Taxi-v3	Ding-Qiang	2023-07-28T10:09:34Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T10:09:32Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 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="Ding-Qiang/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
digiplay/LuckyStrikeMix1.05_Lovelylady	digiplay	2023-07-28T10:05:01Z	532	3	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-28T09:20:36Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/13034/lucky-strike-mix https://civitai.com/models/13034?modelVersionId=127680 *use "photorealism", "8k" keywords, could generate better images. Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9198406a-170c-4d69-8d65-ce961eaca5c2/width=1280/02628-1796431482-1%20supercute%20kitty%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2f87589f-3b8d-4c26-b2e9-5ba9645697cd/width=1280/02644-2789515522-1%20supercute%20cat%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5993b948-3a2a-473f-b21c-928f109f656b/width=1280/02656-2033477510-a%20superc%20ute%20kitty%20wear%20Ultraman%20armor%20and%20Ultraman%20mask,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/13dbc7a2-f1de-493c-a771-91e4746f68b6/width=1280/02690-807894338-lionel%20messi%20in%20pink%20football%20kit,%20(geometric%20mosaic_1.4),%20(digital%20art%20style_1.4).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d562d789-dd0c-4fe3-aca9-486747f92f16/width=1280/02707-3559411941-screenshot%20of%20person's%20profile%20in%20Tinder%20app,%20buttons%20of%20user%20interface,%20her%20name%20and%20age%20in%20large%20headline%20text,%20self-introduct.jpeg)
toto10/Loras	toto10	2023-07-28T10:02:32Z	0	5	null	[ "region:us" ]	null	2023-06-02T17:40:30Z	Found. Redirecting to https://cdn-lfs.hf.co/repos/bc/28/bc28ea4dbdd4bbf7b1443dc6b8b821b0bcef0883733ce11f9302fa46f2041ae9/4b31a3ee3aaf413a5aa371a67e17342321e393df1c304e777db671fe3691b83b?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739270453&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTI3MDQ1M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9iYy8yOC9iYzI4ZWE0ZGJkZDRiYmY3YjE0NDNkYzZiOGI4MjFiMGJjZWYwODgzNzMzY2UxMWY5MzAyZmE0NmYyMDQxYWU5LzRiMzFhM2VlM2FhZjQxM2E1YWEzNzFhNjdlMTczNDIzMjFlMzkzZGYxYzMwNGU3NzdkYjY3MWZlMzY5MWI4M2I%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=O%7EEkH78z3sT3GJWiYHtvxCGab-b%7EsVDsLNR7sNJUVVY1Nlw%7Ei07dtpWm6NpJPAOiQzi0YuvRxXAZ6a7wbaeXkTyOhoU%7EeaFlEJUtGmre8-qPBBXrMK7GmbNwlwvkvpT4VHROsMhcp0RHIILA18mBuGL3XT0Dt2cvOaS1KXXSNILhsNb%7EKLyb5ZRvT%7ELamkF9joxDVyO1NL1N%7EfcpNRxaqqLubiyyKp0awyu59O1CvVKpN%7EsIHflSx1Dq6m3STgGURoS2z6AK1o83%7Ehx4uJEgvxZG9UBY2IzHskgHn7HBWCVcGxV29duRL7L35oYdD%7EiPQoU3UiCfFr2UCdCxjgG9lQ__&Key-Pair-Id=K3RPWS32NSSJCE
tommilyjones/swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured	tommilyjones	2023-07-28T09:57:28Z	212	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T09:36:50Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.53 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7166 - Accuracy: 0.53 ## Model description More information needed ## Intended uses & 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6507 \| 0.99 \| 66 \| 0.7352 \| 0.502 \| \| 0.6411 \| 2.0 \| 133 \| 0.7070 \| 0.528 \| \| 0.6268 \| 2.99 \| 199 \| 0.7166 \| 0.53 \| \| 0.6007 \| 4.0 \| 266 \| 0.7934 \| 0.506 \| \| 0.5875 \| 4.99 \| 332 \| 0.8053 \| 0.52 \| \| 0.5554 \| 6.0 \| 399 \| 0.7534 \| 0.524 \| \| 0.5613 \| 6.99 \| 465 \| 0.8075 \| 0.524 \| \| 0.5714 \| 8.0 \| 532 \| 0.7882 \| 0.522 \| \| 0.5244 \| 8.99 \| 598 \| 0.8380 \| 0.518 \| \| 0.5251 \| 9.92 \| 660 \| 0.8331 \| 0.52 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
accuracy-maker/ppo-LunarLander-v2	accuracy-maker	2023-07-28T09:53:38Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:53:15Z	--- 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: 264.96 +/- 17.69 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 ... ```
Ding-Qiang/q-FrozenLake-v1-4x4-Slippery	Ding-Qiang	2023-07-28T09:43:58Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T09:42:45Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.29 +/- 0.45 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="Ding-Qiang/q-FrozenLake-v1-4x4-Slippery", 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"]) ```
rashmi035/wav2vec2-large-mms-1b-hindi-colab	rashmi035	2023-07-28T09:33:51Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-12T05:29:24Z	--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer datasets: - common_voice_6_1 metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_6_1 type: common_voice_6_1 config: hi split: test args: hi metrics: - name: Wer type: wer value: 0.32018561484918795 --- <!-- 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-mms-1b-hindi-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on the common_voice_6_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3535 - Wer: 0.3202 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 16.7585 \| 0.14 \| 10 \| 10.2106 \| 2.0951 \| \| 6.9602 \| 0.27 \| 20 \| 3.7700 \| 1.0046 \| \| 2.4653 \| 0.41 \| 30 \| 1.3321 \| 0.6763 \| \| 1.0919 \| 0.55 \| 40 \| 0.6594 \| 0.4664 \| \| 0.7645 \| 0.68 \| 50 \| 0.4930 \| 0.3910 \| \| 0.8434 \| 0.82 \| 60 \| 0.4819 \| 0.3898 \| \| 0.5118 \| 0.96 \| 70 \| 0.4492 \| 0.3817 \| \| 0.6097 \| 1.1 \| 80 \| 0.4299 \| 0.4327 \| \| 0.4698 \| 1.23 \| 90 \| 0.4308 \| 0.3643 \| \| 0.5402 \| 1.37 \| 100 \| 0.4042 \| 0.4107 \| \| 0.5622 \| 1.51 \| 110 \| 0.4156 \| 0.3701 \| \| 0.4084 \| 1.64 \| 120 \| 0.4138 \| 0.3701 \| \| 0.4888 \| 1.78 \| 130 \| 0.3917 \| 0.3434 \| \| 0.4253 \| 1.92 \| 140 \| 0.3852 \| 0.3457 \| \| 0.5004 \| 2.05 \| 150 \| 0.3843 \| 0.3364 \| \| 0.3791 \| 2.19 \| 160 \| 0.3841 \| 0.3469 \| \| 0.3302 \| 2.33 \| 170 \| 0.3764 \| 0.3271 \| \| 0.4047 \| 2.47 \| 180 \| 0.3689 \| 0.3364 \| \| 0.2951 \| 2.6 \| 190 \| 0.3657 \| 0.3329 \| \| 0.3545 \| 2.74 \| 200 \| 0.3582 \| 0.3306 \| \| 0.3736 \| 2.88 \| 210 \| 0.3585 \| 0.3248 \| \| 0.388 \| 3.01 \| 220 \| 0.3602 \| 0.3237 \| \| 0.2997 \| 3.15 \| 230 \| 0.3624 \| 0.3167 \| \| 0.3704 \| 3.29 \| 240 \| 0.3625 \| 0.3190 \| \| 0.2095 \| 3.42 \| 250 \| 0.3571 \| 0.3248 \| \| 0.3564 \| 3.56 \| 260 \| 0.3570 \| 0.3202 \| \| 0.2119 \| 3.7 \| 270 \| 0.3550 \| 0.3225 \| \| 0.3697 \| 3.84 \| 280 \| 0.3542 \| 0.3190 \| \| 0.3551 \| 3.97 \| 290 \| 0.3535 \| 0.3202 \| ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
tommilyjones/swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured	tommilyjones	2023-07-28T09:26:38Z	215	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T09:04:55Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.52 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8519 - Accuracy: 0.52 ## Model description More information needed ## Intended uses & 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6441 \| 0.99 \| 66 \| 0.7419 \| 0.492 \| \| 0.6368 \| 2.0 \| 133 \| 0.7235 \| 0.51 \| \| 0.6157 \| 2.99 \| 199 \| 0.7516 \| 0.504 \| \| 0.5928 \| 4.0 \| 266 \| 0.8009 \| 0.502 \| \| 0.5735 \| 4.99 \| 332 \| 0.8270 \| 0.508 \| \| 0.5559 \| 6.0 \| 399 \| 0.7804 \| 0.502 \| \| 0.5533 \| 6.99 \| 465 \| 0.8053 \| 0.486 \| \| 0.5541 \| 8.0 \| 532 \| 0.8078 \| 0.504 \| \| 0.5218 \| 8.99 \| 598 \| 0.8519 \| 0.52 \| \| 0.5226 \| 9.92 \| 660 \| 0.8522 \| 0.508 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
lixsh6/wsdm23_pretrain	lixsh6	2023-07-28T09:19:26Z	0	0	null	[ "arxiv:2302.13756", "arxiv:2302.13498", "region:us" ]	null	2023-01-13T07:08:47Z	# WSDM Cup 2023 BERT Checkpoints: - This repo contains the checkpoints of our competition in WSDM Cup 2023: [Pre-training for Web Search](https://aistudio.baidu.com/aistudio/competition/detail/536/0/leaderboard) and [Unbiased Learning for Web Search](https://aistudio.baidu.com/aistudio/competition/detail/534/0/leaderboard). ## Paper released Please refer to our paper for details in this competition: - Task1 Unbiased Learning to rank: [Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation](https://arxiv.org/pdf/2302.13756.pdf) - Task2 Pretraining for web search: [Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking](https://arxiv.org/pdf/2302.13498.pdf) ## Method Overview - Pre-training BERT with MLM and CTR prediction loss (or multi-task CTR prediction loss). - Finetuning BERT with pairwise ranking loss. - Obtain prediction scores from different BERTs. - Ensemble learning to combine BERT features and sparse features. Details will be updated in the submission paper. #### BERT features: ##### 1) Model details: [Checkpoints Download Here](https://huggingface.co/lixsh6/wsdm23_pretrain/tree/main) \| Index\| Model Flag \| Method \| Pretrain step \| Finetune step \| DCG on leaderboard \| \| --------\| -------- \| ------- \|---------------\| ------- \| ------- \| \| 1\| large_group2_wwm_from_unw4625K \| M1 \| 1700K \| 5130 \| 11.96214 \| \| 2\| large_group2_wwm_from_unw4625K \| M1 \| 1700K \| 5130 \| NAN \| \| 3\| base_group2_wwm \| M2 \| 2150K \| 5130 \| ~11.32363 \| \| 4\| large_group2_wwm_from_unw4625K \| M1 \| 590K \| 5130 \| 11.94845 \| \| 5\| large_group2_wwm_from_unw4625K \| M1 \| 1700K \| 4180 \| NAN \| \| 6\| large_group2_mt_pretrain \| M3 \| 1940K \| 5130 \| NAN \| ##### 2) Method details \| Method \| Model Layers \| Details \| \| -------- \| ------- \| ------- \| \| M1 \| 24 \| WWM & CTR prediction as pretraining tasks\| \| M2 \| 12 \| WWM & CTR prediction as pretraining tasks \| \| M3 \| 24 \| WWM & Multi-task CTR prediction as pretraining tasks\| ## Contacts - Xiangsheng Li: [[email protected]]([email protected]). - Xiaoshu Chen: [[email protected]]([email protected])
digiplay/PotoPhotoRealism_v1	digiplay	2023-07-28T09:18:04Z	499	7	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-28T08:59:23Z	--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/117538/poto-photo-realism Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f0dc3495-2968-42b5-8435-758ec4fb954c/width=1280/580662874.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b1022894-834e-4f86-9e0e-927342d5ef34/width=1280/2598429369.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06f5fbd8-c46d-4171-9b62-657b988e74cc/width=1728/3944133732.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/de31e0b3-33f8-42e8-872b-bb28854b2490/width=1728/3607815262.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eba70078-9c00-4e2e-ae3a-053c9dca9900/width=2160/4292364610.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/384c94fa-200f-459f-8138-8c8c17be0484/width=1280/1116309337.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7c901984-0f84-4deb-81ed-9cb67ae67d46/width=1280/2611639573.jpeg)
kamalchibrani/yolov8_fall_detection_25	kamalchibrani	2023-07-28T09:10:52Z	0	0	null	[ "dataset:kamalchibrani/fall_detection", "license:openrail", "region:us" ]	null	2023-07-28T08:59:29Z	--- license: openrail datasets: - kamalchibrani/fall_detection metrics: - accuracy ---
openlamm/lamm_13b_lora32_98k	openlamm	2023-07-28T09:08:57Z	4	0	transformers	[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-10T03:46:34Z	--- license: apache-2.0 Model: - Vicuna13B - LoRA32 - openlamm/LAMM-98K ---
sdocio/bne-spacy-corgale-ner-es	sdocio	2023-07-28T09:08:31Z	2	0	spacy	[ "spacy", "token-classification", "es", "license:gpl-3.0", "model-index", "region:us" ]	token-classification	2023-01-07T23:02:41Z	--- license: gpl-3.0 language: - es library_name: spacy pipeline_tag: token-classification tags: - spacy - token-classification widget: - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." - text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral." - text: "Y allí precisamente es Santiago el patrón del pueblo." model-index: - name: bne-spacy-corgale-ner-es results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9721311475 - name: NER Recall type: recall value: 0.9732708089 - name: NER F Score type: f_score value: 0.9727006444 --- # Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). It was fine-tuned using `PlanTL-GOB-ES/roberta-base-bne`. \| Feature \| Description \| \| --- \| --- \| \| Name \| `bne-spacy-corgale-ner-es` \| \| Version \| `0.0.2` \| \| spaCy \| `>=3.5.2,<3.6.0` \| \| Default Pipeline \| `transformer`, `ner` \| \| Components \| `transformer`, `ner` \| ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `ner` \| `LOC`, `MISC`, `ORG`, `PER` \| </details> ## Usage You can use this model with the spaCy pipeline for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("bne-spacy-corgale-ner-es") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo." ner_pipe = nlp(example) print(ner_pipe.ents) for token in merge_entities(ner_pipe): print(token.text, token.ent_type_) ``` ## Dataset ToDo ## Model performance entity\|precision\|recall\|f1 -\|-\|-\|- LOC\|0.985\|0.987\|0.986 MISC\|0.862\|0.865\|0.863 ORG\|0.938\|0.779\|0.851 PER\|0.921\|0.941\|0.931 micro avg\|0.971\|0.972\|0.971 macro avg\|0.926\|0.893\|0.908 weighted avg\|0.971\|0.972\|0.971
caozhanqiang/llama2-glora-finetunined-french	caozhanqiang	2023-07-28T09:08:14Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-28T09:07:56Z	--- 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 0.5.0.dev0
oljike/nurtas_db_lora	oljike	2023-07-28T09:04:38Z	0	1	diffusers	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-28T07:05:24Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of kairat nurtas tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - oljike/nurtas_db_lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of kairat nurtas using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) LoRA for the text encoder was enabled: False.
tommilyjones/resnet-50-finetuned-hateful-meme-restructured	tommilyjones	2023-07-28T09:03:09Z	229	0	transformers	[ "transformers", "pytorch", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T08:40:52Z	--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: resnet-50-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.5 --- <!-- 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. --> # resnet-50-finetuned-hateful-meme-restructured This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7132 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6633 \| 0.99 \| 66 \| 0.7132 \| 0.5 \| \| 0.6561 \| 2.0 \| 133 \| 0.7309 \| 0.5 \| \| 0.6497 \| 2.99 \| 199 \| 0.7314 \| 0.5 \| \| 0.6529 \| 4.0 \| 266 \| 0.7296 \| 0.5 \| \| 0.6336 \| 4.99 \| 332 \| 0.7386 \| 0.5 \| \| 0.625 \| 6.0 \| 399 \| 0.7403 \| 0.5 \| \| 0.6511 \| 6.99 \| 465 \| 0.7425 \| 0.5 \| \| 0.6567 \| 8.0 \| 532 \| 0.7314 \| 0.5 \| \| 0.6389 \| 8.99 \| 598 \| 0.7380 \| 0.5 \| \| 0.6446 \| 9.92 \| 660 \| 0.7426 \| 0.5 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
openlamm/lamm_7b_lora32_98k	openlamm	2023-07-28T09:00:14Z	6	0	transformers	[ "transformers", "llama", "text-generation", "en", "dataset:caojianjian/LAMM", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-06-10T15:18:52Z	--- license: apache-2.0 datasets: - caojianjian/LAMM language: - en library_name: transformers ---
dummycouchspud/adapter_demo	dummycouchspud	2023-07-28T08:39:54Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T08:39:52Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0
iliyaML/my_awesome_eli5_clm-model	iliyaML	2023-07-28T08:33:55Z	132	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T08:00:06Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.8773 \| 1.0 \| 1121 \| 3.7568 \| \| 3.7788 \| 2.0 \| 2242 \| 3.7430 \| \| 3.7441 \| 3.0 \| 3363 \| 3.7399 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
xianbin/Reinforce-Pixelcopter-PLE-v0	xianbin	2023-07-28T08:28:33Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T08:14:41Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 106.00 +/- 86.94 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
darveen/llama2-qlora-finetuned-alpaca-40steps	darveen	2023-07-28T08:27:20Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-23T03:32:03Z	--- 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 0.5.0.dev0
Imxxn/RLCourseU5-Pyramids	Imxxn	2023-07-28T08:22:54Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-28T08:22:50Z	--- 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: Imxxn/RLCourseU5-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
HaziqRazali/dqn-SpaceInvadersNoFrameskip-v4	HaziqRazali	2023-07-28T08:12:40Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T08:12:17Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 133.50 +/- 35.22 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HaziqRazali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HaziqRazali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga HaziqRazali ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 1000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 1000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Imxxn/RLCourseU5-SnowballTarget	Imxxn	2023-07-28T07:49:37Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-28T07:49:33Z	--- 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: Imxxn/RLCourseU5-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
loucad/swin-tiny-patch4-window7-224-finetuned-eurosat	loucad	2023-07-28T07:48:19Z	214	0	transformers	[ "transformers", "pytorch", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-05-19T07:03:50Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.9751851851851852 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0797 - Accuracy: 0.9752 ## Model description More information needed ## Intended uses & 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.284 \| 1.0 \| 190 \| 0.1307 \| 0.9559 \| \| 0.1839 \| 2.0 \| 380 \| 0.1056 \| 0.9681 \| \| 0.1339 \| 3.0 \| 570 \| 0.0797 \| 0.9752 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3
DAMO-NLP-MT/polylm-chat-13b	DAMO-NLP-MT	2023-07-28T07:43:26Z	1,472	6	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "arxiv:2307.06018", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T06:46:08Z	--- license: apache-2.0 --- # Model Card for PolyLM-Multialpaca This model is finetuned on [polyLM-13b](https://huggingface.co/DAMO-NLP-MT/polylm-13b) using the following datasets: # Demo [Open](https://modelscope.cn/studios/damo/demo-polylm-multialpaca-13b/summary) # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf): > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. # Citation BibTeX: ```bibtex @misc{wei2023polylm, title={PolyLM: An Open Source Polyglot Large Language Model}, author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie}, year={2023}, eprint={2307.06018}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload	Lajonbot	2023-07-28T07:31:07Z	1,398	1	transformers	[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-28T07:20:24Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
BlunderPanini/Taxi-v3	BlunderPanini	2023-07-28T07:26:32Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T07:26:28Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="BlunderPanini/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
ToolBench/ToolLLaMA-7b-LoRA-v1	ToolBench	2023-07-28T07:25:03Z	0	8	null	[ "license:apache-2.0", "region:us" ]	null	2023-07-27T08:36:08Z	--- license: apache-2.0 --- # Model Card for Model ID This is a lora version ToolLLaMA model introduced in [ToolBench](https://github.com/OpenBMB/ToolBench). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - License: apache-2.0 - Finetuned from model [optional]: LLaMA-7b ## Uses Refer to [ToolBench](https://github.com/OpenBMB/ToolBench). ## Training Details Trained with the new version data in ToolBench.
xianbin/ppo-LunarLander-v2-2	xianbin	2023-07-28T07:22:23Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T07:22:17Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -165.00 +/- 73.23 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 100000 'torch_deterministic': True 'cuda': True 'capture_video': False 'num_envs': 32 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.995 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xianbin/ppo-LunarLander-v2-2' 'batch_size': 4096 'minibatch_size': 1024 'env_id': 'LunarLander-v2'} ```
Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_GGML	Lajonbot	2023-07-28T07:20:24Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-28T07:12:07Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
xianbin/ppo-LunarLander-v2-new	xianbin	2023-07-28T07:18:41Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T07:11:23Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -98.47 +/- 49.60 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 100000 'torch_deterministic': True 'cuda': True 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.995 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xianbin/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128 'env_id': 'LunarLander-v2'} ```
sm136599/chatfoodie-koalpaca-polyglot-5_8b-1000step-4batch	sm136599	2023-07-28T06:48:16Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-28T06:48:11Z	--- 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
CobraMamba/mamba-gpt-3b	CobraMamba	2023-07-28T06:42:23Z	1,402	4	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "gpt", "llm", "large language model", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-06-12T06:08:57Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model inference: false thumbnail: >- https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico license: apache-2.0 --- # Model Card ## Github https://github.com/chi2liu/mamba-gpt-3b \| Metric \| Value \| \|-----------------------\|-------\| \| MMLU (5-shot) \| 25.3 \| \| ARC (25-shot) \| 40.5 \| \| HellaSwag (10-shot) \| 64.9 \| \| TruthfulQA (0-shot) \| 37.1 \| \| Avg. \| 42.0 \| We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above. ## Summary We have fine-tuned the open-lama model and surpassed the original model in multiple evaluation subtasks, making it currently the best performing 3B model with comparable performance to llama-7b - Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.29.2 pip install accelerate==0.19.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="CobraMamba/mamba-gpt-3b", torch_dtype="auto", trust_remote_code=True, use_fast=False, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?</s><\|answer\|> ``` Alternatively, you can download the mamba_gpt_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from mamba_gpt_pipeline import MambaGPTTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "CobraMamba/mamba-gpt-3b", use_fast=False, padding_side="left", trust_remote_code=False, ) model = AutoModelForCausalLM.from_pretrained( "CobraMamba/mamba-gpt-3b", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=False, ) generate_text = MambaGPTTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "CobraMamba/mamba-gpt-3b" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?</s><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=False, trust_remote_code=False, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=False, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( inputs, min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=4096, bias=False) (v_proj): Linear(in_features=4096, out_features=4096, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=4096, out_features=11008, bias=False) (down_proj): Linear(in_features=11008, out_features=4096, bias=False) (up_proj): Linear(in_features=4096, out_features=11008, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. \| Task/Metric \| finetuned-GPT 3B \| OpenLLaMA 3B \| \| ---------------------- \| -------- \| ------------ \| \| anli_r1/acc \| 0.35 \| 0.33 \| \| anli_r2/acc \| 0.33 \| 0.32 \| \| anli_r3/acc \| 0.35 \| 0.35 \| \| arc_challenge/acc \| 0.35 \| 0.34 \| \| arc_challenge/acc_norm \| 0.37 \| 0.37 \| \| arc_easy/acc \| 0.71 \| 0.69 \| \| arc_easy/acc_norm \| 0.65 \| 0.65 \| \| boolq/acc \| 0.72 \| 0.66 \| \| hellaswag/acc \| 0.49 \| 0.43 \| \| hellaswag/acc_norm \| 0.66 \| 0.67 \| \| openbookqa/acc \| 0.26 \| 0.27 \| \| openbookqa/acc_norm \| 0.40 \| 0.40 \| \| piqa/acc \| 0.76 \| 0.75 \| \| piqa/acc_norm \| 0.76 \| 0.76 \| \| record/em \| 0.88 \| 0.88 \| \| record/f1 \| 0.88 \| 0.89 \| \| rte/acc \| 0.55 \| 0.58 \| \| truthfulqa_mc/mc1 \| 0.27 \| 0.22 \| \| truthfulqa_mc/mc2 \| 0.37 \| 0.35 \| \| wic/acc \| 0.49 \| 0.48 \| \| winogrande/acc \| 0.63 \| 0.62 \| \| Average \| 0.53** \| 0.52 \| We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
jjohn23/diffuser-anime-faces-32	jjohn23	2023-07-28T06:27:52Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-28T06:25:41Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute anime faces. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jjohn23/diffuser-anime-faces-32') image = pipeline().images[0] image ```
Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding	Evan-Lin	2023-07-28T06:19:46Z	47	0	transformers	[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-07-28T06:12:29Z	--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
alibaba-pai/pai-diffusion-food-large-zh	alibaba-pai	2023-07-28T06:15:26Z	5	3	diffusers	[ "diffusers", "pytorch", "text-to-image", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-30T03:30:53Z	--- license: apache-2.0 tags: - pytorch - diffusers - text-to-image --- # Chinese Latent Diffusion Model 我们开源了一个中文 Lattent Diffusion 模型（美食） * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ```python from diffusers import StableDiffusionPipeline model_id = "alibaba-pai/pai-diffusion-food-large-zh" pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") prompt = "番茄炒蛋" image = pipe(prompt).images[0] image.save("result.png") ```
terwrt/ppo-Huggy	terwrt	2023-07-28T06:01:17Z	5	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-28T06:01:11Z	--- 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: terwrt/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
yijhen38/llama2-7B-chat-qlora-FT-chainsea	yijhen38	2023-07-28T05:58:04Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T05:15:12Z	--- library_name: peft --- 參考 Fine Turning https://colab.research.google.com/drive/12dVqXZMIVxGI0uutU6HG9RWbWPXL3vts?usp=sharing#scrollTo=mNnkgBq7Q3EU Model：[meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) dataset：COT_Answer.txt ## 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 0.5.0.dev0
sm136599/chatfoodie-koalpaca-polyglot-5.8b-200step-4batch	sm136599	2023-07-28T05:56:33Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T05:56:27Z	--- 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
beaugogh/pythia-1.4b-deduped-sharegpt	beaugogh	2023-07-28T05:52:54Z	1,613	0	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-25T13:21:09Z	--- license: apache-2.0 --- pythia-1.4b-deduped model finetuned on sharegpt data
chriskim2273/IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert	chriskim2273	2023-07-28T05:45:38Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-28T05:06:34Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Imxxn/RLCourseU4-CartPole-v1	Imxxn	2023-07-28T05:41:57Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T05:41:46Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RLCourseU4-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
sm136599/chatfoodie-koalpaca-polyglot-5.8b-200step	sm136599	2023-07-28T04:43:54Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T04:43:49Z	--- 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
xianbin/a2c-AntBulletEnv-v0	xianbin	2023-07-28T04:30:29Z	2	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T04:28:24Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1346.83 +/- 53.98 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 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 ... ```
sm136599/chatfoodie-koalpaca-polyglot-5.8b-1000step	sm136599	2023-07-28T04:06:52Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T04:06:47Z	--- 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
dilip025/dummy-model	dilip025	2023-07-28T03:56:34Z	59	0	transformers	[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-07-28T03:55:31Z	--- license: mit base_model: camembert-base tags: - generated_from_keras_callback model-index: - name: dummy-model 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. --> # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Tokenizers 0.13.3
svalcin/q-Taxi-v3	svalcin	2023-07-28T03:26:37Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-11T13:24:15Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="svalcin/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
PAIXAI/Astrid-7B	PAIXAI	2023-07-28T03:02:52Z	19	22	transformers	[ "transformers", "pytorch", "RefinedWebModel", "text-generation", "gpt", "llm", "large language model", "PAIX", "custom_code", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-07T01:56:35Z	--- language: - en library_name: transformers tags: - gpt - llm - large language model - PAIX inference: true thumbnail: https://static.wixstatic.com/media/bdee4e_d0af74523fa64a998d4cfb894e8cd3bb~mv2.png/v1/crop/x_40,y_663,w_1954,h_663/fill/w_342,h_116,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/PAIX%20Logo%20(2).png --- # Model Card ## Summary Model Card Summary The model Astrid-7B-1 architecture includes a RWForCausalLM transformer with word embeddings, a module list of 32 DecoderLayers, and a linear lm_head. The DecoderLayer includes an input layer normalization, self-attention mechanism, and a multi-layer perceptron (MLP). It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance. Trained in English, it's a versatile tool for a variety of applications. This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model. This model was trained by [PAIX.Cloud](https://www.paix.cloud/). - Wait list: [Wait List](https://www.paix.cloud/join-waitlist) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.30.1 pip install accelerate==0.20.3 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="<path_to_local_folder>", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|prompt\|>Why is drinking water so healthy?<\|endoftext\|><\|answer\|> ``` ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "<path_to_local_folder>", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "<path_to_local_folder>", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "<path_to_local_folder>" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|prompt\|>How are you?<\|endoftext\|><\|answer\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` RWForCausalLM( (transformer): RWModel( (word_embeddings): Embedding(65024, 4544) (h): ModuleList( (0-31): 32 x DecoderLayer( (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) (self_attention): Attention( (maybe_rotary): RotaryEmbedding() (query_key_value): Linear(in_features=4544, out_features=4672, bias=False) (dense): Linear(in_features=4544, out_features=4544, bias=False) (attention_dropout): Dropout(p=0.0, inplace=False) ) (mlp): MLP( (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False) (act): GELU(approximate='none') (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False) ) ) ) (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=4544, out_features=65024, bias=False) ) ``` ## Model Configuration ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). ```bash CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=<path_to_local_folder> --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log ``` ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
jordyvl/rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5	jordyvl	2023-07-28T02:58:46Z	165	0	transformers	[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-27T18:49:50Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4561 - Accuracy: 0.802 - Brier Loss: 0.3399 - Nll: 1.6335 - F1 Micro: 0.802 - F1 Macro: 0.8037 - Ece: 0.1478 - Aurc: 0.0576 ## Model description More information needed ## Intended uses & 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Brier Loss \| Nll \| F1 Micro \| F1 Macro \| Ece \| Aurc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:----------:\|:------:\|:--------:\|:--------:\|:------:\|:------:\| \| No log \| 1.0 \| 125 \| 5.6533 \| 0.5288 \| 0.6696 \| 3.8280 \| 0.5288 \| 0.4993 \| 0.2170 \| 0.2294 \| \| No log \| 2.0 \| 250 \| 5.3016 \| 0.6285 \| 0.5364 \| 2.7651 \| 0.6285 \| 0.6089 \| 0.1861 \| 0.1465 \| \| No log \| 3.0 \| 375 \| 5.1153 \| 0.696 \| 0.4775 \| 2.4052 \| 0.696 \| 0.6956 \| 0.2003 \| 0.1078 \| \| 5.7003 \| 4.0 \| 500 \| 4.9491 \| 0.7358 \| 0.3968 \| 2.1532 \| 0.7358 \| 0.7375 \| 0.1406 \| 0.0813 \| \| 5.7003 \| 5.0 \| 625 \| 4.8556 \| 0.754 \| 0.3676 \| 1.8243 \| 0.754 \| 0.7472 \| 0.1001 \| 0.0756 \| \| 5.7003 \| 6.0 \| 750 \| 4.8060 \| 0.7625 \| 0.3475 \| 1.8558 \| 0.7625 \| 0.7636 \| 0.0808 \| 0.0696 \| \| 5.7003 \| 7.0 \| 875 \| 4.8301 \| 0.7648 \| 0.3320 \| 1.7367 \| 0.7648 \| 0.7663 \| 0.0434 \| 0.0677 \| \| 4.6459 \| 8.0 \| 1000 \| 4.7883 \| 0.7692 \| 0.3305 \| 1.8366 \| 0.7692 \| 0.7728 \| 0.0532 \| 0.0666 \| \| 4.6459 \| 9.0 \| 1125 \| 4.8347 \| 0.7762 \| 0.3282 \| 1.7122 \| 0.7762 \| 0.7789 \| 0.0610 \| 0.0675 \| \| 4.6459 \| 10.0 \| 1250 \| 4.8679 \| 0.7682 \| 0.3338 \| 1.8225 \| 0.7682 \| 0.7713 \| 0.0634 \| 0.0672 \| \| 4.6459 \| 11.0 \| 1375 \| 4.9875 \| 0.7655 \| 0.3521 \| 1.9651 \| 0.7655 \| 0.7647 \| 0.0914 \| 0.0692 \| \| 4.2436 \| 12.0 \| 1500 \| 4.9708 \| 0.77 \| 0.3410 \| 2.0195 \| 0.7700 \| 0.7694 \| 0.0838 \| 0.0684 \| \| 4.2436 \| 13.0 \| 1625 \| 4.9246 \| 0.7752 \| 0.3349 \| 1.8150 \| 0.7752 \| 0.7758 \| 0.0801 \| 0.0666 \| \| 4.2436 \| 14.0 \| 1750 \| 4.9235 \| 0.776 \| 0.3327 \| 1.8364 \| 0.776 \| 0.7782 \| 0.0896 \| 0.0628 \| \| 4.2436 \| 15.0 \| 1875 \| 4.9149 \| 0.7817 \| 0.3348 \| 1.9243 \| 0.7817 \| 0.7857 \| 0.0917 \| 0.0650 \| \| 4.0997 \| 16.0 \| 2000 \| 4.8998 \| 0.7837 \| 0.3255 \| 1.8326 \| 0.7837 \| 0.7874 \| 0.0901 \| 0.0637 \| \| 4.0997 \| 17.0 \| 2125 \| 4.9658 \| 0.7792 \| 0.3358 \| 1.8156 \| 0.7792 \| 0.7815 \| 0.1025 \| 0.0640 \| \| 4.0997 \| 18.0 \| 2250 \| 4.9819 \| 0.7905 \| 0.3256 \| 1.8605 \| 0.7905 \| 0.7919 \| 0.1016 \| 0.0613 \| \| 4.0997 \| 19.0 \| 2375 \| 5.0040 \| 0.778 \| 0.3417 \| 1.9392 \| 0.778 \| 0.7800 \| 0.1095 \| 0.0638 \| \| 4.0325 \| 20.0 \| 2500 \| 5.0084 \| 0.7817 \| 0.3387 \| 1.9882 \| 0.7817 \| 0.7833 \| 0.1043 \| 0.0642 \| \| 4.0325 \| 21.0 \| 2625 \| 5.0680 \| 0.7805 \| 0.3473 \| 1.8641 \| 0.7805 \| 0.7803 \| 0.1200 \| 0.0631 \| \| 4.0325 \| 22.0 \| 2750 \| 5.0324 \| 0.7808 \| 0.3395 \| 1.8541 \| 0.7808 \| 0.7835 \| 0.1124 \| 0.0620 \| \| 4.0325 \| 23.0 \| 2875 \| 5.0734 \| 0.7845 \| 0.3446 \| 1.9087 \| 0.7845 \| 0.7884 \| 0.1170 \| 0.0625 \| \| 3.99 \| 24.0 \| 3000 \| 5.2144 \| 0.782 \| 0.3564 \| 1.9540 \| 0.782 \| 0.7845 \| 0.1293 \| 0.0640 \| \| 3.99 \| 25.0 \| 3125 \| 5.0299 \| 0.7873 \| 0.3387 \| 1.8106 \| 0.7873 \| 0.7887 \| 0.1167 \| 0.0614 \| \| 3.99 \| 26.0 \| 3250 \| 5.0673 \| 0.792 \| 0.3318 \| 1.7538 \| 0.792 \| 0.7930 \| 0.1134 \| 0.0599 \| \| 3.99 \| 27.0 \| 3375 \| 5.0854 \| 0.791 \| 0.3379 \| 1.8144 \| 0.791 \| 0.7932 \| 0.1253 \| 0.0586 \| \| 3.9606 \| 28.0 \| 3500 \| 5.0962 \| 0.787 \| 0.3403 \| 1.7780 \| 0.787 \| 0.7884 \| 0.1224 \| 0.0592 \| \| 3.9606 \| 29.0 \| 3625 \| 5.0812 \| 0.7877 \| 0.3379 \| 1.7721 \| 0.7877 \| 0.7900 \| 0.1247 \| 0.0592 \| \| 3.9606 \| 30.0 \| 3750 \| 5.1318 \| 0.7905 \| 0.3359 \| 1.8105 \| 0.7905 \| 0.7931 \| 0.1290 \| 0.0597 \| \| 3.9606 \| 31.0 \| 3875 \| 5.0330 \| 0.7953 \| 0.3276 \| 1.7361 \| 0.7953 \| 0.7978 \| 0.1144 \| 0.0584 \| \| 3.9355 \| 32.0 \| 4000 \| 5.0843 \| 0.7975 \| 0.3276 \| 1.7556 \| 0.7975 \| 0.7990 \| 0.1236 \| 0.0560 \| \| 3.9355 \| 33.0 \| 4125 \| 5.1843 \| 0.7995 \| 0.3315 \| 1.7084 \| 0.7995 \| 0.8004 \| 0.1297 \| 0.0575 \| \| 3.9355 \| 34.0 \| 4250 \| 5.1703 \| 0.7987 \| 0.3333 \| 1.6918 \| 0.7987 \| 0.8000 \| 0.1257 \| 0.0580 \| \| 3.9355 \| 35.0 \| 4375 \| 5.1933 \| 0.7937 \| 0.3372 \| 1.7084 \| 0.7937 \| 0.7941 \| 0.1307 \| 0.0561 \| \| 3.9148 \| 36.0 \| 4500 \| 5.1404 \| 0.7987 \| 0.3275 \| 1.6423 \| 0.7987 \| 0.8011 \| 0.1308 \| 0.0547 \| \| 3.9148 \| 37.0 \| 4625 \| 5.1734 \| 0.8017 \| 0.3272 \| 1.6836 \| 0.8017 \| 0.8034 \| 0.1272 \| 0.0572 \| \| 3.9148 \| 38.0 \| 4750 \| 5.2479 \| 0.802 \| 0.3322 \| 1.7081 \| 0.802 \| 0.8032 \| 0.1353 \| 0.0550 \| \| 3.9148 \| 39.0 \| 4875 \| 5.1921 \| 0.8 \| 0.3320 \| 1.6554 \| 0.8000 \| 0.8012 \| 0.1334 \| 0.0538 \| \| 3.9001 \| 40.0 \| 5000 \| 5.2477 \| 0.801 \| 0.3353 \| 1.6333 \| 0.801 \| 0.8022 \| 0.1390 \| 0.0539 \| \| 3.9001 \| 41.0 \| 5125 \| 5.2140 \| 0.801 \| 0.3299 \| 1.6370 \| 0.801 \| 0.8017 \| 0.1340 \| 0.0544 \| \| 3.9001 \| 42.0 \| 5250 \| 5.2660 \| 0.807 \| 0.3303 \| 1.6090 \| 0.807 \| 0.8079 \| 0.1339 \| 0.0545 \| \| 3.9001 \| 43.0 \| 5375 \| 5.2884 \| 0.8007 \| 0.3319 \| 1.6816 \| 0.8007 \| 0.8022 \| 0.1394 \| 0.0547 \| \| 3.8892 \| 44.0 \| 5500 \| 5.3358 \| 0.804 \| 0.3352 \| 1.6399 \| 0.804 \| 0.8049 \| 0.1387 \| 0.0560 \| \| 3.8892 \| 45.0 \| 5625 \| 5.3545 \| 0.8043 \| 0.3349 \| 1.6445 \| 0.8043 \| 0.8060 \| 0.1408 \| 0.0555 \| \| 3.8892 \| 46.0 \| 5750 \| 5.4026 \| 0.8033 \| 0.3373 \| 1.6493 \| 0.8033 \| 0.8049 \| 0.1439 \| 0.0567 \| \| 3.8892 \| 47.0 \| 5875 \| 5.4195 \| 0.8015 \| 0.3386 \| 1.6393 \| 0.8015 \| 0.8031 \| 0.1468 \| 0.0570 \| \| 3.8834 \| 48.0 \| 6000 \| 5.4409 \| 0.803 \| 0.3396 \| 1.6392 \| 0.803 \| 0.8046 \| 0.1458 \| 0.0574 \| \| 3.8834 \| 49.0 \| 6125 \| 5.4501 \| 0.8023 \| 0.3395 \| 1.6367 \| 0.8023 \| 0.8039 \| 0.1468 \| 0.0574 \| \| 3.8834 \| 50.0 \| 6250 \| 5.4561 \| 0.802 \| 0.3399 \| 1.6335 \| 0.802 \| 0.8037 \| 0.1478 \| 0.0576 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-10	Jonathaniu	2023-07-28T02:46:15Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-28T02:45:54Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
Muniyaraj/output_model	Muniyaraj	2023-07-28T02:39:10Z	1	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	2023-07-27T15:44:23Z	--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of muniyarajs tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Muniyaraj/output_model These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of muniyarajs 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: None.
dleiferives/dwane	dleiferives	2023-07-28T02:30:30Z	193	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-28T02:30:22Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: dwane results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.20000000298023224 --- # dwane Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### dwayne johnson ![dwayne johnson](images/dwayne_johnson.jpg) #### dwayne the rock johnson ![dwayne the rock johnson](images/dwayne_the_rock_johnson.jpg)
khnhar/capturingYoutubeVideo	khnhar	2023-07-28T02:22:46Z	0	0	null	[ "region:us" ]	null	2023-07-28T01:58:32Z	Youtube 동영상에서 '찾고 싶은 키워드가 들어간 이미지'를 찾아주는 ai모듈이다. 응용 : CCTV 범죄자 찾기, 도난차량 번호판 조회 등
MichelNivard/starcoderbase_3b_for_R	MichelNivard	2023-07-28T02:22:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T12:43:24Z	--- 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
cgr28/a2c-PandaReachDense-v2	cgr28	2023-07-28T02:06:34Z	1	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-28T02:03:40Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.76 +/- 0.55 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v2 This is a trained model of a A2C agent playing PandaReachDense-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
manuu01/Reinforce-Pixelcopter-PLE-v0	manuu01	2023-07-28T02:05:03Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T13:22:54Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 51.90 +/- 39.87 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
mskhattori/wav2vec2phone-large-xlsr-jp-jdrt5N-demo	mskhattori	2023-07-28T01:58:48Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "base_model:finetune:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-27T22:24:17Z	--- license: apache-2.0 base_model: jonatasgrosman/wav2vec2-large-xlsr-53-japanese tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2phone-large-xlsr-jp-jdrt5N-demo 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. --> # wav2vec2phone-large-xlsr-jp-jdrt5N-demo This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3714 - Wer: 0.4730 - Cer: 0.5054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 1.5238 \| 1.0 \| 567 \| 1.3532 \| 0.8709 \| 0.6208 \| \| 1.2812 \| 2.0 \| 1134 \| 0.8674 \| 0.6835 \| 0.5633 \| \| 1.1329 \| 3.0 \| 1701 \| 0.7105 \| 0.6164 \| 0.5564 \| \| 1.0267 \| 4.0 \| 2268 \| 0.6111 \| 0.5775 \| 0.5401 \| \| 1.0415 \| 5.0 \| 2835 \| 0.5505 \| 0.5499 \| 0.5482 \| \| 0.9767 \| 6.0 \| 3402 \| 0.4986 \| 0.5210 \| 0.5204 \| \| 1.0392 \| 7.0 \| 3969 \| 0.4655 \| 0.5082 \| 0.5194 \| \| 0.9235 \| 8.0 \| 4536 \| 0.4457 \| 0.4989 \| 0.5136 \| \| 0.9511 \| 9.0 \| 5103 \| 0.4201 \| 0.4917 \| 0.5106 \| \| 0.8998 \| 10.0 \| 5670 \| 0.4031 \| 0.4869 \| 0.5081 \| \| 0.8883 \| 11.0 \| 6237 \| 0.3920 \| 0.4814 \| 0.5107 \| \| 0.856 \| 12.0 \| 6804 \| 0.3834 \| 0.4790 \| 0.5094 \| \| 0.8814 \| 13.0 \| 7371 \| 0.3772 \| 0.4761 \| 0.5081 \| \| 0.8352 \| 14.0 \| 7938 \| 0.3737 \| 0.4735 \| 0.5052 \| \| 0.9001 \| 15.0 \| 8505 \| 0.3714 \| 0.4730 \| 0.5054 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Jinmane/jafamix	Jinmane	2023-07-28T01:30:30Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-28T00:53:39Z	--- license: creativeml-openrail-m ---
lucas0/empath-falcon-40b	lucas0	2023-07-28T01:04:53Z	0	0	null	[ "generated_from_trainer", "base_model:tiiuae/falcon-40b-instruct", "base_model:finetune:tiiuae/falcon-40b-instruct", "license:apache-2.0", "region:us" ]	null	2023-07-20T18:29:06Z	--- license: apache-2.0 base_model: tiiuae/falcon-40b-instruct tags: - generated_from_trainer model-index: - name: empath-falcon-40b 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. --> # empath-falcon-40b This model is a fine-tuned version of [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
NasimB/bnc-cbt-log-rarity-mixed	NasimB	2023-07-28T01:03:51Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T22:53:51Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-log-rarity-mixed 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. --> # bnc-cbt-log-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3678 \| 0.29 \| 500 \| 5.3083 \| \| 5.0506 \| 0.58 \| 1000 \| 4.8988 \| \| 4.727 \| 0.87 \| 1500 \| 4.6664 \| \| 4.4606 \| 1.16 \| 2000 \| 4.5274 \| \| 4.3096 \| 1.45 \| 2500 \| 4.4089 \| \| 4.213 \| 1.75 \| 3000 \| 4.3013 \| \| 4.0894 \| 2.04 \| 3500 \| 4.2301 \| \| 3.9068 \| 2.33 \| 4000 \| 4.1861 \| \| 3.8675 \| 2.62 \| 4500 \| 4.1276 \| \| 3.8433 \| 2.91 \| 5000 \| 4.0819 \| \| 3.6581 \| 3.2 \| 5500 \| 4.0743 \| \| 3.5934 \| 3.49 \| 6000 \| 4.0511 \| \| 3.5814 \| 3.78 \| 6500 \| 4.0203 \| \| 3.4978 \| 4.07 \| 7000 \| 4.0150 \| \| 3.326 \| 4.36 \| 7500 \| 4.0140 \| \| 3.3207 \| 4.65 \| 8000 \| 4.0007 \| \| 3.308 \| 4.94 \| 8500 \| 3.9894 \| \| 3.1751 \| 5.24 \| 9000 \| 4.0029 \| \| 3.144 \| 5.53 \| 9500 \| 4.0021 \| \| 3.1408 \| 5.82 \| 10000 \| 4.0013 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
chriskim2273/IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta	chriskim2273	2023-07-28T00:59:28Z	120	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-07-27T23:49:20Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3326 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
Bellaaazzzzz/model_Xray	Bellaaazzzzz	2023-07-28T00:24:07Z	3	0	diffusers	[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "controlnet", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2023-07-24T18:08:42Z	--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_Xray These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png) Validation result of 3 round. ![images_2_0)](./images_2_0.png) Validation result of 4 round. ![images_3_0)](./images_3_0.png) Validation result of 5 round. ![images_4_0)](./images_4_0.png) Validation result of 6 round. ![images_5_0)](./images_5_0.png) Validation result of 7 round. ![images_6_0)](./images_6_0.png) Validation result of 8 round. ![images_7_0)](./images_7_0.png) Validation result of 9 round. ![images_8_0)](./images_8_0.png) Validation result of 10 round. ![images_9_0)](./images_9_0.png)
NasimB/aochildes-guten-fixed-rarity-mixed	NasimB	2023-07-28T00:03:14Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T21:48:59Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-guten-fixed-rarity-mixed 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. --> # aochildes-guten-fixed-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.365 \| 0.29 \| 500 \| 5.3433 \| \| 5.0504 \| 0.59 \| 1000 \| 4.9338 \| \| 4.72 \| 0.88 \| 1500 \| 4.6948 \| \| 4.4681 \| 1.17 \| 2000 \| 4.5525 \| \| 4.3117 \| 1.47 \| 2500 \| 4.4371 \| \| 4.2069 \| 1.76 \| 3000 \| 4.3333 \| \| 4.0872 \| 2.05 \| 3500 \| 4.2616 \| \| 3.9143 \| 2.35 \| 4000 \| 4.2156 \| \| 3.88 \| 2.64 \| 4500 \| 4.1594 \| \| 3.8477 \| 2.93 \| 5000 \| 4.1171 \| \| 3.6334 \| 3.23 \| 5500 \| 4.1165 \| \| 3.607 \| 3.52 \| 6000 \| 4.0836 \| \| 3.5905 \| 3.81 \| 6500 \| 4.0549 \| \| 3.4793 \| 4.11 \| 7000 \| 4.0555 \| \| 3.3298 \| 4.4 \| 7500 \| 4.0536 \| \| 3.3312 \| 4.69 \| 8000 \| 4.0366 \| \| 3.3098 \| 4.99 \| 8500 \| 4.0267 \| \| 3.1519 \| 5.28 \| 9000 \| 4.0423 \| \| 3.1522 \| 5.57 \| 9500 \| 4.0401 \| \| 3.1459 \| 5.87 \| 10000 \| 4.0393 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
chandrasutrisnotjhong/Reinforce-CartPole	chandrasutrisnotjhong	2023-07-27T23:59:26Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T11:16:50Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
PhilSad/phil-lora-2	PhilSad	2023-07-27T23:44:11Z	0	0	diffusers	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "license:openrail++", "region:us" ]	text-to-image	2023-07-27T22:35:46Z	--- license: openrail++ base_model: ./stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks man tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - PhilSad/phil-lora-2 These are LoRA adaption weights for ./stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks man 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) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
brunoboat/Reinforce-Pixelcopter-PLE-v0	brunoboat	2023-07-27T23:43:45Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T23:08:18Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.00 +/- 11.42 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
EulerianKnight/ppo-Huggy	EulerianKnight	2023-07-27T23:25:17Z	17	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-27T23:25:07Z	--- 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: EulerianKnight/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
brunoboat/Reinforce-CartPole-v1	brunoboat	2023-07-27T23:19:15Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T23:18:39Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
digitaljungle/reinforce-cart-01	digitaljungle	2023-07-27T23:09:17Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T23:09:07Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-cart-01 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
JeffGuo/llama2-qlora-finetunined-french	JeffGuo	2023-07-27T22:38:26Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-27T22:38:09Z	--- 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 0.5.0.dev0
NasimB/bnc-cbt-rarity-mixed	NasimB	2023-07-27T22:29:27Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-27T20:11:40Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-rarity-mixed 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. --> # bnc-cbt-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3665 \| 0.29 \| 500 \| 5.3115 \| \| 5.0498 \| 0.58 \| 1000 \| 4.9024 \| \| 4.7317 \| 0.87 \| 1500 \| 4.6646 \| \| 4.4609 \| 1.16 \| 2000 \| 4.5206 \| \| 4.3169 \| 1.45 \| 2500 \| 4.4078 \| \| 4.2185 \| 1.75 \| 3000 \| 4.3041 \| \| 4.0998 \| 2.04 \| 3500 \| 4.2236 \| \| 3.9145 \| 2.33 \| 4000 \| 4.1835 \| \| 3.8745 \| 2.62 \| 4500 \| 4.1284 \| \| 3.8531 \| 2.91 \| 5000 \| 4.0763 \| \| 3.6685 \| 3.2 \| 5500 \| 4.0703 \| \| 3.598 \| 3.49 \| 6000 \| 4.0412 \| \| 3.5813 \| 3.78 \| 6500 \| 4.0117 \| \| 3.5089 \| 4.07 \| 7000 \| 4.0030 \| \| 3.3334 \| 4.36 \| 7500 \| 3.9999 \| \| 3.3274 \| 4.65 \| 8000 \| 3.9871 \| \| 3.317 \| 4.94 \| 8500 \| 3.9757 \| \| 3.1751 \| 5.24 \| 9000 \| 3.9865 \| \| 3.1414 \| 5.53 \| 9500 \| 3.9854 \| \| 3.1567 \| 5.82 \| 10000 \| 3.9837 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
magnustragardh/ppo-LunarLander-v2	magnustragardh	2023-07-27T22:22:24Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-06-18T15:50:00Z	--- 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: 286.65 +/- 8.52 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 ... ```
zacdennis/gradientascent	zacdennis	2023-07-27T22:16:30Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T22:16:26Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: gradientascent results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 109.50 +/- 14.23 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ksaw39/ksawmegga	ksaw39	2023-07-27T22:11:13Z	0	0	keras	[ "keras", "reinforcement-learning", "en", "region:us" ]	reinforcement-learning	2023-07-27T22:08:14Z	--- language: - en metrics: - accuracy - code_eval library_name: keras pipeline_tag: reinforcement-learning ---
hyunussarioglu/dqn-SpaceInvadersNoFrameskip-v4	hyunussarioglu	2023-07-27T22:08:28Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T22:07:54Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 556.50 +/- 125.72 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hyunussarioglu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hyunussarioglu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hyunussarioglu ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
zpattdev/Reinforce-pixelcopterV0	zpattdev	2023-07-27T21:56:00Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-26T17:49:36Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopterV0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 60.30 +/- 47.86 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
jariasn/q-Taxi-v3	jariasn	2023-07-27T21:32:51Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T21:32:49Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="jariasn/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
sghirardelli/vit-base-patch16-224-rgbd1k2	sghirardelli	2023-07-27T21:26:49Z	65	0	transformers	[ "transformers", "tf", "tensorboard", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-21T21:15:59Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_keras_callback model-index: - name: sghirardelli/vit-base-patch16-224-rgbd1k2 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. --> # sghirardelli/vit-base-patch16-224-rgbd1k2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9711 - Train Accuracy: 0.4384 - Train Top-3-accuracy: 0.6297 - Validation Loss: 0.2537 - Validation Accuracy: 0.9323 - Validation Top-3-accuracy: 0.9940 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.002, 'decay_steps': 544, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Train Top-3-accuracy \| Validation Loss \| Validation Accuracy \| Validation Top-3-accuracy \| Epoch \| \|:----------:\|:--------------:\|:--------------------:\|:---------------:\|:-------------------:\|:-------------------------:\|:-----:\| \| 1.9711 \| 0.4384 \| 0.6297 \| 0.2537 \| 0.9323 \| 0.9940 \| 0 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
shivarama23/llama_v2_finetuned_redaction	shivarama23	2023-07-27T21:18:42Z	2	0	peft	[ "peft", "pytorch", "region:us" ]	null	2023-07-27T21:17:43Z	--- 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 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 - PEFT 0.4.0
vivianchen98/distilbert-base-uncased-finetuned-cola	vivianchen98	2023-07-27T21:06:15Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-27T19:48:05Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5317477654019562 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8526 - Matthews Correlation: 0.5317 ## Model description More information needed ## Intended uses & 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5224 \| 1.0 \| 535 \| 0.4742 \| 0.4397 \| \| 0.3484 \| 2.0 \| 1070 \| 0.5877 \| 0.4558 \| \| 0.2357 \| 3.0 \| 1605 \| 0.6307 \| 0.5301 \| \| 0.1668 \| 4.0 \| 2140 \| 0.7054 \| 0.5288 \| \| 0.1218 \| 5.0 \| 2675 \| 0.8526 \| 0.5317 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
YLI317/llama2-qlora-finetunined-SOP	YLI317	2023-07-27T21:05:56Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T21:05:39Z	--- 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 0.5.0.dev0
qanastek/LLaMa-2-FrenchMedMCQA	qanastek	2023-07-27T20:47:33Z	4	2	peft	[ "peft", "safetensors", "llama", "region:us" ]	null	2023-07-27T02:38:32Z	--- library_name: peft --- ## Inference ```python from transformers import AutoTokenizer import transformers import torch model = "qanastek/LLaMa-2-FrenchMedMCQA" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: We are giving you a scientific question (easy level) and five answers options (associated to « A », « B », « C », « D », « E »). Your task is to find the correct(s) answer(s) based on scientific facts, knowledge and reasoning. Don't generate anything other than one of the following characters : 'A B C D E'. ### Input: Parmi les propositions suivantes, quelle est celle qui est exacte? Lorsqu'on ajoute un acide fort à une solution tampon: (A) Le pH reste constant (B) Le pH diminue légèrement (C) Le constituant basique du tampon reste constant (D) Le constituant acide du tampon réagit (E) Le rapport acide/base reste inchangé ### Response: " seq = pipeline( prompt, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, )[0] print(seq['generated_text'][len(prompt):]) ``` ## 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 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 - PEFT 0.4.0
YoonSeul/LawBot-5.8B	YoonSeul	2023-07-27T20:40:46Z	4	0	peft	[ "peft", "region:us" ]	null	2023-07-26T07:47:12Z	--- library_name: peft --- <img src=https://github.com/taemin6697/Paper_Review/assets/96530685/54ecd6cf-8695-4caa-bdc8-fb85c9b7d70d style="max-width: 700px; width: 100%" /> ## 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.dev0
chh6/ppo-Pyramids	chh6	2023-07-27T20:22:39Z	9	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-27T20:21:43Z	--- 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: chh6/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
NicolasDenier/speecht5-finetuned-voxpopuli-sl	NicolasDenier	2023-07-27T20:21:31Z	89	0	transformers	[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "sl", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "model-index", "endpoints_compatible", "region:us" ]	text-to-speech	2023-07-27T17:17:36Z	--- language: - sl license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5-finetuned-voxpopuli-sl results: - task: name: Text to Speech type: text-to-speech dataset: name: Voxpopuli type: facebook/voxpopuli config: sl split: train args: all metrics: - name: Loss type: loss value: 0.4546 --- <!-- 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-finetuned-voxpopuli-sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4546 ## Model description More information needed ## Intended uses & 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: 4 - eval_batch_size: 2 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.4942 \| 21.68 \| 1000 \| 0.4567 \| \| 0.4698 \| 43.36 \| 2000 \| 0.4544 \| \| 0.4615 \| 65.04 \| 3000 \| 0.4541 \| \| 0.462 \| 86.72 \| 4000 \| 0.4546 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3
patonw/q-FrozenLake-v1-4x4-noSlippery	patonw	2023-07-27T20:14:02Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-27T20:14:00Z	--- 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="patonw/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"]) ```
Ronan14232/Omar	Ronan14232	2023-07-27T20:12:29Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-07-27T20:12:29Z	--- license: bigscience-openrail-m ---
Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-8	Jonathaniu	2023-07-27T20:09:53Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-27T20:09:32Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
SH-W/60emotions	SH-W	2023-07-27T20:01:52Z	107	0	transformers	[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "autotrain", "unk", "dataset:SH-W/autotrain-data-5000_koi", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-27T19:55:16Z	--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain" datasets: - SH-W/autotrain-data-5000_koi co2_eq_emissions: emissions: 3.920765439350259 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 77927140735 - CO2 Emissions (in grams): 3.9208 ## Validation Metrics - Loss: 2.432 - Accuracy: 0.415 - Macro F1: 0.410 - Micro F1: 0.415 - Weighted F1: 0.410 - Macro Precision: 0.459 - Micro Precision: 0.415 - Weighted Precision: 0.456 - Macro Recall: 0.413 - Micro Recall: 0.415 - Weighted Recall: 0.415 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/SH-W/autotrain-5000_koi-77927140735 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("SH-W/autotrain-5000_koi-77927140735", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("SH-W/autotrain-5000_koi-77927140735", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
ianvaz/llama2-qlora-finetunined-french	ianvaz	2023-07-27T20:00:58Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-27T20:00:54Z	--- 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 0.5.0.dev0

zjunlp/mt5-ie

zjunlp

2023-07-28T11:46:33Z

110

1

transformers

[ "transformers", "pytorch", "safetensors", "mt5", "text2text-generation", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2023-05-17T11:39:03Z

--- license: mit --- We trained the MT5-base model for the CCKS2023 Instruction-based KGC task using 27W weakly supervised data without employing any additional techniques. To learn more about the training process and how to utilize the model, please consult the following GitHub repository: https://github.com/zjunlp/DeepKE/tree/main/example/triple/mt5. There, you will find detailed information on how to train the model and leverage its capabilities for the given task.

jcy204/heat

jcy204

2023-07-28T11:32:20Z

62

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-28T10:57:46Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: jcy204/heat results: [] ---  # jcy204/heat This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2735 - Validation Loss: 0.5758 - Train Accuracy: 0.7927 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.6362 | 0.5353 | 0.7838 | 0 | | 0.4101 | 0.5194 | 0.7927 | 1 | | 0.2735 | 0.5758 | 0.7927 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

mw00/yolov7-lego

mw00

2023-07-28T11:09:08Z

0

1

null

[ "lego", "brick", "object-detection", "license:cc0-1.0", "region:us" ]

object-detection

2023-07-26T16:52:21Z

--- license: cc0-1.0 pipeline_tag: object-detection tags: - lego - brick --- # Overview The model(s) in this repository are trained with the [dreamfactor/biggest-lego-dataset-600-parts](https://www.kaggle.com/datasets/dreamfactor/biggest-lego-dataset-600-parts) from Kaggle and the [Yolov7](https://github.com/WongKinYiu/yolov7) training script. ## Limitations The `zero-shot-1000-single-class.pt` was trained in the `training-zero-shot-1000-single-class.ipynb` notebook with 1000 images and does not differentiate lego classes but only tries to predict Lego objects. This can be easily reconfigured and retrained in the notebook, but the current implementation leads to many false positives on non-Lego objects and therefore can be improved upon. Also, it could be worth investigating if the metrics improve with a bigger training dataset, as currently only 1000 images are being used (approx. 0.6% of the full dataset).

hruslen/LunarLander-v2-ppo-self

hruslen

2023-07-28T11:04:54Z

0

null

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

reinforcement-learning

2023-07-28T11:04:47Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -126.43 +/- 74.98 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'f': None 'exp_name': 'ppo-selfmade2' 'seed': 1 'repo_id': 'hruslen/LunarLander-v2-ppo-self' 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'batch_size': 512 'minibatch_size': 128} ```

manuu01/ppo-Pyramids

manuu01

2023-07-28T10:35:59Z

25

0

ml-agents

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

reinforcement-learning

2023-07-28T10:35:58Z

--- 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: manuu01/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Imxxn/RLCourseU4-Pixelcopter-v0

Imxxn

2023-07-28T10:29:13Z

0

null

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

reinforcement-learning

2023-07-28T06:18:26Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RLCourseU4-Pixelcopter-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 54.30 +/- 41.99 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Kexa/Kx_01

Kexa

2023-07-28T10:16:31Z

0

allennlp

[ "allennlp", "chemistry", "question-answering", "es", "dataset:Open-Orca/OpenOrca", "arxiv:1910.09700", "license:unknown", "region:us" ]

question-answering

2023-07-28T10:14:03Z

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

HaziqRazali/Reinforce-pixelcopter

HaziqRazali

2023-07-28T10:13:26Z

0

null

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

reinforcement-learning

2023-07-28T10:11:15Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: -5.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

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

RTT

2023-07-28T10:09:43Z

0

null

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

reinforcement-learning

2023-07-28T10:09:42Z

--- 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="RTT/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"]) ```

Ding-Qiang/q-Taxi-v3

Ding-Qiang

2023-07-28T10:09:34Z

0

null

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

reinforcement-learning

2023-07-28T10:09:32Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 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="Ding-Qiang/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

digiplay/LuckyStrikeMix1.05_Lovelylady

digiplay

2023-07-28T10:05:01Z

532

3

diffusers

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

text-to-image

2023-07-28T09:20:36Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/13034/lucky-strike-mix https://civitai.com/models/13034?modelVersionId=127680 *use "photorealism", "8k" keywords, could generate better images. Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/9198406a-170c-4d69-8d65-ce961eaca5c2/width=1280/02628-1796431482-1%20supercute%20kitty%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2f87589f-3b8d-4c26-b2e9-5ba9645697cd/width=1280/02644-2789515522-1%20supercute%20cat%20wear%20a%20origami%20gundam%20armor,fur.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/5993b948-3a2a-473f-b21c-928f109f656b/width=1280/02656-2033477510-a%20superc%20ute%20kitty%20wear%20Ultraman%20armor%20and%20Ultraman%20mask,.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/13dbc7a2-f1de-493c-a771-91e4746f68b6/width=1280/02690-807894338-lionel%20messi%20in%20pink%20football%20kit,%20(geometric%20mosaic_1.4),%20(digital%20art%20style_1.4).jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d562d789-dd0c-4fe3-aca9-486747f92f16/width=1280/02707-3559411941-screenshot%20of%20person's%20profile%20in%20Tinder%20app,%20buttons%20of%20user%20interface,%20her%20name%20and%20age%20in%20large%20headline%20text,%20self-introduct.jpeg)

toto10/Loras

toto10

2023-07-28T10:02:32Z

0

5

null

[ "region:us" ]

null

2023-06-02T17:40:30Z

Found. Redirecting to https://cdn-lfs.hf.co/repos/bc/28/bc28ea4dbdd4bbf7b1443dc6b8b821b0bcef0883733ce11f9302fa46f2041ae9/4b31a3ee3aaf413a5aa371a67e17342321e393df1c304e777db671fe3691b83b?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1739270453&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczOTI3MDQ1M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5oZi5jby9yZXBvcy9iYy8yOC9iYzI4ZWE0ZGJkZDRiYmY3YjE0NDNkYzZiOGI4MjFiMGJjZWYwODgzNzMzY2UxMWY5MzAyZmE0NmYyMDQxYWU5LzRiMzFhM2VlM2FhZjQxM2E1YWEzNzFhNjdlMTczNDIzMjFlMzkzZGYxYzMwNGU3NzdkYjY3MWZlMzY5MWI4M2I%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=O%7EEkH78z3sT3GJWiYHtvxCGab-b%7EsVDsLNR7sNJUVVY1Nlw%7Ei07dtpWm6NpJPAOiQzi0YuvRxXAZ6a7wbaeXkTyOhoU%7EeaFlEJUtGmre8-qPBBXrMK7GmbNwlwvkvpT4VHROsMhcp0RHIILA18mBuGL3XT0Dt2cvOaS1KXXSNILhsNb%7EKLyb5ZRvT%7ELamkF9joxDVyO1NL1N%7EfcpNRxaqqLubiyyKp0awyu59O1CvVKpN%7EsIHflSx1Dq6m3STgGURoS2z6AK1o83%7Ehx4uJEgvxZG9UBY2IzHskgHn7HBWCVcGxV29duRL7L35oYdD%7EiPQoU3UiCfFr2UCdCxjgG9lQ__&Key-Pair-Id=K3RPWS32NSSJCE

tommilyjones/swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured

tommilyjones

2023-07-28T09:57:28Z

212

0

transformers

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

image-classification

2023-07-28T09:36:50Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.53 ---  # swin-tiny-patch4-window7-224-finetuned-masked-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7166 - Accuracy: 0.53 ## Model description More information needed ## Intended uses & 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6507 | 0.99 | 66 | 0.7352 | 0.502 | | 0.6411 | 2.0 | 133 | 0.7070 | 0.528 | | 0.6268 | 2.99 | 199 | 0.7166 | 0.53 | | 0.6007 | 4.0 | 266 | 0.7934 | 0.506 | | 0.5875 | 4.99 | 332 | 0.8053 | 0.52 | | 0.5554 | 6.0 | 399 | 0.7534 | 0.524 | | 0.5613 | 6.99 | 465 | 0.8075 | 0.524 | | 0.5714 | 8.0 | 532 | 0.7882 | 0.522 | | 0.5244 | 8.99 | 598 | 0.8380 | 0.518 | | 0.5251 | 9.92 | 660 | 0.8331 | 0.52 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

accuracy-maker/ppo-LunarLander-v2

accuracy-maker

2023-07-28T09:53:38Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-28T09:53:15Z

--- 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: 264.96 +/- 17.69 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 ... ```

Ding-Qiang/q-FrozenLake-v1-4x4-Slippery

Ding-Qiang

2023-07-28T09:43:58Z

0

null

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

reinforcement-learning

2023-07-28T09:42:45Z

--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.29 +/- 0.45 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="Ding-Qiang/q-FrozenLake-v1-4x4-Slippery", 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"]) ```

rashmi035/wav2vec2-large-mms-1b-hindi-colab

rashmi035

2023-07-28T09:33:51Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_6_1", "base_model:facebook/mms-1b-fl102", "base_model:finetune:facebook/mms-1b-fl102", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-12T05:29:24Z

--- license: cc-by-nc-4.0 base_model: facebook/mms-1b-fl102 tags: - generated_from_trainer datasets: - common_voice_6_1 metrics: - wer model-index: - name: wav2vec2-large-mms-1b-hindi-colab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_6_1 type: common_voice_6_1 config: hi split: test args: hi metrics: - name: Wer type: wer value: 0.32018561484918795 ---  # wav2vec2-large-mms-1b-hindi-colab This model is a fine-tuned version of [facebook/mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102) on the common_voice_6_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.3535 - Wer: 0.3202 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 16.7585 | 0.14 | 10 | 10.2106 | 2.0951 | | 6.9602 | 0.27 | 20 | 3.7700 | 1.0046 | | 2.4653 | 0.41 | 30 | 1.3321 | 0.6763 | | 1.0919 | 0.55 | 40 | 0.6594 | 0.4664 | | 0.7645 | 0.68 | 50 | 0.4930 | 0.3910 | | 0.8434 | 0.82 | 60 | 0.4819 | 0.3898 | | 0.5118 | 0.96 | 70 | 0.4492 | 0.3817 | | 0.6097 | 1.1 | 80 | 0.4299 | 0.4327 | | 0.4698 | 1.23 | 90 | 0.4308 | 0.3643 | | 0.5402 | 1.37 | 100 | 0.4042 | 0.4107 | | 0.5622 | 1.51 | 110 | 0.4156 | 0.3701 | | 0.4084 | 1.64 | 120 | 0.4138 | 0.3701 | | 0.4888 | 1.78 | 130 | 0.3917 | 0.3434 | | 0.4253 | 1.92 | 140 | 0.3852 | 0.3457 | | 0.5004 | 2.05 | 150 | 0.3843 | 0.3364 | | 0.3791 | 2.19 | 160 | 0.3841 | 0.3469 | | 0.3302 | 2.33 | 170 | 0.3764 | 0.3271 | | 0.4047 | 2.47 | 180 | 0.3689 | 0.3364 | | 0.2951 | 2.6 | 190 | 0.3657 | 0.3329 | | 0.3545 | 2.74 | 200 | 0.3582 | 0.3306 | | 0.3736 | 2.88 | 210 | 0.3585 | 0.3248 | | 0.388 | 3.01 | 220 | 0.3602 | 0.3237 | | 0.2997 | 3.15 | 230 | 0.3624 | 0.3167 | | 0.3704 | 3.29 | 240 | 0.3625 | 0.3190 | | 0.2095 | 3.42 | 250 | 0.3571 | 0.3248 | | 0.3564 | 3.56 | 260 | 0.3570 | 0.3202 | | 0.2119 | 3.7 | 270 | 0.3550 | 0.3225 | | 0.3697 | 3.84 | 280 | 0.3542 | 0.3190 | | 0.3551 | 3.97 | 290 | 0.3535 | 0.3202 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

tommilyjones/swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured

tommilyjones

2023-07-28T09:26:38Z

215

0

transformers

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

image-classification

2023-07-28T09:04:55Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.52 ---  # swin-tiny-patch4-window7-224-finetuned-hateful-meme-restructured This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8519 - Accuracy: 0.52 ## Model description More information needed ## Intended uses & 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6441 | 0.99 | 66 | 0.7419 | 0.492 | | 0.6368 | 2.0 | 133 | 0.7235 | 0.51 | | 0.6157 | 2.99 | 199 | 0.7516 | 0.504 | | 0.5928 | 4.0 | 266 | 0.8009 | 0.502 | | 0.5735 | 4.99 | 332 | 0.8270 | 0.508 | | 0.5559 | 6.0 | 399 | 0.7804 | 0.502 | | 0.5533 | 6.99 | 465 | 0.8053 | 0.486 | | 0.5541 | 8.0 | 532 | 0.8078 | 0.504 | | 0.5218 | 8.99 | 598 | 0.8519 | 0.52 | | 0.5226 | 9.92 | 660 | 0.8522 | 0.508 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

lixsh6/wsdm23_pretrain

lixsh6

2023-07-28T09:19:26Z

0

null

[ "arxiv:2302.13756", "arxiv:2302.13498", "region:us" ]

null

2023-01-13T07:08:47Z

# WSDM Cup 2023 BERT Checkpoints: - This repo contains the checkpoints of our competition in WSDM Cup 2023: [Pre-training for Web Search](https://aistudio.baidu.com/aistudio/competition/detail/536/0/leaderboard) and [Unbiased Learning for Web Search](https://aistudio.baidu.com/aistudio/competition/detail/534/0/leaderboard). ## Paper released Please refer to our paper for details in this competition: - Task1 Unbiased Learning to rank: [Multi-Feature Integration for Perception-Dependent Examination-Bias Estimation](https://arxiv.org/pdf/2302.13756.pdf) - Task2 Pretraining for web search: [Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking](https://arxiv.org/pdf/2302.13498.pdf) ## Method Overview - Pre-training BERT with MLM and CTR prediction loss (or multi-task CTR prediction loss). - Finetuning BERT with pairwise ranking loss. - Obtain prediction scores from different BERTs. - Ensemble learning to combine BERT features and sparse features. Details will be updated in the submission paper. #### BERT features: ##### 1) Model details: [Checkpoints Download Here](https://huggingface.co/lixsh6/wsdm23_pretrain/tree/main) | Index| Model Flag | Method | Pretrain step | Finetune step | DCG on leaderboard | | --------| -------- | ------- |---------------| ------- | ------- | | 1| large_group2_wwm_from_unw4625K | M1 | 1700K | 5130 | 11.96214 | | 2| large_group2_wwm_from_unw4625K | M1 | 1700K | 5130 | NAN | | 3| base_group2_wwm | M2 | 2150K | 5130 | ~11.32363 | | 4| large_group2_wwm_from_unw4625K | M1 | 590K | 5130 | 11.94845 | | 5| large_group2_wwm_from_unw4625K | M1 | 1700K | 4180 | NAN | | 6| large_group2_mt_pretrain | M3 | 1940K | 5130 | NAN | ##### 2) Method details | Method | Model Layers | Details | | -------- | ------- | ------- | | M1 | 24 | WWM & CTR prediction as pretraining tasks| | M2 | 12 | WWM & CTR prediction as pretraining tasks | | M3 | 24 | WWM & Multi-task CTR prediction as pretraining tasks| ## Contacts - Xiangsheng Li: [[email protected]]([email protected]). - Xiaoshu Chen: [[email protected]]([email protected])

digiplay/PotoPhotoRealism_v1

digiplay

2023-07-28T09:18:04Z

499

7

diffusers

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

text-to-image

2023-07-28T08:59:23Z

--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info : https://civitai.com/models/117538/poto-photo-realism Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f0dc3495-2968-42b5-8435-758ec4fb954c/width=1280/580662874.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b1022894-834e-4f86-9e0e-927342d5ef34/width=1280/2598429369.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/06f5fbd8-c46d-4171-9b62-657b988e74cc/width=1728/3944133732.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/de31e0b3-33f8-42e8-872b-bb28854b2490/width=1728/3607815262.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/eba70078-9c00-4e2e-ae3a-053c9dca9900/width=2160/4292364610.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/384c94fa-200f-459f-8138-8c8c17be0484/width=1280/1116309337.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7c901984-0f84-4deb-81ed-9cb67ae67d46/width=1280/2611639573.jpeg)

kamalchibrani/yolov8_fall_detection_25

kamalchibrani

2023-07-28T09:10:52Z

0

null

[ "dataset:kamalchibrani/fall_detection", "license:openrail", "region:us" ]

null

2023-07-28T08:59:29Z

--- license: openrail datasets: - kamalchibrani/fall_detection metrics: - accuracy ---

openlamm/lamm_13b_lora32_98k

openlamm

2023-07-28T09:08:57Z

4

0

transformers

[ "transformers", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-10T03:46:34Z

--- license: apache-2.0 Model: - Vicuna13B - LoRA32 - openlamm/LAMM-98K ---

sdocio/bne-spacy-corgale-ner-es

sdocio

2023-07-28T09:08:31Z

2

0

spacy

[ "spacy", "token-classification", "es", "license:gpl-3.0", "model-index", "region:us" ]

token-classification

2023-01-07T23:02:41Z

--- license: gpl-3.0 language: - es library_name: spacy pipeline_tag: token-classification tags: - spacy - token-classification widget: - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." - text: "Si te metes en el Franco desde la Alameda, vas hacia la Catedral." - text: "Y allí precisamente es Santiago el patrón del pueblo." model-index: - name: bne-spacy-corgale-ner-es results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.9721311475 - name: NER Recall type: recall value: 0.9732708089 - name: NER F Score type: f_score value: 0.9727006444 --- # Introduction spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC). It was fine-tuned using `PlanTL-GOB-ES/roberta-base-bne`. | Feature | Description | | --- | --- | | **Name** | `bne-spacy-corgale-ner-es` | | **Version** | `0.0.2` | | **spaCy** | `>=3.5.2,<3.6.0` | | **Default Pipeline** | `transformer`, `ner` | | **Components** | `transformer`, `ner` | ### Label Scheme <details> <summary>View label scheme (4 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | </details> ## Usage You can use this model with the spaCy *pipeline* for NER. ```python import spacy from spacy.pipeline import merge_entities nlp = spacy.load("bne-spacy-corgale-ner-es") nlp.add_pipe('sentencizer') example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo." ner_pipe = nlp(example) print(ner_pipe.ents) for token in merge_entities(ner_pipe): print(token.text, token.ent_type_) ``` ## Dataset ToDo ## Model performance entity|precision|recall|f1 -|-|-|- LOC|0.985|0.987|0.986 MISC|0.862|0.865|0.863 ORG|0.938|0.779|0.851 PER|0.921|0.941|0.931 micro avg|0.971|0.972|0.971 macro avg|0.926|0.893|0.908 weighted avg|0.971|0.972|0.971

caozhanqiang/llama2-glora-finetunined-french

caozhanqiang

2023-07-28T09:08:14Z

3

0

peft

[ "peft", "region:us" ]

null

2023-07-28T09:07:56Z

--- 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 0.5.0.dev0

oljike/nurtas_db_lora

oljike

2023-07-28T09:04:38Z

0

1

diffusers

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

text-to-image

2023-07-28T07:05:24Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of kairat nurtas tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - oljike/nurtas_db_lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of kairat nurtas using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) LoRA for the text encoder was enabled: False.

tommilyjones/resnet-50-finetuned-hateful-meme-restructured

tommilyjones

2023-07-28T09:03:09Z

229

0

transformers

[ "transformers", "pytorch", "resnet", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/resnet-50", "base_model:finetune:microsoft/resnet-50", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2023-07-28T08:40:52Z

--- license: apache-2.0 base_model: microsoft/resnet-50 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: resnet-50-finetuned-hateful-meme-restructured results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.5 ---  # resnet-50-finetuned-hateful-meme-restructured This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7132 - Accuracy: 0.5 ## Model description More information needed ## Intended uses & 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6633 | 0.99 | 66 | 0.7132 | 0.5 | | 0.6561 | 2.0 | 133 | 0.7309 | 0.5 | | 0.6497 | 2.99 | 199 | 0.7314 | 0.5 | | 0.6529 | 4.0 | 266 | 0.7296 | 0.5 | | 0.6336 | 4.99 | 332 | 0.7386 | 0.5 | | 0.625 | 6.0 | 399 | 0.7403 | 0.5 | | 0.6511 | 6.99 | 465 | 0.7425 | 0.5 | | 0.6567 | 8.0 | 532 | 0.7314 | 0.5 | | 0.6389 | 8.99 | 598 | 0.7380 | 0.5 | | 0.6446 | 9.92 | 660 | 0.7426 | 0.5 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

openlamm/lamm_7b_lora32_98k

openlamm

2023-07-28T09:00:14Z

6

0

transformers

[ "transformers", "llama", "text-generation", "en", "dataset:caojianjian/LAMM", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2023-06-10T15:18:52Z

--- license: apache-2.0 datasets: - caojianjian/LAMM language: - en library_name: transformers ---

dummycouchspud/adapter_demo

dummycouchspud

2023-07-28T08:39:54Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T08:39:52Z

--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0 - PEFT 0.5.0.dev0

iliyaML/my_awesome_eli5_clm-model

iliyaML

2023-07-28T08:33:55Z

132

0

transformers

[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T08:00:06Z

--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: my_awesome_eli5_clm-model results: [] ---  # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 3.8773 | 1.0 | 1121 | 3.7568 | | 3.7788 | 2.0 | 2242 | 3.7430 | | 3.7441 | 3.0 | 3363 | 3.7399 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

xianbin/Reinforce-Pixelcopter-PLE-v0

xianbin

2023-07-28T08:28:33Z

0

null

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

reinforcement-learning

2023-07-28T08:14:41Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 106.00 +/- 86.94 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

darveen/llama2-qlora-finetuned-alpaca-40steps

darveen

2023-07-28T08:27:20Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-23T03:32:03Z

--- 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 0.5.0.dev0

Imxxn/RLCourseU5-Pyramids

Imxxn

2023-07-28T08:22:54Z

0

ml-agents

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

reinforcement-learning

2023-07-28T08:22:50Z

--- 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: Imxxn/RLCourseU5-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

HaziqRazali/dqn-SpaceInvadersNoFrameskip-v4

HaziqRazali

2023-07-28T08:12:40Z

4

0

stable-baselines3

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

reinforcement-learning

2023-07-28T08:12:17Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 133.50 +/- 35.22 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HaziqRazali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga HaziqRazali -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga HaziqRazali ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 1000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 1000), ('n_timesteps', 10000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

Imxxn/RLCourseU5-SnowballTarget

Imxxn

2023-07-28T07:49:37Z

0

ml-agents

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

reinforcement-learning

2023-07-28T07:49:33Z

--- 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: Imxxn/RLCourseU5-SnowballTarget 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

loucad/swin-tiny-patch4-window7-224-finetuned-eurosat

loucad

2023-07-28T07:48:19Z

214

0

transformers

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

image-classification

2023-05-19T07:03:50Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.9751851851851852 ---  # swin-tiny-patch4-window7-224-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0797 - Accuracy: 0.9752 ## Model description More information needed ## Intended uses & 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.284 | 1.0 | 190 | 0.1307 | 0.9559 | | 0.1839 | 2.0 | 380 | 0.1056 | 0.9681 | | 0.1339 | 3.0 | 570 | 0.0797 | 0.9752 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3

DAMO-NLP-MT/polylm-chat-13b

DAMO-NLP-MT

2023-07-28T07:43:26Z

1,472

6

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "arxiv:2307.06018", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T06:46:08Z

--- license: apache-2.0 --- # Model Card for PolyLM-Multialpaca This model is finetuned on [polyLM-13b](https://huggingface.co/DAMO-NLP-MT/polylm-13b) using the following datasets: # Demo [Open](https://modelscope.cn/studios/damo/demo-polylm-multialpaca-13b/summary) # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf): > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. # Citation **BibTeX:** ```bibtex @misc{wei2023polylm, title={PolyLM: An Open Source Polyglot Large Language Model}, author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie}, year={2023}, eprint={2307.06018}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload

Lajonbot

2023-07-28T07:31:07Z

1,398

1

transformers

[ "transformers", "pytorch", "llama", "text-generation", "facebook", "meta", "llama-2", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-28T07:20:24Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

BlunderPanini/Taxi-v3

BlunderPanini

2023-07-28T07:26:32Z

0

null

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

reinforcement-learning

2023-07-28T07:26:28Z

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

ToolBench/ToolLLaMA-7b-LoRA-v1

ToolBench

2023-07-28T07:25:03Z

0

8

null

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

null

2023-07-27T08:36:08Z

--- license: apache-2.0 --- # Model Card for Model ID This is a lora version ToolLLaMA model introduced in [ToolBench](https://github.com/OpenBMB/ToolBench). ## Model Details ### Model Description  - **License:** apache-2.0 - **Finetuned from model [optional]:** LLaMA-7b ## Uses Refer to [ToolBench](https://github.com/OpenBMB/ToolBench). ## Training Details Trained with the new version data in ToolBench.

xianbin/ppo-LunarLander-v2-2

xianbin

2023-07-28T07:22:23Z

0

null

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

reinforcement-learning

2023-07-28T07:22:17Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -165.00 +/- 73.23 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 100000 'torch_deterministic': True 'cuda': True 'capture_video': False 'num_envs': 32 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.995 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xianbin/ppo-LunarLander-v2-2' 'batch_size': 4096 'minibatch_size': 1024 'env_id': 'LunarLander-v2'} ```

Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_GGML

Lajonbot

2023-07-28T07:20:24Z

0

null

[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]

text-generation

2023-07-28T07:12:07Z

--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---

xianbin/ppo-LunarLander-v2-new

xianbin

2023-07-28T07:18:41Z

0

null

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

reinforcement-learning

2023-07-28T07:11:23Z

--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -98.47 +/- 49.60 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'gym_id': 'LunarLander-v2' 'learning_rate': 0.00025 'seed': 1 'total_timesteps': 100000 'torch_deterministic': True 'cuda': True 'capture_video': False 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.995 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'xianbin/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128 'env_id': 'LunarLander-v2'} ```

sm136599/chatfoodie-koalpaca-polyglot-5_8b-1000step-4batch

sm136599

2023-07-28T06:48:16Z

0

peft

[ "peft", "region:us" ]

null

2023-07-28T06:48:11Z

--- 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

CobraMamba/mamba-gpt-3b

CobraMamba

2023-07-28T06:42:23Z

1,402

4

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "gpt", "llm", "large language model", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2023-06-12T06:08:57Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model inference: false thumbnail: >- https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico license: apache-2.0 --- # Model Card ## Github https://github.com/chi2liu/mamba-gpt-3b | Metric | Value | |-----------------------|-------| | MMLU (5-shot) | 25.3 | | ARC (25-shot) | 40.5 | | HellaSwag (10-shot) | 64.9 | | TruthfulQA (0-shot) | 37.1 | | Avg. | 42.0 | We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above. ## Summary We have fine-tuned the open-lama model and surpassed the original model in multiple evaluation subtasks, making it currently the best performing 3B model with comparable performance to llama-7b - Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.29.2 pip install accelerate==0.19.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="CobraMamba/mamba-gpt-3b", torch_dtype="auto", trust_remote_code=True, use_fast=False, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?</s><|answer|> ``` Alternatively, you can download the mamba_gpt_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from mamba_gpt_pipeline import MambaGPTTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "CobraMamba/mamba-gpt-3b", use_fast=False, padding_side="left", trust_remote_code=False, ) model = AutoModelForCausalLM.from_pretrained( "CobraMamba/mamba-gpt-3b", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=False, ) generate_text = MambaGPTTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "CobraMamba/mamba-gpt-3b" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?</s><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=False, trust_remote_code=False, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=False, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=1024, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 4096, padding_idx=0) (layers): ModuleList( (0-31): 32 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=4096, out_features=4096, bias=False) (k_proj): Linear(in_features=4096, out_features=4096, bias=False) (v_proj): Linear(in_features=4096, out_features=4096, bias=False) (o_proj): Linear(in_features=4096, out_features=4096, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=4096, out_features=11008, bias=False) (down_proj): Linear(in_features=11008, out_features=4096, bias=False) (up_proj): Linear(in_features=4096, out_features=11008, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) ) ``` ## Evaluation We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/). The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks. | **Task/Metric** | finetuned-GPT 3B | OpenLLaMA 3B | | ---------------------- | -------- | ------------ | | anli_r1/acc | **0.35** | 0.33 | | anli_r2/acc | **0.33** | 0.32 | | anli_r3/acc | 0.35 | 0.35 | | arc_challenge/acc | **0.35** | 0.34 | | arc_challenge/acc_norm | 0.37 | 0.37 | | arc_easy/acc | **0.71** | 0.69 | | arc_easy/acc_norm | 0.65 | 0.65 | | boolq/acc | **0.72** | 0.66 | | hellaswag/acc | **0.49** | 0.43 | | hellaswag/acc_norm | 0.66 | **0.67** | | openbookqa/acc | 0.26 | **0.27** | | openbookqa/acc_norm | 0.40 | 0.40 | | piqa/acc | **0.76** | 0.75 | | piqa/acc_norm | 0.76 | 0.76 | | record/em | 0.88 | 0.88 | | record/f1 | 0.88 | **0.89** | | rte/acc | 0.55 | **0.58** | | truthfulqa_mc/mc1 | **0.27** | 0.22 | | truthfulqa_mc/mc2 | **0.37** | 0.35 | | wic/acc | **0.49** | 0.48 | | winogrande/acc | **0.63** | 0.62 | | Average | **0.53** | 0.52 | We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

jjohn23/diffuser-anime-faces-32

jjohn23

2023-07-28T06:27:52Z

30

0

diffusers

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

unconditional-image-generation

2023-07-28T06:25:41Z

--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute anime faces. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('jjohn23/diffuser-anime-faces-32') image = pipeline().images[0] image ```

Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding

Evan-Lin

2023-07-28T06:19:46Z

47

0

transformers

[ "transformers", "pytorch", "bart", "text2text-generation", "trl", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

reinforcement-learning

2023-07-28T06:12:29Z

--- license: apache-2.0 tags: - trl - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") model = AutoModelForCausalLMWithValueHead.from_pretrained("Evan-Lin//tmp/tmp3_ykz16z/Evan-Lin/Bart-Yelp-rougelastbatch-attractive1-keywordmax1-decoding") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```

alibaba-pai/pai-diffusion-food-large-zh

alibaba-pai

2023-07-28T06:15:26Z

5

3

diffusers

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

text-to-image

2022-11-30T03:30:53Z

--- license: apache-2.0 tags: - pytorch - diffusers - text-to-image --- # Chinese Latent Diffusion Model 我们开源了一个中文 Lattent Diffusion 模型（美食） * Github: [EasyNLP](https://github.com/alibaba/EasyNLP) ```python from diffusers import StableDiffusionPipeline model_id = "alibaba-pai/pai-diffusion-food-large-zh" pipe = StableDiffusionPipeline.from_pretrained(model_id) pipe = pipe.to("cuda") prompt = "番茄炒蛋" image = pipe(prompt).images[0] image.save("result.png") ```

terwrt/ppo-Huggy

terwrt

2023-07-28T06:01:17Z

5

0

ml-agents

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

reinforcement-learning

2023-07-28T06:01:11Z

--- 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: terwrt/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

yijhen38/llama2-7B-chat-qlora-FT-chainsea

yijhen38

2023-07-28T05:58:04Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T05:15:12Z

--- library_name: peft --- 參考 Fine Turning https://colab.research.google.com/drive/12dVqXZMIVxGI0uutU6HG9RWbWPXL3vts?usp=sharing#scrollTo=mNnkgBq7Q3EU Model：[meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) dataset：COT_Answer.txt ## 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 0.5.0.dev0

sm136599/chatfoodie-koalpaca-polyglot-5.8b-200step-4batch

sm136599

2023-07-28T05:56:33Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T05:56:27Z

--- 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

beaugogh/pythia-1.4b-deduped-sharegpt

beaugogh

2023-07-28T05:52:54Z

1,613

0

transformers

[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-25T13:21:09Z

--- license: apache-2.0 --- pythia-1.4b-deduped model finetuned on sharegpt data

chriskim2273/IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert

chriskim2273

2023-07-28T05:45:38Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-28T05:06:34Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert results: [] ---  # IOTNation_CompanyName_AND_Location_AND_Series_Extraction_QA_Model_1.5_DistilBert This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Imxxn/RLCourseU4-CartPole-v1

Imxxn

2023-07-28T05:41:57Z

0

null

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

reinforcement-learning

2023-07-28T05:41:46Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: RLCourseU4-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

sm136599/chatfoodie-koalpaca-polyglot-5.8b-200step

sm136599

2023-07-28T04:43:54Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T04:43:49Z

--- 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

xianbin/a2c-AntBulletEnv-v0

xianbin

2023-07-28T04:30:29Z

2

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2023-07-28T04:28:24Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1346.83 +/- 53.98 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** 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 ... ```

sm136599/chatfoodie-koalpaca-polyglot-5.8b-1000step

sm136599

2023-07-28T04:06:52Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T04:06:47Z

--- 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

dilip025/dummy-model

dilip025

2023-07-28T03:56:34Z

59

0

transformers

[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2023-07-28T03:55:31Z

--- license: mit base_model: camembert-base tags: - generated_from_keras_callback model-index: - name: dummy-model results: [] ---  # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Tokenizers 0.13.3

svalcin/q-Taxi-v3

svalcin

2023-07-28T03:26:37Z

0

null

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

reinforcement-learning

2023-07-11T13:24:15Z

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

PAIXAI/Astrid-7B

PAIXAI

2023-07-28T03:02:52Z

19

22

transformers

[ "transformers", "pytorch", "RefinedWebModel", "text-generation", "gpt", "llm", "large language model", "PAIX", "custom_code", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-07-07T01:56:35Z

--- language: - en library_name: transformers tags: - gpt - llm - large language model - PAIX inference: true thumbnail: https://static.wixstatic.com/media/bdee4e_d0af74523fa64a998d4cfb894e8cd3bb~mv2.png/v1/crop/x_40,y_663,w_1954,h_663/fill/w_342,h_116,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/PAIX%20Logo%20(2).png --- # Model Card ## Summary Model Card Summary The model Astrid-7B-1 architecture includes a RWForCausalLM transformer with word embeddings, a module list of 32 DecoderLayers, and a linear lm_head. The DecoderLayer includes an input layer normalization, self-attention mechanism, and a multi-layer perceptron (MLP). It's part of our mission to make AI technology accessible to everyone, focusing on personalization, data privacy, and transparent AI governance. Trained in English, it's a versatile tool for a variety of applications. This model is one of the many models available on our platform, and we currently have a 1B and 7B open-source model. This model was trained by [PAIX.Cloud](https://www.paix.cloud/). - Wait list: [Wait List](https://www.paix.cloud/join-waitlist) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.30.1 pip install accelerate==0.20.3 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="<path_to_local_folder>", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|> ``` ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "<path_to_local_folder>", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "<path_to_local_folder>", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "<path_to_local_folder>" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?<|endoftext|><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( **inputs, min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Model Architecture ``` RWForCausalLM( (transformer): RWModel( (word_embeddings): Embedding(65024, 4544) (h): ModuleList( (0-31): 32 x DecoderLayer( (input_layernorm): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) (self_attention): Attention( (maybe_rotary): RotaryEmbedding() (query_key_value): Linear(in_features=4544, out_features=4672, bias=False) (dense): Linear(in_features=4544, out_features=4544, bias=False) (attention_dropout): Dropout(p=0.0, inplace=False) ) (mlp): MLP( (dense_h_to_4h): Linear(in_features=4544, out_features=18176, bias=False) (act): GELU(approximate='none') (dense_4h_to_h): Linear(in_features=18176, out_features=4544, bias=False) ) ) ) (ln_f): LayerNorm((4544,), eps=1e-05, elementwise_affine=True) ) (lm_head): Linear(in_features=4544, out_features=65024, bias=False) ) ``` ## Model Configuration ## Model Validation Model validation results using [EleutherAI lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). ```bash CUDA_VISIBLE_DEVICES=0 python main.py --model hf-causal-experimental --model_args pretrained=<path_to_local_folder> --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq --device cuda &> eval.log ``` ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.

jordyvl/rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5

jordyvl

2023-07-28T02:58:46Z

165

0

transformers

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

image-classification

2023-07-27T18:49:50Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 results: [] ---  # rvlcdip-tiny_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5 This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.4561 - Accuracy: 0.802 - Brier Loss: 0.3399 - Nll: 1.6335 - F1 Micro: 0.802 - F1 Macro: 0.8037 - Ece: 0.1478 - Aurc: 0.0576 ## Model description More information needed ## Intended uses & 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | No log | 1.0 | 125 | 5.6533 | 0.5288 | 0.6696 | 3.8280 | 0.5288 | 0.4993 | 0.2170 | 0.2294 | | No log | 2.0 | 250 | 5.3016 | 0.6285 | 0.5364 | 2.7651 | 0.6285 | 0.6089 | 0.1861 | 0.1465 | | No log | 3.0 | 375 | 5.1153 | 0.696 | 0.4775 | 2.4052 | 0.696 | 0.6956 | 0.2003 | 0.1078 | | 5.7003 | 4.0 | 500 | 4.9491 | 0.7358 | 0.3968 | 2.1532 | 0.7358 | 0.7375 | 0.1406 | 0.0813 | | 5.7003 | 5.0 | 625 | 4.8556 | 0.754 | 0.3676 | 1.8243 | 0.754 | 0.7472 | 0.1001 | 0.0756 | | 5.7003 | 6.0 | 750 | 4.8060 | 0.7625 | 0.3475 | 1.8558 | 0.7625 | 0.7636 | 0.0808 | 0.0696 | | 5.7003 | 7.0 | 875 | 4.8301 | 0.7648 | 0.3320 | 1.7367 | 0.7648 | 0.7663 | 0.0434 | 0.0677 | | 4.6459 | 8.0 | 1000 | 4.7883 | 0.7692 | 0.3305 | 1.8366 | 0.7692 | 0.7728 | 0.0532 | 0.0666 | | 4.6459 | 9.0 | 1125 | 4.8347 | 0.7762 | 0.3282 | 1.7122 | 0.7762 | 0.7789 | 0.0610 | 0.0675 | | 4.6459 | 10.0 | 1250 | 4.8679 | 0.7682 | 0.3338 | 1.8225 | 0.7682 | 0.7713 | 0.0634 | 0.0672 | | 4.6459 | 11.0 | 1375 | 4.9875 | 0.7655 | 0.3521 | 1.9651 | 0.7655 | 0.7647 | 0.0914 | 0.0692 | | 4.2436 | 12.0 | 1500 | 4.9708 | 0.77 | 0.3410 | 2.0195 | 0.7700 | 0.7694 | 0.0838 | 0.0684 | | 4.2436 | 13.0 | 1625 | 4.9246 | 0.7752 | 0.3349 | 1.8150 | 0.7752 | 0.7758 | 0.0801 | 0.0666 | | 4.2436 | 14.0 | 1750 | 4.9235 | 0.776 | 0.3327 | 1.8364 | 0.776 | 0.7782 | 0.0896 | 0.0628 | | 4.2436 | 15.0 | 1875 | 4.9149 | 0.7817 | 0.3348 | 1.9243 | 0.7817 | 0.7857 | 0.0917 | 0.0650 | | 4.0997 | 16.0 | 2000 | 4.8998 | 0.7837 | 0.3255 | 1.8326 | 0.7837 | 0.7874 | 0.0901 | 0.0637 | | 4.0997 | 17.0 | 2125 | 4.9658 | 0.7792 | 0.3358 | 1.8156 | 0.7792 | 0.7815 | 0.1025 | 0.0640 | | 4.0997 | 18.0 | 2250 | 4.9819 | 0.7905 | 0.3256 | 1.8605 | 0.7905 | 0.7919 | 0.1016 | 0.0613 | | 4.0997 | 19.0 | 2375 | 5.0040 | 0.778 | 0.3417 | 1.9392 | 0.778 | 0.7800 | 0.1095 | 0.0638 | | 4.0325 | 20.0 | 2500 | 5.0084 | 0.7817 | 0.3387 | 1.9882 | 0.7817 | 0.7833 | 0.1043 | 0.0642 | | 4.0325 | 21.0 | 2625 | 5.0680 | 0.7805 | 0.3473 | 1.8641 | 0.7805 | 0.7803 | 0.1200 | 0.0631 | | 4.0325 | 22.0 | 2750 | 5.0324 | 0.7808 | 0.3395 | 1.8541 | 0.7808 | 0.7835 | 0.1124 | 0.0620 | | 4.0325 | 23.0 | 2875 | 5.0734 | 0.7845 | 0.3446 | 1.9087 | 0.7845 | 0.7884 | 0.1170 | 0.0625 | | 3.99 | 24.0 | 3000 | 5.2144 | 0.782 | 0.3564 | 1.9540 | 0.782 | 0.7845 | 0.1293 | 0.0640 | | 3.99 | 25.0 | 3125 | 5.0299 | 0.7873 | 0.3387 | 1.8106 | 0.7873 | 0.7887 | 0.1167 | 0.0614 | | 3.99 | 26.0 | 3250 | 5.0673 | 0.792 | 0.3318 | 1.7538 | 0.792 | 0.7930 | 0.1134 | 0.0599 | | 3.99 | 27.0 | 3375 | 5.0854 | 0.791 | 0.3379 | 1.8144 | 0.791 | 0.7932 | 0.1253 | 0.0586 | | 3.9606 | 28.0 | 3500 | 5.0962 | 0.787 | 0.3403 | 1.7780 | 0.787 | 0.7884 | 0.1224 | 0.0592 | | 3.9606 | 29.0 | 3625 | 5.0812 | 0.7877 | 0.3379 | 1.7721 | 0.7877 | 0.7900 | 0.1247 | 0.0592 | | 3.9606 | 30.0 | 3750 | 5.1318 | 0.7905 | 0.3359 | 1.8105 | 0.7905 | 0.7931 | 0.1290 | 0.0597 | | 3.9606 | 31.0 | 3875 | 5.0330 | 0.7953 | 0.3276 | 1.7361 | 0.7953 | 0.7978 | 0.1144 | 0.0584 | | 3.9355 | 32.0 | 4000 | 5.0843 | 0.7975 | 0.3276 | 1.7556 | 0.7975 | 0.7990 | 0.1236 | 0.0560 | | 3.9355 | 33.0 | 4125 | 5.1843 | 0.7995 | 0.3315 | 1.7084 | 0.7995 | 0.8004 | 0.1297 | 0.0575 | | 3.9355 | 34.0 | 4250 | 5.1703 | 0.7987 | 0.3333 | 1.6918 | 0.7987 | 0.8000 | 0.1257 | 0.0580 | | 3.9355 | 35.0 | 4375 | 5.1933 | 0.7937 | 0.3372 | 1.7084 | 0.7937 | 0.7941 | 0.1307 | 0.0561 | | 3.9148 | 36.0 | 4500 | 5.1404 | 0.7987 | 0.3275 | 1.6423 | 0.7987 | 0.8011 | 0.1308 | 0.0547 | | 3.9148 | 37.0 | 4625 | 5.1734 | 0.8017 | 0.3272 | 1.6836 | 0.8017 | 0.8034 | 0.1272 | 0.0572 | | 3.9148 | 38.0 | 4750 | 5.2479 | 0.802 | 0.3322 | 1.7081 | 0.802 | 0.8032 | 0.1353 | 0.0550 | | 3.9148 | 39.0 | 4875 | 5.1921 | 0.8 | 0.3320 | 1.6554 | 0.8000 | 0.8012 | 0.1334 | 0.0538 | | 3.9001 | 40.0 | 5000 | 5.2477 | 0.801 | 0.3353 | 1.6333 | 0.801 | 0.8022 | 0.1390 | 0.0539 | | 3.9001 | 41.0 | 5125 | 5.2140 | 0.801 | 0.3299 | 1.6370 | 0.801 | 0.8017 | 0.1340 | 0.0544 | | 3.9001 | 42.0 | 5250 | 5.2660 | 0.807 | 0.3303 | 1.6090 | 0.807 | 0.8079 | 0.1339 | 0.0545 | | 3.9001 | 43.0 | 5375 | 5.2884 | 0.8007 | 0.3319 | 1.6816 | 0.8007 | 0.8022 | 0.1394 | 0.0547 | | 3.8892 | 44.0 | 5500 | 5.3358 | 0.804 | 0.3352 | 1.6399 | 0.804 | 0.8049 | 0.1387 | 0.0560 | | 3.8892 | 45.0 | 5625 | 5.3545 | 0.8043 | 0.3349 | 1.6445 | 0.8043 | 0.8060 | 0.1408 | 0.0555 | | 3.8892 | 46.0 | 5750 | 5.4026 | 0.8033 | 0.3373 | 1.6493 | 0.8033 | 0.8049 | 0.1439 | 0.0567 | | 3.8892 | 47.0 | 5875 | 5.4195 | 0.8015 | 0.3386 | 1.6393 | 0.8015 | 0.8031 | 0.1468 | 0.0570 | | 3.8834 | 48.0 | 6000 | 5.4409 | 0.803 | 0.3396 | 1.6392 | 0.803 | 0.8046 | 0.1458 | 0.0574 | | 3.8834 | 49.0 | 6125 | 5.4501 | 0.8023 | 0.3395 | 1.6367 | 0.8023 | 0.8039 | 0.1468 | 0.0574 | | 3.8834 | 50.0 | 6250 | 5.4561 | 0.802 | 0.3399 | 1.6335 | 0.802 | 0.8037 | 0.1478 | 0.0576 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2

Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-10

Jonathaniu

2023-07-28T02:46:15Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-28T02:45:54Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0

Muniyaraj/output_model

Muniyaraj

2023-07-28T02:39:10Z

1

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

2023-07-27T15:44:23Z

--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of muniyarajs tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - Muniyaraj/output_model These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of muniyarajs 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: None.

dleiferives/dwane

dleiferives

2023-07-28T02:30:30Z

193

0

transformers

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

image-classification

2023-07-28T02:30:22Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: dwane results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.20000000298023224 --- # dwane Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### dwayne johnson ![dwayne johnson](images/dwayne_johnson.jpg) #### dwayne the rock johnson ![dwayne the rock johnson](images/dwayne_the_rock_johnson.jpg)

khnhar/capturingYoutubeVideo

khnhar

2023-07-28T02:22:46Z

0

null

[ "region:us" ]

null

2023-07-28T01:58:32Z

Youtube 동영상에서 '찾고 싶은 키워드가 들어간 이미지'를 찾아주는 ai모듈이다. 응용 : CCTV 범죄자 찾기, 도난차량 번호판 조회 등

MichelNivard/starcoderbase_3b_for_R

MichelNivard

2023-07-28T02:22:27Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T12:43:24Z

--- 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

cgr28/a2c-PandaReachDense-v2

cgr28

2023-07-28T02:06:34Z

1

0

stable-baselines3

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

reinforcement-learning

2023-07-28T02:03:40Z

--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -2.76 +/- 0.55 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v2** This is a trained model of a **A2C** agent playing **PandaReachDense-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

manuu01/Reinforce-Pixelcopter-PLE-v0

manuu01

2023-07-28T02:05:03Z

0

null

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

reinforcement-learning

2023-07-27T13:22:54Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 51.90 +/- 39.87 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

mskhattori/wav2vec2phone-large-xlsr-jp-jdrt5N-demo

mskhattori

2023-07-28T01:58:48Z

5

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "base_model:finetune:jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2023-07-27T22:24:17Z

--- license: apache-2.0 base_model: jonatasgrosman/wav2vec2-large-xlsr-53-japanese tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2phone-large-xlsr-jp-jdrt5N-demo results: [] ---  # wav2vec2phone-large-xlsr-jp-jdrt5N-demo This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-japanese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3714 - Wer: 0.4730 - Cer: 0.5054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.5238 | 1.0 | 567 | 1.3532 | 0.8709 | 0.6208 | | 1.2812 | 2.0 | 1134 | 0.8674 | 0.6835 | 0.5633 | | 1.1329 | 3.0 | 1701 | 0.7105 | 0.6164 | 0.5564 | | 1.0267 | 4.0 | 2268 | 0.6111 | 0.5775 | 0.5401 | | 1.0415 | 5.0 | 2835 | 0.5505 | 0.5499 | 0.5482 | | 0.9767 | 6.0 | 3402 | 0.4986 | 0.5210 | 0.5204 | | 1.0392 | 7.0 | 3969 | 0.4655 | 0.5082 | 0.5194 | | 0.9235 | 8.0 | 4536 | 0.4457 | 0.4989 | 0.5136 | | 0.9511 | 9.0 | 5103 | 0.4201 | 0.4917 | 0.5106 | | 0.8998 | 10.0 | 5670 | 0.4031 | 0.4869 | 0.5081 | | 0.8883 | 11.0 | 6237 | 0.3920 | 0.4814 | 0.5107 | | 0.856 | 12.0 | 6804 | 0.3834 | 0.4790 | 0.5094 | | 0.8814 | 13.0 | 7371 | 0.3772 | 0.4761 | 0.5081 | | 0.8352 | 14.0 | 7938 | 0.3737 | 0.4735 | 0.5052 | | 0.9001 | 15.0 | 8505 | 0.3714 | 0.4730 | 0.5054 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3

Jinmane/jafamix

Jinmane

2023-07-28T01:30:30Z

0

null

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

null

2023-07-28T00:53:39Z

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

lucas0/empath-falcon-40b

lucas0

2023-07-28T01:04:53Z

0

null

[ "generated_from_trainer", "base_model:tiiuae/falcon-40b-instruct", "base_model:finetune:tiiuae/falcon-40b-instruct", "license:apache-2.0", "region:us" ]

null

2023-07-20T18:29:06Z

--- license: apache-2.0 base_model: tiiuae/falcon-40b-instruct tags: - generated_from_trainer model-index: - name: empath-falcon-40b results: [] ---  # empath-falcon-40b This model is a fine-tuned version of [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3

NasimB/bnc-cbt-log-rarity-mixed

NasimB

2023-07-28T01:03:51Z

4

0

transformers

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

text-generation

2023-07-27T22:53:51Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-log-rarity-mixed results: [] ---  # bnc-cbt-log-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0803 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3678 | 0.29 | 500 | 5.3083 | | 5.0506 | 0.58 | 1000 | 4.8988 | | 4.727 | 0.87 | 1500 | 4.6664 | | 4.4606 | 1.16 | 2000 | 4.5274 | | 4.3096 | 1.45 | 2500 | 4.4089 | | 4.213 | 1.75 | 3000 | 4.3013 | | 4.0894 | 2.04 | 3500 | 4.2301 | | 3.9068 | 2.33 | 4000 | 4.1861 | | 3.8675 | 2.62 | 4500 | 4.1276 | | 3.8433 | 2.91 | 5000 | 4.0819 | | 3.6581 | 3.2 | 5500 | 4.0743 | | 3.5934 | 3.49 | 6000 | 4.0511 | | 3.5814 | 3.78 | 6500 | 4.0203 | | 3.4978 | 4.07 | 7000 | 4.0150 | | 3.326 | 4.36 | 7500 | 4.0140 | | 3.3207 | 4.65 | 8000 | 4.0007 | | 3.308 | 4.94 | 8500 | 3.9894 | | 3.1751 | 5.24 | 9000 | 4.0029 | | 3.144 | 5.53 | 9500 | 4.0021 | | 3.1408 | 5.82 | 10000 | 4.0013 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

chriskim2273/IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta

chriskim2273

2023-07-28T00:59:28Z

120

0

transformers

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2023-07-27T23:49:20Z

--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta results: [] ---  # IOTNation_CompanyName_AND_Location_Extraction_QA_Model_1.4_Roberta This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3326 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

Bellaaazzzzz/model_Xray

Bellaaazzzzz

2023-07-28T00:24:07Z

3

0

diffusers

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

text-to-image

2023-07-24T18:08:42Z

--- license: creativeml-openrail-m base_model: runwayml/stable-diffusion-v1-5 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - controlnet inference: true --- # controlnet-Bellaaazzzzz/model_Xray These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning. You can find some example images below. Validation result of 1 round. ![images_0_0)](./images_0_0.png) Validation result of 2 round. ![images_1_0)](./images_1_0.png) Validation result of 3 round. ![images_2_0)](./images_2_0.png) Validation result of 4 round. ![images_3_0)](./images_3_0.png) Validation result of 5 round. ![images_4_0)](./images_4_0.png) Validation result of 6 round. ![images_5_0)](./images_5_0.png) Validation result of 7 round. ![images_6_0)](./images_6_0.png) Validation result of 8 round. ![images_7_0)](./images_7_0.png) Validation result of 9 round. ![images_8_0)](./images_8_0.png) Validation result of 10 round. ![images_9_0)](./images_9_0.png)

NasimB/aochildes-guten-fixed-rarity-mixed

NasimB

2023-07-28T00:03:14Z

5

0

transformers

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

text-generation

2023-07-27T21:48:59Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes-guten-fixed-rarity-mixed results: [] ---  # aochildes-guten-fixed-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.365 | 0.29 | 500 | 5.3433 | | 5.0504 | 0.59 | 1000 | 4.9338 | | 4.72 | 0.88 | 1500 | 4.6948 | | 4.4681 | 1.17 | 2000 | 4.5525 | | 4.3117 | 1.47 | 2500 | 4.4371 | | 4.2069 | 1.76 | 3000 | 4.3333 | | 4.0872 | 2.05 | 3500 | 4.2616 | | 3.9143 | 2.35 | 4000 | 4.2156 | | 3.88 | 2.64 | 4500 | 4.1594 | | 3.8477 | 2.93 | 5000 | 4.1171 | | 3.6334 | 3.23 | 5500 | 4.1165 | | 3.607 | 3.52 | 6000 | 4.0836 | | 3.5905 | 3.81 | 6500 | 4.0549 | | 3.4793 | 4.11 | 7000 | 4.0555 | | 3.3298 | 4.4 | 7500 | 4.0536 | | 3.3312 | 4.69 | 8000 | 4.0366 | | 3.3098 | 4.99 | 8500 | 4.0267 | | 3.1519 | 5.28 | 9000 | 4.0423 | | 3.1522 | 5.57 | 9500 | 4.0401 | | 3.1459 | 5.87 | 10000 | 4.0393 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

chandrasutrisnotjhong/Reinforce-CartPole

chandrasutrisnotjhong

2023-07-27T23:59:26Z

0

null

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

reinforcement-learning

2023-07-27T11:16:50Z

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

PhilSad/phil-lora-2

PhilSad

2023-07-27T23:44:11Z

0

diffusers

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "license:openrail++", "region:us" ]

text-to-image

2023-07-27T22:35:46Z

--- license: openrail++ base_model: ./stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks man tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - PhilSad/phil-lora-2 These are LoRA adaption weights for ./stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks man 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) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

brunoboat/Reinforce-Pixelcopter-PLE-v0

brunoboat

2023-07-27T23:43:45Z

0

null

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

reinforcement-learning

2023-07-27T23:08:18Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 13.00 +/- 11.42 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

EulerianKnight/ppo-Huggy

EulerianKnight

2023-07-27T23:25:17Z

17

0

ml-agents

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

reinforcement-learning

2023-07-27T23:25:07Z

--- 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: EulerianKnight/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

brunoboat/Reinforce-CartPole-v1

brunoboat

2023-07-27T23:19:15Z

0

null

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

reinforcement-learning

2023-07-27T23:18:39Z

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

digitaljungle/reinforce-cart-01

digitaljungle

2023-07-27T23:09:17Z

0

null

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

reinforcement-learning

2023-07-27T23:09:07Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: reinforce-cart-01 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

JeffGuo/llama2-qlora-finetunined-french

JeffGuo

2023-07-27T22:38:26Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-27T22:38:09Z

--- 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 0.5.0.dev0

NasimB/bnc-cbt-rarity-mixed

NasimB

2023-07-27T22:29:27Z

5

0

transformers

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

text-generation

2023-07-27T20:11:40Z

--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc-cbt-rarity-mixed results: [] ---  # bnc-cbt-rarity-mixed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.0594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3665 | 0.29 | 500 | 5.3115 | | 5.0498 | 0.58 | 1000 | 4.9024 | | 4.7317 | 0.87 | 1500 | 4.6646 | | 4.4609 | 1.16 | 2000 | 4.5206 | | 4.3169 | 1.45 | 2500 | 4.4078 | | 4.2185 | 1.75 | 3000 | 4.3041 | | 4.0998 | 2.04 | 3500 | 4.2236 | | 3.9145 | 2.33 | 4000 | 4.1835 | | 3.8745 | 2.62 | 4500 | 4.1284 | | 3.8531 | 2.91 | 5000 | 4.0763 | | 3.6685 | 3.2 | 5500 | 4.0703 | | 3.598 | 3.49 | 6000 | 4.0412 | | 3.5813 | 3.78 | 6500 | 4.0117 | | 3.5089 | 4.07 | 7000 | 4.0030 | | 3.3334 | 4.36 | 7500 | 3.9999 | | 3.3274 | 4.65 | 8000 | 3.9871 | | 3.317 | 4.94 | 8500 | 3.9757 | | 3.1751 | 5.24 | 9000 | 3.9865 | | 3.1414 | 5.53 | 9500 | 3.9854 | | 3.1567 | 5.82 | 10000 | 3.9837 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3

magnustragardh/ppo-LunarLander-v2

magnustragardh

2023-07-27T22:22:24Z

0

stable-baselines3

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

reinforcement-learning

2023-06-18T15:50:00Z

--- 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: 286.65 +/- 8.52 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 ... ```

zacdennis/gradientascent

zacdennis

2023-07-27T22:16:30Z

0

null

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

reinforcement-learning

2023-07-27T22:16:26Z

--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: gradientascent results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 109.50 +/- 14.23 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

ksaw39/ksawmegga

ksaw39

2023-07-27T22:11:13Z

0

keras

[ "keras", "reinforcement-learning", "en", "region:us" ]

reinforcement-learning

2023-07-27T22:08:14Z

--- language: - en metrics: - accuracy - code_eval library_name: keras pipeline_tag: reinforcement-learning ---

hyunussarioglu/dqn-SpaceInvadersNoFrameskip-v4

hyunussarioglu

2023-07-27T22:08:28Z

0

stable-baselines3

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

reinforcement-learning

2023-07-27T22:07:54Z

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 556.50 +/- 125.72 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hyunussarioglu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga hyunussarioglu -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga hyunussarioglu ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

zpattdev/Reinforce-pixelcopterV0

zpattdev

2023-07-27T21:56:00Z

0

null

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

reinforcement-learning

2023-07-26T17:49:36Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-pixelcopterV0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 60.30 +/- 47.86 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

jariasn/q-Taxi-v3

jariasn

2023-07-27T21:32:51Z

0

null

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

reinforcement-learning

2023-07-27T21:32:49Z

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

sghirardelli/vit-base-patch16-224-rgbd1k2

sghirardelli

2023-07-27T21:26:49Z

65

0

transformers

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

image-classification

2023-07-21T21:15:59Z

--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_keras_callback model-index: - name: sghirardelli/vit-base-patch16-224-rgbd1k2 results: [] ---  # sghirardelli/vit-base-patch16-224-rgbd1k2 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.9711 - Train Accuracy: 0.4384 - Train Top-3-accuracy: 0.6297 - Validation Loss: 0.2537 - Validation Accuracy: 0.9323 - Validation Top-3-accuracy: 0.9940 - 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.002, 'decay_steps': 544, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 1.9711 | 0.4384 | 0.6297 | 0.2537 | 0.9323 | 0.9940 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3

shivarama23/llama_v2_finetuned_redaction

shivarama23

2023-07-27T21:18:42Z

2

0

peft

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

null

2023-07-27T21:17:43Z

--- 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 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 - PEFT 0.4.0

vivianchen98/distilbert-base-uncased-finetuned-cola

vivianchen98

2023-07-27T21:06:15Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-27T19:48:05Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: validation args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5317477654019562 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.8526 - Matthews Correlation: 0.5317 ## Model description More information needed ## Intended uses & 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5224 | 1.0 | 535 | 0.4742 | 0.4397 | | 0.3484 | 2.0 | 1070 | 0.5877 | 0.4558 | | 0.2357 | 3.0 | 1605 | 0.6307 | 0.5301 | | 0.1668 | 4.0 | 2140 | 0.7054 | 0.5288 | | 0.1218 | 5.0 | 2675 | 0.8526 | 0.5317 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3

YLI317/llama2-qlora-finetunined-SOP

YLI317

2023-07-27T21:05:56Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T21:05:39Z

--- 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 0.5.0.dev0

qanastek/LLaMa-2-FrenchMedMCQA

qanastek

2023-07-27T20:47:33Z

4

2

peft

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

null

2023-07-27T02:38:32Z

--- library_name: peft --- ## Inference ```python from transformers import AutoTokenizer import transformers import torch model = "qanastek/LLaMa-2-FrenchMedMCQA" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: We are giving you a scientific question (easy level) and five answers options (associated to « A », « B », « C », « D », « E »). Your task is to find the correct(s) answer(s) based on scientific facts, knowledge and reasoning. Don't generate anything other than one of the following characters : 'A B C D E'. ### Input: Parmi les propositions suivantes, quelle est celle qui est exacte? Lorsqu'on ajoute un acide fort à une solution tampon: (A) Le pH reste constant (B) Le pH diminue légèrement (C) Le constituant basique du tampon reste constant (D) Le constituant acide du tampon réagit (E) Le rapport acide/base reste inchangé ### Response: " seq = pipeline( prompt, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, )[0] print(seq['generated_text'][len(prompt):]) ``` ## 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 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 - PEFT 0.4.0

YoonSeul/LawBot-5.8B

YoonSeul

2023-07-27T20:40:46Z

4

0

peft

[ "peft", "region:us" ]

null

2023-07-26T07:47:12Z

--- library_name: peft --- <img src=https://github.com/taemin6697/Paper_Review/assets/96530685/54ecd6cf-8695-4caa-bdc8-fb85c9b7d70d style="max-width: 700px; width: 100%" /> ## 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.dev0

chh6/ppo-Pyramids

chh6

2023-07-27T20:22:39Z

9

0

ml-agents

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

reinforcement-learning

2023-07-27T20:21:43Z

--- 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: chh6/ppo-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

NicolasDenier/speecht5-finetuned-voxpopuli-sl

NicolasDenier

2023-07-27T20:21:31Z

89

0

transformers

[ "transformers", "pytorch", "tensorboard", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "sl", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "model-index", "endpoints_compatible", "region:us" ]

text-to-speech

2023-07-27T17:17:36Z

--- language: - sl license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer - text-to-speech datasets: - facebook/voxpopuli model-index: - name: speecht5-finetuned-voxpopuli-sl results: - task: name: Text to Speech type: text-to-speech dataset: name: Voxpopuli type: facebook/voxpopuli config: sl split: train args: all metrics: - name: Loss type: loss value: 0.4546 ---  # speecht5-finetuned-voxpopuli-sl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4546 ## Model description More information needed ## Intended uses & 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: 4 - eval_batch_size: 2 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.4942 | 21.68 | 1000 | 0.4567 | | 0.4698 | 43.36 | 2000 | 0.4544 | | 0.4615 | 65.04 | 3000 | 0.4541 | | 0.462 | 86.72 | 4000 | 0.4546 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.0 - Tokenizers 0.13.3

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

patonw

2023-07-27T20:14:02Z

0

null

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

reinforcement-learning

2023-07-27T20:14:00Z

--- 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="patonw/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"]) ```

Ronan14232/Omar

Ronan14232

2023-07-27T20:12:29Z

0

null

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

null

2023-07-27T20:12:29Z

--- license: bigscience-openrail-m ---

Jonathaniu/llama2-breast-cancer-13b-knowledge-epoch-8

Jonathaniu

2023-07-27T20:09:53Z

0

peft

[ "peft", "region:us" ]

null

2023-07-27T20:09:32Z

--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0

SH-W/60emotions

SH-W

2023-07-27T20:01:52Z

107

0

transformers

[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "autotrain", "unk", "dataset:SH-W/autotrain-data-5000_koi", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2023-07-27T19:55:16Z

--- tags: - autotrain - text-classification language: - unk widget: - text: "I love AutoTrain" datasets: - SH-W/autotrain-data-5000_koi co2_eq_emissions: emissions: 3.920765439350259 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 77927140735 - CO2 Emissions (in grams): 3.9208 ## Validation Metrics - Loss: 2.432 - Accuracy: 0.415 - Macro F1: 0.410 - Micro F1: 0.415 - Weighted F1: 0.410 - Macro Precision: 0.459 - Micro Precision: 0.415 - Weighted Precision: 0.456 - Macro Recall: 0.413 - Micro Recall: 0.415 - Weighted Recall: 0.415 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/SH-W/autotrain-5000_koi-77927140735 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("SH-W/autotrain-5000_koi-77927140735", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("SH-W/autotrain-5000_koi-77927140735", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

ianvaz/llama2-qlora-finetunined-french

ianvaz

2023-07-27T20:00:58Z

1

0

peft

[ "peft", "region:us" ]

null

2023-07-27T20:00:54Z

--- 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 0.5.0.dev0