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librarian-bots
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2025-07-30 00:44:18
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feic36/xlm-roberta-base-finetuned-panx-de-fr
feic36
2023-07-30T17:09:48Z
105
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-30T16:58:02Z
--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1606 - F1: 0.8620 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2873 | 1.0 | 715 | 0.1802 | 0.8245 | | 0.1446 | 2.0 | 1430 | 0.1601 | 0.8512 | | 0.0925 | 3.0 | 2145 | 0.1606 | 0.8620 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l54_v50
KingKazma
2023-07-30T16:59:36Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:59:33Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e4_s6789_v3_l4_v50
KingKazma
2023-07-30T16:51:57Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:51:56Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
ctrltokyo/llm_prompt_mask_fill_model
ctrltokyo
2023-07-30T16:47:26Z
62
1
transformers
[ "transformers", "tf", "distilbert", "fill-mask", "generated_from_keras_callback", "en", "dataset:sahil2801/code_instructions_120k", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
fill-mask
2023-07-29T12:13:23Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_keras_callback model-index: - name: ctrltokyo/llm_prompt_mask_fill_model results: [] datasets: - sahil2801/code_instructions_120k metrics: - accuracy language: - en widget: - text: "A web application with a REST API on Rails. This will be used for [MASK]." --- <!-- 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. --> # ctrltokyo/llm_prompt_mask_fill_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k) dataset. It achieves the following results on the evaluation set: - Train Loss: 2.1215 - Validation Loss: 1.5672 - Epoch: 0 ## Model description It's just distilbert-base-uncased with some fine tuning. ## Intended uses & limitations This model could be used for live autocompletion of PROMPTS in a coding-specific chatbot. Don't try this on code, because it won't work. ## Training and evaluation data Evaluated on 5% of training data. No further evaluation performed at this point. Trained on NVIDIA V100. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 108, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.1215 | 1.5672 | 0 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.14.1 - Tokenizers 0.13.3
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l4_v50
KingKazma
2023-07-30T16:44:00Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:43:59Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e2_s6789_v3_l4_v50
KingKazma
2023-07-30T16:36:03Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:36:01Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
feic36/xlm-roberta-base-finetuned-panx-de
feic36
2023-07-30T16:35:15Z
125
0
transformers
[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2023-07-30T16:25:41Z
--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8653353814644136 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1339 - F1: 0.8653 ## 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: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 | | 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 | | 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 | ### Framework versions - Transformers 4.16.2 - Pytorch 2.0.1+cu118 - Datasets 1.16.1 - Tokenizers 0.13.3
kimetsu/Whisper-Small-TF-TIMIT-FLEUR
kimetsu
2023-07-30T16:33:39Z
76
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-03-29T09:43:39Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper-Small-TF-TIMIT-FLEUR 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. --> # Whisper-Small-TF-TIMIT-FLEUR This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8885 - Wer: 35.0461 ## 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: 6.25e-06 - 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 - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.4965 | 1.27 | 500 | 0.9304 | 37.3857 | | 0.1668 | 2.54 | 1000 | 0.8561 | 32.7384 | | 0.069 | 3.81 | 1500 | 0.8093 | 52.7441 | | 0.0152 | 5.08 | 2000 | 0.9021 | 54.9437 | | 0.0083 | 6.35 | 2500 | 0.8471 | 57.3611 | | 0.0021 | 7.61 | 3000 | 0.8885 | 35.0461 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
kimetsu/Whisper-Small-TF-TIMIT
kimetsu
2023-07-30T16:32:47Z
78
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-03-06T16:37:15Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper-Small-TF-TIMIT 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. --> # Whisper-Small-TF-TIMIT This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7104 - Wer: 98.0856 ## 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: 32 - 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_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.3408 | 3.45 | 500 | 0.3994 | 83.6838 | | 0.2057 | 6.9 | 1000 | 0.4079 | 92.3470 | | 0.0616 | 10.34 | 1500 | 0.5076 | 94.2053 | | 0.023 | 13.79 | 2000 | 0.5998 | 95.3184 | | 0.0043 | 17.24 | 2500 | 0.6825 | 97.1284 | | 0.0023 | 20.69 | 3000 | 0.7104 | 98.0856 | ### Framework versions - Transformers 4.27.0.dev0 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
kimetsu/Whisper-Small-TF-TIMIT-FLEUR-Normalizado
kimetsu
2023-07-30T16:31:42Z
85
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-04-04T16:17:00Z
--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper-Small-TF-TIMIT-FLEUR-Normalizado 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. --> # Whisper-Small-TF-TIMIT-FLEUR-Normalizado This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7395 - Wer: 85.3796 ## 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: 8 - 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: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.5923 | 1.27 | 500 | 0.9379 | 98.7612 | | 0.1823 | 2.54 | 1000 | 0.6721 | 89.3262 | | 0.0852 | 3.81 | 1500 | 0.6534 | 86.1141 | | 0.0327 | 5.08 | 2000 | 0.6794 | 84.4019 | | 0.0106 | 6.35 | 2500 | 0.7170 | 82.5587 | | 0.0064 | 7.61 | 3000 | 0.7395 | 85.3796 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 1.13.0 - Datasets 2.1.0 - Tokenizers 0.13.2
efainman/rl_course_vizdoom_health_gathering_supreme
efainman
2023-07-30T16:28:20Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T16:28:15Z
--- 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_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 10.31 +/- 4.54 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 efainman/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` 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 .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
teftef/LARM_mix_xl
teftef
2023-07-30T16:20:10Z
0
2
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-30T13:06:56Z
--- license: creativeml-openrail-m --- # LARM_mix_xl StableDiffusionXL model merge for anime. Negative Prompt Embedding can be obtained [here](https://huggingface.co/gsdf/CounterfeitXL/tree/main/embeddings) . ### Examples <img src="https://cdn-uploads.huggingface.co/production/uploads/63056e2d99870e13d3df4e73/GPBK1UTXyejQpEaHSaBpu.png" width="1200" > - Prompt: face focus, dynamic angle, masterpiece, best quality, solo, 1girl, face focus, cute, masterpiece, best quality, 1girl holding lycoris, black background, light particle, solo, black hair, red eyes, standing, pixiv, depth of field, cinematic compotision, best lighting, looking up - Negative prompt: (low quality, worst quality:1.3), 3d, embedding:negativeXL_C.safetensors, watermark, signature, ugly, nsfw, (worst quality, low quality, normal quality:1.2), (bad anatomy, bad hands, missing fingers, extra digit, fewer digits:1.1) - Steps: 30 - Sampler: ddpm_2s_ancestral - CFG scale: 12 - Seed: 3178 - Size: 1024x1024 - Scheduler normal - Denoise: 1.0 <img src="https://cdn-uploads.huggingface.co/production/uploads/63056e2d99870e13d3df4e73/3oI1BQh1NxU0JuHja7VOJ.png" width="1200" > - Prompt: face focus, dynamic angle, masterpiece, best quality, solo, 1girl, looking at viewer, solo, brown hair, outdoors, brown eyes, falling autumn leaves, plaid brown dress, medium hair, black boots, white coat, pixiv, depth of field, smile - Negative prompt: (low quality, worst quality:1.3), 3d, embedding:negativeXL_C.safetensors, watermark, signature, ugly, nsfw, (worst quality, low quality, normal quality:1.2), (bad anatomy, bad hands, missing fingers, extra digit, fewer digits:1.1), - Steps: 30 - Sampler: ddpm_2s_ancestral - CFG scale: 12 - Seed: 3157 - Size: 1024x1024 - Scheduler normal - Denoise: 1.0 ### Notes ・Feel free to use it for merging. ・Do not sell this model for commercial purposes. ・Do not use for crimes. thanks to the author of [Counterfeit XL](https://civitai.com/models/118406/counterfeitxl) [Reproduction](https://civitai.com/models/118729?modelVersionId=128846) [Swim In Latent](https://civitai.com/models/118525/swim-in-latent) [not waifu](https://huggingface.co/gmonsoon/notwaifu-diffusion-xl/tree/main) Public : 2023/07/30 teftef
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l4_v50
KingKazma
2023-07-30T16:20:08Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:20:06Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l54_v50
KingKazma
2023-07-30T16:19:01Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:18:58Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
yeiker/Canservero
yeiker
2023-07-30T16:17:12Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-30T16:17:12Z
--- license: creativeml-openrail-m ---
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3_l4_v50
KingKazma
2023-07-30T16:12:05Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:12:04Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e-1_s6789_v3_l54_v50
KingKazma
2023-07-30T16:11:04Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T16:11:00Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Ningxin/llama2_wikitext_centralized_4
Ningxin
2023-07-30T15:58:28Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T15:56:15Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
iamnambiar/q-FrozenLake-v1-4x4-noSlippery
iamnambiar
2023-07-30T15:55:43Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T15:32:31Z
--- 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="iamnambiar/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"]) ```
matnord/PPO-LunarLander
matnord
2023-07-30T15:44:04Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T15:43:42Z
--- 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: 261.51 +/- 17.11 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 ... ```
pratsy/poca-SoccerTwos
pratsy
2023-07-30T15:31:10Z
4
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos", "region:us" ]
reinforcement-learning
2023-07-30T15:30:34Z
--- 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: pratsy/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
d-karpone/whisper-small-dv
d-karpone
2023-07-30T15:14:26Z
78
0
transformers
[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-07-30T14:36:45Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-small-dv results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3305785123966942 --- <!-- 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 This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.6857 - Wer Ortho: 32.4491 - Wer: 0.3306 ## 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 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0008 | 17.86 | 500 | 0.6857 | 32.4491 | 0.3306 | ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.1 - Datasets 2.13.1 - Tokenizers 0.13.3
csabad/ppo-LunarLander-v2
csabad
2023-07-30T14:42:50Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T14:42:27Z
--- 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: 223.00 +/- 20.20 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 ... ```
reach-vb/bark-endpoint
reach-vb
2023-07-30T14:34:35Z
13
0
transformers
[ "transformers", "pytorch", "bark", "text-to-audio", "audio", "text-to-speech", "en", "de", "es", "fr", "hi", "it", "ja", "ko", "pl", "pt", "ru", "tr", "zh", "license:cc-by-nc-4.0", "region:us" ]
text-to-speech
2023-07-30T14:34:35Z
--- language: - en - de - es - fr - hi - it - ja - ko - pl - pt - ru - tr - zh thumbnail: >- https://user-images.githubusercontent.com/5068315/230698495-cbb1ced9-c911-4c9a-941d-a1a4a1286ac6.png library: bark license: cc-by-nc-4.0 tags: - bark - audio - text-to-speech pipeline_tag: text-to-speech inference: false duplicated_from: suno/bark --- # Bark Bark is a transformer-based text-to-audio model created by [Suno](https://www.suno.ai). Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints ready for inference. The original github repo and model card can be found [here](https://github.com/suno-ai/bark). This model is meant for research purposes only. The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk. Two checkpoints are released: - [small](https://huggingface.co/suno/bark-small) - [**large** (this checkpoint)](https://huggingface.co/suno/bark) ## Example Try out Bark yourself! * Bark Colab: <a target="_blank" href="https://colab.research.google.com/drive/1eJfA2XUa-mXwdMy7DoYKVYHI1iTd9Vkt?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/drive/1dWWkZzvu7L9Bunq9zvD-W02RFUXoW-Pd?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/suno/bark"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run Bark locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) from main: ``` pip install git+https://github.com/huggingface/transformers.git ``` 2. Run the following Python code to generate speech samples: ```python from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("suno/bark-small") model = AutoModel.from_pretrained("suno/bark-small") inputs = processor( text=["Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe."], return_tensors="pt", ) speech_values = model.generate(**inputs, do_sample=True) ``` 3. Listen to the speech samples either in an ipynb notebook: ```python from IPython.display import Audio sampling_rate = model.generation_config.sample_rate Audio(speech_values.cpu().numpy().squeeze(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```python import scipy sampling_rate = model.config.sample_rate scipy.io.wavfile.write("bark_out.wav", rate=sampling_rate, data=speech_values.cpu().numpy().squeeze()) ``` For more details on using the Bark model for inference using the 🤗 Transformers library, refer to the [Bark docs](https://huggingface.co/docs/transformers/model_doc/bark). ## Suno Usage You can also run Bark locally through the original [Bark library]((https://github.com/suno-ai/bark): 1. First install the [`bark` library](https://github.com/suno-ai/bark) 3. Run the following Python code: ```python from bark import SAMPLE_RATE, generate_audio, preload_models from IPython.display import Audio # download and load all models preload_models() # generate audio from text text_prompt = """ Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe. """ speech_array = generate_audio(text_prompt) # play text in notebook Audio(speech_array, rate=SAMPLE_RATE) ``` [pizza.webm](https://user-images.githubusercontent.com/5068315/230490503-417e688d-5115-4eee-9550-b46a2b465ee3.webm) To save `audio_array` as a WAV file: ```python from scipy.io.wavfile import write as write_wav write_wav("/path/to/audio.wav", SAMPLE_RATE, audio_array) ``` ## Model Details The following is additional information about the models released here. Bark is a series of three transformer models that turn text into audio. ### Text to semantic tokens - Input: text, tokenized with [BERT tokenizer from Hugging Face](https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertTokenizer) - Output: semantic tokens that encode the audio to be generated ### Semantic to coarse tokens - Input: semantic tokens - Output: tokens from the first two codebooks of the [EnCodec Codec](https://github.com/facebookresearch/encodec) from facebook ### Coarse to fine tokens - Input: the first two codebooks from EnCodec - Output: 8 codebooks from EnCodec ### Architecture | Model | Parameters | Attention | Output Vocab size | |:-------------------------:|:----------:|------------|:-----------------:| | Text to semantic tokens | 80/300 M | Causal | 10,000 | | Semantic to coarse tokens | 80/300 M | Causal | 2x 1,024 | | Coarse to fine tokens | 80/300 M | Non-causal | 6x 1,024 | ### Release date April 2023 ## Broader Implications We anticipate that this model's text to audio capabilities can be used to improve accessbility tools in a variety of languages. While we hope that this release will enable users to express their creativity and build applications that are a force for good, we acknowledge that any text to audio model has the potential for dual use. While it is not straightforward to voice clone known people with Bark, it can still be used for nefarious purposes. To further reduce the chances of unintended use of Bark, we also release a simple classifier to detect Bark-generated audio with high accuracy (see notebooks section of the main repository).
digiplay/DreamShaper_8
digiplay
2023-07-30T14:30:18Z
2,414
15
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-30T13:39:08Z
--- license: other tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers inference: true --- Model info: https://civitai.com/models/4384?modelVersionId=128713 Original Author's DEMO images : ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/dd9b038c-bd15-43ab-86ab-66e145ad7ff2/width=1096/26072158-132340247-8k%20portrait%20of%20beautiful%20cyborg%20with%20brown%20hair,%20intricate,%20elegant,%20highly%20detailed,%20majestic,%20digital%20photography,%20art%20by%20artg_ed.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c1033497-007c-4a73-b812-915c8e32e8fe/width=1120/26072224-5775713-(masterpiece),%20(extremely%20intricate_1.3),%20(realistic),%20portrait%20of%20a%20girl,%20the%20most%20beautiful%20in%20the%20world,%20(medieval%20armor),%20me.jpeg) ![](https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e5f49ec5-62f7-4511-8be6-517042729091/width=1352/26072419-1584580292-masterpiece,%20(photorealistic_1.4),%20best%20quality,%20beautiful%20lighting,%20(ulzzang-6500_0.5),%20lucy%20_(cyberpunk_),%201girl,%20white%20hair,.jpeg) Sample image generated by huggingface's API : ![a04f24a1-7301-4458-a292-a8f41636c617.jpeg](https://cdn-uploads.huggingface.co/production/uploads/646c83c871d0c8a6e4455854/8wrFo-Rq2mHn0x8Kwu-KZ.jpeg) *generated by huggingface's API
minatosnow/swinv2-small-patch4-window16-256-mineral
minatosnow
2023-07-30T14:23:14Z
6
0
transformers
[ "transformers", "pytorch", "swinv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swinv2-small-patch4-window16-256", "base_model:finetune:microsoft/swinv2-small-patch4-window16-256", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-29T18:28:25Z
--- license: apache-2.0 base_model: microsoft/swinv2-small-patch4-window16-256 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swinv2-small-patch4-window16-256-mineral results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.24 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swinv2-small-patch4-window16-256-mineral This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 4.9130 - Accuracy: 0.24 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:------:|:----:|:---------------:|:--------:| | 5.6941 | 0.96 | 18 | 5.6921 | 0.005 | | 5.6886 | 1.97 | 37 | 5.6825 | 0.005 | | 5.6735 | 2.99 | 56 | 5.6691 | 0.005 | | 5.6521 | 4.0 | 75 | 5.6549 | 0.0033 | | 5.6394 | 4.96 | 93 | 5.6416 | 0.0033 | | 5.6078 | 5.97 | 112 | 5.6278 | 0.0033 | | 5.5743 | 6.99 | 131 | 5.6128 | 0.0017 | | 5.5509 | 8.0 | 150 | 5.5918 | 0.0017 | | 5.5115 | 8.96 | 168 | 5.5696 | 0.0067 | | 5.4411 | 9.97 | 187 | 5.5440 | 0.01 | | 5.3335 | 10.99 | 206 | 5.5135 | 0.0167 | | 5.2413 | 12.0 | 225 | 5.4640 | 0.0217 | | 5.1738 | 12.96 | 243 | 5.4084 | 0.0333 | | 5.0222 | 13.97 | 262 | 5.3321 | 0.045 | | 4.8594 | 14.99 | 281 | 5.2485 | 0.0533 | | 4.7441 | 16.0 | 300 | 5.1509 | 0.065 | | 4.5946 | 16.96 | 318 | 5.0701 | 0.0717 | | 4.3382 | 17.97 | 337 | 4.9767 | 0.0867 | | 4.2008 | 18.99 | 356 | 4.8622 | 0.105 | | 4.0563 | 20.0 | 375 | 4.7726 | 0.1033 | | 3.8064 | 20.96 | 393 | 4.6898 | 0.115 | | 3.5584 | 21.97 | 412 | 4.5997 | 0.125 | | 3.3377 | 22.99 | 431 | 4.4848 | 0.1367 | | 3.1119 | 24.0 | 450 | 4.4052 | 0.1533 | | 2.8686 | 24.96 | 468 | 4.3705 | 0.15 | | 2.7649 | 25.97 | 487 | 4.2980 | 0.165 | | 2.5698 | 26.99 | 506 | 4.2363 | 0.1767 | | 2.4344 | 28.0 | 525 | 4.1733 | 0.1767 | | 2.2186 | 28.96 | 543 | 4.1783 | 0.1733 | | 2.0227 | 29.97 | 562 | 4.1306 | 0.18 | | 1.9153 | 30.99 | 581 | 4.0948 | 0.175 | | 1.7363 | 32.0 | 600 | 4.0612 | 0.1783 | | 1.6171 | 32.96 | 618 | 4.0209 | 0.185 | | 1.4865 | 33.97 | 637 | 4.0194 | 0.185 | | 1.3194 | 34.99 | 656 | 3.9881 | 0.205 | | 1.2811 | 36.0 | 675 | 3.9862 | 0.215 | | 1.1703 | 36.96 | 693 | 3.9905 | 0.2033 | | 1.114 | 37.97 | 712 | 3.9514 | 0.2133 | | 0.9645 | 38.99 | 731 | 3.9678 | 0.2067 | | 0.8976 | 40.0 | 750 | 3.9874 | 0.2167 | | 0.8147 | 40.96 | 768 | 3.9257 | 0.2083 | | 0.7239 | 41.97 | 787 | 3.9394 | 0.2217 | | 0.7732 | 42.99 | 806 | 3.9473 | 0.215 | | 0.7009 | 44.0 | 825 | 3.9461 | 0.215 | | 0.5945 | 44.96 | 843 | 4.0207 | 0.2133 | | 0.555 | 45.97 | 862 | 4.0353 | 0.2083 | | 0.5241 | 46.99 | 881 | 4.0232 | 0.2167 | | 0.4789 | 48.0 | 900 | 4.0026 | 0.22 | | 0.4284 | 48.96 | 918 | 4.0031 | 0.22 | | 0.4701 | 49.97 | 937 | 4.0572 | 0.215 | | 0.4501 | 50.99 | 956 | 4.0877 | 0.215 | | 0.3966 | 52.0 | 975 | 4.0207 | 0.2167 | | 0.3564 | 52.96 | 993 | 4.0827 | 0.215 | | 0.3472 | 53.97 | 1012 | 4.0902 | 0.235 | | 0.3731 | 54.99 | 1031 | 4.0953 | 0.2417 | | 0.3161 | 56.0 | 1050 | 4.1660 | 0.2033 | | 0.3352 | 56.96 | 1068 | 4.1153 | 0.2217 | | 0.3317 | 57.97 | 1087 | 4.1096 | 0.2167 | | 0.294 | 58.99 | 1106 | 4.1856 | 0.215 | | 0.3299 | 60.0 | 1125 | 4.1476 | 0.2233 | | 0.2847 | 60.96 | 1143 | 4.2046 | 0.225 | | 0.2924 | 61.97 | 1162 | 4.1568 | 0.2183 | | 0.2818 | 62.99 | 1181 | 4.1519 | 0.2333 | | 0.2698 | 64.0 | 1200 | 4.2275 | 0.215 | | 0.2579 | 64.96 | 1218 | 4.1626 | 0.235 | | 0.2597 | 65.97 | 1237 | 4.2277 | 0.2217 | | 0.2443 | 66.99 | 1256 | 4.1929 | 0.2367 | | 0.2532 | 68.0 | 1275 | 4.2779 | 0.2233 | | 0.2305 | 68.96 | 1293 | 4.2441 | 0.2367 | | 0.2423 | 69.97 | 1312 | 4.2583 | 0.2217 | | 0.222 | 70.99 | 1331 | 4.2935 | 0.23 | | 0.2096 | 72.0 | 1350 | 4.2714 | 0.23 | | 0.1776 | 72.96 | 1368 | 4.2348 | 0.225 | | 0.2009 | 73.97 | 1387 | 4.2930 | 0.2283 | | 0.2087 | 74.99 | 1406 | 4.3071 | 0.235 | | 0.1818 | 76.0 | 1425 | 4.2960 | 0.235 | | 0.2236 | 76.96 | 1443 | 4.2910 | 0.24 | | 0.1802 | 77.97 | 1462 | 4.2896 | 0.25 | | 0.2037 | 78.99 | 1481 | 4.3314 | 0.245 | | 0.1912 | 80.0 | 1500 | 4.2612 | 0.2333 | | 0.2305 | 80.96 | 1518 | 4.2790 | 0.2367 | | 0.2188 | 81.97 | 1537 | 4.3069 | 0.2217 | | 0.1639 | 82.99 | 1556 | 4.3539 | 0.2183 | | 0.1741 | 84.0 | 1575 | 4.3211 | 0.225 | | 0.1937 | 84.96 | 1593 | 4.3576 | 0.2117 | | 0.1712 | 85.97 | 1612 | 4.3434 | 0.2233 | | 0.1665 | 86.99 | 1631 | 4.3349 | 0.2117 | | 0.1846 | 88.0 | 1650 | 4.4170 | 0.235 | | 0.1827 | 88.96 | 1668 | 4.3350 | 0.23 | | 0.1591 | 89.97 | 1687 | 4.3397 | 0.215 | | 0.1508 | 90.99 | 1706 | 4.3273 | 0.2317 | | 0.1808 | 92.0 | 1725 | 4.3315 | 0.2317 | | 0.17 | 92.96 | 1743 | 4.2760 | 0.24 | | 0.14 | 93.97 | 1762 | 4.3144 | 0.2333 | | 0.1734 | 94.99 | 1781 | 4.3667 | 0.2283 | | 0.1593 | 96.0 | 1800 | 4.3903 | 0.225 | | 0.1523 | 96.96 | 1818 | 4.3314 | 0.24 | | 0.1599 | 97.97 | 1837 | 4.4115 | 0.23 | | 0.1352 | 98.99 | 1856 | 4.3626 | 0.2467 | | 0.1406 | 100.0 | 1875 | 4.3555 | 0.2383 | | 0.1486 | 100.96 | 1893 | 4.3116 | 0.2383 | | 0.149 | 101.97 | 1912 | 4.3894 | 0.23 | | 0.115 | 102.99 | 1931 | 4.3755 | 0.2233 | | 0.1301 | 104.0 | 1950 | 4.3765 | 0.2317 | | 0.1429 | 104.96 | 1968 | 4.4027 | 0.235 | | 0.1209 | 105.97 | 1987 | 4.3803 | 0.2317 | | 0.1287 | 106.99 | 2006 | 4.3235 | 0.2467 | | 0.1318 | 108.0 | 2025 | 4.3484 | 0.24 | | 0.1136 | 108.96 | 2043 | 4.3977 | 0.225 | | 0.1326 | 109.97 | 2062 | 4.3978 | 0.2267 | | 0.1415 | 110.99 | 2081 | 4.3214 | 0.2383 | | 0.1229 | 112.0 | 2100 | 4.3699 | 0.2467 | | 0.1004 | 112.96 | 2118 | 4.3828 | 0.2583 | | 0.0961 | 113.97 | 2137 | 4.3564 | 0.2517 | | 0.1132 | 114.99 | 2156 | 4.3384 | 0.2533 | | 0.1166 | 116.0 | 2175 | 4.4152 | 0.2417 | | 0.1193 | 116.96 | 2193 | 4.3634 | 0.2417 | | 0.096 | 117.97 | 2212 | 4.3826 | 0.235 | | 0.1158 | 118.99 | 2231 | 4.4524 | 0.235 | | 0.099 | 120.0 | 2250 | 4.4978 | 0.2233 | | 0.1065 | 120.96 | 2268 | 4.4124 | 0.24 | | 0.129 | 121.97 | 2287 | 4.3814 | 0.235 | | 0.1047 | 122.99 | 2306 | 4.3663 | 0.2467 | | 0.101 | 124.0 | 2325 | 4.5113 | 0.23 | | 0.1076 | 124.96 | 2343 | 4.4553 | 0.2367 | | 0.1135 | 125.97 | 2362 | 4.4351 | 0.23 | | 0.1066 | 126.99 | 2381 | 4.4874 | 0.235 | | 0.1256 | 128.0 | 2400 | 4.4635 | 0.2333 | | 0.0932 | 128.96 | 2418 | 4.4576 | 0.2383 | | 0.1189 | 129.97 | 2437 | 4.5770 | 0.2267 | | 0.1096 | 130.99 | 2456 | 4.4921 | 0.2317 | | 0.0791 | 132.0 | 2475 | 4.5090 | 0.2267 | | 0.1152 | 132.96 | 2493 | 4.4572 | 0.2417 | | 0.1264 | 133.97 | 2512 | 4.5109 | 0.25 | | 0.1009 | 134.99 | 2531 | 4.5236 | 0.2283 | | 0.0956 | 136.0 | 2550 | 4.4783 | 0.245 | | 0.0919 | 136.96 | 2568 | 4.5484 | 0.2467 | | 0.1042 | 137.97 | 2587 | 4.5423 | 0.2433 | | 0.1039 | 138.99 | 2606 | 4.4918 | 0.245 | | 0.094 | 140.0 | 2625 | 4.5456 | 0.2467 | | 0.1056 | 140.96 | 2643 | 4.5219 | 0.245 | | 0.0918 | 141.97 | 2662 | 4.5255 | 0.245 | | 0.0877 | 142.99 | 2681 | 4.4923 | 0.2383 | | 0.105 | 144.0 | 2700 | 4.5352 | 0.235 | | 0.0892 | 144.96 | 2718 | 4.4715 | 0.245 | | 0.0963 | 145.97 | 2737 | 4.5060 | 0.245 | | 0.095 | 146.99 | 2756 | 4.5593 | 0.2433 | | 0.0997 | 148.0 | 2775 | 4.5804 | 0.24 | | 0.0839 | 148.96 | 2793 | 4.5917 | 0.23 | | 0.0924 | 149.97 | 2812 | 4.5931 | 0.2267 | | 0.0781 | 150.99 | 2831 | 4.5784 | 0.2317 | | 0.0986 | 152.0 | 2850 | 4.6546 | 0.2283 | | 0.0823 | 152.96 | 2868 | 4.5985 | 0.2367 | | 0.0887 | 153.97 | 2887 | 4.6148 | 0.23 | | 0.0671 | 154.99 | 2906 | 4.6397 | 0.2333 | | 0.0897 | 156.0 | 2925 | 4.5834 | 0.235 | | 0.093 | 156.96 | 2943 | 4.5397 | 0.2433 | | 0.0973 | 157.97 | 2962 | 4.5532 | 0.2333 | | 0.1001 | 158.99 | 2981 | 4.5827 | 0.24 | | 0.0884 | 160.0 | 3000 | 4.5728 | 0.235 | | 0.084 | 160.96 | 3018 | 4.6542 | 0.235 | | 0.0902 | 161.97 | 3037 | 4.6366 | 0.2417 | | 0.0944 | 162.99 | 3056 | 4.5957 | 0.2383 | | 0.0828 | 164.0 | 3075 | 4.6521 | 0.23 | | 0.0812 | 164.96 | 3093 | 4.6761 | 0.2367 | | 0.0817 | 165.97 | 3112 | 4.6272 | 0.225 | | 0.07 | 166.99 | 3131 | 4.6536 | 0.2433 | | 0.0746 | 168.0 | 3150 | 4.5671 | 0.245 | | 0.0782 | 168.96 | 3168 | 4.5915 | 0.24 | | 0.0677 | 169.97 | 3187 | 4.6373 | 0.2433 | | 0.0626 | 170.99 | 3206 | 4.6723 | 0.2583 | | 0.0697 | 172.0 | 3225 | 4.6817 | 0.245 | | 0.077 | 172.96 | 3243 | 4.6793 | 0.23 | | 0.068 | 173.97 | 3262 | 4.7110 | 0.2417 | | 0.0875 | 174.99 | 3281 | 4.7012 | 0.2433 | | 0.0787 | 176.0 | 3300 | 4.7113 | 0.2367 | | 0.0779 | 176.96 | 3318 | 4.6998 | 0.24 | | 0.0823 | 177.97 | 3337 | 4.7092 | 0.24 | | 0.0685 | 178.99 | 3356 | 4.6763 | 0.245 | | 0.0698 | 180.0 | 3375 | 4.7181 | 0.2567 | | 0.0924 | 180.96 | 3393 | 4.7151 | 0.2483 | | 0.084 | 181.97 | 3412 | 4.7231 | 0.2417 | | 0.0508 | 182.99 | 3431 | 4.6856 | 0.2317 | | 0.0637 | 184.0 | 3450 | 4.7041 | 0.2417 | | 0.06 | 184.96 | 3468 | 4.7205 | 0.24 | | 0.0659 | 185.97 | 3487 | 4.7251 | 0.2433 | | 0.0842 | 186.99 | 3506 | 4.7215 | 0.23 | | 0.0733 | 188.0 | 3525 | 4.7068 | 0.24 | | 0.0647 | 188.96 | 3543 | 4.7594 | 0.2367 | | 0.0569 | 189.97 | 3562 | 4.7831 | 0.2233 | | 0.0883 | 190.99 | 3581 | 4.7212 | 0.235 | | 0.0622 | 192.0 | 3600 | 4.6878 | 0.2417 | | 0.057 | 192.96 | 3618 | 4.6654 | 0.2467 | | 0.0654 | 193.97 | 3637 | 4.6358 | 0.2517 | | 0.0868 | 194.99 | 3656 | 4.6621 | 0.2333 | | 0.0789 | 196.0 | 3675 | 4.6985 | 0.2333 | | 0.0657 | 196.96 | 3693 | 4.6636 | 0.2567 | | 0.0648 | 197.97 | 3712 | 4.7698 | 0.2467 | | 0.0635 | 198.99 | 3731 | 4.7226 | 0.2417 | | 0.0637 | 200.0 | 3750 | 4.7481 | 0.245 | | 0.0665 | 200.96 | 3768 | 4.7789 | 0.2483 | | 0.0799 | 201.97 | 3787 | 4.7014 | 0.235 | | 0.064 | 202.99 | 3806 | 4.7528 | 0.2417 | | 0.0772 | 204.0 | 3825 | 4.7401 | 0.2383 | | 0.0438 | 204.96 | 3843 | 4.7678 | 0.2417 | | 0.0766 | 205.97 | 3862 | 4.7180 | 0.2367 | | 0.0687 | 206.99 | 3881 | 4.7058 | 0.2433 | | 0.0801 | 208.0 | 3900 | 4.7584 | 0.235 | | 0.0772 | 208.96 | 3918 | 4.7304 | 0.2433 | | 0.0663 | 209.97 | 3937 | 4.6940 | 0.2367 | | 0.0529 | 210.99 | 3956 | 4.6940 | 0.235 | | 0.0568 | 212.0 | 3975 | 4.7333 | 0.235 | | 0.0697 | 212.96 | 3993 | 4.6673 | 0.2367 | | 0.0394 | 213.97 | 4012 | 4.6733 | 0.245 | | 0.0625 | 214.99 | 4031 | 4.7383 | 0.225 | | 0.0588 | 216.0 | 4050 | 4.7674 | 0.24 | | 0.0594 | 216.96 | 4068 | 4.6873 | 0.2417 | | 0.0451 | 217.97 | 4087 | 4.6718 | 0.2433 | | 0.047 | 218.99 | 4106 | 4.7146 | 0.2283 | | 0.0445 | 220.0 | 4125 | 4.7174 | 0.2283 | | 0.0746 | 220.96 | 4143 | 4.6702 | 0.2367 | | 0.0697 | 221.97 | 4162 | 4.6462 | 0.2367 | | 0.0562 | 222.99 | 4181 | 4.6956 | 0.2333 | | 0.047 | 224.0 | 4200 | 4.7278 | 0.2383 | | 0.0612 | 224.96 | 4218 | 4.7307 | 0.235 | | 0.0625 | 225.97 | 4237 | 4.6670 | 0.2567 | | 0.0739 | 226.99 | 4256 | 4.7110 | 0.2317 | | 0.0637 | 228.0 | 4275 | 4.7039 | 0.22 | | 0.0461 | 228.96 | 4293 | 4.7119 | 0.2267 | | 0.0506 | 229.97 | 4312 | 4.7099 | 0.23 | | 0.0412 | 230.99 | 4331 | 4.6714 | 0.2317 | | 0.057 | 232.0 | 4350 | 4.6921 | 0.2367 | | 0.0402 | 232.96 | 4368 | 4.7545 | 0.2317 | | 0.058 | 233.97 | 4387 | 4.7573 | 0.225 | | 0.0661 | 234.99 | 4406 | 4.6800 | 0.2283 | | 0.0613 | 236.0 | 4425 | 4.6533 | 0.2433 | | 0.0462 | 236.96 | 4443 | 4.6748 | 0.2283 | | 0.0494 | 237.97 | 4462 | 4.6874 | 0.23 | | 0.0643 | 238.99 | 4481 | 4.7291 | 0.2333 | | 0.0422 | 240.0 | 4500 | 4.7088 | 0.23 | | 0.0376 | 240.96 | 4518 | 4.7422 | 0.225 | | 0.0696 | 241.97 | 4537 | 4.8011 | 0.2283 | | 0.0609 | 242.99 | 4556 | 4.8013 | 0.2217 | | 0.0637 | 244.0 | 4575 | 4.7603 | 0.225 | | 0.0529 | 244.96 | 4593 | 4.7895 | 0.2233 | | 0.0603 | 245.97 | 4612 | 4.7639 | 0.235 | | 0.0365 | 246.99 | 4631 | 4.7285 | 0.2433 | | 0.0732 | 248.0 | 4650 | 4.7252 | 0.2283 | | 0.0709 | 248.96 | 4668 | 4.7620 | 0.23 | | 0.0485 | 249.97 | 4687 | 4.7529 | 0.2367 | | 0.0449 | 250.99 | 4706 | 4.8006 | 0.2417 | | 0.0506 | 252.0 | 4725 | 4.8028 | 0.2333 | | 0.0455 | 252.96 | 4743 | 4.7778 | 0.2367 | | 0.0594 | 253.97 | 4762 | 4.7439 | 0.2383 | | 0.0551 | 254.99 | 4781 | 4.8069 | 0.2367 | | 0.0435 | 256.0 | 4800 | 4.8171 | 0.2383 | | 0.042 | 256.96 | 4818 | 4.7961 | 0.2383 | | 0.0403 | 257.97 | 4837 | 4.8172 | 0.2383 | | 0.0524 | 258.99 | 4856 | 4.8537 | 0.23 | | 0.0461 | 260.0 | 4875 | 4.7698 | 0.2283 | | 0.05 | 260.96 | 4893 | 4.8058 | 0.2483 | | 0.0545 | 261.97 | 4912 | 4.8398 | 0.2333 | | 0.0405 | 262.99 | 4931 | 4.8228 | 0.2367 | | 0.0615 | 264.0 | 4950 | 4.8395 | 0.2367 | | 0.0381 | 264.96 | 4968 | 4.8231 | 0.2233 | | 0.0464 | 265.97 | 4987 | 4.8180 | 0.2367 | | 0.058 | 266.99 | 5006 | 4.8744 | 0.235 | | 0.0553 | 268.0 | 5025 | 4.8866 | 0.2367 | | 0.0505 | 268.96 | 5043 | 4.8534 | 0.24 | | 0.049 | 269.97 | 5062 | 4.8702 | 0.2333 | | 0.0444 | 270.99 | 5081 | 4.8715 | 0.2267 | | 0.0457 | 272.0 | 5100 | 4.8274 | 0.225 | | 0.0546 | 272.96 | 5118 | 4.8441 | 0.225 | | 0.0378 | 273.97 | 5137 | 4.8229 | 0.225 | | 0.0374 | 274.99 | 5156 | 4.8053 | 0.2217 | | 0.047 | 276.0 | 5175 | 4.8619 | 0.2333 | | 0.0526 | 276.96 | 5193 | 4.8793 | 0.2417 | | 0.0503 | 277.97 | 5212 | 4.9060 | 0.2283 | | 0.0414 | 278.99 | 5231 | 4.8687 | 0.24 | | 0.0361 | 280.0 | 5250 | 4.8537 | 0.24 | | 0.0449 | 280.96 | 5268 | 4.8204 | 0.2383 | | 0.0596 | 281.97 | 5287 | 4.8030 | 0.2367 | | 0.0494 | 282.99 | 5306 | 4.8060 | 0.2483 | | 0.0483 | 284.0 | 5325 | 4.7878 | 0.235 | | 0.0338 | 284.96 | 5343 | 4.8254 | 0.2383 | | 0.0319 | 285.97 | 5362 | 4.8264 | 0.2383 | | 0.0454 | 286.99 | 5381 | 4.8426 | 0.2367 | | 0.0409 | 288.0 | 5400 | 4.8198 | 0.2483 | | 0.0435 | 288.96 | 5418 | 4.8339 | 0.2367 | | 0.0498 | 289.97 | 5437 | 4.8387 | 0.225 | | 0.0447 | 290.99 | 5456 | 4.8342 | 0.23 | | 0.0402 | 292.0 | 5475 | 4.8496 | 0.2333 | | 0.0366 | 292.96 | 5493 | 4.8671 | 0.2317 | | 0.0369 | 293.97 | 5512 | 4.8366 | 0.2467 | | 0.0361 | 294.99 | 5531 | 4.7992 | 0.2433 | | 0.0448 | 296.0 | 5550 | 4.8486 | 0.2267 | | 0.055 | 296.96 | 5568 | 4.8979 | 0.2267 | | 0.0585 | 297.97 | 5587 | 4.8660 | 0.2367 | | 0.0477 | 298.99 | 5606 | 4.8717 | 0.2433 | | 0.0247 | 300.0 | 5625 | 4.8838 | 0.2283 | | 0.047 | 300.96 | 5643 | 4.8248 | 0.2383 | | 0.0608 | 301.97 | 5662 | 4.8330 | 0.2367 | | 0.0417 | 302.99 | 5681 | 4.8236 | 0.2317 | | 0.0494 | 304.0 | 5700 | 4.8070 | 0.2383 | | 0.0316 | 304.96 | 5718 | 4.8213 | 0.2267 | | 0.0421 | 305.97 | 5737 | 4.8634 | 0.2317 | | 0.0411 | 306.99 | 5756 | 4.8770 | 0.24 | | 0.0404 | 308.0 | 5775 | 4.9030 | 0.2383 | | 0.0397 | 308.96 | 5793 | 4.9433 | 0.2383 | | 0.053 | 309.97 | 5812 | 4.9301 | 0.2333 | | 0.0303 | 310.99 | 5831 | 4.8961 | 0.2283 | | 0.0369 | 312.0 | 5850 | 4.8560 | 0.2433 | | 0.0423 | 312.96 | 5868 | 4.9177 | 0.225 | | 0.0343 | 313.97 | 5887 | 4.8928 | 0.2233 | | 0.0216 | 314.99 | 5906 | 4.8958 | 0.23 | | 0.0287 | 316.0 | 5925 | 4.8803 | 0.235 | | 0.0286 | 316.96 | 5943 | 4.8615 | 0.23 | | 0.0304 | 317.97 | 5962 | 4.8736 | 0.2317 | | 0.0486 | 318.99 | 5981 | 4.8825 | 0.2233 | | 0.0404 | 320.0 | 6000 | 4.8618 | 0.2283 | | 0.0439 | 320.96 | 6018 | 4.8848 | 0.23 | | 0.0428 | 321.97 | 6037 | 4.8975 | 0.2267 | | 0.0498 | 322.99 | 6056 | 4.8614 | 0.2383 | | 0.0314 | 324.0 | 6075 | 4.8718 | 0.235 | | 0.0334 | 324.96 | 6093 | 4.9021 | 0.2383 | | 0.0431 | 325.97 | 6112 | 4.8973 | 0.2283 | | 0.0473 | 326.99 | 6131 | 4.8671 | 0.24 | | 0.0348 | 328.0 | 6150 | 4.9050 | 0.2333 | | 0.0718 | 328.96 | 6168 | 4.8869 | 0.2417 | | 0.0387 | 329.97 | 6187 | 4.8552 | 0.245 | | 0.0335 | 330.99 | 6206 | 4.8932 | 0.2367 | | 0.0355 | 332.0 | 6225 | 4.9195 | 0.245 | | 0.0407 | 332.96 | 6243 | 4.9163 | 0.2333 | | 0.0471 | 333.97 | 6262 | 4.8860 | 0.225 | | 0.0334 | 334.99 | 6281 | 4.8943 | 0.235 | | 0.0301 | 336.0 | 6300 | 4.9223 | 0.2367 | | 0.0281 | 336.96 | 6318 | 4.9101 | 0.2433 | | 0.0305 | 337.97 | 6337 | 4.8897 | 0.24 | | 0.0505 | 338.99 | 6356 | 4.9290 | 0.2417 | | 0.024 | 340.0 | 6375 | 4.9442 | 0.2333 | | 0.0504 | 340.96 | 6393 | 4.9183 | 0.2367 | | 0.0259 | 341.97 | 6412 | 4.8832 | 0.235 | | 0.0313 | 342.99 | 6431 | 4.8958 | 0.2317 | | 0.0293 | 344.0 | 6450 | 4.8979 | 0.2433 | | 0.0427 | 344.96 | 6468 | 4.9055 | 0.2417 | | 0.0399 | 345.97 | 6487 | 4.8957 | 0.2433 | | 0.0273 | 346.99 | 6506 | 4.8989 | 0.24 | | 0.0388 | 348.0 | 6525 | 4.9087 | 0.2367 | | 0.0306 | 348.96 | 6543 | 4.9264 | 0.2283 | | 0.0411 | 349.97 | 6562 | 4.9219 | 0.2367 | | 0.0394 | 350.99 | 6581 | 4.8998 | 0.24 | | 0.0507 | 352.0 | 6600 | 4.9304 | 0.2317 | | 0.0263 | 352.96 | 6618 | 4.9232 | 0.23 | | 0.0395 | 353.97 | 6637 | 4.9241 | 0.2367 | | 0.0394 | 354.99 | 6656 | 4.9263 | 0.2433 | | 0.0391 | 356.0 | 6675 | 4.9273 | 0.26 | | 0.0647 | 356.96 | 6693 | 4.9034 | 0.2633 | | 0.038 | 357.97 | 6712 | 4.8910 | 0.2467 | | 0.0368 | 358.99 | 6731 | 4.8830 | 0.245 | | 0.0308 | 360.0 | 6750 | 4.8867 | 0.2367 | | 0.0346 | 360.96 | 6768 | 4.8657 | 0.2433 | | 0.0279 | 361.97 | 6787 | 4.8678 | 0.24 | | 0.0443 | 362.99 | 6806 | 4.8723 | 0.2433 | | 0.027 | 364.0 | 6825 | 4.8756 | 0.2433 | | 0.0447 | 364.96 | 6843 | 4.8742 | 0.235 | | 0.028 | 365.97 | 6862 | 4.9042 | 0.235 | | 0.0483 | 366.99 | 6881 | 4.9086 | 0.2367 | | 0.034 | 368.0 | 6900 | 4.8886 | 0.24 | | 0.0363 | 368.96 | 6918 | 4.8778 | 0.2467 | | 0.0417 | 369.97 | 6937 | 4.9051 | 0.2417 | | 0.0326 | 370.99 | 6956 | 4.9112 | 0.2367 | | 0.028 | 372.0 | 6975 | 4.9116 | 0.2333 | | 0.0343 | 372.96 | 6993 | 4.9104 | 0.245 | | 0.0229 | 373.97 | 7012 | 4.9401 | 0.2367 | | 0.0337 | 374.99 | 7031 | 4.9341 | 0.245 | | 0.0356 | 376.0 | 7050 | 4.9336 | 0.2317 | | 0.029 | 376.96 | 7068 | 4.9132 | 0.2333 | | 0.0272 | 377.97 | 7087 | 4.9102 | 0.2367 | | 0.0256 | 378.99 | 7106 | 4.9255 | 0.2317 | | 0.0276 | 380.0 | 7125 | 4.9282 | 0.2267 | | 0.026 | 380.96 | 7143 | 4.9527 | 0.22 | | 0.0385 | 381.97 | 7162 | 4.9411 | 0.2217 | | 0.026 | 382.99 | 7181 | 4.9530 | 0.2367 | | 0.0444 | 384.0 | 7200 | 4.9387 | 0.2383 | | 0.0369 | 384.96 | 7218 | 4.9042 | 0.2333 | | 0.0203 | 385.97 | 7237 | 4.8860 | 0.23 | | 0.0238 | 386.99 | 7256 | 4.8775 | 0.2333 | | 0.0315 | 388.0 | 7275 | 4.8641 | 0.2333 | | 0.0349 | 388.96 | 7293 | 4.8677 | 0.2467 | | 0.038 | 389.97 | 7312 | 4.8688 | 0.24 | | 0.0301 | 390.99 | 7331 | 4.8932 | 0.245 | | 0.0363 | 392.0 | 7350 | 4.9023 | 0.2417 | | 0.0329 | 392.96 | 7368 | 4.8825 | 0.24 | | 0.0174 | 393.97 | 7387 | 4.8711 | 0.24 | | 0.0284 | 394.99 | 7406 | 4.8762 | 0.2433 | | 0.0178 | 396.0 | 7425 | 4.8684 | 0.2417 | | 0.0359 | 396.96 | 7443 | 4.8660 | 0.245 | | 0.029 | 397.97 | 7462 | 4.8799 | 0.2433 | | 0.0227 | 398.99 | 7481 | 4.8845 | 0.25 | | 0.0135 | 400.0 | 7500 | 4.8898 | 0.2383 | | 0.0297 | 400.96 | 7518 | 4.8967 | 0.2383 | | 0.0263 | 401.97 | 7537 | 4.8884 | 0.2333 | | 0.0386 | 402.99 | 7556 | 4.8719 | 0.24 | | 0.0298 | 404.0 | 7575 | 4.8609 | 0.2433 | | 0.0232 | 404.96 | 7593 | 4.8602 | 0.2483 | | 0.0232 | 405.97 | 7612 | 4.8667 | 0.2467 | | 0.032 | 406.99 | 7631 | 4.8684 | 0.2483 | | 0.0306 | 408.0 | 7650 | 4.8755 | 0.2433 | | 0.0299 | 408.96 | 7668 | 4.8687 | 0.245 | | 0.0307 | 409.97 | 7687 | 4.8724 | 0.24 | | 0.0304 | 410.99 | 7706 | 4.8798 | 0.25 | | 0.0293 | 412.0 | 7725 | 4.8901 | 0.2483 | | 0.0273 | 412.96 | 7743 | 4.9025 | 0.24 | | 0.0184 | 413.97 | 7762 | 4.8870 | 0.24 | | 0.0377 | 414.99 | 7781 | 4.8901 | 0.2417 | | 0.0278 | 416.0 | 7800 | 4.8895 | 0.2417 | | 0.0345 | 416.96 | 7818 | 4.9046 | 0.2533 | | 0.0301 | 417.97 | 7837 | 4.9002 | 0.2483 | | 0.0159 | 418.99 | 7856 | 4.8982 | 0.245 | | 0.0203 | 420.0 | 7875 | 4.9008 | 0.2483 | | 0.0182 | 420.96 | 7893 | 4.9113 | 0.2467 | | 0.0258 | 421.97 | 7912 | 4.9180 | 0.25 | | 0.0266 | 422.99 | 7931 | 4.9134 | 0.2433 | | 0.0304 | 424.0 | 7950 | 4.9005 | 0.2417 | | 0.0247 | 424.96 | 7968 | 4.8937 | 0.2417 | | 0.0493 | 425.97 | 7987 | 4.8835 | 0.245 | | 0.0286 | 426.99 | 8006 | 4.8968 | 0.24 | | 0.0228 | 428.0 | 8025 | 4.9066 | 0.2383 | | 0.0362 | 428.96 | 8043 | 4.9031 | 0.245 | | 0.0244 | 429.97 | 8062 | 4.8997 | 0.2467 | | 0.0204 | 430.99 | 8081 | 4.9059 | 0.2433 | | 0.0344 | 432.0 | 8100 | 4.9052 | 0.2433 | | 0.0252 | 432.96 | 8118 | 4.8975 | 0.2433 | | 0.0242 | 433.97 | 8137 | 4.8961 | 0.2467 | | 0.0135 | 434.99 | 8156 | 4.9086 | 0.2467 | | 0.0296 | 436.0 | 8175 | 4.9135 | 0.2417 | | 0.0432 | 436.96 | 8193 | 4.9079 | 0.2433 | | 0.0242 | 437.97 | 8212 | 4.8981 | 0.24 | | 0.0227 | 438.99 | 8231 | 4.8857 | 0.24 | | 0.021 | 440.0 | 8250 | 4.8874 | 0.2383 | | 0.0244 | 440.96 | 8268 | 4.8847 | 0.24 | | 0.0234 | 441.97 | 8287 | 4.8964 | 0.2367 | | 0.0278 | 442.99 | 8306 | 4.9161 | 0.2383 | | 0.0322 | 444.0 | 8325 | 4.9212 | 0.2367 | | 0.038 | 444.96 | 8343 | 4.9251 | 0.24 | | 0.0327 | 445.97 | 8362 | 4.9340 | 0.24 | | 0.0256 | 446.99 | 8381 | 4.9246 | 0.2417 | | 0.0327 | 448.0 | 8400 | 4.9294 | 0.2367 | | 0.0246 | 448.96 | 8418 | 4.9311 | 0.2417 | | 0.0239 | 449.97 | 8437 | 4.9220 | 0.2383 | | 0.0219 | 450.99 | 8456 | 4.9205 | 0.24 | | 0.0287 | 452.0 | 8475 | 4.9249 | 0.2367 | | 0.0244 | 452.96 | 8493 | 4.9275 | 0.24 | | 0.0222 | 453.97 | 8512 | 4.9322 | 0.2417 | | 0.0277 | 454.99 | 8531 | 4.9318 | 0.2383 | | 0.0315 | 456.0 | 8550 | 4.9291 | 0.2383 | | 0.021 | 456.96 | 8568 | 4.9293 | 0.2367 | | 0.0288 | 457.97 | 8587 | 4.9233 | 0.2333 | | 0.0229 | 458.99 | 8606 | 4.9236 | 0.2383 | | 0.0257 | 460.0 | 8625 | 4.9225 | 0.2367 | | 0.0291 | 460.96 | 8643 | 4.9222 | 0.2383 | | 0.0325 | 461.97 | 8662 | 4.9216 | 0.2367 | | 0.0268 | 462.99 | 8681 | 4.9202 | 0.2367 | | 0.0156 | 464.0 | 8700 | 4.9175 | 0.2367 | | 0.0196 | 464.96 | 8718 | 4.9147 | 0.2333 | | 0.0448 | 465.97 | 8737 | 4.9100 | 0.2333 | | 0.0232 | 466.99 | 8756 | 4.9088 | 0.2333 | | 0.0274 | 468.0 | 8775 | 4.9096 | 0.2367 | | 0.029 | 468.96 | 8793 | 4.9105 | 0.2367 | | 0.0337 | 469.97 | 8812 | 4.9125 | 0.235 | | 0.0178 | 470.99 | 8831 | 4.9120 | 0.235 | | 0.0286 | 472.0 | 8850 | 4.9125 | 0.2367 | | 0.0159 | 472.96 | 8868 | 4.9102 | 0.2367 | | 0.0318 | 473.97 | 8887 | 4.9116 | 0.2383 | | 0.0302 | 474.99 | 8906 | 4.9113 | 0.24 | | 0.0184 | 476.0 | 8925 | 4.9120 | 0.24 | | 0.025 | 476.96 | 8943 | 4.9128 | 0.24 | | 0.027 | 477.97 | 8962 | 4.9126 | 0.24 | | 0.0298 | 478.99 | 8981 | 4.9130 | 0.24 | | 0.0349 | 480.0 | 9000 | 4.9130 | 0.24 | ### Framework versions - Transformers 4.31.0 - Pytorch 1.13.1 - Datasets 2.14.0 - Tokenizers 0.13.3
jasonching/output
jasonching
2023-07-30T14:20:39Z
0
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
2023-07-30T10:29:18Z
--- 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 - jasonching/output 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.
Maldopast/whisper-tiny-finetuned-en-us
Maldopast
2023-07-30T14:05:31Z
84
0
transformers
[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:PolyAI/minds14", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2023-07-30T13:59:42Z
--- license: apache-2.0 tags: - generated_from_trainer datasets: - PolyAI/minds14 metrics: - wer model-index: - name: whisper-tiny-en_us results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: PolyAI/minds14 type: PolyAI/minds14 config: en-US split: train args: en-US metrics: - name: Wer type: wer value: 0.3305785123966942 --- <!-- 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-tiny-en_us This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set: - Loss: 0.4863 - Wer Ortho: 0.3362 - Wer: 0.3306 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 0.0233 | 4.42 | 500 | 0.4863 | 0.3362 | 0.3306 | ### Framework versions - Transformers 4.30.1 - Pytorch 2.0.1+cu117 - Datasets 2.14.0 - Tokenizers 0.13.3
chandrasutrisnotjhong/Pixelcopter-PLE-v0
chandrasutrisnotjhong
2023-07-30T13:42:41Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T13:42:01Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: 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: 27.90 +/- 18.81 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
VinEuro/rl_course_vizdoom_health_gathering_supreme
VinEuro
2023-07-30T13:40:30Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T13:14:09Z
--- 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_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 8.35 +/- 3.27 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 VinEuro/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` 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 .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
accuracy-maker/ppo-Huggy
accuracy-maker
2023-07-30T13:29:53Z
18
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-07-30T13:29:50Z
--- 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: chrisght/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
sshalini6/whisper-small-5e4-r16-a32-d0.1
sshalini6
2023-07-30T13:12:45Z
2
0
peft
[ "peft", "region:us" ]
null
2023-07-30T07:34:57Z
--- 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
Technotech/sd-prompt-instruct-3b-epoch-0.4-ggml
Technotech
2023-07-30T12:54:59Z
1
0
transformers
[ "transformers", "llama", "stable-diffusion", "instruct", "magic-prompt", "natural language inference", "en", "dataset:Technotech/sd-prompt-instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]
null
2023-07-30T10:39:26Z
--- library_name: transformers license: apache-2.0 datasets: - Technotech/sd-prompt-instruct language: - en tags: - stable-diffusion - instruct - magic-prompt - natural language inference --- # Stable Diffusion Prompt Instruct 3B GGML (OpenLlama v2 3B) Trained for 0.4 epochs (test) on [Technotech/sd-prompt-instruct](https://huggingface.co/datasets/Technotech/sd-prompt-instruct). ## Prompt Format ``` ### Instruction: {prompt} ### Response: {response} ``` ## Formats At the moment, k-quants are not compatible with OpenLlama v2 3B, which this model is fine tuned from. | Quant | Name | Size | | ----- | ----- | ----- | | `q4_0` | `sd-prompt-instruct-ggml.q4_0.bin` | `(1.93 GB)` | `q4_1` | `sd-prompt-instruct-ggml.q4_1.bin` | `(2.14 GB)` | `q5_0` | `sd-prompt-instruct-ggml.q5_0.bin` | `(2.36 GB)` | `q5_1` | `sd-prompt-instruct-ggml.q5_1.bin` | `(2.57 GB)`
Qasim30/Reinforce-mycopter
Qasim30
2023-07-30T12:45:31Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T12:12:17Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-mycopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 17.50 +/- 10.12 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
xiao12346/t5-large_PREFIX_TUNING_SEQ2SEQ_c1
xiao12346
2023-07-30T12:39:31Z
2
0
peft
[ "peft", "region:us" ]
null
2023-07-30T12:39:31Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
noystl/corpify_t5_large
noystl
2023-07-30T12:33:32Z
5
0
transformers
[ "transformers", "pytorch", "t5", "text2text-generation", "license:cc", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2023-07-30T09:48:35Z
--- license: cc --- Corpify-t5 is "corpy" textual style-transfer model which involves the transformation of casual and informal English text into a style suited for a professional workplace setting. Usage example: ``` from transformers import pipeline pipe = pipeline("text2text-generation", model="noystl/corpify_t5_large") input_text = "I can't stand you farting in the office all the time" generated_text = pipe(input_text) print(generated_text[0]['generated_text']) ``` Output: ``` I'm not sure if I can accommodate you in the office. ``` The data, code and more information on the project could be found here: https://github.com/maayansharon10/Corpify
NasimB/simple_wikipedia-log-rarity-seed
NasimB
2023-07-30T12:29:42Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-30T08:44:43Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: simple_wikipedia-log-rarity-seed 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. --> # simple_wikipedia-log-rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1528 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.3397 | 0.29 | 500 | 5.3500 | | 5.0305 | 0.58 | 1000 | 4.9322 | | 4.7176 | 0.87 | 1500 | 4.7007 | | 4.4695 | 1.17 | 2000 | 4.5715 | | 4.3034 | 1.46 | 2500 | 4.4625 | | 4.2247 | 1.75 | 3000 | 4.3657 | | 4.1027 | 2.04 | 3500 | 4.3050 | | 3.9238 | 2.33 | 4000 | 4.2594 | | 3.8913 | 2.62 | 4500 | 4.2022 | | 3.8633 | 2.91 | 5000 | 4.1553 | | 3.6726 | 3.21 | 5500 | 4.1434 | | 3.6113 | 3.5 | 6000 | 4.1167 | | 3.6006 | 3.79 | 6500 | 4.0839 | | 3.5168 | 4.08 | 7000 | 4.0827 | | 3.3434 | 4.37 | 7500 | 4.0770 | | 3.3399 | 4.66 | 8000 | 4.0610 | | 3.3254 | 4.95 | 8500 | 4.0501 | | 3.1918 | 5.24 | 9000 | 4.0638 | | 3.1599 | 5.54 | 9500 | 4.0629 | | 3.1599 | 5.83 | 10000 | 4.0621 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Mahmoud-Ghareeb/relation_between_two_sentences
Mahmoud-Ghareeb
2023-07-30T12:28:43Z
61
0
transformers
[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-29T14:07:24Z
--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: Repoo/relation_between_two_sentences 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. --> # Repoo/relation_between_two_sentences This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.1074 - Validation Loss: 1.0986 - Train Accuracy: 0.3419 - Epoch: 9 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 1.1794 | 1.1038 | 0.3187 | 0 | | 1.1724 | 1.0996 | 0.3402 | 1 | | 1.1570 | 1.1092 | 0.3427 | 2 | | 1.1470 | 1.0993 | 0.3411 | 3 | | 1.1145 | 1.1131 | 0.3419 | 4 | | 1.1042 | 1.1027 | 0.3171 | 5 | | 1.1060 | 1.0988 | 0.3402 | 6 | | 1.1073 | 1.1132 | 0.3411 | 7 | | 1.1074 | 1.0997 | 0.3411 | 8 | | 1.1074 | 1.0986 | 0.3419 | 9 | ### Framework versions - Transformers 4.30.2 - TensorFlow 2.12.0 - Datasets 2.1.0 - Tokenizers 0.13.3
AhmedSSoliman/DistilBERT-Marian-Model-on-DJANGO
AhmedSSoliman
2023-07-30T12:01:43Z
109
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "Code Generation", "Machine translation", "Text generation", "translation", "en", "dataset:AhmedSSoliman/DJANGO", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-01-11T21:54:43Z
--- license: mit datasets: - AhmedSSoliman/DJANGO language: - en metrics: - bleu - accuracy pipeline_tag: translation tags: - Code Generation - Machine translation - Text generation ---
AhmedSSoliman/MarianCG-CoNaLa-Large
AhmedSSoliman
2023-07-30T11:58:54Z
112
0
transformers
[ "transformers", "pytorch", "safetensors", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-05-24T22:50:16Z
--- widget: - text: "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]" - text: "check if all elements in list `mylist` are identical" - text: "enable debug mode on flask application `app`" - text: "getting the length of `my_tuple`" - text: 'find all files in directory "/mydir" with extension ".txt"' --- ``` ``` [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mariancg-a-code-generation-transformer-model/code-generation-on-conala)](https://paperswithcode.com/sota/code-generation-on-conala?p=mariancg-a-code-generation-transformer-model) ``` ``` # MarianCG: a code generation transformer model inspired by machine translation This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implemetation with the code of training and the generated output is available at this repository: https://github.com/AhmedSSoliman/MarianCG-NL-to-Code CoNaLa Dataset for Code Generation is available at https://huggingface.co/datasets/AhmedSSoliman/CoNaLa-Large This is the model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-CoNaLa-Large ```python # Model and Tokenizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # model_name = "AhmedSSoliman/MarianCG-NL-to-Code" model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa-Large") tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa-Large") # Input (Natural Language) and Output (Python Code) NL_input = "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]" output = model.generate(**tokenizer(NL_input, padding="max_length", truncation=True, max_length=512, return_tensors="pt")) output_code = tokenizer.decode(output[0], skip_special_tokens=True) ``` This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-CoNaLa-Large --- Tasks: - Translation - Code Generation - Text2Text Generation - Text Generation --- # Citation We now have a [paper](https://doi.org/10.1186/s44147-022-00159-4) for this work and you can cite: ``` @article{soliman2022mariancg, title={MarianCG: a code generation transformer model inspired by machine translation}, author={Soliman, Ahmed S and Hadhoud, Mayada M and Shaheen, Samir I}, journal={Journal of Engineering and Applied Science}, volume={69}, number={1}, pages={1--23}, year={2022}, publisher={SpringerOpen} url={https://doi.org/10.1186/s44147-022-00159-4} } ```
AhmedSSoliman/MarianCG-DJANGO
AhmedSSoliman
2023-07-30T11:58:02Z
123
0
transformers
[ "transformers", "pytorch", "marian", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2022-08-30T12:14:00Z
--- widget: - text: "define the method i with an argument self." - text: "substitute asvar for self.asvar." - text: "convert host to lowercase." - text: "for every var in self.vars," - text: "call the method parser.delete_first_token." --- ``` ``` [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mariancg-a-code-generation-transformer-model/code-generation-on-django)](https://paperswithcode.com/sota/code-generation-on-django?p=mariancg-a-code-generation-transformer-model) ``` ``` # MarianCG: a code generation transformer model inspired by machine translation This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset. MarianCG model and its implementation with the code of training and the generated output is available at this repository: https://github.com/AhmedSSoliman/MarianCG-NL-to-Code DJANGO dataset is available at https://huggingface.co/datasets/AhmedSSoliman/DJANGO This model is avialable on the huggingface hub https://huggingface.co/AhmedSSoliman/MarianCG-DJANGO ```python # Model and Tokenizer from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # model_name = "AhmedSSoliman/MarianCG-NL-to-Code" model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-DJANGO") tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-DJANGO") # Input (Natural Language) and Output (Python Code) NL_input = "define the method i with an argument self." output = model.generate(**tokenizer(NL_input, padding="max_length", truncation=True, max_length=512, return_tensors="pt")) output_code = tokenizer.decode(output[0], skip_special_tokens=True) ``` This model is available in spaces using gradio at: https://huggingface.co/spaces/AhmedSSoliman/MarianCG-DJANGO --- Tasks: - Translation - Code Generation - Text2Text Generation - Text Generation --- # Citation We now have a [paper](https://doi.org/10.1186/s44147-022-00159-4) for this work and you can cite: ``` @article{soliman2022mariancg, title={MarianCG: a code generation transformer model inspired by machine translation}, author={Soliman, Ahmed S and Hadhoud, Mayada M and Shaheen, Samir I}, journal={Journal of Engineering and Applied Science}, volume={69}, number={1}, pages={1--23}, year={2022}, publisher={SpringerOpen} url={https://doi.org/10.1186/s44147-022-00159-4} } ```
AhmedSSoliman/LUKE-Marian-Model-on-DJANGO
AhmedSSoliman
2023-07-30T11:57:00Z
94
0
transformers
[ "transformers", "pytorch", "encoder-decoder", "text2text-generation", "Machine Translation ", "Code Generation", "Text Generation", "translation", "en", "dataset:AhmedSSoliman/DJANGO", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-01-11T22:18:29Z
--- license: mit datasets: - AhmedSSoliman/DJANGO language: - en metrics: - bleu - accuracy pipeline_tag: translation tags: - 'Machine Translation ' - Code Generation - Text Generation ---
sshalini6/whisper-small-5e4-r8-a32-d0.1
sshalini6
2023-07-30T11:39:54Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T11:39:53Z
--- 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
NasimB/aochildes_cbt_rarity-seed
NasimB
2023-07-30T11:26:12Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-30T07:41:30Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: aochildes_cbt_rarity-seed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aochildes_cbt_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1220 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.365 | 0.29 | 500 | 5.3408 | | 5.0483 | 0.59 | 1000 | 4.9282 | | 4.7186 | 0.88 | 1500 | 4.6933 | | 4.4606 | 1.17 | 2000 | 4.5590 | | 4.315 | 1.47 | 2500 | 4.4381 | | 4.2073 | 1.76 | 3000 | 4.3375 | | 4.0917 | 2.05 | 3500 | 4.2624 | | 3.9138 | 2.35 | 4000 | 4.2183 | | 3.8813 | 2.64 | 4500 | 4.1641 | | 3.8464 | 2.93 | 5000 | 4.1152 | | 3.6477 | 3.23 | 5500 | 4.1130 | | 3.6014 | 3.52 | 6000 | 4.0847 | | 3.5838 | 3.81 | 6500 | 4.0509 | | 3.4799 | 4.11 | 7000 | 4.0527 | | 3.3288 | 4.4 | 7500 | 4.0486 | | 3.328 | 4.69 | 8000 | 4.0350 | | 3.3186 | 4.99 | 8500 | 4.0239 | | 3.1615 | 5.28 | 9000 | 4.0374 | | 3.1453 | 5.58 | 9500 | 4.0360 | | 3.1478 | 5.87 | 10000 | 4.0353 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
Qasim30/Reinforce-mycartmodel
Qasim30
2023-07-30T11:18:47Z
0
0
null
[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T11:18:36Z
--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-mycartmodel results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
toto10/Ooga
toto10
2023-07-30T11:17:57Z
13
0
transformers
[ "transformers", "arxiv:2211.06679", "endpoints_compatible", "region:us" ]
null
2023-07-30T11:10:12Z
# Stable Diffusion web UI-UX Not just a browser interface based on Gradio library for Stable Diffusion. A pixel perfect design, mobile friendly, customizable interface that adds accessibility, ease of use and extended functionallity to the stable diffusion web ui. Enjoy! Default theme ![anapnoe_uiux](https://user-images.githubusercontent.com/124302297/227973574-6003142d-0c7c-41c6-9966-0792a94549e9.png) ## Features of ui-ux - resizable viewport - switchable viewports (DoubleClick on the split handler to swap views) option in settings for default position - mobile navigation - top header tabs (option setting) - hidden tabs (option setting) no need to restart this is a different implementation - drag and drop reordable quick settings offcanvas aside view - drag and drop images to txt2img and img2img and import generation info parameters along with a preview image - ignore - remove overrides when import [multiselect] (option setting) - resizable cards for extra networks and number of rows (option setting) - lazy loading alternative offcanvas aside view for extra networks (option setting) - live preview image fit method (option setting) - generated image fit method (option setting) - max resolution output for txt2img and img2img (option setting) - performant dispatch for gradio's range slider and input number field issue: https://github.com/gradio-app/gradio/issues/3204 (option setting) latest update uses only one instance clone to mediate for the release event - ticks input range sliders (option setting) - pacman preloader unified colors on reload ui - frame border animation when generating images - progress bar on top of the page always visible (when scroll for mobile) - remix icons - style theme configurator extension to customize every aspect of theme in real time with cool global functions to change the hue / saturation / brightness or invert the theme colors - pan and zoom in out functionality for sketch, inpaint, inpaint sketch - fullscreen support for sketch, inpaint, inpaint sketch - better lightbox with zoom in-out mobile gestures support etc.. ## TODO - small arrows next to icons sent to inpaint, extras, img2img etc - component gallery navigate to previous generations inside the txt2img, img2img interface - and auto load the current generation settings - credits/about page display all 300+ contributors so far inside the UI Quick Settings aside off-canvas view - drag and drop to custom sort your settings ![anapnoe_uiux_quicksettings](https://user-images.githubusercontent.com/124302297/227967695-f8bb01b5-5cc9-4238-80dd-06e261378d6e.png) Extra Networks aside off-canvas view ![anapnoe_uiux_extra_networks](https://user-images.githubusercontent.com/124302297/227968001-20eab8f5-da91-4a11-9fe0-230fec4ba720.png) Detail img2img sketch view ![anapnoe_uiux_sketch](https://user-images.githubusercontent.com/124302297/227973727-084da8e0-931a-4c62-ab73-39e988fd4523.png) Theme Configurator - aside off-canvas view ![anapnoe_uiux_theme_config](https://user-images.githubusercontent.com/124302297/227967844-45063edb-eb40-4224-9666-f506d21d7780.png) Mobile 395px width ![anapnoe_uiux_mobile](https://user-images.githubusercontent.com/124302297/227987709-36231d30-e6da-424a-8930-cc0c55a0b979.png) ## Features [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): - Original txt2img and img2img modes - One click install and run script (but you still must install python and git) - Outpainting - Inpainting - Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to - a man in a `((tuxedo))` - will pay more attention to tuxedo - a man in a `(tuxedo:1.21)` - alternative syntax - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) - Loopback, run img2img processing multiple times - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters - Textual Inversion - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN - RealESRGAN, neural network upscaler - ESRGAN, neural network upscaler with a lot of third party models - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers - LDSR, Latent diffusion super resolution upscaling - Resizing aspect ratio options - Sampling method selection - Adjust sampler eta values (noise multiplier) - More advanced noise setting options - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches - Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings - drag and drop an image/text-parameters to promptbox - Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with --allow-code to enable) - Mouseover hints for most UI elements - Possible to change defaults/mix/max/step values for UI elements via text config - Tiling support, a checkbox to create images that can be tiled like textures - Progress bar and live image generation preview - Can use a separate neural network to produce previews with almost none VRAM or compute requirement - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image - Styles, a way to save part of prompt and easily apply them via dropdown later - Variations, a way to generate same image but with tiny differences - Seed resizing, a way to generate same image but at slightly different resolution - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img - Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI - Generate forever option - Training tab - hypernetworks and embeddings options - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) - Clip skip - Hypernetworks - Loras (same as Hypernetworks but more pretty) - A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt. - Can select to load a different VAE from settings screen - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions - Now without any bad letters! - Load checkpoints in safetensors format - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64 - Now with a license! - Reorder elements in the UI from settings screen - ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH". 2. Install [git](https://git-scm.com/download/win). 3. Download the stable-diffusion-webui-ux repository, for example by running `git clone https://github.com/anapnoe/stable-diffusion-webui-ux.git`. 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. ### Installation on Linux 1. Install the dependencies: ```bash # Debian-based: sudo apt install wget git python3 python3-venv # Red Hat-based: sudo dnf install wget git python3 # Arch-based: sudo pacman -S wget git python3 ``` 2. Navigate to the directory you would like the webui to be installed and execute the following command: ```bash bash <(wget -qO- https://raw.githubusercontent.com/anapnoe/stable-diffusion-webui-ux/master/webui.sh) ``` 3. Run `webui.sh`. 4. Check `webui-user.sh` for options. ### Installation on Apple Silicon Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). and replace the path in step 3 with `git clone https://github.com/anapnoe/stable-diffusion-webui-ux` ## Contributing Here's how to add code to the original repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers - k-diffusion - https://github.com/crowsonkb/k-diffusion.git - GFPGAN - https://github.com/TencentARC/GFPGAN.git - CodeFormer - https://github.com/sczhou/CodeFormer - ESRGAN - https://github.com/xinntao/ESRGAN - SwinIR - https://github.com/JingyunLiang/SwinIR - Swin2SR - https://github.com/mv-lab/swin2sr - LDSR - https://github.com/Hafiidz/latent-diffusion - MiDaS - https://github.com/isl-org/MiDaS - Ideas for optimizations - https://github.com/basujindal/stable-diffusion - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) - Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - xformers - https://github.com/facebookresearch/xformers - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru - Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd - LyCORIS - KohakuBlueleaf - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You)
msladic/rl_course_vizdoom_health_gathering_supreme
msladic
2023-07-30T11:13:02Z
0
0
sample-factory
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T11:10:38Z
--- 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_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 13.53 +/- 5.22 name: mean_reward verified: false --- A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 msladic/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` 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 .usr.local.lib.python3.10.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
emaeon/lora-large-healthcare-model-17_desc
emaeon
2023-07-30T11:05:51Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-28T08:13:22Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Aityz/reviews_model
Aityz
2023-07-30T11:05:33Z
127
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-29T09:13:54Z
--- license: apache-2.0 base_model: aityz/reviews_model tags: - generated_from_trainer model-index: - name: reviews_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # reviews_model This model is a fine-tuned version of [aityz/reviews_model](https://huggingface.co/aityz/reviews_model) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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+cpu - Tokenizers 0.13.3
AliGhiasvand86/digit_recognition2
AliGhiasvand86
2023-07-30T11:05:29Z
216
0
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-30T11:05:22Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: digit_recognition2 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.19801980257034302 --- # digit_recognition2 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### number 1 ![number 1](images/number_1.jpg) #### number 2 ![number 2](images/number_2.jpg) #### number 3 ![number 3](images/number_3.jpg) #### number 4 ![number 4](images/number_4.jpg) #### number 5 ![number 5](images/number_5.jpg) #### number 6 ![number 6](images/number_6.jpg) #### number 7 ![number 7](images/number_7.jpg) #### number 8 ![number 8](images/number_8.jpg) #### number 9 ![number 9](images/number_9.jpg)
mlabonne/llama-2-13b-miniguanaco
mlabonne
2023-07-30T11:03:45Z
128
2
transformers
[ "transformers", "pytorch", "llama", "text-generation", "dataset:mlabonne/guanaco-llama2-1k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-30T11:00:10Z
--- license: apache-2.0 datasets: - mlabonne/guanaco-llama2-1k pipeline_tag: text-generation --- # 🦙🧠 Miniguanaco-13b 📝 [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) | 💻 [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing) | 📄 [Script](https://gist.github.com/mlabonne/b5718e1b229ce6553564e3f56df72c5c) <center><img src="https://i.imgur.com/1IZmjU4.png" width="300"></center> This is a `Llama-2-13b-chat-hf` model fine-tuned using QLoRA (4-bit precision) on the [`mlabonne/guanaco-llama2-1k`](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k) dataset, which is a subset of the [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco). ## 🔧 Training It was trained on an RTX 3090. It is mainly designed for educational purposes, not for inference. Parameters: ``` max_seq_length = 2048 use_nested_quant = True bnb_4bit_compute_dtype=bfloat16 lora_r=8 lora_alpha=16 lora_dropout=0.05 per_device_train_batch_size=2 ``` ## 💻 Usage ``` python # pip install transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "mlabonne/llama-2-13b-miniguanaco" prompt = "What is a large language model?" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( f'<s>[INST] {prompt} [/INST]', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ```
intanm/bri_topic_modeling_baseline_30_001
intanm
2023-07-30T10:59:06Z
108
1
transformers
[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "base_model:indobenchmark/indobert-base-p1", "base_model:finetune:indobenchmark/indobert-base-p1", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-30T10:55:15Z
--- license: mit base_model: indobenchmark/indobert-base-p1 tags: - generated_from_trainer metrics: - accuracy model-index: - name: bri_topic_modeling_baseline_30_001 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. --> # bri_topic_modeling_baseline_30_001 This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co/indobenchmark/indobert-base-p1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8029 - Accuracy: 0.7748 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 223 | 0.9959 | 0.7284 | | No log | 2.0 | 446 | 0.8029 | 0.7748 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
emaeon/lora-large-healthcare-model-10_desc
emaeon
2023-07-30T10:56:54Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-28T08:04:24Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-9_desc
emaeon
2023-07-30T10:55:36Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-28T07:23:46Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-8_desc
emaeon
2023-07-30T10:54:20Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-20T08:41:23Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-7_desc
emaeon
2023-07-30T10:53:02Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-20T08:37:03Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
AliGhiasvand86/digit_recognition
AliGhiasvand86
2023-07-30T10:52:26Z
196
1
transformers
[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2023-07-30T10:52:18Z
--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: digit_recognition results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.10891088843345642 --- # digit_recognition Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### 1 ![1](images/1.jpg) #### 2 ![2](images/2.jpg) #### 3 ![3](images/3.jpg) #### 4 ![4](images/4.jpg) #### 5 ![5](images/5.jpg) #### 6 ![6](images/6.jpg) #### 7 ![7](images/7.jpg) #### 8 ![8](images/8.jpg) #### 9 ![9](images/9.jpg)
emaeon/lora-large-healthcare-model-5_desc
emaeon
2023-07-30T10:50:27Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-20T08:28:23Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-4_desc
emaeon
2023-07-30T10:49:09Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-20T08:24:03Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-3_desc
emaeon
2023-07-30T10:47:53Z
1
0
peft
[ "peft", "region:us" ]
null
2023-07-20T07:21:29Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
emaeon/lora-large-healthcare-model-0_desc
emaeon
2023-07-30T10:44:02Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-20T07:08:30Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
yukangcao/cat_toy_dreambooth
yukangcao
2023-07-30T10:42:04Z
31
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-30T10:28:24Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of cat toy tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - RaikkonenCao/cat_toy_dreambooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of cat toy using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
AbstractQbit/electra_large_imdb_htsplice
AbstractQbit
2023-07-30T10:32:13Z
107
0
transformers
[ "transformers", "pytorch", "electra", "text-classification", "arxiv:1905.05583", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2023-07-30T10:03:48Z
`google/electra-large-discriminator` finetuned on imdb dataset for 2 epoches. Large examples tokenized with head and tail parts of a review, as described in [How to Fine-Tune BERT for Text Classification?](https://arxiv.org/abs/1905.05583) ```python def preprocess_function(example): tokens = tokenizer(example["text"], truncation=False) if len(tokens['input_ids']) > 512: tokens['input_ids'] = tokens['input_ids'][:129] + \ [102] + tokens['input_ids'][-382:] tokens['token_type_ids'] = [0]*512 tokens['attention_mask'] = [1]*512 return tokens ```
Daniil-plotnikov/russian-vision-v5-1
Daniil-plotnikov
2023-07-30T10:22:58Z
29
0
diffusers
[ "diffusers", "text-to-image", "stable-diffusion", "ru", "en", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-29T17:01:58Z
--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion language: - ru - en --- ### Russian-Vision-V5.1 Данная модель просто идеально по сравнению с другими! Примеры картинок: <img src="https://ibb.co/pRNF7jr" alt="." width="1024" height="683"> https://ibb.co/8MwnXJ4 https://ibb.co/W21dfHQ https://ibb.co/KWcqKjx https://ibb.co/2dzvg2j https://ibb.co/yNqhS6x https://ibb.co/0hCnFBP https://ibb.co/1sFTZCB https://ibb.co/hY5KHG6 https://ibb.co/CsVX64L https://ibb.co/HBr5mZw https://ibb.co/gFnLbhw https://ibb.co/CBKfyHZ https://ibb.co/H4RBJRn
TFLai/llama-2-13b-4bit-alpaca-gpt4
TFLai
2023-07-30T10:21:52Z
8
2
peft
[ "peft", "dataset:vicgalle/alpaca-gpt4", "region:us" ]
null
2023-07-21T13:37:13Z
--- library_name: peft datasets: - vicgalle/alpaca-gpt4 --- ## 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
AdiOO7/Azure-tickets-Classifier-llama-2
AdiOO7
2023-07-30T10:20:09Z
1
0
peft
[ "peft", "region:us" ]
null
2023-07-30T10:20:08Z
--- 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
yukangcao/dog_dreambooth
yukangcao
2023-07-30T10:10:39Z
29
0
diffusers
[ "diffusers", "tensorboard", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2023-07-30T09:45:44Z
--- license: creativeml-openrail-m base_model: stabilityai/stable-diffusion-2-1 instance_prompt: a photo of sks dog tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - RaikkonenCao/dog_dreambooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. 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.
StupidTree/llama2-qlora-finetunined-french
StupidTree
2023-07-30T10:04:52Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T10:04:47Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e5_s6789_v3_l5_v100
KingKazma
2023-07-30T10:00:26Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T09:57:20Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
jondurbin/airoboros-33b-gpt4-2.0-peft
jondurbin
2023-07-30T09:48:48Z
0
0
null
[ "license:cc-by-nc-4.0", "region:us" ]
null
2023-07-27T13:11:27Z
--- license: cc-by-nc-4.0 --- Adapter model for https://hf.co/jondurbin/airoboros-33b-gpt4-2.0
JNK789/Taxi-v3-unit2-assignment
JNK789
2023-07-30T09:43:42Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T09:43:40Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3-unit2-assignment results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="JNK789/Taxi-v3-unit2-assignment", 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"]) ```
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e3_s6789_v3_l5_v100
KingKazma
2023-07-30T09:42:37Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T09:40:38Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
Amandm77/LunarLander-v2-ppo
Amandm77
2023-07-30T09:39:58Z
2
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T09:39:41Z
--- 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: 272.44 +/- 16.63 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 ... ```
Skie0007/lander_v2
Skie0007
2023-07-30T09:32:38Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T09:32:18Z
--- 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: 268.31 +/- 20.20 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 ... ```
Conquer2020/llama2-qlora-finetunined-french
Conquer2020
2023-07-30T09:22:58Z
4
0
peft
[ "peft", "region:us" ]
null
2023-07-30T09:22:53Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
OmarEmam99/distilbert-base-uncased-finetuned-emotion
OmarEmam99
2023-07-30T09:20:05Z
107
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
2023-07-10T09:04:27Z
--- 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: 0.9335 - name: F1 type: f1 value: 0.9336312134570528 --- <!-- 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: 0.1507 - Accuracy: 0.9335 - F1: 0.9336 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.1737 | 1.0 | 250 | 0.1817 | 0.931 | 0.9320 | | 0.1136 | 2.0 | 500 | 0.1629 | 0.9305 | 0.9312 | | 0.0985 | 3.0 | 750 | 0.1507 | 0.9335 | 0.9336 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3000_8_e0_s6789_v3_l5_v100
KingKazma
2023-07-30T09:15:54Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T09:15:33Z
--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
elhindih/llama-2-7b-rp
elhindih
2023-07-30T09:15:26Z
1
0
peft
[ "peft", "region:us" ]
null
2023-07-30T09:14:42Z
--- 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 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 The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.4.0 - PEFT 0.4.0 - PEFT 0.4.0
EarthnDusk/Epic-Poltergeist-Backups-2023
EarthnDusk
2023-07-30T09:15:04Z
0
0
null
[ "en", "license:creativeml-openrail-m", "region:us" ]
null
2023-05-19T00:55:23Z
--- license: creativeml-openrail-m language: - en --- Test these out here: start your ONLINE GENERATION now! https://tensor.art/images/618050252349634200?post_id=618057377695934307&source_id=nz-3plnjkUG1ofAvanb09hMv Join our Reddit: https://www.reddit.com/r/earthndusk/ WE ARE PROUDLY SPONSORED BY: https://www.piratediffusion.com/ If you got requests, or concerns, We're still looking for beta testers: JOIN THE DISCORD AND DEMAND THINGS OF US: https://discord.gg/Da7s8d3KJ7 Listen to the music that we've made that goes with our art: https://open.spotify.com/playlist/00R8x00YktB4u541imdSSf?si=b60d209385a74b38 We stream a lot of our testing on twitch: https://www.twitch.tv/duskfallcrew any chance you can spare a coffee or three? https://ko-fi.com/DUSKFALLcrew [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/Z8Z8L4EO) Merge permissions: MUST CREDIT, linkback to Earth & DUSK or to the respective civitAI profile. You may not sell your merge loras or lora bind checkpoints, or merged models you make from our content but you may use them on generative services.
lovelybbq/clear
lovelybbq
2023-07-30T09:14:28Z
0
0
null
[ "en", "region:us" ]
null
2023-07-26T16:03:18Z
--- language: - en --- Cropped version of NoCrypt's repo.tar.lz4 All credits to https://huggingface.co/NoCrypt
vin293/buffalo
vin293
2023-07-30T09:06:12Z
0
0
null
[ "license:bigscience-openrail-m", "region:us" ]
null
2023-07-30T09:06:12Z
--- license: bigscience-openrail-m ---
goreactdev/lora-trained-xl
goreactdev
2023-07-30T08:41:35Z
2
2
diffusers
[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]
text-to-image
2023-07-30T07:49:26Z
--- 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 - goreactdev/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: None.
pratsy/a2c-AntBulletEnv-v0
pratsy
2023-07-30T08:31:54Z
0
0
stable-baselines3
[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T08:30:44Z
--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1904.37 +/- 158.79 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Carlosavc/llama2-qlora-finetunined-french
Carlosavc
2023-07-30T08:29:46Z
0
0
peft
[ "peft", "region:us" ]
null
2023-07-30T08:23:26Z
--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0.dev0
efainman/ppo-CartPole-v1
efainman
2023-07-30T08:28:12Z
0
0
null
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T08:23:28Z
--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -165.04 +/- 72.42 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 50000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'efainman/ppo-CartPole-v1' 'batch_size': 512 'minibatch_size': 128} ```
Mtc2/ppo-with-rnd-Pyramids
Mtc2
2023-07-30T08:14:32Z
9
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]
reinforcement-learning
2023-07-30T08:14:29Z
--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Mtc2/ppo-with-rnd-Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
kejolong/pinkbunny
kejolong
2023-07-30T07:56:16Z
0
0
null
[ "license:creativeml-openrail-m", "region:us" ]
null
2023-07-30T07:52:44Z
--- license: creativeml-openrail-m ---
rahul-appu/q-FrozenLake-v1-4x4-noSlippery
rahul-appu
2023-07-30T07:51:35Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T07:51:33Z
--- 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="rahul-appu/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"]) ```
NasimB/bnc_spoken_gutenberg_fixed_rarity-seed
NasimB
2023-07-30T07:07:38Z
5
0
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:generator", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-30T04:18:55Z
--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: bnc_spoken_gutenberg_fixed_rarity-seed results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bnc_spoken_gutenberg_fixed_rarity-seed This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 4.1378 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.36 | 0.29 | 500 | 5.3399 | | 5.0618 | 0.59 | 1000 | 4.9347 | | 4.7196 | 0.88 | 1500 | 4.7026 | | 4.4644 | 1.17 | 2000 | 4.5576 | | 4.3203 | 1.46 | 2500 | 4.4384 | | 4.2142 | 1.76 | 3000 | 4.3396 | | 4.0914 | 2.05 | 3500 | 4.2702 | | 3.9075 | 2.34 | 4000 | 4.2264 | | 3.8854 | 2.63 | 4500 | 4.1740 | | 3.8439 | 2.93 | 5000 | 4.1252 | | 3.6556 | 3.22 | 5500 | 4.1193 | | 3.6025 | 3.51 | 6000 | 4.0910 | | 3.5842 | 3.8 | 6500 | 4.0570 | | 3.4933 | 4.1 | 7000 | 4.0614 | | 3.3389 | 4.39 | 7500 | 4.0542 | | 3.3321 | 4.68 | 8000 | 4.0411 | | 3.3168 | 4.97 | 8500 | 4.0319 | | 3.1664 | 5.27 | 9000 | 4.0464 | | 3.1532 | 5.56 | 9500 | 4.0444 | | 3.1517 | 5.85 | 10000 | 4.0435 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
DucQuynh/roberta-base-finetune-subjqa
DucQuynh
2023-07-30T06:52:12Z
108
0
transformers
[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]
question-answering
2023-05-22T05:44:34Z
--- license: cc-by-4.0 tags: - generated_from_trainer model-index: - name: roberta-base-finetune-subjqa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-finetune-subjqa This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 14 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
bluefoxcreation/Codeformer-ONNX
bluefoxcreation
2023-07-30T06:46:16Z
0
5
null
[ "onnx", "license:other", "region:us" ]
null
2023-07-30T06:39:59Z
--- license: other tags: - onnx ---
thehive/everyjourney-sdxl-0.9-finetuned
thehive
2023-07-30T06:37:45Z
0
39
null
[ "stable-diffusion-xl", "text-to-image", "en", "license:other", "region:us" ]
text-to-image
2023-07-06T14:37:45Z
--- license: other language: - en pipeline_tag: text-to-image tags: - stable-diffusion-xl --- **Please for anyone, due to StabilityAI SDXL 0.9 Research License, don't reupload my finetuned models to other site, like Civitai or image generating site like Seeart other sites. Thank you for understanding this.** Like my works and want to collaboration or funding my projects? contact me. Finetuned on SDXL Base 0.9 Official Release, Expected to be successor of [Everyjourney](https://huggingface.co/aiartindo/Everyjourney), currently in alpha stage, since i'm captioned this model with BLIP2, the image generated with this model may not meet your expectations, waiting for SDXL finetune/training process to be more polished. My other works: - https://huggingface.co/gmonsoon/notwaifu-diffusion-xl - https://huggingface.co/gmonsoon/waifujourney-xl **Recommended Settings** - Sampler: any DPM++ Karras samplers - Sampling Steps: 42 (because 42 is the answer to the Ultimate Question of Life, the Universe and Everything. :D ) - CFG Scale: 8 **Result & Comparison** ![sample](https://i.imgur.com/IUy7moz.png) ![sample](https://i.imgur.com/8aClZDT.png) ![sample](https://i.imgur.com/n8I1Emb.png) ![sample](https://i.imgur.com/RANeSWl.png) ![sample](https://i.imgur.com/berYOkG.png) ![sample](https://i.imgur.com/cgV4prm.png) **Review** [review by The Prompt Wizard](https://youtu.be/kDid7cxKLq0) ### Model Description - **Finetuned by:** [Gorilla Monsoon III](https://huggingface.co/gmonsoon) - **Model type:** Diffusion-based text-to-image generative model - **License:** [SDXL 0.9 Research License](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/LICENSE.md) **Credits** - Stability AI - Kohya_SS - Linaqruf
twbrandon7/rl-course-unit2-taxi-v3
twbrandon7
2023-07-30T06:22:29Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T06:22:27Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: rl-course-unit2-taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="twbrandon7/rl-course-unit2-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"]) ```
dvs/videomae-base-finetuned-movienet-take2
dvs
2023-07-30T06:18:00Z
3
0
transformers
[ "transformers", "pytorch", "tensorboard", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base", "base_model:finetune:MCG-NJU/videomae-base", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2023-07-28T21:29:04Z
--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-movienet-take2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # videomae-base-finetuned-movienet-take2 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0793 - eval_accuracy: 0.7969 - eval_runtime: 131.1948 - eval_samples_per_second: 1.463 - eval_steps_per_second: 0.183 - epoch: 9.01 - step: 1704 ## 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 - training_steps: 2960 ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
twbrandon7/q-FrozenLake-v1-4x4-noSlippery
twbrandon7
2023-07-30T06:11:50Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2023-07-30T06:11:47Z
--- 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="twbrandon7/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"]) ```
sanka85/rstp_instruct_new_2
sanka85
2023-07-30T06:09:31Z
1
0
peft
[ "peft", "region:us" ]
null
2023-07-30T06:09:25Z
--- 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
jonng1000/git-base-pokemon
jonng1000
2023-07-30T06:01:33Z
66
0
transformers
[ "transformers", "pytorch", "tensorboard", "git", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/git-base", "base_model:finetune:microsoft/git-base", "license:mit", "endpoints_compatible", "region:us" ]
image-text-to-text
2023-07-29T08:48:40Z
--- license: mit base_model: microsoft/git-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: git-base-pokemon 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. --> # git-base-pokemon This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.1 - Tokenizers 0.13.3
taehoon1lee/ppo-Huggy
taehoon1lee
2023-07-30T05:05:01Z
3
0
ml-agents
[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]
reinforcement-learning
2023-07-30T05:04:55Z
--- 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: taehoon1lee/ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀
hasibul1ah/article19_500r_data_clm-model
hasibul1ah
2023-07-30T04:57:13Z
198
0
transformers
[ "transformers", "pytorch", "tensorboard", "bloom", "text-generation", "generated_from_trainer", "base_model:bigscience/bloom-560m", "base_model:finetune:bigscience/bloom-560m", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-30T04:29:04Z
--- license: bigscience-bloom-rail-1.0 base_model: bigscience/bloom-560m tags: - generated_from_trainer model-index: - name: article19_500r_data_clm-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # article19_500r_data_clm-model This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.8149 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 259 | 3.5163 | | 3.0863 | 2.0 | 518 | 3.5642 | | 3.0863 | 3.0 | 777 | 3.8149 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.14.1 - Tokenizers 0.13.3
MichelNivard/Rchat_3b_peft
MichelNivard
2023-07-30T04:47:44Z
1
0
peft
[ "peft", "region:us" ]
null
2023-07-29T19:58:12Z
--- 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