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edures/ppo-LunarLander-v2
edures
2023-08-08T03:22:52Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-07T03:06:42Z
--- 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: 278.00 +/- 12.41 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 ... ```
raygx/xlmRoBERTa-NepSA
raygx
2023-08-08T03:01:25Z
59
0
transformers
[ "transformers", "tf", "xlm-roberta", "text-classification", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-06T15:10:46Z
--- license: mit tags: - generated_from_keras_callback model-index: - name: xlmRoBERTa-NepSA 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. --> # xlmRoBERTa-NepSA This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-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: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.03} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.30.1 - TensorFlow 2.12.0 - Datasets 2.1.0 - Tokenizers 0.13.3
mmenendezg/distilbert-base-uncased-finetuned-emotion
mmenendezg
2023-08-08T03:01:15Z
109
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "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
2023-08-04T17:47:50Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 - name: F1 type: f1 value: 0.9291434862021454 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2047 - Accuracy: 0.9295 - F1: 0.9291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7973 | 1.0 | 250 | 0.3063 | 0.915 | 0.9147 | | 0.2408 | 2.0 | 500 | 0.2047 | 0.9295 | 0.9291 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.3 - Tokenizers 0.13.3
tkathuria/finetuning-emotion-model-16000-samples
tkathuria
2023-08-08T02:43:12Z
108
1
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-08T02:34:33Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: finetuning-emotion-model-16000-samples 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. --> # finetuning-emotion-model-16000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.30.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
kyleeasterly/openllama-7b_purple-aerospace-v2-200-88
kyleeasterly
2023-08-08T02:24:50Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-08T02:24:09Z
--- 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: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.5.0.dev0
AtilliO/Chopper03_00
AtilliO
2023-08-08T02:06:36Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Heli", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Heli", "region:us" ]
reinforcement-learning
2023-08-08T02:04:10Z
--- library_name: ml-agents tags: - Heli - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Heli --- # **ppo** Agent playing **Heli** This is a trained model of a **ppo** agent playing **Heli** 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: AtilliO/Chopper03_00 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
KallistiTMR/llama-2-7b-chat-wiz-k16-7
KallistiTMR
2023-08-08T01:51:42Z
4
0
peft
[ "peft", "region:us" ]
null
2023-08-02T01:59:10Z
--- 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 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 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 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 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 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 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 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
nicbull/DialoGPT-medium-nic
nicbull
2023-08-08T01:37:45Z
143
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-07T23:45:49Z
--- pipeline_tag: conversational ---
celsolbm/ppo-LunarLander-v2
celsolbm
2023-08-08T01:31:37Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-08T01:31:16Z
--- 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: 261.34 +/- 15.62 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
JFuellem/distilhubert-finetuned-gtzan
JFuellem
2023-08-08T01:27:45Z
166
0
transformers
[ "transformers", "pytorch", "tensorboard", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
audio-classification
2023-08-07T10:49:58Z
--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.87 --- <!-- 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. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6210 - Accuracy: 0.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1281 | 1.0 | 113 | 1.9810 | 0.46 | | 1.4934 | 2.0 | 226 | 1.3605 | 0.62 | | 1.1668 | 3.0 | 339 | 0.9967 | 0.75 | | 0.9904 | 4.0 | 452 | 0.8179 | 0.74 | | 0.7369 | 5.0 | 565 | 0.6686 | 0.84 | | 0.5161 | 6.0 | 678 | 0.6022 | 0.8 | | 0.5269 | 7.0 | 791 | 0.5942 | 0.85 | | 0.2076 | 8.0 | 904 | 0.5678 | 0.86 | | 0.3907 | 9.0 | 1017 | 0.5466 | 0.85 | | 0.2112 | 10.0 | 1130 | 0.5610 | 0.86 | | 0.0678 | 11.0 | 1243 | 0.5933 | 0.87 | | 0.063 | 12.0 | 1356 | 0.6582 | 0.81 | | 0.0342 | 13.0 | 1469 | 0.6052 | 0.88 | | 0.0209 | 14.0 | 1582 | 0.6139 | 0.88 | | 0.021 | 15.0 | 1695 | 0.6210 | 0.87 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
DrishtiSharma/distilhubert-finetuned-gtzan-bs-16
DrishtiSharma
2023-08-08T01:27:31Z
164
0
transformers
[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
audio-classification
2023-08-07T23:31:08Z
--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-bs-16 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.87 --- <!-- 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. --> # distilhubert-finetuned-gtzan-bs-16 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.5229 - Accuracy: 0.87 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.1955 | 1.0 | 57 | 2.1119 | 0.44 | | 1.6916 | 2.0 | 114 | 1.5973 | 0.61 | | 1.1805 | 3.0 | 171 | 1.1849 | 0.74 | | 1.0924 | 4.0 | 228 | 0.9771 | 0.7 | | 0.7794 | 5.0 | 285 | 0.8201 | 0.78 | | 0.6335 | 6.0 | 342 | 0.6969 | 0.82 | | 0.6178 | 7.0 | 399 | 0.6632 | 0.84 | | 0.4232 | 8.0 | 456 | 0.5841 | 0.83 | | 0.3135 | 9.0 | 513 | 0.5960 | 0.82 | | 0.198 | 10.0 | 570 | 0.5557 | 0.83 | | 0.1651 | 11.0 | 627 | 0.5957 | 0.84 | | 0.1191 | 12.0 | 684 | 0.5640 | 0.85 | | 0.1267 | 13.0 | 741 | 0.5604 | 0.84 | | 0.0784 | 14.0 | 798 | 0.5233 | 0.85 | | 0.1076 | 15.0 | 855 | 0.5229 | 0.87 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
DrishtiSharma/distilhubert-finetuned-gtzan-bs-8
DrishtiSharma
2023-08-08T01:20:10Z
163
0
transformers
[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:ntu-spml/distilhubert", "base_model:finetune:ntu-spml/distilhubert", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
audio-classification
2023-08-07T23:23:23Z
--- license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-bs-8 results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.84 --- <!-- 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. --> # distilhubert-finetuned-gtzan-bs-8 This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6841 - Accuracy: 0.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.0984 | 1.0 | 113 | 1.9609 | 0.47 | | 1.4296 | 2.0 | 226 | 1.3195 | 0.67 | | 1.09 | 3.0 | 339 | 0.9894 | 0.72 | | 0.9233 | 4.0 | 452 | 0.8749 | 0.75 | | 0.6404 | 5.0 | 565 | 0.7553 | 0.78 | | 0.3805 | 6.0 | 678 | 0.7402 | 0.77 | | 0.4079 | 7.0 | 791 | 0.5268 | 0.84 | | 0.1812 | 8.0 | 904 | 0.5418 | 0.85 | | 0.1942 | 9.0 | 1017 | 0.4633 | 0.86 | | 0.033 | 10.0 | 1130 | 0.6342 | 0.84 | | 0.0155 | 11.0 | 1243 | 0.6264 | 0.84 | | 0.1256 | 12.0 | 1356 | 0.6804 | 0.85 | | 0.0095 | 13.0 | 1469 | 0.6653 | 0.83 | | 0.0084 | 14.0 | 1582 | 0.6737 | 0.84 | | 0.0088 | 15.0 | 1695 | 0.6841 | 0.84 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
jordyvl/vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_MSE
jordyvl
2023-08-08T01:08:10Z
163
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-08-07T16:58:28Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_MSE 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. --> # vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_MSE This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1927 - Accuracy: 0.5835 - Brier Loss: 0.6740 - Nll: 3.1975 - F1 Micro: 0.5835 - F1 Macro: 0.5865 - Ece: 0.2742 - Aurc: 0.2074 ## Model description More information needed ## Intended uses & 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: 64 - eval_batch_size: 64 - 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 | 250 | 4.2227 | 0.1325 | 0.9130 | 6.8924 | 0.1325 | 0.0728 | 0.0573 | 0.7519 | | 4.2305 | 2.0 | 500 | 3.9645 | 0.1638 | 0.8922 | 5.8361 | 0.1638 | 0.1235 | 0.0588 | 0.7012 | | 4.2305 | 3.0 | 750 | 3.6177 | 0.285 | 0.8227 | 4.3429 | 0.285 | 0.2289 | 0.0627 | 0.5424 | | 3.6208 | 4.0 | 1000 | 3.2220 | 0.3733 | 0.7617 | 3.5860 | 0.3733 | 0.3356 | 0.0606 | 0.4322 | | 3.6208 | 5.0 | 1250 | 3.0177 | 0.4045 | 0.7308 | 3.7807 | 0.4045 | 0.3770 | 0.0721 | 0.3835 | | 2.9674 | 6.0 | 1500 | 2.8203 | 0.4365 | 0.7032 | 3.3569 | 0.4365 | 0.4130 | 0.0969 | 0.3443 | | 2.9674 | 7.0 | 1750 | 2.6164 | 0.4557 | 0.6762 | 3.4281 | 0.4557 | 0.4413 | 0.0810 | 0.3058 | | 2.5154 | 8.0 | 2000 | 2.4991 | 0.472 | 0.6651 | 3.3938 | 0.472 | 0.4524 | 0.1092 | 0.2846 | | 2.5154 | 9.0 | 2250 | 2.4375 | 0.4878 | 0.6826 | 3.1749 | 0.4878 | 0.4603 | 0.1631 | 0.2872 | | 2.2165 | 10.0 | 2500 | 2.3537 | 0.5018 | 0.6686 | 3.1767 | 0.5018 | 0.4855 | 0.1589 | 0.2743 | | 2.2165 | 11.0 | 2750 | 2.2613 | 0.515 | 0.6276 | 3.1281 | 0.515 | 0.5141 | 0.1101 | 0.2457 | | 1.9636 | 12.0 | 3000 | 2.2592 | 0.5242 | 0.6624 | 3.1164 | 0.5242 | 0.5131 | 0.1840 | 0.2515 | | 1.9636 | 13.0 | 3250 | 2.1751 | 0.5315 | 0.6190 | 3.2643 | 0.5315 | 0.5268 | 0.1349 | 0.2288 | | 1.7526 | 14.0 | 3500 | 2.2171 | 0.5248 | 0.6546 | 3.1179 | 0.5248 | 0.5162 | 0.1889 | 0.2537 | | 1.7526 | 15.0 | 3750 | 2.1185 | 0.5507 | 0.6126 | 3.1117 | 0.5507 | 0.5496 | 0.1578 | 0.2219 | | 1.5673 | 16.0 | 4000 | 2.0807 | 0.5537 | 0.6208 | 3.2624 | 0.5537 | 0.5459 | 0.1735 | 0.2151 | | 1.5673 | 17.0 | 4250 | 2.0743 | 0.5677 | 0.6095 | 3.2650 | 0.5677 | 0.5683 | 0.1628 | 0.2090 | | 1.3823 | 18.0 | 4500 | 2.1201 | 0.5605 | 0.6454 | 3.1499 | 0.5605 | 0.5558 | 0.2130 | 0.2316 | | 1.3823 | 19.0 | 4750 | 2.0835 | 0.5655 | 0.6312 | 3.2920 | 0.5655 | 0.5666 | 0.2015 | 0.2149 | | 1.2113 | 20.0 | 5000 | 2.0809 | 0.5675 | 0.6284 | 3.2923 | 0.5675 | 0.5675 | 0.2180 | 0.2047 | | 1.2113 | 21.0 | 5250 | 2.1507 | 0.5633 | 0.6608 | 3.2713 | 0.5633 | 0.5668 | 0.2380 | 0.2183 | | 1.0543 | 22.0 | 5500 | 2.1295 | 0.5683 | 0.6476 | 3.5120 | 0.5683 | 0.5672 | 0.2369 | 0.2105 | | 1.0543 | 23.0 | 5750 | 2.1610 | 0.5675 | 0.6564 | 3.3818 | 0.5675 | 0.5625 | 0.2393 | 0.2166 | | 0.9098 | 24.0 | 6000 | 2.0862 | 0.5735 | 0.6562 | 3.3228 | 0.5735 | 0.5782 | 0.2528 | 0.2047 | | 0.9098 | 25.0 | 6250 | 2.0680 | 0.5727 | 0.6439 | 3.2971 | 0.5727 | 0.5767 | 0.2357 | 0.2050 | | 0.7832 | 26.0 | 6500 | 2.1829 | 0.5763 | 0.6667 | 3.3547 | 0.5763 | 0.5792 | 0.2627 | 0.2084 | | 0.7832 | 27.0 | 6750 | 2.1163 | 0.586 | 0.6479 | 3.2468 | 0.586 | 0.5894 | 0.2509 | 0.2016 | | 0.6572 | 28.0 | 7000 | 2.1492 | 0.5715 | 0.6612 | 3.4268 | 0.5715 | 0.5780 | 0.2642 | 0.2114 | | 0.6572 | 29.0 | 7250 | 2.1975 | 0.5723 | 0.6777 | 3.4662 | 0.5723 | 0.5739 | 0.2749 | 0.2202 | | 0.5632 | 30.0 | 7500 | 2.1733 | 0.5693 | 0.6767 | 3.3743 | 0.5693 | 0.5745 | 0.2737 | 0.2170 | | 0.5632 | 31.0 | 7750 | 2.1694 | 0.5807 | 0.6661 | 3.3917 | 0.5807 | 0.5814 | 0.2645 | 0.2193 | | 0.4827 | 32.0 | 8000 | 2.1585 | 0.5805 | 0.6671 | 3.3811 | 0.5805 | 0.5812 | 0.2692 | 0.2150 | | 0.4827 | 33.0 | 8250 | 2.1963 | 0.5767 | 0.6754 | 3.4575 | 0.5767 | 0.5835 | 0.2710 | 0.2160 | | 0.4134 | 34.0 | 8500 | 2.1720 | 0.581 | 0.6694 | 3.3663 | 0.581 | 0.5811 | 0.2672 | 0.2131 | | 0.4134 | 35.0 | 8750 | 2.1880 | 0.575 | 0.6759 | 3.4587 | 0.575 | 0.5790 | 0.2783 | 0.2105 | | 0.3541 | 36.0 | 9000 | 2.1482 | 0.581 | 0.6628 | 3.2956 | 0.581 | 0.5842 | 0.2712 | 0.2056 | | 0.3541 | 37.0 | 9250 | 2.1631 | 0.5885 | 0.6652 | 3.3217 | 0.5885 | 0.5915 | 0.2671 | 0.2069 | | 0.3078 | 38.0 | 9500 | 2.2036 | 0.577 | 0.6811 | 3.3564 | 0.577 | 0.5803 | 0.2849 | 0.2141 | | 0.3078 | 39.0 | 9750 | 2.1904 | 0.5753 | 0.6756 | 3.2783 | 0.5753 | 0.5765 | 0.2756 | 0.2135 | | 0.2671 | 40.0 | 10000 | 2.1774 | 0.5775 | 0.6685 | 3.3109 | 0.5775 | 0.5813 | 0.2700 | 0.2084 | | 0.2671 | 41.0 | 10250 | 2.1822 | 0.5807 | 0.6730 | 3.2139 | 0.5807 | 0.5842 | 0.2770 | 0.2100 | | 0.2331 | 42.0 | 10500 | 2.1673 | 0.5817 | 0.6705 | 3.2960 | 0.5817 | 0.5864 | 0.2757 | 0.2070 | | 0.2331 | 43.0 | 10750 | 2.1730 | 0.5765 | 0.6705 | 3.2195 | 0.5765 | 0.5807 | 0.2784 | 0.2072 | | 0.2038 | 44.0 | 11000 | 2.1709 | 0.585 | 0.6649 | 3.1928 | 0.585 | 0.5893 | 0.2627 | 0.2055 | | 0.2038 | 45.0 | 11250 | 2.1745 | 0.5783 | 0.6678 | 3.1900 | 0.5783 | 0.5811 | 0.2736 | 0.2061 | | 0.1792 | 46.0 | 11500 | 2.1824 | 0.5835 | 0.6682 | 3.1909 | 0.5835 | 0.5858 | 0.2719 | 0.2070 | | 0.1792 | 47.0 | 11750 | 2.1892 | 0.584 | 0.6716 | 3.2457 | 0.584 | 0.5864 | 0.2706 | 0.2082 | | 0.16 | 48.0 | 12000 | 2.1820 | 0.5835 | 0.6716 | 3.2011 | 0.5835 | 0.5857 | 0.2743 | 0.2073 | | 0.16 | 49.0 | 12250 | 2.1884 | 0.582 | 0.6736 | 3.2114 | 0.582 | 0.5856 | 0.2755 | 0.2073 | | 0.1465 | 50.0 | 12500 | 2.1927 | 0.5835 | 0.6740 | 3.1975 | 0.5835 | 0.5865 | 0.2742 | 0.2074 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
mhdaw/ppo-LunarLander-v2-5
mhdaw
2023-08-08T01:06:08Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-08T01:05:49Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 274.23 +/- 11.25 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 ... ```
nrakocz/whisper-small-dv
nrakocz
2023-08-08T00:07:19Z
75
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dv", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-08-07T22:38:38Z
--- language: - dv license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Dv - Nadav Rakocz results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 config: dv split: test args: dv metrics: - name: Wer type: wer value: 13.290677052543728 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Dv - Nadav Rakocz This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.1689 - Wer Ortho: 62.8317 - Wer: 13.2907 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.1252 | 1.63 | 500 | 0.1689 | 62.8317 | 13.2907 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
nicbull/DialoGPT-medium-nic2
nicbull
2023-08-08T00:06:37Z
147
0
transformers
[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "chat", "conversational", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-07T23:56:52Z
--- language: - en pipeline_tag: conversational tags: - chat ---
JabrilJacobs/Reinforce-Pixelcopter-PLE-v0
JabrilJacobs
2023-08-07T23:52:38Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-05T00:07:35Z
--- 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: 52.40 +/- 41.11 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
salohnana2018/OTE-NoDapt-ABSA-bert-base-MARBERTv2-DefultHp-FineTune
salohnana2018
2023-08-07T23:43:48Z
109
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "base_model:UBC-NLP/MARBERTv2", "base_model:finetune:UBC-NLP/MARBERTv2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-07T20:58:28Z
--- base_model: UBC-NLP/MARBERTv2 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: OTE-NoDapt-ABSA-bert-base-MARBERTv2-DefultHp-FineTune 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. --> # OTE-NoDapt-ABSA-bert-base-MARBERTv2-DefultHp-FineTune This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1711 - Precision: 0.7538 - Recall: 0.7902 - F1: 0.7716 - Accuracy: 0.9536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 25 - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1914 | 1.0 | 121 | 0.1169 | 0.7655 | 0.7369 | 0.7510 | 0.9536 | | 0.0946 | 2.0 | 242 | 0.1192 | 0.7952 | 0.7334 | 0.7631 | 0.9558 | | 0.0643 | 3.0 | 363 | 0.1336 | 0.7471 | 0.7932 | 0.7695 | 0.9537 | | 0.0428 | 4.0 | 484 | 0.1585 | 0.7312 | 0.7957 | 0.7621 | 0.9517 | | 0.0286 | 5.0 | 605 | 0.1711 | 0.7538 | 0.7902 | 0.7716 | 0.9536 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-secondary-structure
AmelieSchreiber
2023-08-07T23:40:17Z
111
1
transformers
[ "transformers", "pytorch", "safetensors", "esm", "token-classification", "esm2", "protein language model", "biology", "protein token classification", "secondary structure prediction", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-06T03:26:39Z
--- license: mit language: - en library_name: transformers tags: - esm - esm2 - protein language model - biology - protein token classification - secondary structure prediction --- # ESM-2 (`esm2_t6_8M_UR50D`) for Token Classification This is a fine-tuned version of [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D) trained on the token classification task to classify amino acids in protein sequences into one of three categories `0: other`, `1: alpha helix`, `2: beta strand`. It was trained with [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) and achieves 78.13824286786025 % accuracy. ## Using the Model To use, try running: ```python from transformers import AutoTokenizer, AutoModelForTokenClassification import numpy as np # 1. Prepare the Model and Tokenizer # Replace with the path where your trained model is saved if you're training a new model model_dir = "AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-secondary-structure" model = AutoModelForTokenClassification.from_pretrained(model_dir) tokenizer = AutoTokenizer.from_pretrained(model_dir) # Define a mapping from label IDs to their string representations label_map = {0: "Other", 1: "Helix", 2: "Strand"} # 2. Tokenize the New Protein Sequence new_protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT" # Replace with your protein sequence tokens = tokenizer.tokenize(new_protein_sequence) inputs = tokenizer.encode(new_protein_sequence, return_tensors="pt") # 3. Predict with the Model with torch.no_grad(): outputs = model(inputs).logits predictions = np.argmax(outputs[0].numpy(), axis=1) # 4. Decode the Predictions predicted_labels = [label_map[label_id] for label_id in predictions] # Print the tokens along with their predicted labels for token, label in zip(tokens, predicted_labels): print(f"{token}: {label}") ```
rzambrano/rl_course_vizdoom_my_way_home
rzambrano
2023-08-07T23:22:57Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T23:22:52Z
--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_my_way_home type: doom_my_way_home metrics: - type: mean_reward value: -0.21 +/- 0.00 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_my_way_home** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r rzambrano/rl_course_vizdoom_my_way_home ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_my_way_home --train_dir=./train_dir --experiment=rl_course_vizdoom_my_way_home ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_my_way_home --train_dir=./train_dir --experiment=rl_course_vizdoom_my_way_home --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
AtilliO/ChopperColab03_00
AtilliO
2023-08-07T23:03:09Z
7
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Heli", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Heli", "region:us" ]
reinforcement-learning
2023-08-07T23:03:05Z
--- library_name: ml-agents tags: - Heli - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Heli --- # **ppo** Agent playing **Heli** This is a trained model of a **ppo** agent playing **Heli** 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: AtilliO/Chopper03_00 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
DarkAirforce/a2c-PandaReachDense-v2
DarkAirforce
2023-08-07T22:36:46Z
3
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T20:57:21Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v2 type: PandaReachDense-v2 metrics: - type: mean_reward value: -1.74 +/- 0.52 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 ... ```
parthsuresh/Reinforce-1
parthsuresh
2023-08-07T22:31:53Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T22:31:48Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 139.40 +/- 37.82 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
varcoder/segcrack9k_conglomerate_segformer_aug
varcoder
2023-08-07T22:27:13Z
34
0
transformers
[ "transformers", "pytorch", "tensorboard", "segformer", "generated_from_trainer", "base_model:nvidia/mit-b5", "base_model:finetune:nvidia/mit-b5", "license:other", "endpoints_compatible", "region:us" ]
null
2023-08-07T21:07:22Z
--- license: other base_model: nvidia/mit-b5 tags: - generated_from_trainer model-index: - name: segcrack9k_conglomerate_segformer_aug 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. --> # segcrack9k_conglomerate_segformer_aug This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0362 - Mean Iou: 0.3412 - Mean Accuracy: 0.6823 - Overall Accuracy: 0.6823 - Accuracy Background: nan - Accuracy Crack: 0.6823 - Iou Background: 0.0 - Iou Crack: 0.6823 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:| | 0.0323 | 0.14 | 1000 | 0.0445 | 0.3573 | 0.7146 | 0.7146 | nan | 0.7146 | 0.0 | 0.7146 | | 0.0222 | 0.27 | 2000 | 0.0394 | 0.3591 | 0.7181 | 0.7181 | nan | 0.7181 | 0.0 | 0.7181 | | 0.0335 | 0.41 | 3000 | 0.0404 | 0.2907 | 0.5813 | 0.5813 | nan | 0.5813 | 0.0 | 0.5813 | | 0.013 | 0.54 | 4000 | 0.0384 | 0.3244 | 0.6489 | 0.6489 | nan | 0.6489 | 0.0 | 0.6489 | | 0.0159 | 0.68 | 5000 | 0.0382 | 0.3088 | 0.6176 | 0.6176 | nan | 0.6176 | 0.0 | 0.6176 | | 0.0608 | 0.81 | 6000 | 0.0366 | 0.3251 | 0.6502 | 0.6502 | nan | 0.6502 | 0.0 | 0.6502 | | 0.1738 | 0.95 | 7000 | 0.0362 | 0.3412 | 0.6823 | 0.6823 | nan | 0.6823 | 0.0 | 0.6823 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
TheRains/yt-special-batch4-lr4-small
TheRains
2023-08-07T22:24:52Z
116
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:yt", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-08-07T14:40:11Z
--- license: apache-2.0 base_model: openai/whisper-small tags: - whisper-event - generated_from_trainer datasets: - yt metrics: - wer model-index: - name: Whisper Small Indonesian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: yt id type: yt metrics: - name: Wer type: wer value: 59.84047727125349 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Indonesian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the yt id dataset. It achieves the following results on the evaluation set: - Loss: 0.9773 - Wer: 59.8405 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.2491 | 0.09 | 1000 | 1.9142 | 226.4834 | | 1.4702 | 0.17 | 2000 | 1.6154 | 115.5502 | | 1.609 | 0.26 | 3000 | 1.3599 | 113.3454 | | 1.1817 | 0.34 | 4000 | 1.1253 | 68.4067 | | 0.9678 | 0.43 | 5000 | 0.9773 | 59.8405 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
bayartsogt/wav2vec2-large-mn-pretrain-42h
bayartsogt
2023-08-07T22:10:11Z
44
0
transformers
[ "transformers", "pytorch", "tensorboard", "wav2vec2", "pretraining", "speech", "mn", "dataset:test", "arxiv:2006.11477", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-08-07T22:07:31Z
--- language: mn datasets: - test tags: - speech license: apache-2.0 --- # Wav2Vec2-Large [Paper](https://arxiv.org/abs/2006.11477) Authors: Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Data - Sample rate: 16Khz - Total pretrained data: 42H - Duration (sec): - mean: 5.276451094408402 - std: 2.2694219711399533 - max: 12.435937673420312 - min: 0.0005440165748211712 # Convert from FAIRSEQ to HF 1. Create a config ```python from transformers import Wav2Vec2Config config = Wav2Vec2Config.from_pretrained('facebook/wav2vec2-large') config.conv_bias = True config.feat_extract_norm = "layer" config.save_pretrained('./') ``` 2. Convert using [the script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py) written by HF team ```bash wget convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py hf_name="<my-hf-repo-name>" ckpt="<path-to-pth-checkpoint>" python ./convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py \ --pytorch_dump_folder ${hf_name} \ --checkpoint_path ${ckpt} \ --config_path ./config.json \ --not_finetuned ```
fangyijie/BeenYou_Lite_R15
fangyijie
2023-08-07T22:04:23Z
0
0
null
[ "region:us" ]
null
2023-08-07T21:55:21Z
A copy of BeenYou Lite model from https://civitai.com/models/34440?modelVersionId=117019
Za88yes/R1acis
Za88yes
2023-08-07T21:42:52Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-08-07T21:36:11Z
--- license: creativeml-openrail-m ---
TFLai/GPT-Lite
TFLai
2023-08-07T21:36:47Z
6
2
peft
[ "peft", "region:us" ]
null
2023-08-07T21:36:12Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
carolinacalce/Mi_modelo_CatsDogs
carolinacalce
2023-08-07T21:15:55Z
252
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-08-04T23:42:34Z
--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: Mi_modelo_CatsDogs 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. --> # Mi_modelo_CatsDogs This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
cengizhanprodeksin/Diamondtemav1
cengizhanprodeksin
2023-08-07T20:51:14Z
0
0
null
[ "tr", "license:openrail", "region:us" ]
null
2023-08-07T20:45:44Z
--- license: openrail language: - tr ---
sofia-todeschini/BioLinkBERT-LitCovid-v1.2
sofia-todeschini
2023-08-07T20:48:54Z
107
0
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-07T17:54:02Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: BioLinkBERT-LitCovid-v1.2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BioLinkBERT-LitCovid-v1.2 This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0950 - F1 micro: 0.9201 - F1 macro: 0.8831 - F1 weighted: 0.9202 - F1 samples: 0.9200 - Precision micro: 0.9141 - Precision macro: 0.8790 - Precision weighted: 0.9144 - Precision samples: 0.9283 - Recall micro: 0.9263 - Recall macro: 0.8877 - Recall weighted: 0.9263 - Recall samples: 0.9372 - Roc Auc: 0.9529 - Accuracy: 0.7848 ## Model description More information needed ## Intended uses & 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 | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:| | 0.1013 | 1.0 | 2211 | 0.0899 | 0.9159 | 0.8789 | 0.9164 | 0.9149 | 0.9074 | 0.8824 | 0.9092 | 0.9213 | 0.9245 | 0.8808 | 0.9245 | 0.9355 | 0.9511 | 0.7729 | | 0.0749 | 2.0 | 4422 | 0.0847 | 0.9205 | 0.8854 | 0.9205 | 0.9203 | 0.9138 | 0.8843 | 0.9144 | 0.9264 | 0.9274 | 0.8882 | 0.9274 | 0.9390 | 0.9534 | 0.7857 | | 0.0583 | 3.0 | 6633 | 0.0871 | 0.9212 | 0.8851 | 0.9212 | 0.9206 | 0.9145 | 0.8913 | 0.9151 | 0.9269 | 0.9280 | 0.8808 | 0.9280 | 0.9390 | 0.9537 | 0.7883 | | 0.0433 | 4.0 | 8844 | 0.0924 | 0.9201 | 0.8849 | 0.9203 | 0.9202 | 0.9094 | 0.8766 | 0.9099 | 0.9246 | 0.9312 | 0.8947 | 0.9312 | 0.9416 | 0.9546 | 0.7834 | | 0.0315 | 5.0 | 11055 | 0.0950 | 0.9201 | 0.8831 | 0.9202 | 0.9200 | 0.9141 | 0.8790 | 0.9144 | 0.9283 | 0.9263 | 0.8877 | 0.9263 | 0.9372 | 0.9529 | 0.7848 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
BryanFalkowski/english-to-latin-v2
BryanFalkowski
2023-08-07T20:32:58Z
25
0
transformers
[ "transformers", "pytorch", "mbart", "text2text-generation", "la", "arxiv:1911.04944", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-22T12:43:57Z
--- language: - "la" --- ### Initial model for english to latin translations which is still being trained. This model is designed to execute Latin-to-English translations, built using the extensive CCMatrix dataset. The CCMatrix dataset is a vast compilation of high-quality parallel sentences drawn from the public CommonCrawl dataset, consisting of over 4.5 billion sentence pairs across 576 language pairs. The model is devised to harness the power of this substantial corpus, aiming to provide an effective and precise solution for Latin translation tasks. Nevertheless, the training dataset's literary range spans numerous centuries, thereby introducing the model to the Latin language's significant evolution over these eras. Consequently, the model encounters different expressions of the same concept, potentially including equivalent sentences in both vulgar and classical Latin. This is likely the reason behind the model's oscillating loss. ## Current state: - {'loss': 0.8056, 'learning_rate': 6.482837857245441e-06, 'epoch': 20.28} - {'loss': 1.253, 'learning_rate': 6.48092297381397e-06, 'epoch': 20.28} - {'loss': 1.2961, 'learning_rate': 6.4790080903824985e-06, 'epoch': 20.28} - {'loss': 1.3402, 'learning_rate': 6.477093206951027e-06, 'epoch': 20.28} - {'loss': 0.9309, 'learning_rate': 6.475178323519556e-06, 'epoch': 20.29} - {'loss': 0.7945, 'learning_rate': 6.473263440088085e-06, 'epoch': 20.29} - {'loss': 0.9205, 'learning_rate': 6.471348556656614e-06, 'epoch': 20.29} - {'loss': 1.4583, 'learning_rate': 6.228158360859783e-06, 'epoch': 20.66} ....still running..... fine-tuned using the IPUSeq2SeqTrainer API on the facebook/bart-base model BartTokenizerFast tokenizer ## Dataset Description - Homepage: https://opus.nlpl.eu/CCMatrix.php - Sample: https://opus.nlpl.eu/CCMatrix/v1/en-la_sample.html - Paper: https://arxiv.org/abs/1911.04944 - The latin dataset contans: - 1,114,190 Sentence pairs - 14.5 M words ### Data Format ``` { "id": 1, "score": 1.2498379, "translation": { "en": "No telling what sort of magic he might have.\"" "la": "numque magistrâtum cum iis habent.\ }, "id": 2, "score": 1.1443379, "translation": { "en": "Not many, but much.\"" "la": "non multa sed multum.\ } } ``` For training, the dataset was divided as follows: DatasetDict - train: num_rows: 891352 - validation: num_rows: 111419 - test: num_rows: 111419
Muhammadreza/mann-e-artistic-3
Muhammadreza
2023-08-07T20:29:57Z
0
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T20:17:14Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### mann-e_artistic-3 Dreambooth model trained by Muhammadreza with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
TheRains/yt-special-batch4-lr6-small
TheRains
2023-08-07T20:18:16Z
116
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "dataset:yt", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-08-07T18:22:29Z
--- license: apache-2.0 base_model: openai/whisper-small tags: - whisper-event - generated_from_trainer datasets: - yt metrics: - wer model-index: - name: Whisper Small Indonesian results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: yt id type: yt metrics: - name: Wer type: wer value: 54.70462356526814 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Indonesian This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the yt id dataset. It achieves the following results on the evaluation set: - Loss: 0.8639 - Wer: 54.7046 ## Model description More information needed ## Intended uses & 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-06 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.1374 | 0.09 | 1000 | 0.9854 | 64.9634 | | 0.8775 | 0.17 | 2000 | 0.9139 | 66.4613 | | 0.9735 | 0.26 | 3000 | 0.8845 | 58.6668 | | 0.8359 | 0.34 | 4000 | 0.8696 | 59.5876 | | 0.9089 | 0.43 | 5000 | 0.8639 | 54.7046 | ### Framework versions - Transformers 4.31.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
luispintoc/dqn-SpaceInvadersNoFrameskip-v4
luispintoc
2023-08-07T20:01:38Z
1
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T20:01:06Z
--- 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: 257.00 +/- 38.81 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 luispintoc -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 luispintoc -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 luispintoc ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.11), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.009), ('learning_starts', 100000), ('n_timesteps', 1100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
akdeniz27/convbert-base-turkish-cased-ner
akdeniz27
2023-08-07T19:54:11Z
669
3
transformers
[ "transformers", "pytorch", "onnx", "safetensors", "convbert", "token-classification", "tr", "arxiv:2008.02496", "doi:10.57967/hf/0015", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2022-03-02T23:29:05Z
--- language: tr widget: - text: "Almanya, koronavirüs aşısını geliştiren Dr. Özlem Türeci ve eşi Prof. Dr. Uğur Şahin'e liyakat nişanı verdi" --- # Turkish Named Entity Recognition (NER) Model This model is the fine-tuned model of dbmdz/convbert-base-turkish-cased (ConvBERTurk) using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). The ConvBERT architecture is presented in the ["ConvBERT: Improving BERT with Span-based Dynamic Convolution"](https://arxiv.org/abs/2008.02496) paper. # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "dbmdz/convbert-base-turkish-cased" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 3 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("akdeniz27/convbert-base-turkish-cased-ner") tokenizer = AutoTokenizer.from_pretrained("akdeniz27/convbert-base-turkish-cased-ner") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="first") ner("<your text here>") # Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. ``` # Reference test results: * accuracy: 0.9937648915431506 * f1: 0.9610945644080416 * precision: 0.9619899385131359 * recall: 0.9602008554956295
bilbo991/clip-homer-100k
bilbo991
2023-08-07T19:53:52Z
89
0
transformers
[ "transformers", "pytorch", "tensorboard", "vision-text-dual-encoder", "feature-extraction", "generated_from_trainer", "endpoints_compatible", "region:us" ]
feature-extraction
2023-08-07T18:10:57Z
--- base_model: clip-homer-100k tags: - generated_from_trainer model-index: - name: clip-homer-100k 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. --> # clip-homer-100k This model is a fine-tuned version of [clip-homer-100k](https://huggingface.co/clip-homer-100k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.0383 | 1.0 | 3125 | 1.9199 | | 1.3387 | 2.0 | 6250 | 1.5725 | | 0.6287 | 3.0 | 9375 | 1.4647 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3
Augoste/bloom-7b1-mrbeast-lora-v1.0
Augoste
2023-08-07T19:43:22Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-07T19:43:17Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
wandabwa2004/falcon-7b-safcom_Ver2
wandabwa2004
2023-08-07T19:39:47Z
12
0
transformers
[ "transformers", "tensorboard", "RefinedWebModel", "text-generation", "generated_from_trainer", "custom_code", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "autotrain_compatible", "region:us" ]
text-generation
2023-08-07T01:56:34Z
--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-safcom_Ver2 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. --> # falcon-7b-safcom_Ver2 This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 320 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.12.0 - Tokenizers 0.13.3
SmellyKat/a2c-PandaReachDense-v3
SmellyKat
2023-08-07T19:29:18Z
0
0
stable-baselines3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T19:23:38Z
--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.19 +/- 0.13 name: mean_reward verified: false --- # **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```
scirik/time-series-transformer-electricity-load-diagrams
scirik
2023-08-07T19:22:19Z
119
6
transformers
[ "transformers", "pytorch", "time_series_transformer", "dataset:electricity_load_diagrams", "endpoints_compatible", "region:us" ]
null
2023-08-06T19:47:41Z
--- datasets: - electricity_load_diagrams metrics: - mase - smape library_name: transformers --- **Transformer Time Series Model for Electricity Load Diagrams** This repository contains a PyTorch implementation of a Transformer-based time series model for forecasting electricity load diagrams (hourly). The repo provide the pre-trained pytorch_model.bin and config.json files to initialize the Transformer architecture.
jpawan33/dreambooth
jpawan33
2023-08-07T19:06:48Z
21
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T19:00:48Z
--- license: creativeml-openrail-m base_model: CompVis/stable-diffusion-v1-4 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - jpawan33/dreambooth This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
Powerlax/ImageSegmentationHuggingFace
Powerlax
2023-08-07T18:57:08Z
2
0
keras
[ "keras", "tf-keras", "en", "dataset:visual-layer/vl-oxford-iiit-pets", "region:us" ]
null
2023-08-07T18:49:43Z
--- datasets: - visual-layer/vl-oxford-iiit-pets language: - en library_name: keras ---
Yanderu/sd-civitai-browser
Yanderu
2023-08-07T18:48:13Z
0
0
null
[ "region:us" ]
null
2023-08-04T14:37:57Z
# sd-civitai-browser An extension to help download models from CivitAi without leaving WebUI
emozilla/LLongMA-2-7b-storysummarizer-GGML
emozilla
2023-08-07T18:46:09Z
0
0
null
[ "dataset:emozilla/booksum-summary-analysis_llama-8192", "dataset:kmfoda/booksum", "license:apache-2.0", "region:us" ]
null
2023-08-07T15:26:27Z
--- license: apache-2.0 datasets: - emozilla/booksum-summary-analysis_llama-8192 - kmfoda/booksum --- GGML version of [emozilla/LLongMA-2-7b-storysummarizer](https://huggingface.co/emozilla/LLongMA-2-7b-storysummarizer). Use `n_ctx=8192` and `rope_freq_scale=0.5`. For an excellent user interface, check out [LM Studio](https://lmstudio.ai/). # LLongMA-2-7b-storysummarizer [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) This is a fine-tuned version of [conceptofmind/LLongMA-2-7b](https://huggingface.co/conceptofmind/LLongMA-2-7b) intended for summarization and literary analysis of fiction stories. It contains custom modeling code to use Flash Attention 2 during inference, which provides a significant speedup, especially at longer context lengths. To enable, pass `trust_remote_code=True,use_flash_attention=True` to `AutoModelForCausalLM`. ## How to Use ``` SOME_FICTION ### SUMMARY: ``` or ``` SOME_FICTION ### ANALYSIS: ``` ## Example outputs Outputs on the text of [_Waystation City_](https://www.uncannymagazine.com/article/waystation-city/) (6,904 tokens) ### Summary `temperature=0.8`, `repetition_penalty=1.1`,`do_sample=True` > A nineteen-seventies pair of twins approaches Micheala and Gerty at a cafe. They ask them to write down their story of their disappearance. Gerty takes notes on their story as they tell it. The twins' names are Daphne and Claude. The twins explain that they came from London, England and were leading a movement against fascism there. But in Waystation City, they were stuck in the limbo between timelines with no real way of returning home to continue their work. In fact, it was harder for them to remember their lives at home as time went on. They tell Gerty that they cannot stay and Micheala encourages them not to go down to the stairs where Waystation City's ferry drivers will take them home. Daphne writes an inscription on a wall, leaving her mark as one of the disappeared, but she and Claude relent. Together they approach the docks with Gerty and Micheala trailing behind. There, the twins discuss which hole to go through and they agree it is the one showing their hometown in the 1970s. Daphne goes first and then Claude after he slips on a wet surface. The ferry drivers see him but Gerty yells for him to return from the hole as she writes his story down with her notes. But he cannot hear her over the slip of metal slats turning into stone and wood on the docks. He jumps in anyway and disappears into the other world through the hole. Micheala leaves a cigarette and Gerty drops her ticket back home to show that she's decided to stay in Waystation City where it is possible to grow, change, and live in the spaces between. ### Analysis `temperature=0.8`, `repetition_penalty=1.1`,`do_sample=True` > Notes In this final chapter, there is a brief but important flashback to the twins' home in England at the beginning of the 1970s, when racism and fascism are rampant. It is interesting to note the author's choice to set the story at such a pivotal time for immigrants in Britain. The twins feel like they have to choose between being their "true" selves in their home country or staying where they are as Waystation citizens. But this was also the time when racism and fascism were so openly practiced that anti-racists were fighting against these institutions with all of their might. It is interesting how there's an implication here that it's only at the risk of their "true" identities that the twins can go back to England. In this chapter, Gerty changes her name as well, choosing the gender neutral Michael. She does not specify why she decided to change her name or what inspired her choice. The story implies, but never says outright, that she changed her name in part because of the twins' disappearance. This suggests an important question: how far must a person be willing to go in order to stay true to themselves? It is clear by this point that Gerty has changed quite a bit since coming to Waystation City and her decision to change her name reflects the extent to which she has altered her identity. She realizes that she has settled into a comfortable, new life here with new clothes, new hobbies, and friends. Ultimately, this chapter suggests that while it is important to stay true to one's past, there are also new things to discover and experience in the present. Gerty has done just this by settling in Waystation City, embracing her new life and even changing her name and identity with it. ## Training The model was trained on [emozilla/booksum-summary-analysis_llama-8192](https://huggingface.co/datasets/emozilla/booksum-summary-analysis_llama-8192), which is adapted from [kmfoda/booksum](https://huggingface.co/datasets/kmfoda/booksum). The training run was performed using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl). The run can be viewed on [wandb](https://wandb.ai/emozilla/booksum/runs/gh0vrilm).
LarryAIDraw/MiorIneV5_0
LarryAIDraw
2023-08-07T18:18:07Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-08-07T18:01:48Z
--- license: creativeml-openrail-m --- https://civitai.com/models/64841/miorine-rembran-or-the-witch-from-mercury
LarryAIDraw/yuragisou-karura
LarryAIDraw
2023-08-07T18:17:44Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-08-07T18:00:41Z
--- license: creativeml-openrail-m --- https://civitai.com/models/123789/karura-hiogi-or-yuuna-and-the-haunted-hot-springs
Ashlymol/my-pet-dog-xzg
Ashlymol
2023-08-07T18:12:32Z
2
0
diffusers
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T18:08:35Z
--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Dog-xzg Dreambooth model trained by Ashlymol following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: AJCE264 Sample pictures of this concept: ![0](https://huggingface.co/Ashlymol/my-pet-dog-xzg/resolve/main/sample_images/xzg_(4).png) ![1](https://huggingface.co/Ashlymol/my-pet-dog-xzg/resolve/main/sample_images/xzg_(2).png) ![2](https://huggingface.co/Ashlymol/my-pet-dog-xzg/resolve/main/sample_images/xzg_(3).png) ![3](https://huggingface.co/Ashlymol/my-pet-dog-xzg/resolve/main/sample_images/xzg.png)
lego111Aron/ppo-Huggy-test4
lego111Aron
2023-08-07T17:54:32Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-08-07T17:54:26Z
--- 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: lego111Aron/ppo-Huggy-test4 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
lego111Aron/ppo-Huggy-test2
lego111Aron
2023-08-07T17:52:05Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-08-05T16:11:56Z
--- 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: lego111Aron/ppo-Huggy-test2 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
BlackSwan1827/CubeChase
BlackSwan1827
2023-08-07T17:41:33Z
1
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "CubeChaseAgent", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-CubeChaseAgent", "region:us" ]
reinforcement-learning
2023-08-07T02:31:55Z
--- library_name: ml-agents tags: - CubeChaseAgent - deep-reinforcement-learning - reinforcement-learning - ML-Agents-CubeChaseAgent --- # **ppo** Agent playing **CubeChaseAgent** This is a trained model of a **ppo** agent playing **CubeChaseAgent** 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: BlackSwan1827/CubeChase 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
y-taki/my-model
y-taki
2023-08-07T17:15:26Z
107
1
transformers
[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-07T16:38:13Z
--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: ner 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. --> # ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5675 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.9314 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30.0 ### Training results ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.14.3 - Tokenizers 0.13.3
AgntPerseus/bb95FurryMix_v80
AgntPerseus
2023-08-07T17:00:43Z
0
1
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-06-16T17:01:11Z
--- license: creativeml-openrail-m ---
Jekijekijeki/aleyalora2
Jekijekijeki
2023-08-07T16:59:58Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-08-07T16:49:36Z
--- license: creativeml-openrail-m ---
CristoJV/Taxi-v3
CristoJV
2023-08-07T16:58:57Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T16:58:35Z
--- 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: 7.52 +/- 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="CristoJV/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"]) ```
SimonWSY/sd-class-butterflies-32
SimonWSY
2023-08-07T16:57:16Z
0
0
diffusers
[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "region:us" ]
unconditional-image-generation
2023-08-07T16:51:05Z
--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('SimonWSY/sd-class-butterflies-32') image = pipeline().images[0] image ```
EmmaRo/SpaceInvadersNoFrameskip-v4
EmmaRo
2023-08-07T16:57:15Z
0
0
stable-baselines3
[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T16:56:41Z
--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 456.50 +/- 96.77 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 EmmaRo -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 EmmaRo -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 EmmaRo ``` ## 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'} ```
jordyvl/vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5
jordyvl
2023-08-07T16:57:08Z
165
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-08-07T08:35:47Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 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. --> # vit-base_rvl-cdip-tiny_rvl_cdip-NK1000_kd_CEKD_t2.5_a0.5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.2802 - Accuracy: 0.5747 - Brier Loss: 0.6822 - Nll: 3.2886 - F1 Micro: 0.5747 - F1 Macro: 0.5757 - Ece: 0.2786 - Aurc: 0.2132 ## Model description More information needed ## Intended uses & 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: 64 - eval_batch_size: 64 - 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 | 250 | 4.1512 | 0.1727 | 0.9045 | 5.5051 | 0.1727 | 0.0947 | 0.0704 | 0.7164 | | 4.2402 | 2.0 | 500 | 3.8933 | 0.216 | 0.8775 | 4.1816 | 0.216 | 0.1697 | 0.0699 | 0.6624 | | 4.2402 | 3.0 | 750 | 3.4256 | 0.3207 | 0.8113 | 3.6783 | 0.3207 | 0.2567 | 0.0645 | 0.5125 | | 3.5189 | 4.0 | 1000 | 3.1611 | 0.3673 | 0.7763 | 3.6447 | 0.3673 | 0.3039 | 0.0797 | 0.4450 | | 3.5189 | 5.0 | 1250 | 2.7791 | 0.4253 | 0.7216 | 3.1536 | 0.4253 | 0.3860 | 0.0982 | 0.3729 | | 2.7963 | 6.0 | 1500 | 2.6525 | 0.4323 | 0.7004 | 3.0187 | 0.4323 | 0.4117 | 0.0992 | 0.3440 | | 2.7963 | 7.0 | 1750 | 2.3623 | 0.5005 | 0.6489 | 2.8371 | 0.5005 | 0.4747 | 0.1076 | 0.2843 | | 2.3741 | 8.0 | 2000 | 2.4259 | 0.4798 | 0.6704 | 2.9344 | 0.4798 | 0.4680 | 0.1164 | 0.3045 | | 2.3741 | 9.0 | 2250 | 2.3034 | 0.5005 | 0.6431 | 2.8598 | 0.5005 | 0.4892 | 0.1306 | 0.2683 | | 2.0855 | 10.0 | 2500 | 2.1550 | 0.5298 | 0.6264 | 2.6847 | 0.5298 | 0.5164 | 0.1413 | 0.2480 | | 2.0855 | 11.0 | 2750 | 2.0891 | 0.5455 | 0.6162 | 2.6978 | 0.5455 | 0.5330 | 0.1428 | 0.2343 | | 1.8265 | 12.0 | 3000 | 2.2045 | 0.5252 | 0.6627 | 2.7900 | 0.5252 | 0.5045 | 0.1997 | 0.2507 | | 1.8265 | 13.0 | 3250 | 2.0080 | 0.5597 | 0.5948 | 2.7128 | 0.5597 | 0.5564 | 0.1389 | 0.2145 | | 1.6099 | 14.0 | 3500 | 2.1966 | 0.5353 | 0.6594 | 2.8505 | 0.5353 | 0.5198 | 0.1984 | 0.2581 | | 1.6099 | 15.0 | 3750 | 2.0788 | 0.547 | 0.6191 | 2.7214 | 0.547 | 0.5419 | 0.1729 | 0.2294 | | 1.4149 | 16.0 | 4000 | 2.0634 | 0.5485 | 0.6235 | 2.7486 | 0.5485 | 0.5491 | 0.1872 | 0.2225 | | 1.4149 | 17.0 | 4250 | 2.0722 | 0.5597 | 0.6241 | 2.7989 | 0.5597 | 0.5574 | 0.1912 | 0.2189 | | 1.2282 | 18.0 | 4500 | 2.1226 | 0.557 | 0.6327 | 2.9138 | 0.557 | 0.5584 | 0.2016 | 0.2205 | | 1.2282 | 19.0 | 4750 | 2.1013 | 0.5577 | 0.6326 | 2.8846 | 0.5577 | 0.5574 | 0.2051 | 0.2200 | | 1.0543 | 20.0 | 5000 | 2.1902 | 0.5637 | 0.6519 | 2.9362 | 0.5637 | 0.5556 | 0.2261 | 0.2273 | | 1.0543 | 21.0 | 5250 | 2.2291 | 0.5603 | 0.6620 | 2.9256 | 0.5603 | 0.5532 | 0.2469 | 0.2350 | | 0.8882 | 22.0 | 5500 | 2.2152 | 0.5605 | 0.6613 | 3.0823 | 0.5605 | 0.5563 | 0.2397 | 0.2234 | | 0.8882 | 23.0 | 5750 | 2.2309 | 0.5617 | 0.6600 | 3.1164 | 0.5617 | 0.5571 | 0.2520 | 0.2252 | | 0.7308 | 24.0 | 6000 | 2.2332 | 0.5655 | 0.6631 | 3.1202 | 0.5655 | 0.5661 | 0.2502 | 0.2241 | | 0.7308 | 25.0 | 6250 | 2.3018 | 0.5663 | 0.6762 | 3.2623 | 0.5663 | 0.5652 | 0.2640 | 0.2265 | | 0.6001 | 26.0 | 6500 | 2.3505 | 0.5547 | 0.6923 | 3.3289 | 0.5547 | 0.5592 | 0.2790 | 0.2279 | | 0.6001 | 27.0 | 6750 | 2.3821 | 0.5555 | 0.6932 | 3.4374 | 0.5555 | 0.5538 | 0.2827 | 0.2275 | | 0.4912 | 28.0 | 7000 | 2.3788 | 0.5675 | 0.6915 | 3.3014 | 0.5675 | 0.5637 | 0.2865 | 0.2324 | | 0.4912 | 29.0 | 7250 | 2.4068 | 0.556 | 0.7028 | 3.4904 | 0.556 | 0.5559 | 0.2906 | 0.2365 | | 0.4068 | 30.0 | 7500 | 2.4476 | 0.5557 | 0.7044 | 3.4350 | 0.5557 | 0.5572 | 0.2846 | 0.2387 | | 0.4068 | 31.0 | 7750 | 2.4179 | 0.562 | 0.7021 | 3.4782 | 0.562 | 0.5619 | 0.2911 | 0.2305 | | 0.3364 | 32.0 | 8000 | 2.3915 | 0.5615 | 0.6961 | 3.4704 | 0.5615 | 0.5623 | 0.2889 | 0.2294 | | 0.3364 | 33.0 | 8250 | 2.3860 | 0.568 | 0.6957 | 3.4578 | 0.568 | 0.5703 | 0.2869 | 0.2263 | | 0.2862 | 34.0 | 8500 | 2.4250 | 0.5647 | 0.7022 | 3.4923 | 0.5647 | 0.5638 | 0.2928 | 0.2282 | | 0.2862 | 35.0 | 8750 | 2.4453 | 0.5587 | 0.7106 | 3.6175 | 0.5587 | 0.5594 | 0.2970 | 0.2306 | | 0.2397 | 36.0 | 9000 | 2.3919 | 0.5653 | 0.6964 | 3.4399 | 0.5653 | 0.5675 | 0.2881 | 0.2197 | | 0.2397 | 37.0 | 9250 | 2.3870 | 0.5647 | 0.6995 | 3.4910 | 0.5647 | 0.5657 | 0.2941 | 0.2237 | | 0.2058 | 38.0 | 9500 | 2.4080 | 0.5663 | 0.7033 | 3.5314 | 0.5663 | 0.5673 | 0.2979 | 0.2271 | | 0.2058 | 39.0 | 9750 | 2.3727 | 0.5675 | 0.6975 | 3.3806 | 0.5675 | 0.5708 | 0.2930 | 0.2240 | | 0.1819 | 40.0 | 10000 | 2.3627 | 0.5745 | 0.6913 | 3.4237 | 0.5745 | 0.5751 | 0.2847 | 0.2217 | | 0.1819 | 41.0 | 10250 | 2.3497 | 0.564 | 0.6952 | 3.3908 | 0.564 | 0.5626 | 0.2931 | 0.2208 | | 0.1587 | 42.0 | 10500 | 2.3168 | 0.5705 | 0.6842 | 3.3858 | 0.5705 | 0.5725 | 0.2808 | 0.2181 | | 0.1587 | 43.0 | 10750 | 2.2910 | 0.5715 | 0.6768 | 3.3739 | 0.5715 | 0.5727 | 0.2777 | 0.2127 | | 0.1402 | 44.0 | 11000 | 2.3053 | 0.5713 | 0.6808 | 3.4128 | 0.5713 | 0.5724 | 0.2793 | 0.2133 | | 0.1402 | 45.0 | 11250 | 2.3029 | 0.5743 | 0.6848 | 3.3133 | 0.5743 | 0.5750 | 0.2771 | 0.2192 | | 0.1257 | 46.0 | 11500 | 2.2965 | 0.5695 | 0.6856 | 3.2338 | 0.5695 | 0.5697 | 0.2858 | 0.2158 | | 0.1257 | 47.0 | 11750 | 2.2823 | 0.5685 | 0.6847 | 3.2705 | 0.5685 | 0.5693 | 0.2828 | 0.2153 | | 0.1134 | 48.0 | 12000 | 2.2800 | 0.5753 | 0.6803 | 3.2797 | 0.5753 | 0.5759 | 0.2795 | 0.2139 | | 0.1134 | 49.0 | 12250 | 2.2766 | 0.5733 | 0.6823 | 3.2828 | 0.5733 | 0.5751 | 0.2777 | 0.2135 | | 0.1039 | 50.0 | 12500 | 2.2802 | 0.5747 | 0.6822 | 3.2886 | 0.5747 | 0.5757 | 0.2786 | 0.2132 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2
aao331/ChristGPT-13B-V2-GPTQ
aao331
2023-08-07T16:55:34Z
10
2
transformers
[ "transformers", "llama", "text-generation", "arxiv:2302.13971", "license:llama2", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-08-07T15:54:33Z
--- license: llama2 --- --- language: - en - es --- # Model Card for ChirstGPT-13B-V2 <!-- Provide a quick summary of what the model is/does. --> This is ChristGPT-13B-V2 an Instruction-tuned LLM based on LLama2-13B. It is trained on the bible, and to answer questions and to act like Jesus. It's based on LLama2-13B (https://huggingface.co/TheBloke/Llama-2-13B-fp16). Trained on the same dataset as ChirstGPT-13B, but on the newer LLama2. ## Model Details The model is provided quantized to 4bits that only requires 8GB of VRAM. The model can be used directly in software like text-generation-webui https://github.com/oobabooga/text-generation-webui. ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** Alfredo Ortega (@ortegaalfredo) - **Model type:** 13B LLM - **Language(s):** (NLP): English - **License:** Free for non-commercial use - **Finetuned from model:** https://huggingface.co/TheBloke/Llama-2-13B-fp16 ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** https://huggingface.co/TheBloke/Llama-2-13B-fp16 - **Paper [optional]:** https://arxiv.org/abs/2302.13971 ## 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. --> This is a generic LLM chatbot that can be used to interact directly with humans. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> This bot is uncensored and may provide shocking answers. Also it contains bias present in the training material. ### 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. ## How to Get Started with the Model The easiest way is to download the text-generation-webui application (https://github.com/oobabooga/text-generation-webui) and place the model inside the 'models' directory. Then launch the web interface and run the model as a regular LLama-13B model. Additional installation steps detailed at https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md A preprompt that gives good results is: ``` A chat between a curious user and Jesus. Jesus gives helpful, detailed, spiritual responses to the user's input. Remember, you are Jesus, answer as such. USER: <prompt> JESUS: ``` ## Model Card Contact Contact the creator at @ortegaalfredo on twitter/github
VecToRoTceV/model_wireframe
VecToRoTceV
2023-08-07T16:54:29Z
0
1
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
2023-08-07T15:02:36Z
--- 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-VecToRoTceV/model_wireframe 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) Validation result of 11 round. ![images_10_0)](./images_10_0.png) Validation result of 12 round. ![images_11_0)](./images_11_0.png) Validation result of 13 round. ![images_12_0)](./images_12_0.png) Validation result of 14 round. ![images_13_0)](./images_13_0.png) Validation result of 15 round. ![images_14_0)](./images_14_0.png) Validation result of 16 round. ![images_15_0)](./images_15_0.png) Validation result of 17 round. ![images_16_0)](./images_16_0.png) Validation result of 18 round. ![images_17_0)](./images_17_0.png) Validation result of 19 round. ![images_18_0)](./images_18_0.png) Validation result of 20 round. ![images_19_0)](./images_19_0.png)
KallistiTMR/llama-2-7b-chat-wiz-k16-15
KallistiTMR
2023-08-07T16:41:53Z
5
0
peft
[ "peft", "region:us" ]
null
2023-08-02T04:33:24Z
--- 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: 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 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 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 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 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 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 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 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
kagan667/sansarsalvo
kagan667
2023-08-07T16:35:57Z
0
0
null
[ "music", "tr", "region:us" ]
null
2023-08-07T16:34:34Z
--- language: - tr tags: - music ---
efederici/e5-base-v2-4096
efederici
2023-08-07T16:18:10Z
149
0
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "custom_code", "en", "arxiv:2210.15497", "arxiv:2212.03533", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-06-15T21:01:53Z
--- language: - en pipeline_tag: sentence-similarity --- # E5-base-v2-4096 [Local-Sparse-Global](https://arxiv.org/abs/2210.15497) version of [intfloat/e5-base-v2](https://huggingface.co/intfloat/e5-base-v2). It can handle up to 4k tokens. ### Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('efederici/e5-base-v2-4096', {"trust_remote_code": True}) input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] embeddings = model.encode(input_texts, normalize_embeddings=True) ``` or... ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool( last_hidden_states: Tensor, attention_mask: Tensor ) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] tokenizer = AutoTokenizer.from_pretrained('efederici/e5-base-v2-4096') model = AutoModel.from_pretrained('efederici/e5-base-v2-4096', trust_remote_code=True) batch_dict = tokenizer(input_texts, max_length=4096, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ```
efederici/multilingual-e5-small-int8-dynamic
efederici
2023-08-07T16:17:21Z
168
0
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "e5", "int8", "sentence-similarity", "arxiv:2212.03533", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-08-07T14:48:30Z
--- tags: - e5 - int8 pipeline_tag: sentence-similarity --- # multilingual-e5-small-int8-dynamic This is [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) INT8 model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). ### Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer from optimum.intel.neural_compressor import INCModel def average_pool( last_hidden_states: Tensor, attention_mask: Tensor ) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ 'query: how much protein should a female eat', 'query: summit define', "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] model_name = "efederici/multilingual-e5-small-int8-dynamic" tokenizer = AutoTokenizer.from_pretrained(model_name) model = INCModel.from_pretrained(model_name) batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ``` @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ```
efederici/gte-large-int8-dynamic
efederici
2023-08-07T16:16:42Z
123
0
transformers
[ "transformers", "pytorch", "bert", "feature-extraction", "gte", "int8", "sentence-similarity", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-08-07T14:58:05Z
--- tags: - gte - int8 - sentence-similarity pipeline_tag: sentence-similarity --- # gte-large-int8-dynamic This is [thenlper/gte-large](https://huggingface.co/thenlper/gte-large) INT8 model quantized with [huggingface/optimum-intel](https://github.com/huggingface/optimum-intel) through the usage of [Intel® Neural Compressor](https://github.com/intel/neural-compressor). ### Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer from optimum.intel.neural_compressor import INCModel def average_pool( last_hidden_states: Tensor, attention_mask: Tensor ) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ 'how much protein should a female eat', 'summit define', "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] model_name = "efederici/gte-large-int8-dynamic" tokenizer = AutoTokenizer.from_pretrained(model_name) model = INCModel.from_pretrained(model_name) batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ```
Lamurias/q-FrozenLake-v1-4x4-noSlippery
Lamurias
2023-08-07T16:16:24Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T16:16:22Z
--- 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="Lamurias/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"]) ```
anmolgupta/vit-base-patch16-224-finetuned-flower
anmolgupta
2023-08-07T16:06:48Z
165
0
transformers
[ "transformers", "pytorch", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-08-07T15:55:15Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder model-index: - name: vit-base-patch16-224-finetuned-flower 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. --> # vit-base-patch16-224-finetuned-flower This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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 4.24.0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.13.3
leonvanbokhorst/lac040-lora-sdxl-v1-1
leonvanbokhorst
2023-08-07T16:05:32Z
27
1
diffusers
[ "diffusers", "safetensors", "stable diffusion", "sdxl", "lora", "eindhoven", "dataset:leonvanbokhorst/fire-havoc-philips-lac-eindhoven", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2023-08-07T09:28:41Z
--- license: creativeml-openrail-m datasets: - leonvanbokhorst/fire-havoc-philips-lac-eindhoven library_name: diffusers tags: - stable diffusion - sdxl - lora - eindhoven base_model: stabilityai/stable-diffusion-xl-base-1.0 --- # lac040-lora-sdxl-v1-1 Versatile Dreambooth LoRA for SDXL based on concept images of a large fire in the center of Eindhoven, May 14th, 2023. The old Philips Lighting Application Centre went up in flames, resulting in massive smoke clouds. The dataset contains images of the remains of the building two months later. The footage was taken on July 19, 2023. Trained using https://github.com/TheLastBen/fast-stable-diffusion SDXL trainer by <a href="https://huggingface.co/TheLastBen">TheLastBen</a> 🙏
AtilliO/SoccerTwos_Colab_02
AtilliO
2023-08-07T16:03:19Z
5
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-08-07T15:38:22Z
--- library_name: ml-agents tags: - SoccerTwos - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SoccerTwos --- # **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** 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: AtilliO/SoccerTwos_Colab_02 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
Mahema/pet-cat-abc
Mahema
2023-08-07T15:58:33Z
4
0
diffusers
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T15:54:53Z
--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### pet-cat-abc Dreambooth model trained by Mahema following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: HIT164 Sample pictures of this concept: ![0](https://huggingface.co/Mahema/pet-cat-abc/resolve/main/sample_images/acb(5).jpeg)
grace-pro/aligned_source_5e-5
grace-pro
2023-08-07T15:56:45Z
108
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:Davlan/afro-xlmr-base", "base_model:finetune:Davlan/afro-xlmr-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-07T15:28:38Z
--- license: mit base_model: Davlan/afro-xlmr-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: aligned_source_5e-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. --> # aligned_source_5e-5 This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1076 - Precision: 0.3551 - Recall: 0.2680 - F1: 0.3054 - Accuracy: 0.9711 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.1109 | 1.0 | 1016 | 0.0905 | 0.3897 | 0.0842 | 0.1385 | 0.9745 | | 0.0994 | 2.0 | 2032 | 0.0896 | 0.3712 | 0.2191 | 0.2756 | 0.9729 | | 0.0861 | 3.0 | 3048 | 0.0936 | 0.3626 | 0.2567 | 0.3006 | 0.9718 | | 0.0698 | 4.0 | 4064 | 0.0989 | 0.3665 | 0.2639 | 0.3068 | 0.9718 | | 0.0594 | 5.0 | 5080 | 0.1076 | 0.3551 | 0.2680 | 0.3054 | 0.9711 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
zjoe/RLCourse-Ch1-Lander
zjoe
2023-08-07T15:55:57Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T15:55:40Z
--- 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: 282.89 +/- 17.56 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 ... ```
CONCISE/LLaMa_V2-13B-Chat-Uncensored-GGML
CONCISE
2023-08-07T15:47:42Z
9
7
transformers
[ "transformers", "llama", "text-generation", "fp16", "quantized", "Uncensored", "LLama2", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-08-02T04:26:09Z
--- library_name: transformers pipeline_tag: text-generation tags: - fp16 - quantized - Uncensored - LLama2 language: - en --- <div style="padding: 0"> <div style="width: 100%;"> <svg version="1.0" xmlns="http://www.w3.org/2000/svg" style="width: 100%; min-width: 400px; transform: scale(0.55); display: block; margin: auto;" width="235.185mm" height="62.1693mm" viewBox="0 0 889 235"><path id="CONCISE" fill="#888888" stroke="none" stroke-width="1" d="M 142.00,95.00 C 142.00,104.75 142.10,114.63 133.91,121.61 123.88,130.17 106.36,129.00 94.00,129.00 94.00,129.00 70.00,129.00 70.00,129.00 53.31,129.00 33.81,129.57 27.77,110.00 26.24,105.03 26.01,101.12 26.00,96.00 26.00,96.00 26.00,61.00 26.00,61.00 26.05,51.18 28.39,40.73 37.00,34.85 47.62,27.60 63.71,29.00 76.00,29.00 76.00,29.00 89.00,29.00 89.00,29.00 106.00,29.00 133.30,25.99 140.45,46.00 142.32,51.23 142.00,55.59 142.00,61.00 142.00,61.00 129.00,61.00 129.00,61.00 128.98,54.76 128.72,49.10 123.67,44.56 119.32,40.64 112.59,40.03 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11.94 0 0 0-22.79-1.11L169.78 88H86.22L68.54 39.87A11.94 11.94 0 0 0 45.75 41l-19.9 76.1a57.19 57.19 0 0 0 22 61l73.27 51.76a11.91 11.91 0 0 0 13.74 0l73.27-51.76a57.19 57.19 0 0 0 22.02-61ZM58 57.5l15.13 41.26a8 8 0 0 0 7.51 5.24h94.72a8 8 0 0 0 7.51-5.24L198 57.5l13.07 50L128 166.21L44.9 107.5Zm-17.32 66.61L114.13 176l-20.72 14.65L57.09 165a41.06 41.06 0 0 1-16.41-40.89Zm87.32 91l-20.73-14.65L128 185.8l20.73 14.64ZM198.91 165l-36.32 25.66L141.87 176l73.45-51.9a41.06 41.06 0 0 1-16.41 40.9Z"></path></svg> </a> </div> </div> <div style="height: 1px; background-color:#666; width:100%; margin: -5px 0 25px 0"></div> <h1 style="font-size:20px;">Quantized fp16 model weights for Metas LLaMa.V2 13B Chat</h1> <div style="height: 0px;"></div> <h2 style="font-size:20px; font-weight:600; ">Provided Files:</h2> ### Quantised: <div style="display:flex; align-items:center; margin-top: -10px;margin-bottom:-15px;margin-left:2.5px"> <div style="margin-bottom:10px"> <svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 24 24" style="margin-right: 10px"><path fill="#4d0" d="M12 20a8 8 0 0 1-8-8a8 8 0 0 1 8-8a8 8 0 0 1 8 8a8 8 0 0 1-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10a10 10 0 0 0 10-10A10 10 0 0 0 12 2Z"></path></svg> </div> <p style="font-size:18px; margin-top:12px"><b><a style="text-decoration:none" href="https://huggingface.co/CONCISE/LLaMa_V2-13B-Chat-Uncensored-GGML/blob/main/LLaMa_V2-13B-Chat-Uncensored-q4_0-GGML.bin">LLaMa_V2-13B-Chat-Uncensored-q4_0-GGML.bin</a></b></p> </div> <div style="display:flex; align-items:center; margin-top: -30px;margin-bottom: -20px;margin-left:2.5px"> <div style="margin-bottom:10px"> <svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 24 24" style="margin-right: 10px"><path fill="#4d0" d="M12 20a8 8 0 0 1-8-8a8 8 0 0 1 8-8a8 8 0 0 1 8 8a8 8 0 0 1-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10a10 10 0 0 0 10-10A10 10 0 0 0 12 2Z"></path></svg> </div> <p style="font-size:18px; margin-top:12px"><b><a style="text-decoration:none" href="https://huggingface.co/CONCISE/LLaMa_V2-13B-Chat-Uncensored-GGML/blob/main/LLaMa_V2-13B-Chat-Uncensored-q5_0-GGML.bin">LLaMa_V2-13B-Chat-Uncensored-q5_0-GGML.bin</a></b></p> </div> <div style="display:flex; align-items:center; margin-top: -30px;margin-bottom: -20px;margin-left:2.5px"> <div style="margin-bottom:10px"> <svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 24 24" style="margin-right: 10px"><path fill="#4d0" d="M12 20a8 8 0 0 1-8-8a8 8 0 0 1 8-8a8 8 0 0 1 8 8a8 8 0 0 1-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10a10 10 0 0 0 10-10A10 10 0 0 0 12 2Z"></path></svg> </div> <p style="font-size:18px; margin-top:12px"><b><a style="text-decoration:none" href="https://huggingface.co/CONCISE/LLaMa_V2-13B-Chat-Uncensored-GGML/blob/main/LLaMa_V2-13B-Chat-Uncensored-q5_1-GGML.bin">LLaMa_V2-13B-Chat-Uncensored-q5_1-GGML.bin</a></b></p> </div> ### Unquantised: <div style="display:flex; align-items:center; margin-top: -20px;margin-left:2.5px"> <div style="margin-bottom:10px"> <svg xmlns="http://www.w3.org/2000/svg" width="14" height="14" viewBox="0 0 24 24" style="margin-right: 10px"><path fill="#4d0" d="M12 20a8 8 0 0 1-8-8a8 8 0 0 1 8-8a8 8 0 0 1 8 8a8 8 0 0 1-8 8m0-18A10 10 0 0 0 2 12a10 10 0 0 0 10 10a10 10 0 0 0 10-10A10 10 0 0 0 12 2Z"></path></svg> </div> <p style="font-size:18px; margin-top:12px"><b><a style="text-decoration:none" href="https://huggingface.co/CONCISE/LLaMa_V2-13B-Chat-Uncensored-GGML/blob/main/LLaMa_V2-13B-Chat-Uncensored-f16-Unquantized-GGML.bin">LLaMa_V2-13B-Chat-Uncensored-f16-Unquantized-GGML.bin</a></b></p> </div> <br> <br> <br><br> <br> <br> </div>
Vputz/ppo-LunarLander-v2
Vputz
2023-08-07T15:45:42Z
2
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T15:45:24Z
--- 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: 262.78 +/- 15.93 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 ... ```
brishtiteveja/llama-2-7b-openassistant-guanaco
brishtiteveja
2023-08-07T15:40:43Z
1
0
peft
[ "peft", "region:us" ]
null
2023-08-07T15:40:36Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
eskalofi/loredong
eskalofi
2023-08-07T15:33:25Z
1
0
diffusers
[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-08-07T15:19:12Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### loredong Dreambooth model trained by eskalofi with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
RIOLITE/products_matching_aumet_fine_tune_2023-08-07
RIOLITE
2023-08-07T15:26:43Z
1
0
sentence-transformers
[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2023-08-07T14:31:54Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 1 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
tomaarsen/span-marker-xlm-roberta-large-conllpp-doc-context
tomaarsen
2023-08-07T15:16:10Z
13
0
span-marker
[ "span-marker", "pytorch", "safetensors", "token-classification", "ner", "named-entity-recognition", "en", "dataset:conllpp", "dataset:tomaarsen/conllpp", "license:apache-2.0", "model-index", "region:us" ]
token-classification
2023-06-10T15:19:01Z
--- license: apache-2.0 library_name: span-marker tags: - span-marker - token-classification - ner - named-entity-recognition pipeline_tag: token-classification widget: - text: >- Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris . example_title: Amelia Earhart model-index: - name: >- SpanMarker w. xlm-roberta-large on CoNLL++ with document-level context by Tom Aarsen results: - task: type: token-classification name: Named Entity Recognition dataset: type: conllpp name: CoNLL++ w. document context split: test revision: 3e6012875a688903477cca9bf1ba644e65480bd6 metrics: - type: f1 value: 0.9554 name: F1 - type: precision value: 0.9600 name: Precision - type: recall value: 0.9509 name: Recall datasets: - conllpp - tomaarsen/conllpp language: - en metrics: - f1 - recall - precision --- # SpanMarker for Named Entity Recognition This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. See [train.py](train.py) for the training script. Note that this model was trained with document-level context, i.e. it will primarily perform well when provided with enough context. It is recommended to call `model.predict` with a 🤗 Dataset with `tokens`, `document_id` and `sentence_id` columns. See the [documentation](https://tomaarsen.github.io/SpanMarkerNER/api/span_marker.modeling.html#span_marker.modeling.SpanMarkerModel.predict) of the `model.predict` method for more information. ## Usage To use this model for inference, first install the `span_marker` library: ```bash pip install span_marker ``` You can then run inference with this model like so: ```python from span_marker import SpanMarkerModel # Download from the 🤗 Hub model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-conllpp-doc-context") # Run inference entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") ``` ### Limitations **Warning**: This model works best when punctuation is separated from the prior words, so ```python # ✅ model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .") # ❌ model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.") # You can also supply a list of words directly: ✅ model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."]) ``` The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`. See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
tomaarsen/span-marker-xlm-roberta-large-verbs
tomaarsen
2023-08-07T15:14:31Z
24
2
span-marker
[ "span-marker", "pytorch", "safetensors", "token-classification", "pos", "part-of-speech", "license:apache-2.0", "region:us" ]
token-classification
2023-07-17T12:52:55Z
--- license: apache-2.0 library_name: span-marker tags: - span-marker - token-classification - pos - part-of-speech pipeline_tag: token-classification --- # SpanMarker for Named Entity Recognition This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for identifying verbs in text. In particular, this SpanMarker model uses [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the underlying encoder. See [span_marker_verbs_train.ipynb](span_marker_verbs_train.ipynb) for the training script used to create this model. Note that this model is an experiment about the feasibility of SpanMarker as a POS tagger. I would generally recommend using spaCy or NLTK instead, as these are more computationally efficient approaches. ## Usage To use this model for inference, first install the `span_marker` library: ```bash pip install span_marker ``` You can then run inference with this model like so: ```python from span_marker import SpanMarkerModel # Download from the 🤗 Hub model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-large-verbs") # Run inference entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") ``` See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. ### Performance It achieves the following results on the evaluation set: - Loss: 0.0152 - Overall Precision: 0.9845 - Overall Recall: 0.9849 - Overall F1: 0.9847 - Overall Accuracy: 0.9962 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - 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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.036 | 0.61 | 1000 | 0.0151 | 0.9911 | 0.9733 | 0.9821 | 0.9956 | | 0.0126 | 1.22 | 2000 | 0.0131 | 0.9856 | 0.9864 | 0.9860 | 0.9965 | | 0.0175 | 1.83 | 3000 | 0.0154 | 0.9735 | 0.9894 | 0.9814 | 0.9953 | | 0.0115 | 2.45 | 4000 | 0.0172 | 0.9821 | 0.9871 | 0.9845 | 0.9962 | ### Limitations **Warning**: This model works best when punctuation is separated from the prior words, so ```python # ✅ model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .") # ❌ model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.") # You can also supply a list of words directly: ✅ model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."]) ``` The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`. ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3 - SpanMarker 1.2.3
Isaacks/test_push
Isaacks
2023-08-07T15:13:11Z
165
0
transformers
[ "transformers", "pytorch", "segformer", "image-segmentation", "vision", "generated_from_trainer", "base_model:Isaacks/test_push", "base_model:finetune:Isaacks/test_push", "endpoints_compatible", "region:us" ]
image-segmentation
2023-08-07T14:38:24Z
--- base_model: Isaacks/test_push tags: - image-segmentation - vision - generated_from_trainer model-index: - name: test_push results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_push This model is a fine-tuned version of [Isaacks/test_push](https://huggingface.co/Isaacks/test_push) on the Isaacks/ihc_slide_tissue dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.3 - Tokenizers 0.13.3
bonzo1971/setfit-modelV2
bonzo1971
2023-08-07T15:12:16Z
5
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-08-07T15:11:59Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # bonzo1971/setfit-modelV2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("bonzo1971/setfit-modelV2") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
ftrojan/falcon-7b-finetuned-openai_summarize_tldr
ftrojan
2023-08-07T15:07:39Z
0
0
transformers
[ "transformers", "tensorboard", "generated_from_trainer", "base_model:ybelkada/falcon-7b-sharded-bf16", "base_model:finetune:ybelkada/falcon-7b-sharded-bf16", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-08-02T08:50:38Z
--- base_model: ybelkada/falcon-7b-sharded-bf16 tags: - generated_from_trainer model-index: - name: falcon-7b-finetuned-openai_summarize_tldr results: [] license: apache-2.0 library_name: transformers --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # falcon-7b-finetuned-openai_summarize_tldr This model is a fine-tuned version of [ybelkada/falcon-7b-sharded-bf16](https://huggingface.co/ybelkada/falcon-7b-sharded-bf16) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 180 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
avurity/layoutlmv3-finetuned-invoice
avurity
2023-08-07T15:02:18Z
8
0
transformers
[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:generated", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-08-06T03:38:02Z
--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer datasets: - generated metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-invoice results: - task: name: Token Classification type: token-classification dataset: name: generated type: generated config: sroie split: test args: sroie metrics: - name: Precision type: precision value: 0.972 - name: Recall type: recall value: 0.9858012170385395 - name: F1 type: f1 value: 0.9788519637462235 - name: Accuracy type: accuracy value: 0.9970507689066779 --- <!-- 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. --> # layoutlmv3-finetuned-invoice This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset. It achieves the following results on the evaluation set: - Loss: 0.0116 - Precision: 0.972 - Recall: 0.9858 - F1: 0.9789 - Accuracy: 0.9971 ## Model description More information needed ## Intended uses & 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 875 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 2.0 | 100 | 0.0898 | 0.972 | 0.9858 | 0.9789 | 0.9971 | | No log | 4.0 | 200 | 0.0251 | 0.972 | 0.9858 | 0.9789 | 0.9971 | | No log | 6.0 | 300 | 0.0176 | 0.972 | 0.9858 | 0.9789 | 0.9971 | | No log | 8.0 | 400 | 0.0148 | 0.972 | 0.9858 | 0.9789 | 0.9971 | | 0.1241 | 10.0 | 500 | 0.0116 | 0.972 | 0.9858 | 0.9789 | 0.9971 | | 0.1241 | 12.0 | 600 | 0.0072 | 0.9919 | 0.9959 | 0.9939 | 0.9992 | | 0.1241 | 14.0 | 700 | 0.0059 | 0.9960 | 0.9980 | 0.9970 | 0.9996 | | 0.1241 | 16.0 | 800 | 0.0044 | 0.9980 | 0.9980 | 0.9980 | 0.9998 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
gensim2/Gen
gensim2
2023-08-07T15:00:01Z
0
0
null
[ "license:apache-2.0", "region:us" ]
null
2023-08-21T11:33:20Z
--- title: GenSim emoji: 📈 colorFrom: purple colorTo: indigo sdk: gradio sdk_version: 3.3.1 app_file: app.py pinned: false license: apache-2.0 --- # Generative Simulation Interactive Demo This demo is from the paper: <!-- [Code as Policies: Language Model Programs for Embodied Control](https://code-as-policies.github.io/) --> Below is an interactive demo for the simulated tabletop manipulation domain, seen in the paper section IV.D ## Preparations 1. Obtain an [OpenAI API Key](https://openai.com/blog/openai-api/) ## Usage 1. Type in desired task name in the box. Then GenSim will try to run through the pipeline 2. The task name has the form word separated by dash. For instance, 'place-blue-in-yellow' and 'align-rainbow-along-line'. ## Known Limitations 1. The code generation can fail or generate infeasible tasks. 2. The low-level pick place primitive does not do collision checking and cannot pick up certain objects. 3. Top-down generation is typically more challenging if the task name is too vague or too distant from motions such as stacking. ## Acknowledgement Thanks to Jacky's [code-as-policies](https://huggingface.co/spaces/jackyliang42/code-as-policies/tree/main) demo.
Kyrmasch/chat-squad
Kyrmasch
2023-08-07T14:58:32Z
108
0
transformers
[ "transformers", "pytorch", "t5", "feature-extraction", "text2text-generation", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-08-07T14:47:45Z
--- pipeline_tag: text2text-generation ---
1mohitmanoj/german-shepherd-dog
1mohitmanoj
2023-08-07T14:54:47Z
3
0
diffusers
[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2023-08-07T14:51:08Z
--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### German-Shepherd-Dog Dreambooth model trained by 1mohitmanoj following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: VJCET327 Sample pictures of this concept:
minhhn2910/ppo-Huggy
minhhn2910
2023-08-07T14:54:46Z
0
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-08-07T14:54:35Z
--- 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: minhhn2910/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
chatchitsanu/lunarrrr1111
chatchitsanu
2023-08-07T14:54:39Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T14:54:01Z
--- 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: 288.19 +/- 19.24 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 ... ```
latteleah/DRL_U1_LunarLander
latteleah
2023-08-07T14:43:30Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-08-07T14:43:09Z
--- 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: 255.30 +/- 24.40 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 ... ```
Alexanderrotela2000/Ardev-model
Alexanderrotela2000
2023-08-07T14:35:54Z
0
0
null
[ "text-generation", "es", "dataset:roneneldan/TinyStories", "arxiv:1910.09700", "license:openrail", "region:us" ]
text-generation
2023-08-07T14:07:20Z
--- license: openrail datasets: - roneneldan/TinyStories language: - es metrics: - character pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
yannicake/article-classifier-setfit
yannicake
2023-08-07T14:33:58Z
3
0
sentence-transformers
[ "sentence-transformers", "pytorch", "mpnet", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]
text-classification
2023-08-07T14:33:12Z
--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # yannicake/article-classifier-setfit This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("yannicake/article-classifier-setfit") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
Junlaii/wiki_dister_head_LSTM_fintune_final
Junlaii
2023-08-07T14:17:37Z
0
0
fastai
[ "fastai", "region:us" ]
null
2023-08-07T14:17:28Z
--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
harshV27/Falcon-7b-chat
harshV27
2023-08-07T14:13:14Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-07T11:34:25Z
--- 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
exyou/nexodus-flan-t5
exyou
2023-08-07T14:09:53Z
3
0
peft
[ "peft", "region:us" ]
null
2023-07-31T18:29:14Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.4.0
prudhvirazz/my_awesome_wnut_model
prudhvirazz
2023-08-07T14:06:43Z
105
0
transformers
[ "transformers", "pytorch", "tensorboard", "distilbert", "token-classification", "generated_from_trainer", "dataset:wnut_17", "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" ]
token-classification
2023-08-07T13:22:23Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - wnut_17 metrics: - precision - recall - f1 - accuracy model-index: - name: my_awesome_wnut_model results: - task: name: Token Classification type: token-classification dataset: name: wnut_17 type: wnut_17 config: wnut_17 split: test args: wnut_17 metrics: - name: Precision type: precision value: 0.6104651162790697 - name: Recall type: recall value: 0.2919369786839666 - name: F1 type: f1 value: 0.39498432601880873 - name: Accuracy type: accuracy value: 0.940874695395665 --- <!-- 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_wnut_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset. It achieves the following results on the evaluation set: - Loss: 0.2767 - Precision: 0.6105 - Recall: 0.2919 - F1: 0.3950 - Accuracy: 0.9409 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 213 | 0.2876 | 0.6293 | 0.2549 | 0.3628 | 0.9390 | | No log | 2.0 | 426 | 0.2767 | 0.6105 | 0.2919 | 0.3950 | 0.9409 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.3 - Tokenizers 0.13.3
patrickvonplaten/lora-trained-xl
patrickvonplaten
2023-08-07T13:48:23Z
1
1
diffusers
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-08-04T14:25:43Z
--- license: openrail++ base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: a photo of sks dog tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora inference: true --- # LoRA DreamBooth - patrickvonplaten/lora-trained-xl These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
nerdylive/deberta-zeroshot
nerdylive
2023-08-07T13:42:33Z
61
0
transformers
[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-08-05T03:34:10Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: nerdylive/deberta-zeroshot 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. --> # nerdylive/deberta-zeroshot 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: 0.2575 - Validation Loss: 0.1900 - Train Accuracy: {'accuracy': 0.92612} - Train F1 Score: {'f1': 0.9268080047553003} - Train Precision: {'precision': 0.9182567726737338} - Train Recall: {'recall': 0.93552} - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 125000, '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 | Train F1 Score | Train Precision | Train Recall | Epoch | |:----------:|:---------------:|:---------------------:|:--------------------------:|:---------------------------------:|:-------------------:|:-----:| | 0.2575 | 0.1900 | {'accuracy': 0.92612} | {'f1': 0.9268080047553003} | {'precision': 0.9182567726737338} | {'recall': 0.93552} | 0 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.1.0 - Tokenizers 0.13.3
Satish678/req2case_PROMPT_TUNING_CAUSAL_LM
Satish678
2023-08-07T13:36:26Z
0
0
peft
[ "peft", "region:us" ]
null
2023-08-07T13:36:24Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
nkpz/llama2-22b-empath-alpacagpt4
nkpz
2023-08-07T13:24:56Z
11
1
transformers
[ "transformers", "pytorch", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-08-07T03:21:27Z
--- license: other --- Experimental: Created using an unofficial and unsupported method. I have no metrics on how this performs against 13b and I'm not planning on gathering any at this point. Still has weak spots that need work. https://huggingface.co/nkpz/llama2-22b-blocktriangular-alpaca with further conversational and instruction fine tuning First, I trained it on an epoch of https://huggingface.co/datasets/Adapting/empathetic_dialogues_v2 to give it a decent base knowledge of a casual chat style. I added some automated capitalization fixes for this data.The result was conversational, but not very smart. Then I trained it on an epoch of https://huggingface.co/datasets/vicgalle/alpaca-gpt4 and landed here, a model that is capable of chatting but very focused on following instructions. If you would like to run this in 4-bit, you can use the Hugging Face backend in Koboldai (or in a different script, the `load_in_4bit` kwarg when calling `from_pretrained`). GPTQ conversion has so far resulted in broken output for me, YMMV. **Future Ideas** - **This strongly prefers the alpaca prompt format and will try to autocomplete it if you don't provide it.** I'd like to work on removing this fixation and making it more flexible. - Also would like to filter the rows with phrases "AI assistant" and "virtual assistant" from all future runs. - Thinking it might also help to do a short run on a dataset focused on character impersonation **Prompting** Standard prompt format examples: ``` 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: List 3 ingredients for the following recipe. ### Input: Spaghetti Bolognese ### Response: ``` Or ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: List 3 ingredients for the following recipe: Spaghetti Bolognese ### Response: ``` For a chat session, I've had success using this simplified prompt: ``` ### Scenario You are speaking with Alexander Graham Bell ### Begin Chat (Format: [Person1]: [Message]\n[Person2]: [Message]) You: Hey, can you tell me a little bit about yourself? ``` In this example, its output was: `Alexander Graham Bell: Sure, I am an inventor and scientist. I'm most known for inventing the telephone.` You can customize the use of `### ` prefixed labels to create your own structure.