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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-02 12:32:32	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 534 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-02 12:31:20	card stringlengths 11 1.01M
DeepakGautam/Gautam	DeepakGautam	2023-07-22T07:51:38Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2023-07-22T07:51:38Z	--- license: bigscience-openrail-m ---
vineetsharma/a2c-AntBulletEnv-v0	vineetsharma	2023-07-22T07:47:29Z	0	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T07:46:54Z	--- 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: 1480.41 +/- 128.67 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 ... ```
ailabturkiye/kratosGOWRAGNAROK	ailabturkiye	2023-07-22T07:40:18Z	0	0	null	[ "region:us" ]	null	2023-07-22T07:36:28Z	[![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Kratos (God Of War Ragnarök) - RVC V2 500 Epoch Kratos'un Serisinin son oyunu olan ragnarök'teki ses kayıtlarından oluşturulmuş ses modelidir. Rvc V2 \| 10 Dakikalık Dataset \| 500 Epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: hydragee - YouTube: CoverLai (https://www.youtube.com/@coverlai) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
josephrich/my_awesome_model_721_2	josephrich	2023-07-22T07:27:32Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-22T04:00:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: my_awesome_model_721_2 results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: test args: plain_text metrics: - name: Accuracy type: accuracy value: 0.93228 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_model_721_2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5942 - Accuracy: 0.9323 ## 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: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4604 \| 1.0 \| 12500 \| 0.6389 \| 0.8761 \| \| 0.2442 \| 2.0 \| 25000 \| 0.4233 \| 0.9264 \| \| 0.1495 \| 3.0 \| 37500 \| 0.4755 \| 0.9303 \| \| 0.0516 \| 4.0 \| 50000 \| 0.5942 \| 0.9323 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.13.1 - Tokenizers 0.13.3
Ryukijano/Mujoco_rl_halfcheetah_Decision_Trasformer	Ryukijano	2023-07-22T07:27:15Z	62	0	transformers	[ "transformers", "pytorch", "decision_transformer", "Generated_From_Trainer", "reinforcement-learning", "Mujoco", "dataset:decision_transformer_gym_replay", "endpoints_compatible", "region:us" ]	reinforcement-learning	2023-07-19T15:13:27Z	--- base_model: '' tags: - Generated_From_Trainer - reinforcement-learning - Mujoco datasets: - decision_transformer_gym_replay model-index: - name: Mujoco_rl_halfcheetah_Decision_Trasformer 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. --> # Mujoco_rl_halfcheetah_Decision_Trasformer This model is a fine-tuned version of [](https://huggingface.co/) on the decision_transformer_gym_replay dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 250 ### Training results ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv2-wt-frz-48-emotion	gokuls	2023-07-22T07:19:29Z	48	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-22T07:09:53Z	--- base_model: gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv2-wt-frz-48-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: 0.927 --- <!-- 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. --> # hbertv2-wt-frz-48-emotion This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_wt_init_48_frz) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2271 - Accuracy: 0.927 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6559 \| 1.0 \| 250 \| 0.2760 \| 0.9015 \| \| 0.2565 \| 2.0 \| 500 \| 0.2507 \| 0.9035 \| \| 0.1862 \| 3.0 \| 750 \| 0.2221 \| 0.919 \| \| 0.1455 \| 4.0 \| 1000 \| 0.2271 \| 0.927 \| \| 0.1218 \| 5.0 \| 1250 \| 0.2059 \| 0.9235 \| \| 0.1003 \| 6.0 \| 1500 \| 0.2576 \| 0.9215 \| \| 0.0812 \| 7.0 \| 1750 \| 0.2603 \| 0.92 \| \| 0.0676 \| 8.0 \| 2000 \| 0.2949 \| 0.9215 \| \| 0.0515 \| 9.0 \| 2250 \| 0.3322 \| 0.919 \| \| 0.0411 \| 10.0 \| 2500 \| 0.3375 \| 0.924 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
qwerty8409/Medical_dataset	qwerty8409	2023-07-22T07:04:27Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-22T07:00:20Z	--- 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
EXrRor3/ppo-Huggy	EXrRor3	2023-07-22T06:47:29Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-22T06:47:19Z	--- 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: EXrRor3/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
AndrewL088/SpaceInvadersNoFrameskip-v4_20230722	AndrewL088	2023-07-22T06:31:47Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T06:31:18Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 29.00 +/- 64.30 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndrewL088 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndrewL088 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AndrewL088 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.025), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 10000000.0), ('learning_starts', 100000), ('n_timesteps', 110000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
NasimB/guten-rarity-neg-log-rarity-end-19p1k	NasimB	2023-07-22T06:23:58Z	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	2023-07-22T04:00:43Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-neg-log-rarity-end-19p1k 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. --> # guten-rarity-neg-log-rarity-end-19p1k 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: 4.1078 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3472 \| 0.29 \| 500 \| 5.3359 \| \| 5.0242 \| 0.59 \| 1000 \| 4.9159 \| \| 4.7018 \| 0.88 \| 1500 \| 4.6868 \| \| 4.4382 \| 1.17 \| 2000 \| 4.5458 \| \| 4.2888 \| 1.47 \| 2500 \| 4.4338 \| \| 4.1941 \| 1.76 \| 3000 \| 4.3265 \| \| 4.0652 \| 2.05 \| 3500 \| 4.2631 \| \| 3.8933 \| 2.34 \| 4000 \| 4.2118 \| \| 3.8664 \| 2.64 \| 4500 \| 4.1589 \| \| 3.8275 \| 2.93 \| 5000 \| 4.1077 \| \| 3.6287 \| 3.22 \| 5500 \| 4.1006 \| \| 3.5847 \| 3.52 \| 6000 \| 4.0707 \| \| 3.5697 \| 3.81 \| 6500 \| 4.0389 \| \| 3.4614 \| 4.1 \| 7000 \| 4.0369 \| \| 3.3179 \| 4.4 \| 7500 \| 4.0323 \| \| 3.307 \| 4.69 \| 8000 \| 4.0175 \| \| 3.3039 \| 4.98 \| 8500 \| 4.0058 \| \| 3.1413 \| 5.28 \| 9000 \| 4.0177 \| \| 3.132 \| 5.57 \| 9500 \| 4.0172 \| \| 3.1349 \| 5.86 \| 10000 \| 4.0158 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
AndrewL088/SpaceInvadersNoFrameskip-v4_0722	AndrewL088	2023-07-22T06:18:45Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T06:09:45Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 257.00 +/- 38.81 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndrewL088 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AndrewL088 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AndrewL088 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.025), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 10000000.0), ('learning_starts', 100000), ('n_timesteps', 70000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ebilal79/watsonx-falcon-7b	ebilal79	2023-07-22T06:04:59Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-19T19:42:58Z	--- 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
4bit/Nous-Hermes-Llama2-13b-GPTQ	4bit	2023-07-22T05:32:28Z	11	3	transformers	[ "transformers", "pytorch", "llama", "text-generation", "llama-2", "self-instruct", "distillation", "synthetic instruction", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-22T05:26:48Z	--- license: llama2 language: - en tags: - llama-2 - self-instruct - distillation - synthetic instruction --- # Model Card: Nous-Hermes-Llama2-13b Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI. ## Model Description Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Emozilla leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to ensure consistency between the old Hermes and new, for anyone who wanted to keep Hermes as similar to the old one, just more capable. This model stands out for its long responses, lower hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 4096 sequence length on an 8x a100 80GB DGX machine. ## Example Outputs: ![Example4](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example5.png "Example 4") ![Example1](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/Example1.png "Example 1") ![Example2](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example2.png "Example 2") ![Example3](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b/resolve/main/example3.png "Example 3") ## Model Training The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style. This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below ## Collaborators The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. Special mention goes to @winglian for assisting in some of the training issues. Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. Among the contributors of datasets: - GPTeacher was made available by Teknium - Wizard LM by nlpxucan - Nous Research Instruct Dataset was provided by Karan4D and HueminArt. - GPT4-LLM and Unnatural Instructions were provided by Microsoft - Airoboros dataset by jondurbin - Camel-AI's domain expert datasets are from Camel-AI - CodeAlpaca dataset by Sahil 2801. If anyone was left out, please open a thread in the community tab. ## Prompt Format The model follows the Alpaca prompt format: ``` ### Instruction: <prompt> ### Response: <leave a newline blank for model to respond> ``` or ``` ### Instruction: <prompt> ### Input: <additional context> ### Response: <leave a newline blank for model to respond> ``` ## Benchmark Results AGI-Eval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|agieval_aqua_rat \| 0\|acc \|0.2362\|± \|0.0267\| \| \| \|acc_norm\|0.2480\|± \|0.0272\| \|agieval_logiqa_en \| 0\|acc \|0.3425\|± \|0.0186\| \| \| \|acc_norm\|0.3472\|± \|0.0187\| \|agieval_lsat_ar \| 0\|acc \|0.2522\|± \|0.0287\| \| \| \|acc_norm\|0.2087\|± \|0.0269\| \|agieval_lsat_lr \| 0\|acc \|0.3510\|± \|0.0212\| \| \| \|acc_norm\|0.3627\|± \|0.0213\| \|agieval_lsat_rc \| 0\|acc \|0.4647\|± \|0.0305\| \| \| \|acc_norm\|0.4424\|± \|0.0303\| \|agieval_sat_en \| 0\|acc \|0.6602\|± \|0.0331\| \| \| \|acc_norm\|0.6165\|± \|0.0340\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4320\|± \|0.0346\| \| \| \|acc_norm\|0.4272\|± \|0.0345\| \|agieval_sat_math \| 0\|acc \|0.2909\|± \|0.0307\| \| \| \|acc_norm\|0.2727\|± \|0.0301\| ``` GPT-4All Benchmark Set ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|arc_challenge\| 0\|acc \|0.5102\|± \|0.0146\| \| \| \|acc_norm\|0.5213\|± \|0.0146\| \|arc_easy \| 0\|acc \|0.7959\|± \|0.0083\| \| \| \|acc_norm\|0.7567\|± \|0.0088\| \|boolq \| 1\|acc \|0.8394\|± \|0.0064\| \|hellaswag \| 0\|acc \|0.6164\|± \|0.0049\| \| \| \|acc_norm\|0.8009\|± \|0.0040\| \|openbookqa \| 0\|acc \|0.3580\|± \|0.0215\| \| \| \|acc_norm\|0.4620\|± \|0.0223\| \|piqa \| 0\|acc \|0.7992\|± \|0.0093\| \| \| \|acc_norm\|0.8069\|± \|0.0092\| \|winogrande \| 0\|acc \|0.7127\|± \|0.0127\| ``` BigBench Reasoning Test ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5526\|± \|0.0362\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.7344\|± \|0.0230\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.2636\|± \|0.0275\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.0195\|± \|0.0073\| \| \| \|exact_str_match \|0.0000\|± \|0.0000\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.2760\|± \|0.0200\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2100\|± \|0.0154\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.4400\|± \|0.0287\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.2440\|± \|0.0192\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.4950\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.5570\|± \|0.0111\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.3728\|± \|0.0229\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.1854\|± \|0.0123\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.6298\|± \|0.0360\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6156\|± \|0.0155\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.3140\|± \|0.0147\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2032\|± \|0.0114\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1406\|± \|0.0083\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.4400\|± \|0.0287\| ``` These are the highest benchmarks Hermes has seen on every metric, achieving the following average scores: - GPT4All benchmark average is now 70.0 - from 68.8 in Hermes-Llama1 - 0.3657 on BigBench, up from 0.328 on hermes-llama1 - 0.372 on AGIEval, up from 0.354 on Hermes-llama1 These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list, supplanting Hermes 1 for the new top position. ## Resources for Applied Use Cases: For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot ## Future Plans We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. ## Model Usage The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
Vsukiyaki/Shungiku-Mix	Vsukiyaki	2023-07-22T05:11:31Z	0	23	null	[ "stable-diffusion", "text-to-image", "ja", "en", "license:other", "region:us" ]	text-to-image	2023-06-03T16:25:04Z	--- license: other language: - ja - en tags: - stable-diffusion - text-to-image --- # Shungiku-Mix <img src="https://huggingface.co/Vsukiyaki/Shungiku-Mix/resolve/main/imgs/header.jpg" style="width: 640px;"> ##　概要 / Overview - Shungiku-Mixは、アニメ風の画風に特化したマージモデルです。 / Shungiku-Mix is a merge model that specializes in an anime-like painting style. - 幻想的な空や光の表現が得意です。 / This model excels in the expression of fantastic skies and light. - VAEはお好きなものをお使いください。VAEが無くても鮮やかな色合いで出力されますが、clearvaeを使用することを推奨しています。 / You can use whatever VAE you like. The output will be vividly tinted without VAE, but we recommend using clearvae. - clearvaeを含んだモデルも提供しています。 / I also offer models that include clearvae. => Shungiku-Mix_v1-better-vae-fp16.safetensors <hr> ## 更新 / UPDATE NOTE - 2023/07/22：ライセンスを変更しました。 / License changed. <hr> ## 推奨設定 / Recommended Settings <pre style="margin: 1em 0; padding: 1em; border-radius: 5px; background: #25292f; color: #fff; white-space: pre-line;"> Steps: 20 ~ 60 Sampler: DPM++ SDE Karras CFG scale: 7.5 Denoising strength: 0.55 Hires steps: 20 Hires upscaler: Latent Clip skip: 2 Negative embeddings: EasyNegative, verybadimagenegative </pre> Negative prompt: <pre style="margin: 1em 0; padding: 1em; border-radius: 5px; background: #25292f; color: #fff; white-space: pre-line;"> (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad,(inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3, </pre> <hr> ## 例 / Examples <img src="https://huggingface.co/Vsukiyaki/Shungiku-Mix/resolve/main/imgs/sample1.png" style="width: 512px;"> <pre style="margin: 1em 0; padding: 1em; border-radius: 5px; background: #25292f; color: #fff; white-space: pre-line;"> ((solo:1.2)),cute girl,(harbor),(blue sky:1.2),looking at viewer,dramatic,fantastic atmosphere,magnificent view,cumulonimbus,(cowboy shot:1.2),scenery,Mediterranean Buildings,silver hair Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad,(inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3, Steps: 60 Sampler: DPM++ SDE Karras CFG scale: 7.5 Seed: 1896063174 Size: 768x768 Denoising strength: 0.58 Clip skip: 2 Hires upscale: 2 Hires steps: 20 Hires upscaler: Latent </pre> <br> <img src="https://huggingface.co/Vsukiyaki/Shungiku-Mix/resolve/main/imgs/sample2.png" style="width: 640px;"> <pre style="margin: 1em 0; padding: 1em; border-radius: 5px; background: #25292f; color: #fff; white-space: pre-line;"> ((solo:1.2)),cute little (1girl:1.3) walking,landscape,beautiful sky,village,head tilt,bloom effect,fantastic atmosphere,magnificent view,cowboy shot,pale-blonde hair,blue eyes,long twintails,blush,light smile,white dress,wind,(petals) Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad,(inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3, Steps: 60 Sampler: DPM++ SDE Karras CFG scale: 7.5 Seed: 400031884 Size: 848x600 Denoising strength: 0.55 Clip skip: 2 Hires upscale: 2.5 Hires steps: 20 Hires upscaler: Latent </pre> <hr> ## ライセンス / License <div class="px-2"> <table class="table-fixed border mt-0 text-xs"> <tbody> <tr> <td class="px-4 text-base text-bold" colspan="2"> <a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license"> 修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license </a> </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> 🚫 </span> </td> <td> このモデルのクレジットを入れずに使用する<br> Use the model without crediting the creator </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> 🚫 </span> </td> <td> このモデルで生成した画像を商用利用する<br> Sell images they generate </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> 🚫 </span> </td> <td> このモデルを商用の画像生成サービスで利用する</br> Run on services that generate images for money </td> </tr> <tr> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> ✅ </span> </td> <td> このモデルを使用したマージモデルを共有する<br> Share merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> 🚫 </span> </td> <td> このモデル、またはこのモデルをマージしたモデルを販売する</br> Sell this model or merges using this model </td> </tr> <tr class="bg-danger-100"> <td class="align-middle px-2 w-8"> <span style="font-size: 18px;"> 🚫 </span> </td> <td> このモデルをマージしたモデルに異なる権限を設定する</br> Have different permissions when sharing merges </td> </tr> </tbody> </table> </div> <hr> Twiter: [@Vsukiyaki_AIArt](https://twitter.com/Vsukiyaki_AIArt) <a href="https://twitter.com/Vsukiyaki_AIArt" class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md" style="background-color: #1da1f2"> <svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24"> <path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" /> </svg> </a>
nvidia/GCViT	nvidia	2023-07-22T04:47:32Z	0	5	null	[ "arxiv:2206.09959", "region:us" ]	null	2023-07-21T19:28:35Z	# Global Context Vision Transformer (GC ViT) This model contains the official PyTorch implementation of Global Context Vision Transformers (ICML2023) \ \ [Global Context Vision Transformers](https://arxiv.org/pdf/2206.09959.pdf) \ [Ali Hatamizadeh](https://research.nvidia.com/person/ali-hatamizadeh), [Hongxu (Danny) Yin](https://scholar.princeton.edu/hongxu), [Greg Heinrich](https://developer.nvidia.com/blog/author/gheinrich/), [Jan Kautz](https://jankautz.com/), and [Pavlo Molchanov](https://www.pmolchanov.com/). GC ViT achieves state-of-the-art results across image classification, object detection and semantic segmentation tasks. On ImageNet-1K dataset for classification, GC ViT variants with `51M`, `90M` and `201M` parameters achieve `84.3`, `85.9` and `85.7` Top-1 accuracy, respectively, surpassing comparably-sized prior art such as CNN-based ConvNeXt and ViT-based Swin Transformer. <p align="center"> <img src="https://github.com/NVlabs/GCVit/assets/26806394/d1820d6d-3aef-470e-a1d3-af370f1c1f77" width=63% height=63% class="center"> </p> The architecture of GC ViT is demonstrated in the following: ![gc_vit](https://github.com/NVlabs/GCVit/assets/26806394/86ca853e-56bc-4907-b3e3-0c4611ef9073) ## Introduction GC ViT leverages global context self-attention modules, joint with local self-attention, to effectively yet efficiently model both long and short-range spatial interactions, without the need for expensive operations such as computing attention masks or shifting local windows. <p align="center"> <img src="https://github.com/NVlabs/GCVit/assets/26806394/da64f22a-e7af-4577-8884-b08ba4e24e49" width=72% height=72% class="center"> </p> ## ImageNet Benchmarks ImageNet-1K Pretrained Models <table> <tr> <th>Model Variant</th> <th>Acc@1</th> <th>#Params(M)</th> <th>FLOPs(G)</th> <th>Download</th> </tr> <tr> <td>GC ViT-XXT</td> <th>79.9</th> <td>12</td> <td>2.1</td> <td><a href="https://drive.google.com/uc?export=download&id=1apSIWQCa5VhWLJws8ugMTuyKzyayw4Eh">model</a></td> </tr> <tr> <td>GC ViT-XT</td> <th>82.0</th> <td>20</td> <td>2.6</td> <td><a href="https://drive.google.com/uc?export=download&id=1OgSbX73AXmE0beStoJf2Jtda1yin9t9m">model</a></td> </tr> <tr> <td>GC ViT-T</td> <th>83.5</th> <td>28</td> <td>4.7</td> <td><a href="https://drive.google.com/uc?export=download&id=11M6AsxKLhfOpD12Nm_c7lOvIIAn9cljy">model</a></td> </tr> <tr> <td>GC ViT-T2</td> <th>83.7</th> <td>34</td> <td>5.5</td> <td><a href="https://drive.google.com/uc?export=download&id=1cTD8VemWFiwAx0FB9cRMT-P4vRuylvmQ">model</a></td> </tr> <tr> <td>GC ViT-S</td> <th>84.3</th> <td>51</td> <td>8.5</td> <td><a href="https://drive.google.com/uc?export=download&id=1Nn6ABKmYjylyWC0I41Q3oExrn4fTzO9Y">model</a></td> </tr> <tr> <td>GC ViT-S2</td> <th>84.8</th> <td>68</td> <td>10.7</td> <td><a href="https://drive.google.com/uc?export=download&id=1E5TtYpTqILznjBLLBTlO5CGq343RbEan">model</a></td> </tr> <tr> <td>GC ViT-B</td> <th>85.0</th> <td>90</td> <td>14.8</td> <td><a href="https://drive.google.com/uc?export=download&id=1PF7qfxKLcv_ASOMetDP75n8lC50gaqyH">model</a></td> </tr> <tr> <td>GC ViT-L</td> <th>85.7</th> <td>201</td> <td>32.6</td> <td><a href="https://drive.google.com/uc?export=download&id=1Lkz1nWKTwCCUR7yQJM6zu_xwN1TR0mxS">model</a></td> </tr> </table> ImageNet-21K Pretrained Models <table> <tr> <th>Model Variant</th> <th>Resolution</th> <th>Acc@1</th> <th>#Params(M)</th> <th>FLOPs(G)</th> <th>Download</th> </tr> <tr> <td>GC ViT-L</td> <td>224 x 224</td> <th>86.6</th> <td>201</td> <td>32.6</td> <td><a href="https://drive.google.com/uc?export=download&id=1maGDr6mJkLyRTUkspMzCgSlhDzNRFGEf">model</a></td> </tr> <tr> <td>GC ViT-L</td> <td>384 x 384</td> <th>87.4</th> <td>201</td> <td>120.4</td> <td><a href="https://drive.google.com/uc?export=download&id=1P-IEhvQbJ3FjnunVkM1Z9dEpKw-tsuWv">model</a></td> </tr> </table> ## Citation Please consider citing GC ViT paper if it is useful for your work: ``` @inproceedings{hatamizadeh2023global, title={Global context vision transformers}, author={Hatamizadeh, Ali and Yin, Hongxu and Heinrich, Greg and Kautz, Jan and Molchanov, Pavlo}, booktitle={International Conference on Machine Learning}, pages={12633--12646}, year={2023}, organization={PMLR} } ``` ## Licenses Copyright © 2023, NVIDIA Corporation. All rights reserved. This work is made available under the Nvidia Source Code License-NC. Click [here](LICENSE) to view a copy of this license. The pre-trained models are shared under [CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. For license information regarding the timm, please refer to its [repository](https://github.com/rwightman/pytorch-image-models). For license information regarding the ImageNet dataset, please refer to the ImageNet [official website](https://www.image-net.org/).
EXrRor3/ppo-LunarLander-v2	EXrRor3	2023-07-22T03:46:54Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T03:40:27Z	--- 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: 251.86 +/- 17.63 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 ... ```
ittailup/lallama-13b-chat	ittailup	2023-07-22T03:20:47Z	1	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2023-07-21T19:10:35Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Jonathaniu/vicuna-breast-cancer-7b-mix-data-epoch-2_5	Jonathaniu	2023-07-22T03:09:35Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-22T03:09:21Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False ### Framework versions - PEFT 0.4.0.dev0
UNIST-Eunchan/Pegasus-x-base-govreport-12288-1024-numepoch-10	UNIST-Eunchan	2023-07-22T03:05:31Z	93	0	transformers	[ "transformers", "pytorch", "pegasus_x", "text2text-generation", "generated_from_trainer", "dataset:govreport-summarization", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-07-20T02:20:44Z	--- tags: - generated_from_trainer datasets: - govreport-summarization model-index: - name: Pegasus-x-base-govreport-12288-1024-numepoch-10 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. --> # Pegasus-x-base-govreport-12288-1024-numepoch-10 This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/google/pegasus-x-base) on the govreport-summarization dataset. It achieves the following results on the evaluation set: - Loss: 1.6234 ## Model description More information needed ## Evaluation Score 'ROUGE': { 'rouge1': 0.5012, 'rouge2': 0.2205, 'rougeL': 0.2552, 'rougeLsum': 0.2554 } 'BERT_SCORE' {'f1': 0.859, 'precision': 0.8619, 'recall': 0.8563 } ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.1149 \| 0.37 \| 100 \| 1.9237 \| \| 1.9545 \| 0.73 \| 200 \| 1.8380 \| \| 1.8835 \| 1.1 \| 300 \| 1.7574 \| \| 1.862 \| 1.46 \| 400 \| 1.7305 \| \| 1.8536 \| 1.83 \| 500 \| 1.7100 \| \| 1.8062 \| 2.19 \| 600 \| 1.6944 \| \| 1.8161 \| 2.56 \| 700 \| 1.6882 \| \| 1.7611 \| 2.92 \| 800 \| 1.6803 \| \| 1.7878 \| 3.29 \| 900 \| 1.6671 \| \| 1.7299 \| 3.65 \| 1000 \| 1.6599 \| \| 1.7636 \| 4.02 \| 1100 \| 1.6558 \| \| 1.7262 \| 4.38 \| 1200 \| 1.6547 \| \| 1.715 \| 4.75 \| 1300 \| 1.6437 \| \| 1.7178 \| 5.12 \| 1400 \| 1.6445 \| \| 1.7163 \| 5.48 \| 1500 \| 1.6386 \| \| 1.7367 \| 5.85 \| 1600 \| 1.6364 \| \| 1.7114 \| 6.21 \| 1700 \| 1.6365 \| \| 1.6452 \| 6.58 \| 1800 \| 1.6309 \| \| 1.7251 \| 6.94 \| 1900 \| 1.6301 \| \| 1.6726 \| 7.31 \| 2000 \| 1.6305 \| \| 1.7104 \| 7.67 \| 2100 \| 1.6285 \| \| 1.6739 \| 8.04 \| 2200 \| 1.6252 \| \| 1.7082 \| 8.4 \| 2300 \| 1.6246 \| \| 1.6888 \| 8.77 \| 2400 \| 1.6244 \| \| 1.6609 \| 9.13 \| 2500 \| 1.6256 \| \| 1.6707 \| 9.5 \| 2600 \| 1.6241 \| \| 1.669 \| 9.86 \| 2700 \| 1.6234 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
Falcinspire/Reinforce-MLP-v1-Cartpole-v1	Falcinspire	2023-07-22T02:22:54Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T00:42:23Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-MLP-v1-Cartpole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 491.10 +/- 26.70 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
LarryAIDraw/YelanV4-09	LarryAIDraw	2023-07-22T02:16:49Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-22T02:15:12Z	--- license: creativeml-openrail-m --- https://civitai.com/models/61470/yelan-lora-genshin-impact
Blackroot/Llama-2-13B-Storyweaver-LORA-Deprecated	Blackroot	2023-07-22T02:12:40Z	0	0	null	[ "region:us" ]	null	2023-07-21T23:33:22Z	Join the Coffee & AI Discord for AI Stuff and things! [![Discord](https://img.shields.io/discord/232596713892872193?logo=discord)](https://discord.gg/2JhHVh7CGu) ## Probably bad model Test results are showing that although this model does produce long outputs, the quality has generally degraded. I'm leaving this up for the time being but I would recommend one of my other loras instead. As an aside, this model is really, really funny, try it if you want a laugh. ## Get the base model here: Base Model Quantizations by The Bloke here: https://huggingface.co/TheBloke/Llama-2-13B-GGML https://huggingface.co/TheBloke/Llama-2-13B-GPTQ ## Prompting for this model: A brief warning that no alignment or attempts to sanitize or otherwise filter the dataset or the outputs have been done. This is a completelty raw model and may behave unpredictably or create scenarios that are unpleasant. The base Llama2 is a text completion model. That means it will continue writing from the story in whatever manner you direct it. This is not an instruct tuned model, so don't try and give it instruction. Correct prompting: ``` He grabbed his sword, his gleaming armor, he readied himself. The battle was coming, he walked into the dawn light and ``` Incorrect prompting: ``` Write a story about... ``` This model has been trained to generate as much text as possible, so you should use some mechanism to force it to stop at N tokens or something. For exmaple, in one prompt I average about 7000 output tokens, basically make sure you have a max sequence length set or it'll just keep going forever. ## Training procedure 22,000 steps @ 7 epochs. Final training loss of 1.8. Total training time was 30 hours on a single 3090 TI. PEFT: The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.5.0.dev0
LarryAIDraw/ots-14	LarryAIDraw	2023-07-22T02:11:14Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-22T02:09:45Z	--- license: creativeml-openrail-m --- https://civitai.com/models/21833/girls-frontline-ots-14
LarryAIDraw/niloutest	LarryAIDraw	2023-07-22T01:50:09Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-22T01:49:28Z	--- license: creativeml-openrail-m --- https://civitai.com/models/101969/nilou-genshin-impact
LarryAIDraw/Genshin_Impact-Nilou_V2_nilou__genshin_impact_-000012	LarryAIDraw	2023-07-22T01:49:53Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2023-07-22T01:46:49Z	--- license: creativeml-openrail-m --- https://civitai.com/models/5367/tsumasaky-nilou-genshin-impact-lora
minhanhtuan/llama2-qlora-finetunined-french	minhanhtuan	2023-07-22T01:25:58Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-22T01:25:51Z	--- 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
jonmay/ppo-LunarLander-v2	jonmay	2023-07-22T01:24:58Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-22T01:24:35Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.45 +/- 20.35 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 ... ```
Mel-Iza0/RedPajama-ZeroShot-20K-new_prompt_classe_bias	Mel-Iza0	2023-07-22T01:12:05Z	2	0	peft	[ "peft", "pytorch", "gpt_neox", "region:us" ]	null	2023-07-21T21:11:26Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
Bainbridge/vilt-b32-mlm-mami	Bainbridge	2023-07-22T01:03:05Z	38	0	transformers	[ "transformers", "pytorch", "tensorboard", "vilt", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-07-22T00:22:38Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - f1 model-index: - name: vilt-b32-mlm-mami 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. --> # vilt-b32-mlm-mami This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the MAMI dataset. It achieves the following results on the evaluation set: - Loss: 0.5796 - F1: 0.7899 ## 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_ratio: 0.1 - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.6898 \| 0.48 \| 100 \| 0.6631 \| 0.6076 \| \| 0.5824 \| 0.96 \| 200 \| 0.5055 \| 0.7545 \| \| 0.4306 \| 1.44 \| 300 \| 0.4586 \| 0.7861 \| \| 0.4207 \| 1.91 \| 400 \| 0.4439 \| 0.7927 \| \| 0.3055 \| 2.39 \| 500 \| 0.4912 \| 0.7949 \| \| 0.2582 \| 2.87 \| 600 \| 0.4921 \| 0.7873 \| \| 0.1875 \| 3.35 \| 700 \| 0.5796 \| 0.7899 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.12.0 - Tokenizers 0.13.3
NasimB/cbt-norm-rarity-neg-log-rarity	NasimB	2023-07-22T00:46:15Z	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	2023-07-21T22:20:45Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: cbt-norm-rarity-neg-log-rarity 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. --> # cbt-norm-rarity-neg-log-rarity 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: 4.1046 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3494 \| 0.29 \| 500 \| 5.3385 \| \| 5.0263 \| 0.58 \| 1000 \| 4.9258 \| \| 4.7061 \| 0.87 \| 1500 \| 4.6888 \| \| 4.4468 \| 1.16 \| 2000 \| 4.5463 \| \| 4.2956 \| 1.46 \| 2500 \| 4.4260 \| \| 4.1947 \| 1.75 \| 3000 \| 4.3302 \| \| 4.0756 \| 2.04 \| 3500 \| 4.2520 \| \| 3.8921 \| 2.33 \| 4000 \| 4.2106 \| \| 3.8655 \| 2.62 \| 4500 \| 4.1572 \| \| 3.8345 \| 2.91 \| 5000 \| 4.1064 \| \| 3.6432 \| 3.2 \| 5500 \| 4.1013 \| \| 3.581 \| 3.49 \| 6000 \| 4.0704 \| \| 3.569 \| 3.79 \| 6500 \| 4.0362 \| \| 3.4919 \| 4.08 \| 7000 \| 4.0338 \| \| 3.3226 \| 4.37 \| 7500 \| 4.0289 \| \| 3.3106 \| 4.66 \| 8000 \| 4.0166 \| \| 3.297 \| 4.95 \| 8500 \| 4.0046 \| \| 3.1568 \| 5.24 \| 9000 \| 4.0152 \| \| 3.1358 \| 5.53 \| 9500 \| 4.0145 \| \| 3.1313 \| 5.82 \| 10000 \| 4.0135 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
SaffalPoosh/falcon-7b-autogptq-custom	SaffalPoosh	2023-07-22T00:17:42Z	6	0	transformers	[ "transformers", "RefinedWebModel", "text-generation", "custom_code", "autotrain_compatible", "region:us" ]	text-generation	2023-07-22T00:02:03Z	autogptq quant. logs ``` >>> model.quantize(examples) 2023-07-21 16:54:47 INFO [auto_gptq.modeling._base] Start quantizing layer 1/32 2023-07-21 16:54:47 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 1/32... 2023-07-21 16:54:48 INFO [auto_gptq.quantization.gptq] duration: 0.8171646595001221 2023-07-21 16:54:48 INFO [auto_gptq.quantization.gptq] avg loss: 3.7546463012695312 2023-07-21 16:54:48 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 1/32... 2023-07-21 16:54:49 INFO [auto_gptq.quantization.gptq] duration: 0.8055715560913086 2023-07-21 16:54:49 INFO [auto_gptq.quantization.gptq] avg loss: 0.2164316177368164 2023-07-21 16:54:49 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 1/32... 2023-07-21 16:54:50 INFO [auto_gptq.quantization.gptq] duration: 0.8417620658874512 2023-07-21 16:54:50 INFO [auto_gptq.quantization.gptq] avg loss: 16.070518493652344 2023-07-21 16:54:50 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 1/32... 2023-07-21 16:54:53 INFO [auto_gptq.quantization.gptq] duration: 3.90244197845459 2023-07-21 16:54:53 INFO [auto_gptq.quantization.gptq] avg loss: 0.5676069855690002 2023-07-21 16:54:53 INFO [auto_gptq.modeling._base] Start quantizing layer 2/32 2023-07-21 16:54:54 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 2/32... 2023-07-21 16:54:54 INFO [auto_gptq.quantization.gptq] duration: 0.8373761177062988 2023-07-21 16:54:54 INFO [auto_gptq.quantization.gptq] avg loss: 4.066518783569336 2023-07-21 16:54:54 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 2/32... 2023-07-21 16:54:55 INFO [auto_gptq.quantization.gptq] duration: 0.8285796642303467 2023-07-21 16:54:55 INFO [auto_gptq.quantization.gptq] avg loss: 0.2558078169822693 2023-07-21 16:55:25 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 2/32... 2023-07-21 16:55:25 INFO [auto_gptq.quantization.gptq] duration: 0.8859198093414307 2023-07-21 16:55:25 INFO [auto_gptq.quantization.gptq] avg loss: 16.571727752685547 2023-07-21 16:55:26 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 2/32... 2023-07-21 16:55:29 INFO [auto_gptq.quantization.gptq] duration: 3.86962890625 2023-07-21 16:55:29 INFO [auto_gptq.quantization.gptq] avg loss: 0.34605544805526733 2023-07-21 16:55:30 INFO [auto_gptq.modeling._base] Start quantizing layer 3/32 2023-07-21 16:55:30 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 3/32... 2023-07-21 16:55:30 INFO [auto_gptq.quantization.gptq] duration: 0.8118832111358643 2023-07-21 16:55:30 INFO [auto_gptq.quantization.gptq] avg loss: 5.4185943603515625 2023-07-21 16:55:30 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 3/32... 2023-07-21 16:55:31 INFO [auto_gptq.quantization.gptq] duration: 0.8096959590911865 2023-07-21 16:55:31 INFO [auto_gptq.quantization.gptq] avg loss: 0.22585009038448334 2023-07-21 16:55:31 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 3/32... 2023-07-21 16:55:32 INFO [auto_gptq.quantization.gptq] duration: 0.8473665714263916 2023-07-21 16:55:32 INFO [auto_gptq.quantization.gptq] avg loss: 27.050426483154297 2023-07-21 16:55:32 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 3/32... 2023-07-21 16:55:36 INFO [auto_gptq.quantization.gptq] duration: 3.8430850505828857 2023-07-21 16:55:36 INFO [auto_gptq.quantization.gptq] avg loss: 0.6839203834533691 2023-07-21 16:55:36 INFO [auto_gptq.modeling._base] Start quantizing layer 4/32 2023-07-21 16:55:36 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 4/32... 2023-07-21 16:55:37 INFO [auto_gptq.quantization.gptq] duration: 0.7948899269104004 2023-07-21 16:55:37 INFO [auto_gptq.quantization.gptq] avg loss: 6.523550987243652 2023-07-21 16:55:37 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 4/32... 2023-07-21 16:55:38 INFO [auto_gptq.quantization.gptq] duration: 0.7990512847900391 2023-07-21 16:55:38 INFO [auto_gptq.quantization.gptq] avg loss: 0.21638213098049164 2023-07-21 16:55:38 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 4/32... 2023-07-21 16:55:39 INFO [auto_gptq.quantization.gptq] duration: 0.8403058052062988 2023-07-21 16:55:39 INFO [auto_gptq.quantization.gptq] avg loss: 36.57025146484375 2023-07-21 16:55:39 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 4/32... 2023-07-21 16:55:43 INFO [auto_gptq.quantization.gptq] duration: 3.856529474258423 2023-07-21 16:55:43 INFO [auto_gptq.quantization.gptq] avg loss: 9.424503326416016 2023-07-21 16:55:43 INFO [auto_gptq.modeling._base] Start quantizing layer 5/32 2023-07-21 16:55:43 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 5/32... 2023-07-21 16:55:44 INFO [auto_gptq.quantization.gptq] duration: 0.7926647663116455 2023-07-21 16:55:44 INFO [auto_gptq.quantization.gptq] avg loss: 6.277029037475586 2023-07-21 16:55:44 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 5/32... 2023-07-21 16:55:44 INFO [auto_gptq.quantization.gptq] duration: 0.7987856864929199 2023-07-21 16:55:44 INFO [auto_gptq.quantization.gptq] avg loss: 0.1324760764837265 2023-07-21 16:55:44 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 5/32... 2023-07-21 16:55:45 INFO [auto_gptq.quantization.gptq] duration: 0.8394050598144531 2023-07-21 16:55:45 INFO [auto_gptq.quantization.gptq] avg loss: 36.26388168334961 2023-07-21 16:55:45 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 5/32... 2023-07-21 16:55:49 INFO [auto_gptq.quantization.gptq] duration: 3.849104166030884 2023-07-21 16:55:49 INFO [auto_gptq.quantization.gptq] avg loss: 2.376619338989258 2023-07-21 16:55:49 INFO [auto_gptq.modeling._base] Start quantizing layer 6/32 2023-07-21 16:55:49 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 6/32... 2023-07-21 16:55:50 INFO [auto_gptq.quantization.gptq] duration: 0.7964150905609131 2023-07-21 16:55:50 INFO [auto_gptq.quantization.gptq] avg loss: 8.479263305664062 2023-07-21 16:55:50 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 6/32... 2023-07-21 16:55:51 INFO [auto_gptq.quantization.gptq] duration: 0.7951827049255371 2023-07-21 16:55:51 INFO [auto_gptq.quantization.gptq] avg loss: 0.14170163869857788 2023-07-21 16:56:21 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 6/32... 2023-07-21 16:56:22 INFO [auto_gptq.quantization.gptq] duration: 0.8720560073852539 2023-07-21 16:56:22 INFO [auto_gptq.quantization.gptq] avg loss: 42.756919860839844 2023-07-21 16:56:22 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 6/32... 2023-07-21 16:56:25 INFO [auto_gptq.quantization.gptq] duration: 3.8685550689697266 2023-07-21 16:56:25 INFO [auto_gptq.quantization.gptq] avg loss: 0.8117952346801758 2023-07-21 16:56:26 INFO [auto_gptq.modeling._base] Start quantizing layer 7/32 2023-07-21 16:56:26 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 7/32... 2023-07-21 16:56:26 INFO [auto_gptq.quantization.gptq] duration: 0.7976808547973633 2023-07-21 16:56:26 INFO [auto_gptq.quantization.gptq] avg loss: 7.019394397735596 2023-07-21 16:56:26 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 7/32... 2023-07-21 16:56:27 INFO [auto_gptq.quantization.gptq] duration: 0.803225040435791 2023-07-21 16:56:27 INFO [auto_gptq.quantization.gptq] avg loss: 0.21443051099777222 2023-07-21 16:56:27 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 7/32... 2023-07-21 16:56:28 INFO [auto_gptq.quantization.gptq] duration: 0.8342931270599365 2023-07-21 16:56:28 INFO [auto_gptq.quantization.gptq] avg loss: 39.33504104614258 2023-07-21 16:56:28 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 7/32... 2023-07-21 16:56:32 INFO [auto_gptq.quantization.gptq] duration: 3.8671581745147705 2023-07-21 16:56:32 INFO [auto_gptq.quantization.gptq] avg loss: 0.9214520454406738 2023-07-21 16:56:32 INFO [auto_gptq.modeling._base] Start quantizing layer 8/32 2023-07-21 16:56:32 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 8/32... 2023-07-21 16:56:33 INFO [auto_gptq.quantization.gptq] duration: 0.7989864349365234 2023-07-21 16:56:33 INFO [auto_gptq.quantization.gptq] avg loss: 7.602280616760254 2023-07-21 16:56:33 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 8/32... 2023-07-21 16:56:34 INFO [auto_gptq.quantization.gptq] duration: 0.8112733364105225 2023-07-21 16:56:34 INFO [auto_gptq.quantization.gptq] avg loss: 0.11391645669937134 2023-07-21 16:56:34 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 8/32... 2023-07-21 16:56:35 INFO [auto_gptq.quantization.gptq] duration: 0.8388988971710205 2023-07-21 16:56:35 INFO [auto_gptq.quantization.gptq] avg loss: 34.74957275390625 2023-07-21 16:56:35 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 8/32... 2023-07-21 16:56:39 INFO [auto_gptq.quantization.gptq] duration: 3.8561182022094727 2023-07-21 16:56:39 INFO [auto_gptq.quantization.gptq] avg loss: 1.1289432048797607 2023-07-21 16:56:39 INFO [auto_gptq.modeling._base] Start quantizing layer 9/32 2023-07-21 16:56:39 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 9/32... 2023-07-21 16:56:40 INFO [auto_gptq.quantization.gptq] duration: 0.7969386577606201 2023-07-21 16:56:40 INFO [auto_gptq.quantization.gptq] avg loss: 6.806826591491699 2023-07-21 16:56:40 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 9/32... 2023-07-21 16:56:41 INFO [auto_gptq.quantization.gptq] duration: 0.7953078746795654 2023-07-21 16:56:41 INFO [auto_gptq.quantization.gptq] avg loss: 0.2318212240934372 2023-07-21 16:56:41 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 9/32... 2023-07-21 16:56:41 INFO [auto_gptq.quantization.gptq] duration: 0.8294937610626221 2023-07-21 16:56:41 INFO [auto_gptq.quantization.gptq] avg loss: 35.324676513671875 2023-07-21 16:56:41 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 9/32... 2023-07-21 16:56:45 INFO [auto_gptq.quantization.gptq] duration: 3.8630259037017822 2023-07-21 16:56:45 INFO [auto_gptq.quantization.gptq] avg loss: 1.4622347354888916 2023-07-21 16:56:45 INFO [auto_gptq.modeling._base] Start quantizing layer 10/32 2023-07-21 16:56:46 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 10/32... 2023-07-21 16:56:46 INFO [auto_gptq.quantization.gptq] duration: 0.8029708862304688 2023-07-21 16:56:46 INFO [auto_gptq.quantization.gptq] avg loss: 6.056252956390381 2023-07-21 16:56:46 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 10/32... 2023-07-21 16:56:47 INFO [auto_gptq.quantization.gptq] duration: 0.8028323650360107 2023-07-21 16:56:47 INFO [auto_gptq.quantization.gptq] avg loss: 1.092197060585022 2023-07-21 16:56:47 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 10/32... 2023-07-21 16:56:48 INFO [auto_gptq.quantization.gptq] duration: 0.8335537910461426 2023-07-21 16:56:48 INFO [auto_gptq.quantization.gptq] avg loss: 30.71457290649414 2023-07-21 16:56:48 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 10/32... 2023-07-21 16:56:52 INFO [auto_gptq.quantization.gptq] duration: 3.8703184127807617 2023-07-21 16:56:52 INFO [auto_gptq.quantization.gptq] avg loss: 1.2208330631256104 2023-07-21 16:56:52 INFO [auto_gptq.modeling._base] Start quantizing layer 11/32 2023-07-21 16:56:52 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 11/32... 2023-07-21 16:56:53 INFO [auto_gptq.quantization.gptq] duration: 0.814570426940918 2023-07-21 16:56:53 INFO [auto_gptq.quantization.gptq] avg loss: 6.145627021789551 2023-07-21 16:56:53 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 11/32... 2023-07-21 16:56:54 INFO [auto_gptq.quantization.gptq] duration: 0.8268287181854248 2023-07-21 16:56:54 INFO [auto_gptq.quantization.gptq] avg loss: 0.24324843287467957 2023-07-21 16:56:54 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 11/32... 2023-07-21 16:56:55 INFO [auto_gptq.quantization.gptq] duration: 0.8359119892120361 2023-07-21 16:56:55 INFO [auto_gptq.quantization.gptq] avg loss: 30.847026824951172 2023-07-21 16:56:55 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 11/32... 2023-07-21 16:56:58 INFO [auto_gptq.quantization.gptq] duration: 3.831470489501953 2023-07-21 16:56:58 INFO [auto_gptq.quantization.gptq] avg loss: 1.3961751461029053 2023-07-21 16:57:26 INFO [auto_gptq.modeling._base] Start quantizing layer 12/32 2023-07-21 16:57:26 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 12/32... 2023-07-21 16:57:27 INFO [auto_gptq.quantization.gptq] duration: 0.7964096069335938 2023-07-21 16:57:27 INFO [auto_gptq.quantization.gptq] avg loss: 6.053964614868164 2023-07-21 16:57:27 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 12/32... 2023-07-21 16:57:28 INFO [auto_gptq.quantization.gptq] duration: 0.799691915512085 2023-07-21 16:57:28 INFO [auto_gptq.quantization.gptq] avg loss: 0.2671034336090088 2023-07-21 16:57:28 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 12/32... 2023-07-21 16:57:29 INFO [auto_gptq.quantization.gptq] duration: 0.8342888355255127 2023-07-21 16:57:29 INFO [auto_gptq.quantization.gptq] avg loss: 29.729408264160156 2023-07-21 16:57:29 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 12/32... 2023-07-21 16:57:33 INFO [auto_gptq.quantization.gptq] duration: 3.8561949729919434 2023-07-21 16:57:33 INFO [auto_gptq.quantization.gptq] avg loss: 1.495622158050537 2023-07-21 16:57:33 INFO [auto_gptq.modeling._base] Start quantizing layer 13/32 2023-07-21 16:57:33 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 13/32... 2023-07-21 16:57:34 INFO [auto_gptq.quantization.gptq] duration: 0.7953364849090576 2023-07-21 16:57:34 INFO [auto_gptq.quantization.gptq] avg loss: 5.408998489379883 2023-07-21 16:57:34 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 13/32... 2023-07-21 16:57:34 INFO [auto_gptq.quantization.gptq] duration: 0.7990250587463379 2023-07-21 16:57:34 INFO [auto_gptq.quantization.gptq] avg loss: 0.5066410303115845 2023-07-21 16:57:34 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 13/32... 2023-07-21 16:57:35 INFO [auto_gptq.quantization.gptq] duration: 0.8330769538879395 2023-07-21 16:57:35 INFO [auto_gptq.quantization.gptq] avg loss: 27.790515899658203 2023-07-21 16:57:35 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 13/32... 2023-07-21 16:57:39 INFO [auto_gptq.quantization.gptq] duration: 3.861015558242798 2023-07-21 16:57:39 INFO [auto_gptq.quantization.gptq] avg loss: 1.3019633293151855 2023-07-21 16:57:39 INFO [auto_gptq.modeling._base] Start quantizing layer 14/32 2023-07-21 16:57:39 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 14/32... 2023-07-21 16:57:40 INFO [auto_gptq.quantization.gptq] duration: 0.8011329174041748 2023-07-21 16:57:40 INFO [auto_gptq.quantization.gptq] avg loss: 6.027165412902832 2023-07-21 16:57:40 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 14/32... 2023-07-21 16:57:41 INFO [auto_gptq.quantization.gptq] duration: 0.7977538108825684 2023-07-21 16:57:41 INFO [auto_gptq.quantization.gptq] avg loss: 0.28969255089759827 2023-07-21 16:57:41 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 14/32... 2023-07-21 16:57:42 INFO [auto_gptq.quantization.gptq] duration: 0.8305981159210205 2023-07-21 16:57:42 INFO [auto_gptq.quantization.gptq] avg loss: 28.996891021728516 2023-07-21 16:57:42 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 14/32... 2023-07-21 16:57:46 INFO [auto_gptq.quantization.gptq] duration: 3.874257802963257 2023-07-21 16:57:46 INFO [auto_gptq.quantization.gptq] avg loss: 1.6258554458618164 2023-07-21 16:57:46 INFO [auto_gptq.modeling._base] Start quantizing layer 15/32 2023-07-21 16:57:46 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 15/32... 2023-07-21 16:57:47 INFO [auto_gptq.quantization.gptq] duration: 0.7982082366943359 2023-07-21 16:57:47 INFO [auto_gptq.quantization.gptq] avg loss: 5.937747001647949 2023-07-21 16:57:47 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 15/32... 2023-07-21 16:57:48 INFO [auto_gptq.quantization.gptq] duration: 0.8004462718963623 2023-07-21 16:57:48 INFO [auto_gptq.quantization.gptq] avg loss: 0.3830963373184204 2023-07-21 16:57:48 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 15/32... 2023-07-21 16:57:48 INFO [auto_gptq.quantization.gptq] duration: 0.8347995281219482 2023-07-21 16:57:48 INFO [auto_gptq.quantization.gptq] avg loss: 30.339778900146484 2023-07-21 16:57:48 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 15/32... 2023-07-21 16:57:52 INFO [auto_gptq.quantization.gptq] duration: 3.8794045448303223 2023-07-21 16:57:52 INFO [auto_gptq.quantization.gptq] avg loss: 1.618453025817871 2023-07-21 16:57:52 INFO [auto_gptq.modeling._base] Start quantizing layer 16/32 2023-07-21 16:57:53 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 16/32... 2023-07-21 16:57:53 INFO [auto_gptq.quantization.gptq] duration: 0.802685022354126 2023-07-21 16:57:53 INFO [auto_gptq.quantization.gptq] avg loss: 5.992144584655762 2023-07-21 16:57:53 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 16/32... 2023-07-21 16:57:54 INFO [auto_gptq.quantization.gptq] duration: 0.8001143932342529 2023-07-21 16:57:54 INFO [auto_gptq.quantization.gptq] avg loss: 0.3652211129665375 2023-07-21 16:57:54 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 16/32... 2023-07-21 16:57:55 INFO [auto_gptq.quantization.gptq] duration: 0.843254566192627 2023-07-21 16:57:55 INFO [auto_gptq.quantization.gptq] avg loss: 29.359691619873047 2023-07-21 16:57:55 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 16/32... 2023-07-21 16:57:59 INFO [auto_gptq.quantization.gptq] duration: 3.8731229305267334 2023-07-21 16:57:59 INFO [auto_gptq.quantization.gptq] avg loss: 1.8666539192199707 2023-07-21 16:57:59 INFO [auto_gptq.modeling._base] Start quantizing layer 17/32 2023-07-21 16:57:59 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 17/32... 2023-07-21 16:58:00 INFO [auto_gptq.quantization.gptq] duration: 0.79642653465271 2023-07-21 16:58:00 INFO [auto_gptq.quantization.gptq] avg loss: 6.463171482086182 2023-07-21 16:58:00 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 17/32... 2023-07-21 16:58:01 INFO [auto_gptq.quantization.gptq] duration: 0.8078687191009521 2023-07-21 16:58:01 INFO [auto_gptq.quantization.gptq] avg loss: 0.24540238082408905 2023-07-21 16:58:01 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 17/32... 2023-07-21 16:58:02 INFO [auto_gptq.quantization.gptq] duration: 0.829270601272583 2023-07-21 16:58:02 INFO [auto_gptq.quantization.gptq] avg loss: 30.825468063354492 2023-07-21 16:58:02 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 17/32... 2023-07-21 16:58:05 INFO [auto_gptq.quantization.gptq] duration: 3.855315923690796 2023-07-21 16:58:05 INFO [auto_gptq.quantization.gptq] avg loss: 1.957414150238037 2023-07-21 16:58:06 INFO [auto_gptq.modeling._base] Start quantizing layer 18/32 2023-07-21 16:58:06 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 18/32... 2023-07-21 16:58:07 INFO [auto_gptq.quantization.gptq] duration: 0.8099801540374756 2023-07-21 16:58:07 INFO [auto_gptq.quantization.gptq] avg loss: 6.510787010192871 2023-07-21 16:58:07 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 18/32... 2023-07-21 16:58:07 INFO [auto_gptq.quantization.gptq] duration: 0.8008811473846436 2023-07-21 16:58:07 INFO [auto_gptq.quantization.gptq] avg loss: 0.3201957941055298 2023-07-21 16:58:07 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 18/32... 2023-07-21 16:58:08 INFO [auto_gptq.quantization.gptq] duration: 0.8365602493286133 2023-07-21 16:58:08 INFO [auto_gptq.quantization.gptq] avg loss: 31.26324462890625 2023-07-21 16:58:08 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 18/32... 2023-07-21 16:58:12 INFO [auto_gptq.quantization.gptq] duration: 3.8536572456359863 2023-07-21 16:58:12 INFO [auto_gptq.quantization.gptq] avg loss: 2.0843615531921387 2023-07-21 16:58:12 INFO [auto_gptq.modeling._base] Start quantizing layer 19/32 2023-07-21 16:58:12 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 19/32... 2023-07-21 16:58:13 INFO [auto_gptq.quantization.gptq] duration: 0.7980837821960449 2023-07-21 16:58:13 INFO [auto_gptq.quantization.gptq] avg loss: 6.686659812927246 2023-07-21 16:58:13 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 19/32... 2023-07-21 16:58:14 INFO [auto_gptq.quantization.gptq] duration: 0.7951889038085938 2023-07-21 16:58:14 INFO [auto_gptq.quantization.gptq] avg loss: 0.3053201138973236 2023-07-21 16:58:14 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 19/32... 2023-07-21 16:58:15 INFO [auto_gptq.quantization.gptq] duration: 0.8315420150756836 2023-07-21 16:58:15 INFO [auto_gptq.quantization.gptq] avg loss: 31.97283935546875 2023-07-21 16:58:15 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 19/32... 2023-07-21 16:58:19 INFO [auto_gptq.quantization.gptq] duration: 3.868382215499878 2023-07-21 16:58:19 INFO [auto_gptq.quantization.gptq] avg loss: 2.382962703704834 2023-07-21 16:58:19 INFO [auto_gptq.modeling._base] Start quantizing layer 20/32 2023-07-21 16:58:19 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 20/32... 2023-07-21 16:58:20 INFO [auto_gptq.quantization.gptq] duration: 0.797062873840332 2023-07-21 16:58:20 INFO [auto_gptq.quantization.gptq] avg loss: 6.721341133117676 2023-07-21 16:58:20 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 20/32... 2023-07-21 16:58:20 INFO [auto_gptq.quantization.gptq] duration: 0.806023120880127 2023-07-21 16:58:20 INFO [auto_gptq.quantization.gptq] avg loss: 0.5635891556739807 2023-07-21 16:58:20 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 20/32... 2023-07-21 16:58:21 INFO [auto_gptq.quantization.gptq] duration: 0.841651201248169 2023-07-21 16:58:21 INFO [auto_gptq.quantization.gptq] avg loss: 33.371273040771484 2023-07-21 16:58:21 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 20/32... 2023-07-21 16:58:25 INFO [auto_gptq.quantization.gptq] duration: 3.8724091053009033 2023-07-21 16:58:25 INFO [auto_gptq.quantization.gptq] avg loss: 2.5540378093719482 2023-07-21 16:58:25 INFO [auto_gptq.modeling._base] Start quantizing layer 21/32 2023-07-21 16:58:25 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 21/32... 2023-07-21 16:58:26 INFO [auto_gptq.quantization.gptq] duration: 0.8135292530059814 2023-07-21 16:58:26 INFO [auto_gptq.quantization.gptq] avg loss: 7.383816242218018 2023-07-21 16:58:26 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 21/32... 2023-07-21 16:58:27 INFO [auto_gptq.quantization.gptq] duration: 0.8004577159881592 2023-07-21 16:58:27 INFO [auto_gptq.quantization.gptq] avg loss: 0.2988166809082031 2023-07-21 16:58:27 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 21/32... 2023-07-21 16:58:28 INFO [auto_gptq.quantization.gptq] duration: 0.8346357345581055 2023-07-21 16:58:28 INFO [auto_gptq.quantization.gptq] avg loss: 34.46820068359375 2023-07-21 16:58:28 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 21/32... 2023-07-21 16:58:32 INFO [auto_gptq.quantization.gptq] duration: 3.8698837757110596 2023-07-21 16:58:32 INFO [auto_gptq.quantization.gptq] avg loss: 2.538421154022217 2023-07-21 16:58:32 INFO [auto_gptq.modeling._base] Start quantizing layer 22/32 2023-07-21 16:58:32 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 22/32... 2023-07-21 16:58:33 INFO [auto_gptq.quantization.gptq] duration: 0.7975707054138184 2023-07-21 16:58:33 INFO [auto_gptq.quantization.gptq] avg loss: 7.026803970336914 2023-07-21 16:58:33 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 22/32... 2023-07-21 16:58:34 INFO [auto_gptq.quantization.gptq] duration: 0.7988865375518799 2023-07-21 16:58:34 INFO [auto_gptq.quantization.gptq] avg loss: 0.5440877079963684 2023-07-21 16:58:34 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 22/32... 2023-07-21 16:58:35 INFO [auto_gptq.quantization.gptq] duration: 0.847116231918335 2023-07-21 16:58:35 INFO [auto_gptq.quantization.gptq] avg loss: 33.8814582824707 2023-07-21 16:58:35 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 22/32... 2023-07-21 16:58:38 INFO [auto_gptq.quantization.gptq] duration: 3.851823091506958 2023-07-21 16:58:38 INFO [auto_gptq.quantization.gptq] avg loss: 2.612248182296753 2023-07-21 16:58:39 INFO [auto_gptq.modeling._base] Start quantizing layer 23/32 2023-07-21 16:58:39 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 23/32... 2023-07-21 16:58:39 INFO [auto_gptq.quantization.gptq] duration: 0.7956225872039795 2023-07-21 16:58:39 INFO [auto_gptq.quantization.gptq] avg loss: 7.3217453956604 2023-07-21 16:58:39 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 23/32... 2023-07-21 16:58:40 INFO [auto_gptq.quantization.gptq] duration: 0.8155944347381592 2023-07-21 16:58:40 INFO [auto_gptq.quantization.gptq] avg loss: 0.3978100121021271 2023-07-21 16:58:40 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 23/32... 2023-07-21 16:58:41 INFO [auto_gptq.quantization.gptq] duration: 0.8472270965576172 2023-07-21 16:58:41 INFO [auto_gptq.quantization.gptq] avg loss: 33.613494873046875 2023-07-21 16:58:41 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 23/32... 2023-07-21 16:58:45 INFO [auto_gptq.quantization.gptq] duration: 3.877121925354004 2023-07-21 16:58:45 INFO [auto_gptq.quantization.gptq] avg loss: 3.0234107971191406 2023-07-21 16:58:45 INFO [auto_gptq.modeling._base] Start quantizing layer 24/32 2023-07-21 16:58:45 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 24/32... 2023-07-21 16:58:46 INFO [auto_gptq.quantization.gptq] duration: 0.8478920459747314 2023-07-21 16:58:46 INFO [auto_gptq.quantization.gptq] avg loss: 7.490325927734375 2023-07-21 16:58:46 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 24/32... 2023-07-21 16:58:47 INFO [auto_gptq.quantization.gptq] duration: 0.8023700714111328 2023-07-21 16:58:47 INFO [auto_gptq.quantization.gptq] avg loss: 0.6462091207504272 2023-07-21 16:58:47 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 24/32... 2023-07-21 16:58:48 INFO [auto_gptq.quantization.gptq] duration: 0.8271210193634033 2023-07-21 16:58:48 INFO [auto_gptq.quantization.gptq] avg loss: 35.156715393066406 2023-07-21 16:58:48 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 24/32... 2023-07-21 16:58:52 INFO [auto_gptq.quantization.gptq] duration: 3.8558664321899414 2023-07-21 16:58:52 INFO [auto_gptq.quantization.gptq] avg loss: 3.4150047302246094 2023-07-21 16:58:52 INFO [auto_gptq.modeling._base] Start quantizing layer 25/32 2023-07-21 16:58:52 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 25/32... 2023-07-21 16:58:53 INFO [auto_gptq.quantization.gptq] duration: 0.804887056350708 2023-07-21 16:58:53 INFO [auto_gptq.quantization.gptq] avg loss: 7.842990875244141 2023-07-21 16:58:53 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 25/32... 2023-07-21 16:58:53 INFO [auto_gptq.quantization.gptq] duration: 0.7986440658569336 2023-07-21 16:58:53 INFO [auto_gptq.quantization.gptq] avg loss: 0.5917433500289917 2023-07-21 16:58:53 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 25/32... 2023-07-21 16:58:54 INFO [auto_gptq.quantization.gptq] duration: 0.8256046772003174 2023-07-21 16:58:54 INFO [auto_gptq.quantization.gptq] avg loss: 36.299095153808594 2023-07-21 16:58:54 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 25/32... 2023-07-21 16:58:58 INFO [auto_gptq.quantization.gptq] duration: 3.86680006980896 2023-07-21 16:58:58 INFO [auto_gptq.quantization.gptq] avg loss: 4.292586326599121 2023-07-21 16:58:58 INFO [auto_gptq.modeling._base] Start quantizing layer 26/32 2023-07-21 16:58:58 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 26/32... 2023-07-21 16:58:59 INFO [auto_gptq.quantization.gptq] duration: 0.7961215972900391 2023-07-21 16:58:59 INFO [auto_gptq.quantization.gptq] avg loss: 8.335006713867188 2023-07-21 16:58:59 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 26/32... 2023-07-21 16:59:00 INFO [auto_gptq.quantization.gptq] duration: 0.7967922687530518 2023-07-21 16:59:00 INFO [auto_gptq.quantization.gptq] avg loss: 0.5929185152053833 2023-07-21 16:59:00 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 26/32... 2023-07-21 16:59:01 INFO [auto_gptq.quantization.gptq] duration: 0.8355779647827148 2023-07-21 16:59:01 INFO [auto_gptq.quantization.gptq] avg loss: 39.31059265136719 2023-07-21 16:59:01 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 26/32... 2023-07-21 16:59:05 INFO [auto_gptq.quantization.gptq] duration: 3.859668731689453 2023-07-21 16:59:05 INFO [auto_gptq.quantization.gptq] avg loss: 5.2629475593566895 2023-07-21 16:59:05 INFO [auto_gptq.modeling._base] Start quantizing layer 27/32 2023-07-21 16:59:05 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 27/32... 2023-07-21 16:59:06 INFO [auto_gptq.quantization.gptq] duration: 0.7974636554718018 2023-07-21 16:59:06 INFO [auto_gptq.quantization.gptq] avg loss: 8.194433212280273 2023-07-21 16:59:06 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 27/32... 2023-07-21 16:59:07 INFO [auto_gptq.quantization.gptq] duration: 0.8030986785888672 2023-07-21 16:59:07 INFO [auto_gptq.quantization.gptq] avg loss: 0.7090796828269958 2023-07-21 16:59:07 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 27/32... 2023-07-21 16:59:07 INFO [auto_gptq.quantization.gptq] duration: 0.8322622776031494 2023-07-21 16:59:07 INFO [auto_gptq.quantization.gptq] avg loss: 39.4634895324707 2023-07-21 16:59:07 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 27/32... 2023-07-21 16:59:11 INFO [auto_gptq.quantization.gptq] duration: 3.878126859664917 2023-07-21 16:59:11 INFO [auto_gptq.quantization.gptq] avg loss: 6.581557750701904 2023-07-21 16:59:11 INFO [auto_gptq.modeling._base] Start quantizing layer 28/32 2023-07-21 16:59:12 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 28/32... 2023-07-21 16:59:12 INFO [auto_gptq.quantization.gptq] duration: 0.7974464893341064 2023-07-21 16:59:12 INFO [auto_gptq.quantization.gptq] avg loss: 9.201988220214844 2023-07-21 16:59:12 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 28/32... 2023-07-21 16:59:13 INFO [auto_gptq.quantization.gptq] duration: 0.8018836975097656 2023-07-21 16:59:13 INFO [auto_gptq.quantization.gptq] avg loss: 1.193915605545044 2023-07-21 16:59:13 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 28/32... 2023-07-21 16:59:14 INFO [auto_gptq.quantization.gptq] duration: 0.832056999206543 2023-07-21 16:59:14 INFO [auto_gptq.quantization.gptq] avg loss: 39.874481201171875 2023-07-21 16:59:14 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 28/32... 2023-07-21 16:59:18 INFO [auto_gptq.quantization.gptq] duration: 3.8739585876464844 2023-07-21 16:59:18 INFO [auto_gptq.quantization.gptq] avg loss: 7.8150634765625 2023-07-21 16:59:18 INFO [auto_gptq.modeling._base] Start quantizing layer 29/32 2023-07-21 16:59:18 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 29/32... 2023-07-21 16:59:19 INFO [auto_gptq.quantization.gptq] duration: 0.7971282005310059 2023-07-21 16:59:19 INFO [auto_gptq.quantization.gptq] avg loss: 8.788995742797852 2023-07-21 16:59:19 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 29/32... 2023-07-21 16:59:20 INFO [auto_gptq.quantization.gptq] duration: 0.8014233112335205 2023-07-21 16:59:20 INFO [auto_gptq.quantization.gptq] avg loss: 0.9004578590393066 2023-07-21 16:59:20 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 29/32... 2023-07-21 16:59:21 INFO [auto_gptq.quantization.gptq] duration: 0.8585555553436279 2023-07-21 16:59:21 INFO [auto_gptq.quantization.gptq] avg loss: 40.52891159057617 2023-07-21 16:59:21 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 29/32... 2023-07-21 16:59:24 INFO [auto_gptq.quantization.gptq] duration: 3.886247396469116 2023-07-21 16:59:24 INFO [auto_gptq.quantization.gptq] avg loss: 7.627683639526367 2023-07-21 16:59:25 INFO [auto_gptq.modeling._base] Start quantizing layer 30/32 2023-07-21 16:59:25 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 30/32... 2023-07-21 16:59:26 INFO [auto_gptq.quantization.gptq] duration: 0.8017170429229736 2023-07-21 16:59:26 INFO [auto_gptq.quantization.gptq] avg loss: 7.885834217071533 2023-07-21 16:59:26 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 30/32... 2023-07-21 16:59:26 INFO [auto_gptq.quantization.gptq] duration: 0.8006551265716553 2023-07-21 16:59:26 INFO [auto_gptq.quantization.gptq] avg loss: 1.0838208198547363 2023-07-21 16:59:26 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 30/32... 2023-07-21 16:59:27 INFO [auto_gptq.quantization.gptq] duration: 0.8757197856903076 2023-07-21 16:59:27 INFO [auto_gptq.quantization.gptq] avg loss: 38.54998779296875 2023-07-21 16:59:27 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 30/32... 2023-07-21 16:59:31 INFO [auto_gptq.quantization.gptq] duration: 3.8700709342956543 2023-07-21 16:59:31 INFO [auto_gptq.quantization.gptq] avg loss: 10.26675796508789 2023-07-21 16:59:31 INFO [auto_gptq.modeling._base] Start quantizing layer 31/32 2023-07-21 16:59:31 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 31/32... 2023-07-21 16:59:32 INFO [auto_gptq.quantization.gptq] duration: 0.7995920181274414 2023-07-21 16:59:32 INFO [auto_gptq.quantization.gptq] avg loss: 7.922703266143799 2023-07-21 16:59:32 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 31/32... 2023-07-21 16:59:33 INFO [auto_gptq.quantization.gptq] duration: 0.7997887134552002 2023-07-21 16:59:33 INFO [auto_gptq.quantization.gptq] avg loss: 0.6395642757415771 2023-07-21 16:59:33 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 31/32... 2023-07-21 16:59:34 INFO [auto_gptq.quantization.gptq] duration: 0.8389708995819092 2023-07-21 16:59:34 INFO [auto_gptq.quantization.gptq] avg loss: 38.0499153137207 2023-07-21 16:59:34 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 31/32... 2023-07-21 16:59:38 INFO [auto_gptq.quantization.gptq] duration: 3.8527672290802 2023-07-21 16:59:38 INFO [auto_gptq.quantization.gptq] avg loss: 14.685250282287598 2023-07-21 16:59:38 INFO [auto_gptq.modeling._base] Start quantizing layer 32/32 2023-07-21 16:59:38 INFO [auto_gptq.modeling._base] Quantizing self_attention.query_key_value in layer 32/32... 2023-07-21 16:59:39 INFO [auto_gptq.quantization.gptq] duration: 0.7899763584136963 2023-07-21 16:59:39 INFO [auto_gptq.quantization.gptq] avg loss: 6.566901206970215 2023-07-21 17:00:08 INFO [auto_gptq.modeling._base] Quantizing self_attention.dense in layer 32/32... 2023-07-21 17:00:09 INFO [auto_gptq.quantization.gptq] duration: 0.890770673751831 2023-07-21 17:00:09 INFO [auto_gptq.quantization.gptq] avg loss: 0.2703491747379303 2023-07-21 17:00:09 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_h_to_4h in layer 32/32... 2023-07-21 17:00:10 INFO [auto_gptq.quantization.gptq] duration: 0.8699018955230713 2023-07-21 17:00:10 INFO [auto_gptq.quantization.gptq] avg loss: 33.582237243652344 2023-07-21 17:00:10 INFO [auto_gptq.modeling._base] Quantizing mlp.dense_4h_to_h in layer 32/32... 2023-07-21 17:00:14 INFO [auto_gptq.quantization.gptq] duration: 3.8666820526123047 2023-07-21 17:00:14 INFO [auto_gptq.quantization.gptq] avg loss: 26.30276107788086 2023-07-21 17:00:14 INFO [auto_gptq.modeling._utils] Packing model... 2023-07-21 17:00:14 INFO [auto_gptq.modeling._utils] transformer.h.0.self_attention.dense 2023-07-21 17:00:15 INFO [auto_gptq.modeling._utils] transformer.h.0.self_attention.query_key_value 2023-07-21 17:00:15 INFO [auto_gptq.modeling._utils] transformer.h.0.mlp.dense_4h_to_h 2023-07-21 17:00:18 INFO [auto_gptq.modeling._utils] transformer.h.0.mlp.dense_h_to_4h 2023-07-21 17:00:19 INFO [auto_gptq.modeling._utils] transformer.h.1.self_attention.dense 2023-07-21 17:00:19 INFO [auto_gptq.modeling._utils] transformer.h.1.self_attention.query_key_value 2023-07-21 17:00:20 INFO [auto_gptq.modeling._utils] transformer.h.1.mlp.dense_4h_to_h 2023-07-21 17:00:22 INFO [auto_gptq.modeling._utils] transformer.h.1.mlp.dense_h_to_4h 2023-07-21 17:00:23 INFO [auto_gptq.modeling._utils] transformer.h.2.self_attention.dense 2023-07-21 17:00:23 INFO [auto_gptq.modeling._utils] transformer.h.2.self_attention.query_key_value 2023-07-21 17:00:24 INFO [auto_gptq.modeling._utils] transformer.h.2.mlp.dense_4h_to_h 2023-07-21 17:00:26 INFO [auto_gptq.modeling._utils] transformer.h.2.mlp.dense_h_to_4h 2023-07-21 17:00:27 INFO [auto_gptq.modeling._utils] transformer.h.3.self_attention.dense 2023-07-21 17:00:28 INFO [auto_gptq.modeling._utils] transformer.h.3.self_attention.query_key_value 2023-07-21 17:00:28 INFO [auto_gptq.modeling._utils] transformer.h.3.mlp.dense_4h_to_h 2023-07-21 17:00:30 INFO [auto_gptq.modeling._utils] transformer.h.3.mlp.dense_h_to_4h 2023-07-21 17:00:31 INFO [auto_gptq.modeling._utils] transformer.h.4.self_attention.dense 2023-07-21 17:00:32 INFO [auto_gptq.modeling._utils] transformer.h.4.self_attention.query_key_value 2023-07-21 17:00:32 INFO [auto_gptq.modeling._utils] transformer.h.4.mlp.dense_4h_to_h 2023-07-21 17:00:34 INFO [auto_gptq.modeling._utils] transformer.h.4.mlp.dense_h_to_4h 2023-07-21 17:00:35 INFO [auto_gptq.modeling._utils] transformer.h.5.self_attention.dense 2023-07-21 17:00:35 INFO [auto_gptq.modeling._utils] transformer.h.5.self_attention.query_key_value 2023-07-21 17:00:36 INFO [auto_gptq.modeling._utils] transformer.h.5.mlp.dense_4h_to_h 2023-07-21 17:00:38 INFO [auto_gptq.modeling._utils] transformer.h.5.mlp.dense_h_to_4h 2023-07-21 17:00:39 INFO [auto_gptq.modeling._utils] transformer.h.6.self_attention.dense 2023-07-21 17:00:39 INFO [auto_gptq.modeling._utils] transformer.h.6.self_attention.query_key_value 2023-07-21 17:00:40 INFO [auto_gptq.modeling._utils] transformer.h.6.mlp.dense_4h_to_h 2023-07-21 17:00:41 INFO [auto_gptq.modeling._utils] transformer.h.6.mlp.dense_h_to_4h 2023-07-21 17:00:42 INFO [auto_gptq.modeling._utils] transformer.h.7.self_attention.dense 2023-07-21 17:00:43 INFO [auto_gptq.modeling._utils] transformer.h.7.self_attention.query_key_value 2023-07-21 17:00:43 INFO [auto_gptq.modeling._utils] transformer.h.7.mlp.dense_4h_to_h 2023-07-21 17:00:45 INFO [auto_gptq.modeling._utils] transformer.h.7.mlp.dense_h_to_4h 2023-07-21 17:00:46 INFO [auto_gptq.modeling._utils] transformer.h.8.self_attention.dense 2023-07-21 17:00:47 INFO [auto_gptq.modeling._utils] transformer.h.8.self_attention.query_key_value 2023-07-21 17:00:47 INFO [auto_gptq.modeling._utils] transformer.h.8.mlp.dense_4h_to_h 2023-07-21 17:00:49 INFO [auto_gptq.modeling._utils] transformer.h.8.mlp.dense_h_to_4h 2023-07-21 17:00:50 INFO [auto_gptq.modeling._utils] transformer.h.9.self_attention.dense 2023-07-21 17:00:50 INFO [auto_gptq.modeling._utils] transformer.h.9.self_attention.query_key_value 2023-07-21 17:00:51 INFO [auto_gptq.modeling._utils] transformer.h.9.mlp.dense_4h_to_h 2023-07-21 17:00:53 INFO [auto_gptq.modeling._utils] transformer.h.9.mlp.dense_h_to_4h 2023-07-21 17:00:54 INFO [auto_gptq.modeling._utils] transformer.h.10.self_attention.dense 2023-07-21 17:00:54 INFO [auto_gptq.modeling._utils] transformer.h.10.self_attention.query_key_value 2023-07-21 17:00:55 INFO [auto_gptq.modeling._utils] transformer.h.10.mlp.dense_4h_to_h 2023-07-21 17:00:56 INFO [auto_gptq.modeling._utils] transformer.h.10.mlp.dense_h_to_4h 2023-07-21 17:00:57 INFO [auto_gptq.modeling._utils] transformer.h.11.self_attention.dense 2023-07-21 17:00:58 INFO [auto_gptq.modeling._utils] transformer.h.11.self_attention.query_key_value 2023-07-21 17:00:58 INFO [auto_gptq.modeling._utils] transformer.h.11.mlp.dense_4h_to_h 2023-07-21 17:01:00 INFO [auto_gptq.modeling._utils] transformer.h.11.mlp.dense_h_to_4h 2023-07-21 17:01:01 INFO [auto_gptq.modeling._utils] transformer.h.12.self_attention.dense 2023-07-21 17:01:02 INFO [auto_gptq.modeling._utils] transformer.h.12.self_attention.query_key_value 2023-07-21 17:01:02 INFO [auto_gptq.modeling._utils] transformer.h.12.mlp.dense_4h_to_h 2023-07-21 17:01:04 INFO [auto_gptq.modeling._utils] transformer.h.12.mlp.dense_h_to_4h 2023-07-21 17:01:05 INFO [auto_gptq.modeling._utils] transformer.h.13.self_attention.dense 2023-07-21 17:01:06 INFO [auto_gptq.modeling._utils] transformer.h.13.self_attention.query_key_value 2023-07-21 17:01:06 INFO [auto_gptq.modeling._utils] transformer.h.13.mlp.dense_4h_to_h 2023-07-21 17:01:08 INFO [auto_gptq.modeling._utils] transformer.h.13.mlp.dense_h_to_4h 2023-07-21 17:01:09 INFO [auto_gptq.modeling._utils] transformer.h.14.self_attention.dense 2023-07-21 17:01:10 INFO [auto_gptq.modeling._utils] transformer.h.14.self_attention.query_key_value 2023-07-21 17:01:10 INFO [auto_gptq.modeling._utils] transformer.h.14.mlp.dense_4h_to_h 2023-07-21 17:01:12 INFO [auto_gptq.modeling._utils] transformer.h.14.mlp.dense_h_to_4h 2023-07-21 17:01:13 INFO [auto_gptq.modeling._utils] transformer.h.15.self_attention.dense 2023-07-21 17:01:13 INFO [auto_gptq.modeling._utils] transformer.h.15.self_attention.query_key_value 2023-07-21 17:01:14 INFO [auto_gptq.modeling._utils] transformer.h.15.mlp.dense_4h_to_h 2023-07-21 17:01:16 INFO [auto_gptq.modeling._utils] transformer.h.15.mlp.dense_h_to_4h 2023-07-21 17:01:17 INFO [auto_gptq.modeling._utils] transformer.h.16.self_attention.dense 2023-07-21 17:01:17 INFO [auto_gptq.modeling._utils] transformer.h.16.self_attention.query_key_value 2023-07-21 17:01:18 INFO [auto_gptq.modeling._utils] transformer.h.16.mlp.dense_4h_to_h 2023-07-21 17:01:19 INFO [auto_gptq.modeling._utils] transformer.h.16.mlp.dense_h_to_4h 2023-07-21 17:01:21 INFO [auto_gptq.modeling._utils] transformer.h.17.self_attention.dense 2023-07-21 17:01:21 INFO [auto_gptq.modeling._utils] transformer.h.17.self_attention.query_key_value 2023-07-21 17:01:21 INFO [auto_gptq.modeling._utils] transformer.h.17.mlp.dense_4h_to_h 2023-07-21 17:01:23 INFO [auto_gptq.modeling._utils] transformer.h.17.mlp.dense_h_to_4h 2023-07-21 17:01:24 INFO [auto_gptq.modeling._utils] transformer.h.18.self_attention.dense 2023-07-21 17:01:25 INFO [auto_gptq.modeling._utils] transformer.h.18.self_attention.query_key_value 2023-07-21 17:01:25 INFO [auto_gptq.modeling._utils] transformer.h.18.mlp.dense_4h_to_h 2023-07-21 17:01:27 INFO [auto_gptq.modeling._utils] transformer.h.18.mlp.dense_h_to_4h 2023-07-21 17:01:28 INFO [auto_gptq.modeling._utils] transformer.h.19.self_attention.dense 2023-07-21 17:01:29 INFO [auto_gptq.modeling._utils] transformer.h.19.self_attention.query_key_value 2023-07-21 17:01:29 INFO [auto_gptq.modeling._utils] transformer.h.19.mlp.dense_4h_to_h 2023-07-21 17:01:31 INFO [auto_gptq.modeling._utils] transformer.h.19.mlp.dense_h_to_4h 2023-07-21 17:01:32 INFO [auto_gptq.modeling._utils] transformer.h.20.self_attention.dense 2023-07-21 17:01:33 INFO [auto_gptq.modeling._utils] transformer.h.20.self_attention.query_key_value 2023-07-21 17:01:33 INFO [auto_gptq.modeling._utils] transformer.h.20.mlp.dense_4h_to_h 2023-07-21 17:01:35 INFO [auto_gptq.modeling._utils] transformer.h.20.mlp.dense_h_to_4h 2023-07-21 17:01:36 INFO [auto_gptq.modeling._utils] transformer.h.21.self_attention.dense 2023-07-21 17:01:37 INFO [auto_gptq.modeling._utils] transformer.h.21.self_attention.query_key_value 2023-07-21 17:01:37 INFO [auto_gptq.modeling._utils] transformer.h.21.mlp.dense_4h_to_h 2023-07-21 17:01:39 INFO [auto_gptq.modeling._utils] transformer.h.21.mlp.dense_h_to_4h 2023-07-21 17:01:40 INFO [auto_gptq.modeling._utils] transformer.h.22.self_attention.dense 2023-07-21 17:01:40 INFO [auto_gptq.modeling._utils] transformer.h.22.self_attention.query_key_value 2023-07-21 17:01:41 INFO [auto_gptq.modeling._utils] transformer.h.22.mlp.dense_4h_to_h 2023-07-21 17:01:43 INFO [auto_gptq.modeling._utils] transformer.h.22.mlp.dense_h_to_4h 2023-07-21 17:01:44 INFO [auto_gptq.modeling._utils] transformer.h.23.self_attention.dense 2023-07-21 17:01:44 INFO [auto_gptq.modeling._utils] transformer.h.23.self_attention.query_key_value 2023-07-21 17:01:45 INFO [auto_gptq.modeling._utils] transformer.h.23.mlp.dense_4h_to_h 2023-07-21 17:01:46 INFO [auto_gptq.modeling._utils] transformer.h.23.mlp.dense_h_to_4h 2023-07-21 17:01:48 INFO [auto_gptq.modeling._utils] transformer.h.24.self_attention.dense 2023-07-21 17:01:48 INFO [auto_gptq.modeling._utils] transformer.h.24.self_attention.query_key_value 2023-07-21 17:01:49 INFO [auto_gptq.modeling._utils] transformer.h.24.mlp.dense_4h_to_h 2023-07-21 17:01:51 INFO [auto_gptq.modeling._utils] transformer.h.24.mlp.dense_h_to_4h 2023-07-21 17:01:52 INFO [auto_gptq.modeling._utils] transformer.h.25.self_attention.dense 2023-07-21 17:01:52 INFO [auto_gptq.modeling._utils] transformer.h.25.self_attention.query_key_value 2023-07-21 17:01:53 INFO [auto_gptq.modeling._utils] transformer.h.25.mlp.dense_4h_to_h 2023-07-21 17:01:54 INFO [auto_gptq.modeling._utils] transformer.h.25.mlp.dense_h_to_4h 2023-07-21 17:01:55 INFO [auto_gptq.modeling._utils] transformer.h.26.self_attention.dense 2023-07-21 17:01:56 INFO [auto_gptq.modeling._utils] transformer.h.26.self_attention.query_key_value 2023-07-21 17:01:56 INFO [auto_gptq.modeling._utils] transformer.h.26.mlp.dense_4h_to_h 2023-07-21 17:01:58 INFO [auto_gptq.modeling._utils] transformer.h.26.mlp.dense_h_to_4h 2023-07-21 17:02:00 INFO [auto_gptq.modeling._utils] transformer.h.27.self_attention.dense 2023-07-21 17:02:00 INFO [auto_gptq.modeling._utils] transformer.h.27.self_attention.query_key_value 2023-07-21 17:02:00 INFO [auto_gptq.modeling._utils] transformer.h.27.mlp.dense_4h_to_h 2023-07-21 17:02:02 INFO [auto_gptq.modeling._utils] transformer.h.27.mlp.dense_h_to_4h 2023-07-21 17:02:03 INFO [auto_gptq.modeling._utils] transformer.h.28.self_attention.dense 2023-07-21 17:02:04 INFO [auto_gptq.modeling._utils] transformer.h.28.self_attention.query_key_value 2023-07-21 17:02:04 INFO [auto_gptq.modeling._utils] transformer.h.28.mlp.dense_4h_to_h 2023-07-21 17:02:06 INFO [auto_gptq.modeling._utils] transformer.h.28.mlp.dense_h_to_4h 2023-07-21 17:02:07 INFO [auto_gptq.modeling._utils] transformer.h.29.self_attention.dense 2023-07-21 17:02:08 INFO [auto_gptq.modeling._utils] transformer.h.29.self_attention.query_key_value 2023-07-21 17:02:08 INFO [auto_gptq.modeling._utils] transformer.h.29.mlp.dense_4h_to_h 2023-07-21 17:02:10 INFO [auto_gptq.modeling._utils] transformer.h.29.mlp.dense_h_to_4h 2023-07-21 17:02:11 INFO [auto_gptq.modeling._utils] transformer.h.30.self_attention.dense 2023-07-21 17:02:12 INFO [auto_gptq.modeling._utils] transformer.h.30.self_attention.query_key_value 2023-07-21 17:02:12 INFO [auto_gptq.modeling._utils] transformer.h.30.mlp.dense_4h_to_h 2023-07-21 17:02:14 INFO [auto_gptq.modeling._utils] transformer.h.30.mlp.dense_h_to_4h 2023-07-21 17:02:15 INFO [auto_gptq.modeling._utils] transformer.h.31.self_attention.dense 2023-07-21 17:02:16 INFO [auto_gptq.modeling._utils] transformer.h.31.self_attention.query_key_value 2023-07-21 17:02:16 INFO [auto_gptq.modeling._utils] transformer.h.31.mlp.dense_4h_to_h 2023-07-21 17:02:18 INFO [auto_gptq.modeling._utils] transformer.h.31.mlp.dense_h_to_4h 2023-07-21 17:02:19 INFO [auto_gptq.modeling._utils] Model packed. ```
Villagerindo/tts-bluearchive	Villagerindo	2023-07-21T23:28:07Z	0	1	null	[ "license:apache-2.0", "region:us" ]	null	2023-07-21T14:59:23Z	--- title: Vits Models emoji: 🏃 colorFrom: pink colorTo: indigo sdk: gradio sdk_version: 3.17.0 app_file: app.py pinned: false license: apache-2.0 --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
asapp/sew-tiny-100k	asapp	2023-07-21T23:05:12Z	2,256	3	transformers	[ "transformers", "pytorch", "safetensors", "sew", "feature-extraction", "speech", "en", "dataset:librispeech_asr", "arxiv:2109.06870", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # SEW-tiny [SEW by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi Abstract This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWForCTC`.
asapp/sew-d-tiny-100k	asapp	2023-07-21T23:05:03Z	2,248	2	transformers	[ "transformers", "pytorch", "safetensors", "sew-d", "feature-extraction", "speech", "en", "dataset:librispeech_asr", "arxiv:2109.06870", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-03-02T23:29:05Z	--- language: en datasets: - librispeech_asr tags: - speech license: apache-2.0 --- # SEW-D-tiny [SEW-D by ASAPP Research](https://github.com/asappresearch/sew) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc... Paper: [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi Abstract This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes. The original model can be found under https://github.com/asappresearch/sew#model-checkpoints . # Usage See [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information on how to fine-tune the model. Note that the class `Wav2Vec2ForCTC` has to be replaced by `SEWDForCTC`.
youw/modelodogiela	youw	2023-07-21T22:51:59Z	0	0	adapter-transformers	[ "adapter-transformers", "music", "pt", "license:openrail", "region:us" ]	null	2023-07-21T22:39:02Z	--- language: - pt library_name: adapter-transformers tags: - music license: openrail ---
Emperor-WS/q-FrozenLake-v1-4x4-noSlippery	Emperor-WS	2023-07-21T22:44:11Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T22:44:09Z	--- 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="Emperor-WS/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"]) ```
ashercn97/OpenOrcaUpload	ashercn97	2023-07-21T22:28:43Z	7	0	peft	[ "peft", "text-generation", "dataset:ashercn97/OpenOrcaPleaseWork", "region:us" ]	text-generation	2023-07-21T14:43:58Z	--- library_name: peft pipeline_tag: text-generation datasets: - ashercn97/OpenOrcaPleaseWork --- ## 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 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 - PEFT 0.5.0.dev0
brunoboat/ppo-Huggy	brunoboat	2023-07-21T22:02:00Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2023-07-21T22:01:49Z	--- 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: brunoboat/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Oburaco/llama2-qlora-finetunined-ptbr	Oburaco	2023-07-21T21:43:25Z	1	1	peft	[ "peft", "region:us" ]	null	2023-07-21T21:43:16Z	--- 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
Pedrampd/NLP-HW5-NerTaggerModel	Pedrampd	2023-07-21T21:25:17Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-21T20:10:05Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: NLP-HW5-NerTaggerModel 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. --> # NLP-HW5-NerTaggerModel This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0218 - Accuracy: 0.9947 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1891 \| 1.0 \| 878 \| 0.0342 \| 0.9909 \| \| 0.0377 \| 2.0 \| 1756 \| 0.0218 \| 0.9947 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
Aspik101/guanaco-7B-HF-pl-lora_adapter_model	Aspik101	2023-07-21T21:15:58Z	0	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-2", "text-generation", "pl", "dataset:Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish", "license:other", "region:us" ]	text-generation	2023-07-21T21:15:57Z	--- language: - pl datasets: - Lajonbot/alpaca-dolly-chrisociepa-instruction-only-polish license: other model_type: llama-2 pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 ---
Mel-Iza0/RedPajama-ZeroShot-20K-new_prompt_classe_nenhuma	Mel-Iza0	2023-07-21T21:07:19Z	2	0	peft	[ "peft", "pytorch", "gpt_neox", "region:us" ]	null	2023-07-21T18:40:27Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
davidkariuki/RentPredictorSouthAfrica	davidkariuki	2023-07-21T20:40:37Z	0	0	null	[ "joblib", "license:apache-2.0", "region:us" ]	null	2023-07-20T19:29:11Z	--- license: apache-2.0 --- README Introduction This repository contains a Gradient Boosting Regressor model trained to predict house rents. The model was trained on a dataset that was preprocessed and cleaned to ensure the best possible predictions. Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Prerequisites You need Python 3.7 or later to run the scripts. You can have multiple Python versions (2.x and 3.x) installed on the same system without problems. In Ubuntu, Mint and Debian you can install Python 3 like this: sudo apt-get install python3 python3-pip For other Linux flavors, macOS, and Windows, packages are available at https://www.python.org/getit/ Required Python Packages You will also need the following Python packages: pandas sklearn joblib These can be installed using pip: pip install pandas sklearn joblib Cloning the Repository To clone this repository, run the following command in your terminal: git clone <repository-link> Running the Script To use the model to predict house rents, run the predict.py script. You will be asked to input data for 'Area' and 'Suburb'. The script will then print the predicted rent. To run the script: python test.py The model you'll be interacting with is a machine-learning model specifically designed to predict house rent prices based on various property features. It's been trained on a dataset of housing information and uses what it learned to make predictions for new, unseen houses. Rent: The existing rent of the house. Property Type: The type of property, such as apartment, house, etc. Area: The area where the house is located. Suburb: The suburb within the area where the house is located. Bedrooms: The number of bedrooms in the house. Bathrooms: The number of bathrooms in the house. Garages: The number of garages the house has. nGparking: The number of non-garage parking spaces the house has. Floor Size: The size of the house in square feet or meters. Pool: Whether the house has a pool (1 if yes, 0 if no). Garden: Whether the house has a garden (1 if yes, 0 if no). Study: Whether the house has a study or office room (1 if yes, 0 if no). Pets: Whether pets are allowed in the house (1 if yes, 0 if no). Furnished: Whether the house is furnished (1 if yes, 0 if no). Fibre: Whether the house has fibre internet connection (1 if yes, 0 if no). Based on the information you provide for a house, the model will give an estimate of what it thinks the house's rent would be. Please note that while the model tries its best to make accurate predictions, there is error in its estimates.
gokuls/hbertv2-Massive-intent-48-emb-comp-gelu	gokuls	2023-07-21T20:39:48Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu", "base_model:finetune:gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T20:31:51Z	--- base_model: gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv2-Massive-intent-48-emb-comp-gelu results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.8421052631578947 --- <!-- 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. --> # hbertv2-Massive-intent-48-emb-comp-gelu This model is a fine-tuned version of [gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu](https://huggingface.co/gokuls/bert_12_layer_model_v2_complete_training_new_emb_compress_48_gelu) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Accuracy: 0.8421 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.9202 \| 1.0 \| 180 \| 1.0767 \| 0.7068 \| \| 0.9104 \| 2.0 \| 360 \| 0.9209 \| 0.7482 \| \| 0.6425 \| 3.0 \| 540 \| 0.8343 \| 0.7821 \| \| 0.4854 \| 4.0 \| 720 \| 0.8159 \| 0.7954 \| \| 0.3682 \| 5.0 \| 900 \| 0.8154 \| 0.8077 \| \| 0.272 \| 6.0 \| 1080 \| 0.8417 \| 0.7993 \| \| 0.204 \| 7.0 \| 1260 \| 0.7931 \| 0.8155 \| \| 0.1363 \| 8.0 \| 1440 \| 0.8740 \| 0.8195 \| \| 0.1016 \| 9.0 \| 1620 \| 0.8993 \| 0.8205 \| \| 0.0689 \| 10.0 \| 1800 \| 0.9309 \| 0.8210 \| \| 0.0478 \| 11.0 \| 1980 \| 0.9877 \| 0.8318 \| \| 0.0254 \| 12.0 \| 2160 \| 1.0041 \| 0.8293 \| \| 0.0133 \| 13.0 \| 2340 \| 0.9982 \| 0.8396 \| \| 0.0068 \| 14.0 \| 2520 \| 1.0049 \| 0.8406 \| \| 0.005 \| 15.0 \| 2700 \| 1.0025 \| 0.8421 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-Massive-intent-48-emb-comp-gelu	gokuls	2023-07-21T20:22:27Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu", "base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T20:14:12Z	--- base_model: gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-Massive-intent-48-emb-comp-gelu results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.8027545499262174 --- <!-- 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. --> # hbertv1-Massive-intent-48-emb-comp-gelu This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_emb_compress_48_gelu) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.9566 - Accuracy: 0.8028 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.4622 \| 1.0 \| 180 \| 3.0181 \| 0.2169 \| \| 2.7526 \| 2.0 \| 360 \| 2.4760 \| 0.3168 \| \| 2.188 \| 3.0 \| 540 \| 1.9627 \| 0.4368 \| \| 1.7069 \| 4.0 \| 720 \| 1.5603 \| 0.5568 \| \| 1.3045 \| 5.0 \| 900 \| 1.3354 \| 0.6345 \| \| 1.0621 \| 6.0 \| 1080 \| 1.1726 \| 0.6862 \| \| 0.8745 \| 7.0 \| 1260 \| 1.0703 \| 0.7226 \| \| 0.7286 \| 8.0 \| 1440 \| 0.9905 \| 0.7516 \| \| 0.6005 \| 9.0 \| 1620 \| 0.9881 \| 0.7644 \| \| 0.5021 \| 10.0 \| 1800 \| 0.9661 \| 0.7732 \| \| 0.4208 \| 11.0 \| 1980 \| 0.9621 \| 0.7787 \| \| 0.3524 \| 12.0 \| 2160 \| 0.9480 \| 0.7939 \| \| 0.282 \| 13.0 \| 2340 \| 0.9614 \| 0.7924 \| \| 0.2327 \| 14.0 \| 2520 \| 0.9525 \| 0.7969 \| \| 0.1912 \| 15.0 \| 2700 \| 0.9566 \| 0.8028 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-tiny-wt-48-Massive-intent-emb-comp	gokuls	2023-07-21T20:06:02Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T20:02:45Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-tiny-wt-48-Massive-intent-emb-comp results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.7899655681259223 --- <!-- 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. --> # hbertv1-tiny-wt-48-Massive-intent-emb-comp This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny_emb_comp) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.8545 - Accuracy: 0.7900 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.6847 \| 1.0 \| 180 \| 3.2207 \| 0.2710 \| \| 2.7795 \| 2.0 \| 360 \| 2.3154 \| 0.4471 \| \| 2.0459 \| 3.0 \| 540 \| 1.7680 \| 0.5627 \| \| 1.5874 \| 4.0 \| 720 \| 1.4363 \| 0.6734 \| \| 1.2902 \| 5.0 \| 900 \| 1.2306 \| 0.7127 \| \| 1.0905 \| 6.0 \| 1080 \| 1.1068 \| 0.7373 \| \| 0.9468 \| 7.0 \| 1260 \| 1.0113 \| 0.7545 \| \| 0.844 \| 8.0 \| 1440 \| 0.9661 \| 0.7580 \| \| 0.7684 \| 9.0 \| 1620 \| 0.9333 \| 0.7649 \| \| 0.7086 \| 10.0 \| 1800 \| 0.9018 \| 0.7772 \| \| 0.6629 \| 11.0 \| 1980 \| 0.8807 \| 0.7831 \| \| 0.6244 \| 12.0 \| 2160 \| 0.8747 \| 0.7796 \| \| 0.5965 \| 13.0 \| 2340 \| 0.8591 \| 0.7875 \| \| 0.5731 \| 14.0 \| 2520 \| 0.8634 \| 0.7875 \| \| 0.5633 \| 15.0 \| 2700 \| 0.8545 \| 0.7900 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-small-wt-frz-48-Massive-intent-emb-comp	gokuls	2023-07-21T20:02:17Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T19:59:48Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-small-wt-frz-48-Massive-intent-emb-comp results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.838170191834727 --- <!-- 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. --> # hbertv1-small-wt-frz-48-Massive-intent-emb-comp This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6512 - Accuracy: 0.8382 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.3044 \| 1.0 \| 180 \| 1.1025 \| 0.7167 \| \| 0.8662 \| 2.0 \| 360 \| 0.7731 \| 0.7934 \| \| 0.5469 \| 3.0 \| 540 \| 0.6981 \| 0.8224 \| \| 0.357 \| 4.0 \| 720 \| 0.6512 \| 0.8382 \| \| 0.228 \| 5.0 \| 900 \| 0.6980 \| 0.8254 \| \| 0.1435 \| 6.0 \| 1080 \| 0.7169 \| 0.8278 \| \| 0.0863 \| 7.0 \| 1260 \| 0.7441 \| 0.8323 \| \| 0.0534 \| 8.0 \| 1440 \| 0.7516 \| 0.8382 \| \| 0.0334 \| 9.0 \| 1620 \| 0.8162 \| 0.8357 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
ALM-AHME/convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled	ALM-AHME	2023-07-21T20:00:08Z	14	1	transformers	[ "transformers", "pytorch", "tensorboard", "convnextv2", "image-classification", "generated_from_trainer", "base_model:facebook/convnextv2-large-1k-224", "base_model:finetune:facebook/convnextv2-large-1k-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-21T14:42:59Z	--- license: apache-2.0 base_model: facebook/convnextv2-large-1k-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled 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. --> # convnextv2-large-1k-224-finetuned-Lesion-Classification-HAM10000-AH-60-20-20-Shuffled This model is a fine-tuned version of [facebook/convnextv2-large-1k-224](https://huggingface.co/facebook/convnextv2-large-1k-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0721 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.9 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.8937 \| 1.0 \| 114 \| 1.9040 \| 0.3144 \| \| 1.7208 \| 2.0 \| 229 \| 1.6891 \| 0.5632 \| \| 1.3822 \| 3.0 \| 343 \| 1.3554 \| 0.6897 \| \| 1.1497 \| 4.0 \| 458 \| 1.2437 \| 0.5755 \| \| 0.8979 \| 5.0 \| 572 \| 0.8548 \| 0.7701 \| \| 0.6382 \| 6.0 \| 687 \| 0.6359 \| 0.8424 \| \| 0.583 \| 7.0 \| 801 \| 0.4687 \| 0.8966 \| \| 0.6295 \| 8.0 \| 916 \| 0.5029 \| 0.8456 \| \| 0.5367 \| 9.0 \| 1030 \| 0.4742 \| 0.8670 \| \| 0.5091 \| 10.0 \| 1145 \| 0.3038 \| 0.9212 \| \| 0.3521 \| 11.0 \| 1259 \| 0.1855 \| 0.9606 \| \| 0.318 \| 12.0 \| 1374 \| 0.1893 \| 0.9573 \| \| 0.2725 \| 13.0 \| 1488 \| 0.2292 \| 0.9409 \| \| 0.2937 \| 14.0 \| 1603 \| 0.0866 \| 0.9836 \| \| 0.1185 \| 14.93 \| 1710 \| 0.0721 \| 0.9869 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-small-wt-48-Massive-intent-emb-comp	gokuls	2023-07-21T19:59:16Z	50	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T19:55:19Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_small_emb_comp tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-small-wt-48-Massive-intent-emb-comp results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.8504672897196262 --- <!-- 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. --> # hbertv1-small-wt-48-Massive-intent-emb-comp This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_emb_comp](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_emb_comp) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.8462 - Accuracy: 0.8505 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.1467 \| 1.0 \| 180 \| 1.0602 \| 0.7393 \| \| 0.8554 \| 2.0 \| 360 \| 0.7646 \| 0.7964 \| \| 0.5593 \| 3.0 \| 540 \| 0.6846 \| 0.8239 \| \| 0.3868 \| 4.0 \| 720 \| 0.6673 \| 0.8278 \| \| 0.2613 \| 5.0 \| 900 \| 0.6909 \| 0.8259 \| \| 0.1681 \| 6.0 \| 1080 \| 0.7123 \| 0.8278 \| \| 0.1096 \| 7.0 \| 1260 \| 0.7193 \| 0.8318 \| \| 0.0687 \| 8.0 \| 1440 \| 0.7653 \| 0.8337 \| \| 0.0405 \| 9.0 \| 1620 \| 0.7966 \| 0.8308 \| \| 0.0255 \| 10.0 \| 1800 \| 0.8047 \| 0.8441 \| \| 0.0145 \| 11.0 \| 1980 \| 0.8415 \| 0.8426 \| \| 0.0092 \| 12.0 \| 2160 \| 0.8462 \| 0.8505 \| \| 0.0053 \| 13.0 \| 2340 \| 0.8635 \| 0.8465 \| \| 0.0031 \| 14.0 \| 2520 \| 0.8625 \| 0.8475 \| \| 0.0023 \| 15.0 \| 2700 \| 0.8632 \| 0.8480 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
theanupdas/llama2-qlora-finetuned-french	theanupdas	2023-07-21T19:57:09Z	2	0	peft	[ "peft", "region:us" ]	null	2023-07-21T19:56:51Z	--- 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
ailabturkiye/incesemicenk	ailabturkiye	2023-07-21T19:53:49Z	0	0	null	[ "music", "tr", "license:openrail", "region:us" ]	null	2023-07-21T19:32:32Z	--- license: openrail language: - tr tags: - music --- Semicenk (Müzisyen) - RVC V2 450 Epoch Şarkıcı Semicenk'in Stüdyo Vokallerinden oluşturulan ses modelidir. Rvc V2 \| 4 Dakikalık Dataset \| 450 Epoch olarak eğitilmiştir. Dataset ve Train Benim Tarafımdan yapılmıştır.. Modelin izinsiz bir şekilde Ai Lab Discord Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir. Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. Discord: onurkilot YouTube: onur (https://youtube.com/@onurkilot)
marouni/miniDolly	marouni	2023-07-21T19:51:51Z	169	1	transformers	[ "transformers", "pytorch", "safetensors", "gpt_neox", "text-generation", "en", "dataset:databricks/databricks-dolly-15k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-14T12:21:43Z	--- license: apache-2.0 datasets: - databricks/databricks-dolly-15k language: - en widget: - text: 'What is the capital of France ?' example_title: Basic question group: Python --- # Summary An instruction-following large language model based on [pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) and trained on [Databricks' 15k instruction](https://huggingface.co/datasets/databricks/databricks-dolly-15k) with capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. This model is an experiment in using small base model ([pythia-70m](https://huggingface.co/EleutherAI/pythia-70m)) to build a model similar to Databricks' [dolly model](https://huggingface.co/databricks/dolly-v2-12b). # Usage To use the model with the transformers library, first make sure you have the transformers and accelerate libraries installed : ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") res = generate_text("What is the capital of France ?") print(res[0]["generated_text"]) ``` # Training The model was trained using [Databricks' 15k instruction](https://huggingface.co/datasets/databricks/databricks-dolly-15k) on a recent Dell PC with 32G of RAM with a core i7 CPU. The training took around 12 hours ! # Accuracy As expected the model performance is very bad ! Especially when compared to [Databricks dolly v2 12b model](https://huggingface.co/databricks/dolly-v2-12b). When prompted with `What is the capital of France ?`, the model answers with : ``` "The World". It is an artwork for "working time" called «The Middle East Today". It comes from Paris, Belgium, in local variation, including large cities as described in English language photographs which portray a crescent and sunrise of late note, Bangourt before Paris. “Countries like Pakistan and throughout East Africa close to Australia have constructed a watered havock which can be felt ever longer. Bombardment and booby traps tend to occupy space by wind and water, as were effectively used for material and equipment which have a green signal leading in the images." ``` Compared with the following asnwer from [Databricks dolly v2 3b model](https://huggingface.co/databricks/dolly-v2-12b) ``` The capital of France is Paris. ``` # Conclusion The accuracy between the base model used in this model (pythia-70m) and the base models used by Databricks (pythia-2.8b and pythia-12b) is huge ! And it makes all the difference in terms of accuracy. The only thing worth mentioning here is the model's size, at around 160M it's orders of magnitude smaller than the Databricks ones.
chh6/Reinforce_pixelcopter	chh6	2023-07-21T19:41:05Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T19:40:59Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce_pixelcopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 29.20 +/- 23.66 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
dnarqq/dqn-SpaceInvadersNoFrameskip-v4	dnarqq	2023-07-21T19:33:07Z	4	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T19:32:37Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 303.50 +/- 78.49 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dnarqq -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga dnarqq -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga dnarqq ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 200000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
josh-salako/ai_generated_image_detector	josh-salako	2023-07-21T19:27:04Z	0	0	keras	[ "keras", "tf-keras", "dataset:competitions/aiornot", "region:us" ]	null	2023-03-13T19:22:12Z	--- library_name: keras datasets: - competitions/aiornot metrics: - accuracy --- ## Model description A model that detects AI generated iamge ## Intended uses & limitations Intended for use cases whenever real images are needed and not AI generated ones. This model however cannot distinguish an AI generated movie whenever it has a close resemblance with a real image. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| 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 \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
mann-e/mann-e_5-new-merge-1	mann-e	2023-07-21T19:24:29Z	0	2	diffusers	[ "diffusers", "text-to-image", "license:mit", "region:us" ]	text-to-image	2023-07-21T19:12:34Z	--- license: mit library_name: diffusers pipeline_tag: text-to-image --- # Mann-E 5 Merge 1 This is only the checkpoint file and will be deprecated soon.
ByteExplorer/Pixelcopter-PLE-v0	ByteExplorer	2023-07-21T19:18:40Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-18T18:59:06Z	--- 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: 42.30 +/- 33.15 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
Naruke/taxi-v3	Naruke	2023-07-21T19:12:38Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T19:12:35Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 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="Naruke/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"]) ```
Naruke/q-FrozenLake-v1-8x8-randommap-noSlippery	Naruke	2023-07-21T19:09:08Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T19:09:05Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-8x8-randommap-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="Naruke/q-FrozenLake-v1-8x8-randommap-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"]) ```
ailabturkiye/semicenkroportaj	ailabturkiye	2023-07-21T18:57:19Z	0	0	null	[ "region:us" ]	null	2023-07-21T18:54:42Z	[![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) # Semicenk (Şarkıcı) - RVC V2 300 Epoch Şarkıcı Semicenk'in Röportaj Kesitlerinden oluşturulan ses modelidir, Şarkıdaki sesini temsil etmez.! Rvc V2 \| 6 Dakikalık Dataset \| 300 Epoch olarak eğitilmiştir. _Dataset ve Train Benim Tarafımdan yapılmıştır.._ __Modelin izinsiz bir şekilde [Ai Lab Discord](discord.gg/ailab) Sunucusu dışında paylaşılması tamamen yasaktır, model openrail lisansına sahiptir.__ ## Credits Herhangi bir platformda model ile yapılan bir cover paylaşımında credits vermeniz rica olunur. - Discord: hydragee - YouTube: CoverLai (https://www.youtube.com/@coverlai) ![Static Badge](https://img.shields.io/badge/Yap%C4%B1mc%C4%B1%20Bilgisi%20Verilmeden%20Payla%C5%9F%C4%B1lmas%C4%B1%20Yasakt%C4%B1r!-s?style=plastic&labelColor=orange&color=red) [![Discord Sunucumuz](https://img.shields.io/badge/Discord.gg%2F-AiLab-ailab )](discord.gg/ailab) ![Static Badge](https://img.shields.io/badge/AI%20LAB%20Hugging%20Face%20Organization-sa?style=plastic&labelColor=blue&color=blue)
jaygdesai/Reinforce-Jay-cartpole	jaygdesai	2023-07-21T18:53:27Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T18:12:34Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Jay-cartpole results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 482.50 +/- 52.50 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
NasimB/guten-rarity-neg-log-rarity-no-cut	NasimB	2023-07-21T18:40:43Z	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	2023-07-21T15:16:05Z	--- license: mit tags: - generated_from_trainer datasets: - generator model-index: - name: guten-rarity-neg-log-rarity-no-cut 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. --> # guten-rarity-neg-log-rarity-no-cut 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: 4.1048 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.3421 \| 0.29 \| 500 \| 5.3363 \| \| 5.0357 \| 0.58 \| 1000 \| 4.9250 \| \| 4.7084 \| 0.87 \| 1500 \| 4.6857 \| \| 4.4492 \| 1.16 \| 2000 \| 4.5455 \| \| 4.2984 \| 1.46 \| 2500 \| 4.4301 \| \| 4.1972 \| 1.75 \| 3000 \| 4.3258 \| \| 4.0832 \| 2.04 \| 3500 \| 4.2503 \| \| 3.8934 \| 2.33 \| 4000 \| 4.2116 \| \| 3.8607 \| 2.62 \| 4500 \| 4.1533 \| \| 3.8323 \| 2.91 \| 5000 \| 4.1090 \| \| 3.6419 \| 3.2 \| 5500 \| 4.0989 \| \| 3.5834 \| 3.49 \| 6000 \| 4.0699 \| \| 3.5762 \| 3.79 \| 6500 \| 4.0398 \| \| 3.4864 \| 4.08 \| 7000 \| 4.0350 \| \| 3.3174 \| 4.37 \| 7500 \| 4.0295 \| \| 3.3153 \| 4.66 \| 8000 \| 4.0165 \| \| 3.304 \| 4.95 \| 8500 \| 4.0047 \| \| 3.1667 \| 5.24 \| 9000 \| 4.0159 \| \| 3.1375 \| 5.53 \| 9500 \| 4.0149 \| \| 3.1343 \| 5.82 \| 10000 \| 4.0139 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.11.0+cu113 - Datasets 2.13.0 - Tokenizers 0.13.3
adarsha30735/3_alpaca-heart-status-dataset	adarsha30735	2023-07-21T18:32:21Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-21T18:32:18Z	--- 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
gokuls/hbertv1-small-wt-frz-48-emotion-emb-comp	gokuls	2023-07-21T18:31:54Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T18:28:37Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv1-small-wt-frz-48-emotion-emb-comp 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: 0.887 --- <!-- 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. --> # hbertv1-small-wt-frz-48-emotion-emb-comp This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small_emb_comp_frz) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.4013 - Accuracy: 0.887 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.1127 \| 1.0 \| 250 \| 0.6028 \| 0.798 \| \| 0.4399 \| 2.0 \| 500 \| 0.4066 \| 0.855 \| \| 0.2726 \| 3.0 \| 750 \| 0.3762 \| 0.866 \| \| 0.1907 \| 4.0 \| 1000 \| 0.3649 \| 0.876 \| \| 0.1412 \| 5.0 \| 1250 \| 0.4169 \| 0.8755 \| \| 0.1065 \| 6.0 \| 1500 \| 0.4013 \| 0.887 \| \| 0.0761 \| 7.0 \| 1750 \| 0.4679 \| 0.884 \| \| 0.0548 \| 8.0 \| 2000 \| 0.5221 \| 0.8775 \| \| 0.0379 \| 9.0 \| 2250 \| 0.5458 \| 0.8835 \| \| 0.0233 \| 10.0 \| 2500 \| 0.5586 \| 0.8805 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
jaimevera1107/all-MiniLM-L6-v2-similarity-es	jaimevera1107	2023-07-21T18:26:31Z	4,970	3	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "es", "dataset:jaimevera1107/similarity-sentences-spanish", "license:mit", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-07-21T17:15:03Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: mit datasets: - jaimevera1107/similarity-sentences-spanish language: - es library_name: sentence-transformers --- # All-MiniLM-L6-v2 Fine Tuned - Sentence Transformers - Embedding Model (Spanish-Español) This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Esta es una frase para ser comparada", "Esta es otra oración"] model = SentenceTransformer('jaimevera1107/roberta-similarity-es') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["Esta es una frase para ser comparada", "Esta es otra oración"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('jaimevera1107/roberta-similarity-es') model = AutoModel.from_pretrained('jaimevera1107/roberta-similarity-es') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results \| Model \| R squared \| Spearman Correlation \| \|----------------------------\|--------------\|-------------------------\| \| Roberta Fine tuned \| 70.67 % \| 80.1 % \| ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 767 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` The data used was the one in the [Similarity Sentences Spanish Dataset](https://huggingface.co/datasets/jaimevera1107/similarity-sentences-spanish) Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 500, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 383, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ```
tyzp-INC/bench1-paraphrase-multilingual-MiniLM-L12-v2	tyzp-INC	2023-07-21T18:25:57Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-21T18:25:32Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # tyzp-INC/bench1-paraphrase-multilingual-MiniLM-L12-v2 This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("tyzp-INC/bench1-paraphrase-multilingual-MiniLM-L12-v2") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
gokuls/hbertv1-mini-wt-48-Massive-intent	gokuls	2023-07-21T18:23:43Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_mini", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_mini", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T18:20:04Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_mini tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-mini-wt-48-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.8544023610427939 --- <!-- 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. --> # hbertv1-mini-wt-48-Massive-intent This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_mini](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_mini) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6406 - Accuracy: 0.8544 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.16 \| 1.0 \| 180 \| 2.1089 \| 0.4934 \| \| 1.6964 \| 2.0 \| 360 \| 1.2208 \| 0.6916 \| \| 1.1107 \| 3.0 \| 540 \| 0.9116 \| 0.7703 \| \| 0.8493 \| 4.0 \| 720 \| 0.7717 \| 0.8155 \| \| 0.692 \| 5.0 \| 900 \| 0.7166 \| 0.8155 \| \| 0.5849 \| 6.0 \| 1080 \| 0.6754 \| 0.8288 \| \| 0.5133 \| 7.0 \| 1260 \| 0.6491 \| 0.8392 \| \| 0.4541 \| 8.0 \| 1440 \| 0.6406 \| 0.8451 \| \| 0.4074 \| 9.0 \| 1620 \| 0.6346 \| 0.8480 \| \| 0.3615 \| 10.0 \| 1800 \| 0.6403 \| 0.8460 \| \| 0.3304 \| 11.0 \| 1980 \| 0.6452 \| 0.8446 \| \| 0.3021 \| 12.0 \| 2160 \| 0.6390 \| 0.8495 \| \| 0.2792 \| 13.0 \| 2340 \| 0.6412 \| 0.8515 \| \| 0.2584 \| 14.0 \| 2520 \| 0.6406 \| 0.8544 \| \| 0.2483 \| 15.0 \| 2700 \| 0.6394 \| 0.8529 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-tiny-wt-48-Massive-intent	gokuls	2023-07-21T18:19:49Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_tiny", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T18:16:50Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_tiny tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-tiny-wt-48-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.7722577471716675 --- <!-- 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. --> # hbertv1-tiny-wt-48-Massive-intent This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.8676 - Accuracy: 0.7723 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 3.7161 \| 1.0 \| 180 \| 3.1936 \| 0.2499 \| \| 2.8544 \| 2.0 \| 360 \| 2.3660 \| 0.4058 \| \| 2.2122 \| 3.0 \| 540 \| 1.8566 \| 0.5430 \| \| 1.7979 \| 4.0 \| 720 \| 1.5269 \| 0.6370 \| \| 1.5083 \| 5.0 \| 900 \| 1.3016 \| 0.6911 \| \| 1.3044 \| 6.0 \| 1080 \| 1.1672 \| 0.7098 \| \| 1.1652 \| 7.0 \| 1260 \| 1.0709 \| 0.7270 \| \| 1.0703 \| 8.0 \| 1440 \| 1.0045 \| 0.7432 \| \| 0.996 \| 9.0 \| 1620 \| 0.9595 \| 0.7511 \| \| 0.9323 \| 10.0 \| 1800 \| 0.9276 \| 0.7550 \| \| 0.8832 \| 11.0 \| 1980 \| 0.9183 \| 0.7565 \| \| 0.8521 \| 12.0 \| 2160 \| 0.8953 \| 0.7649 \| \| 0.8246 \| 13.0 \| 2340 \| 0.8829 \| 0.7649 \| \| 0.8072 \| 14.0 \| 2520 \| 0.8676 \| 0.7723 \| \| 0.7947 \| 15.0 \| 2700 \| 0.8657 \| 0.7708 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
tyzp-INC/few-shot-multilingual-e5-large-xnli	tyzp-INC	2023-07-21T18:17:56Z	44	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-21T18:16:01Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # tyzp-INC/few-shot-multilingual-e5-large-xnli This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("tyzp-INC/few-shot-multilingual-e5-large-xnli") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
gokuls/hbertv1-small-wt-48-Massive-intent	gokuls	2023-07-21T18:05:44Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:gokuls/model_v1_complete_training_wt_init_48_small", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T18:01:57Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_small tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: hbertv1-small-wt-48-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: validation args: en-US metrics: - name: Accuracy type: accuracy value: 0.8671913428430891 --- <!-- 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. --> # hbertv1-small-wt-48-Massive-intent This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6540 - Accuracy: 0.8672 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.0435 \| 1.0 \| 180 \| 0.8648 \| 0.7693 \| \| 0.7809 \| 2.0 \| 360 \| 0.6523 \| 0.8190 \| \| 0.5432 \| 3.0 \| 540 \| 0.5795 \| 0.8441 \| \| 0.4035 \| 4.0 \| 720 \| 0.5657 \| 0.8539 \| \| 0.2976 \| 5.0 \| 900 \| 0.5547 \| 0.8618 \| \| 0.22 \| 6.0 \| 1080 \| 0.5735 \| 0.8598 \| \| 0.1639 \| 7.0 \| 1260 \| 0.5905 \| 0.8554 \| \| 0.1281 \| 8.0 \| 1440 \| 0.5916 \| 0.8618 \| \| 0.0893 \| 9.0 \| 1620 \| 0.6186 \| 0.8642 \| \| 0.0722 \| 10.0 \| 1800 \| 0.6370 \| 0.8642 \| \| 0.0513 \| 11.0 \| 1980 \| 0.6540 \| 0.8672 \| \| 0.039 \| 12.0 \| 2160 \| 0.6762 \| 0.8637 \| \| 0.0307 \| 13.0 \| 2340 \| 0.6796 \| 0.8637 \| \| 0.0223 \| 14.0 \| 2520 \| 0.6895 \| 0.8657 \| \| 0.0169 \| 15.0 \| 2700 \| 0.6918 \| 0.8652 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
christinezh/squad-bloom-3b	christinezh	2023-07-21T17:59:48Z	0	0	peft	[ "peft", "region:us" ]	null	2023-07-21T17:52:06Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
gokuls/hbertv1-mini-wt-48-emotion	gokuls	2023-07-21T17:59:00Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/model_v1_complete_training_wt_init_48_mini", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_mini", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T17:55:32Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_mini tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv1-mini-wt-48-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: 0.908 --- <!-- 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. --> # hbertv1-mini-wt-48-emotion This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_mini](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_mini) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2561 - Accuracy: 0.908 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0852 \| 1.0 \| 250 \| 0.5567 \| 0.8195 \| \| 0.4522 \| 2.0 \| 500 \| 0.3409 \| 0.8775 \| \| 0.3152 \| 3.0 \| 750 \| 0.3007 \| 0.8885 \| \| 0.2646 \| 4.0 \| 1000 \| 0.2999 \| 0.9045 \| \| 0.23 \| 5.0 \| 1250 \| 0.2842 \| 0.8945 \| \| 0.205 \| 6.0 \| 1500 \| 0.2658 \| 0.9035 \| \| 0.1871 \| 7.0 \| 1750 \| 0.2674 \| 0.902 \| \| 0.1623 \| 8.0 \| 2000 \| 0.2561 \| 0.908 \| \| 0.1488 \| 9.0 \| 2250 \| 0.2529 \| 0.9075 \| \| 0.1379 \| 10.0 \| 2500 \| 0.2523 \| 0.908 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
Glen/sd-class-butterflies-32	Glen	2023-07-21T17:55:59Z	30	0	diffusers	[ "diffusers", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-07-21T17:55:48Z	--- license: mit tags: - pytorch - diffusers - unconditional-image-generation - diffusion-models-class --- # Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) This model is a diffusion model for unconditional image generation of cute 🦋. ## Usage ```python from diffusers import DDPMPipeline pipeline = DDPMPipeline.from_pretrained('Glen/sd-class-butterflies-32') image = pipeline().images[0] image ```
gokuls/hbertv1-tiny-wt-48-emotion	gokuls	2023-07-21T17:55:14Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/model_v1_complete_training_wt_init_48_tiny", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_tiny", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T17:52:35Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_tiny tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv1-tiny-wt-48-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: 0.8985 --- <!-- 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. --> # hbertv1-tiny-wt-48-emotion This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_tiny](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_tiny) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2695 - Accuracy: 0.8985 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4321 \| 1.0 \| 250 \| 1.0203 \| 0.6475 \| \| 0.8329 \| 2.0 \| 500 \| 0.5954 \| 0.814 \| \| 0.5347 \| 3.0 \| 750 \| 0.4146 \| 0.8645 \| \| 0.398 \| 4.0 \| 1000 \| 0.3496 \| 0.8805 \| \| 0.3418 \| 5.0 \| 1250 \| 0.3091 \| 0.889 \| \| 0.2932 \| 6.0 \| 1500 \| 0.2864 \| 0.8945 \| \| 0.2646 \| 7.0 \| 1750 \| 0.2782 \| 0.8965 \| \| 0.2532 \| 8.0 \| 2000 \| 0.2695 \| 0.8985 \| \| 0.2342 \| 9.0 \| 2250 \| 0.2632 \| 0.898 \| \| 0.225 \| 10.0 \| 2500 \| 0.2617 \| 0.897 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
gokuls/hbertv1-wt-frz-48-emotion	gokuls	2023-07-21T17:50:29Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz", "base_model:finetune:gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T17:41:30Z	--- base_model: gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv1-wt-frz-48-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: 0.9195 --- <!-- 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. --> # hbertv1-wt-frz-48-emotion This model is a fine-tuned version of [gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz](https://huggingface.co/gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48_frz) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.3306 - Accuracy: 0.9195 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8962 \| 1.0 \| 250 \| 0.3587 \| 0.872 \| \| 0.3328 \| 2.0 \| 500 \| 0.3154 \| 0.889 \| \| 0.2269 \| 3.0 \| 750 \| 0.2463 \| 0.913 \| \| 0.1687 \| 4.0 \| 1000 \| 0.3033 \| 0.912 \| \| 0.1319 \| 5.0 \| 1250 \| 0.2559 \| 0.9105 \| \| 0.1091 \| 6.0 \| 1500 \| 0.2657 \| 0.913 \| \| 0.0809 \| 7.0 \| 1750 \| 0.3015 \| 0.913 \| \| 0.0686 \| 8.0 \| 2000 \| 0.3306 \| 0.9195 \| \| 0.0498 \| 9.0 \| 2250 \| 0.3532 \| 0.9195 \| \| 0.0389 \| 10.0 \| 2500 \| 0.3960 \| 0.9175 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
vineetsharma/ppo-Pyramids	vineetsharma	2023-07-21T17:46:48Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-07-21T17:45:56Z	--- 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: vineetsharma/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
gokuls/hbertv1-small-wt-48-emotion	gokuls	2023-07-21T17:40:32Z	47	0	transformers	[ "transformers", "pytorch", "tensorboard", "hybridbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:gokuls/model_v1_complete_training_wt_init_48_small", "base_model:finetune:gokuls/model_v1_complete_training_wt_init_48_small", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-07-21T17:36:58Z	--- base_model: gokuls/model_v1_complete_training_wt_init_48_small tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: hbertv1-small-wt-48-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: 0.9375 --- <!-- 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. --> # hbertv1-small-wt-48-emotion This model is a fine-tuned version of [gokuls/model_v1_complete_training_wt_init_48_small](https://huggingface.co/gokuls/model_v1_complete_training_wt_init_48_small) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1738 - Accuracy: 0.9375 ## 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: 64 - eval_batch_size: 64 - seed: 33 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.653 \| 1.0 \| 250 \| 0.2924 \| 0.8935 \| \| 0.2315 \| 2.0 \| 500 \| 0.2199 \| 0.9175 \| \| 0.1722 \| 3.0 \| 750 \| 0.1918 \| 0.9235 \| \| 0.1263 \| 4.0 \| 1000 \| 0.1738 \| 0.9375 \| \| 0.1087 \| 5.0 \| 1250 \| 0.1898 \| 0.9295 \| \| 0.0889 \| 6.0 \| 1500 \| 0.1812 \| 0.932 \| \| 0.0756 \| 7.0 \| 1750 \| 0.1978 \| 0.9315 \| \| 0.0652 \| 8.0 \| 2000 \| 0.2070 \| 0.931 \| \| 0.0506 \| 9.0 \| 2250 \| 0.2277 \| 0.9345 \| \| 0.0398 \| 10.0 \| 2500 \| 0.2356 \| 0.9335 \| ### Framework versions - Transformers 4.31.0 - Pytorch 1.14.0a0+410ce96 - Datasets 2.13.1 - Tokenizers 0.13.3
subset-data/falcon-7b-bt	subset-data	2023-07-21T17:38:14Z	6	0	transformers	[ "transformers", "RefinedWeb", "text-generation", "custom_code", "autotrain_compatible", "4-bit", "region:us" ]	text-generation	2023-07-20T15:22:03Z	--- pipeline_tag: text-generation library_name: transformers ---
ethannhzhouu/EthanHorror5	ethannhzhouu	2023-07-21T17:37:57Z	173	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T17:37:18Z	--- license: mit base_model: EleutherAI/gpt-neo-125M tags: - generated_from_trainer model-index: - name: EthanHorror5 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. --> # EthanHorror5 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0747 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 1.1872 \| \| No log \| 2.0 \| 2 \| 0.5813 \| \| No log \| 3.0 \| 3 \| 0.2518 \| \| No log \| 4.0 \| 4 \| 0.1155 \| \| No log \| 5.0 \| 5 \| 0.0747 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
helenai/deepset-xlm-roberta-large-squad2-ov	helenai	2023-07-21T17:36:52Z	4	0	transformers	[ "transformers", "openvino", "xlm-roberta", "question-answering", "en", "endpoints_compatible", "region:us" ]	question-answering	2023-07-21T17:36:03Z	--- language: - en tags: - openvino --- # deepset/xlm-roberta-large-squad2 This is the [deepset/xlm-roberta-large-squad2](https://huggingface.co/deepset/xlm-roberta-large-squad2) model converted to [OpenVINO](https://openvino.ai), for accellerated inference. An example of how to do inference on this model: ```python from optimum.intel.openvino import OVModelForQuestionAnswering from transformers import AutoTokenizer, pipeline # model_id should be set to either a local directory or a model available on the HuggingFace hub. model_id = "helenai/deepset-xlm-roberta-large-squad2-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForQuestionAnswering.from_pretrained(model_id) pipe = pipeline("question-answering", model=model, tokenizer=tokenizer) result = pipe("What is OpenVINO?", "OpenVINO is a framework that accelerates deep learning inferencing") print(result) ```
ethannhzhouu/EthanHorror4	ethannhzhouu	2023-07-21T17:34:14Z	116	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T17:32:21Z	--- license: mit base_model: EleutherAI/gpt-neo-125M tags: - generated_from_trainer model-index: - name: EthanHorror4 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. --> # EthanHorror4 This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.0187 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 1 \| 3.3778 \| \| No log \| 2.0 \| 2 \| 2.7985 \| \| No log \| 3.0 \| 3 \| 2.4210 \| \| No log \| 4.0 \| 4 \| 2.1587 \| \| No log \| 5.0 \| 5 \| 2.0187 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ethannhzhouu/EthanHorror3	ethannhzhouu	2023-07-21T17:29:35Z	216	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T17:28:58Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: EthanHorror3 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. --> # EthanHorror3 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3888 ## 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 \| 1 \| 4.8164 \| \| No log \| 2.0 \| 2 \| 4.5105 \| \| No log \| 3.0 \| 3 \| 4.3888 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
aarnphm/llama-2-dolly-qlora	aarnphm	2023-07-21T17:29:12Z	1	0	peft	[ "peft", "region:us" ]	null	2023-07-21T17:29:09Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0.dev0
au2a/whisper-base-zh-20230721	au2a	2023-07-21T17:29:04Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:-", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-07-21T09:41:35Z	--- language: - zh license: apache-2.0 tags: - whisper - generated_from_trainer datasets: - '-' model-index: - name: whisper-base-zh-20230721 - au2a 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-base-zh-20230721 - au2a This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the some hakka audio dataset. It achieves the following results on the evaluation set: - Loss: 0.4546 - Cer: 16.5974 ## 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-06 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\| \| 0.4669 \| 0.65 \| 1000 \| 0.6528 \| 25.0548 \| \| 0.2208 \| 1.29 \| 2000 \| 0.5006 \| 19.8761 \| \| 0.1452 \| 1.94 \| 3000 \| 0.4546 \| 17.9497 \| \| 0.0951 \| 2.59 \| 4000 \| 0.4431 \| 17.4511 \| \| 0.0526 \| 3.24 \| 5000 \| 0.4450 \| 17.3113 \| \| 0.0422 \| 3.88 \| 6000 \| 0.4440 \| 16.6201 \| \| 0.0271 \| 4.53 \| 7000 \| 0.4471 \| 17.0658 \| \| 0.0179 \| 5.18 \| 8000 \| 0.4509 \| 16.5823 \| \| 0.0166 \| 5.83 \| 9000 \| 0.4535 \| 16.8543 \| \| 0.0129 \| 6.47 \| 10000 \| 0.4546 \| 16.5974 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3
an-atlas/moreHorror	an-atlas	2023-07-21T17:26:45Z	150	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T17:22:15Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: moreHorror 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. --> # moreHorror This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3888 ## 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 \| 1 \| 4.8164 \| \| No log \| 2.0 \| 2 \| 4.5105 \| \| No log \| 3.0 \| 3 \| 4.3888 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ethannhzhouu/EthanHorror2	ethannhzhouu	2023-07-21T17:26:16Z	150	0	transformers	[ "transformers", "pytorch", "tensorboard", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T17:23:56Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: EthanHorror2 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. --> # EthanHorror2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.3888 ## 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 \| 1 \| 4.8164 \| \| No log \| 2.0 \| 2 \| 4.5105 \| \| No log \| 3.0 \| 3 \| 4.3888 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
jorgelzn/ppo-SnowballTarget	jorgelzn	2023-07-21T17:21:56Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-07-21T16:26:04Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: jorgelzn/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
akash0/py-code-complete	akash0	2023-07-21T17:06:34Z	63	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-07-21T14:14:08Z	--- license: mit base_model: gpt2 tags: - generated_from_keras_callback model-index: - name: akash0/py-code-complete 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. --> # akash0/py-code-complete This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 4.1922 - Validation Loss: 3.7943 - Epoch: 0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 6150, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 100, '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} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.1922 \| 3.7943 \| 0 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
naimul011/finetuned_tweet_sentiment_llama-7b-100-hf	naimul011	2023-07-21T17:06:05Z	3	0	peft	[ "peft", "region:us" ]	null	2023-07-21T13:49:08Z	--- 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
nermine123/layoutlmv3-finetuned-cord_100	nermine123	2023-07-21T16:59:48Z	80	0	transformers	[ "transformers", "pytorch", "tensorboard", "layoutlmv3", "token-classification", "generated_from_trainer", "dataset:cord-layoutlmv3", "base_model:microsoft/layoutlmv3-base", "base_model:finetune:microsoft/layoutlmv3-base", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2023-07-21T16:08:24Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv3-base tags: - generated_from_trainer datasets: - cord-layoutlmv3 metrics: - precision - recall - f1 - accuracy model-index: - name: layoutlmv3-finetuned-cord_100 results: - task: name: Token Classification type: token-classification dataset: name: cord-layoutlmv3 type: cord-layoutlmv3 config: cord split: test args: cord metrics: - name: Precision type: precision value: 0.9296817172464841 - name: Recall type: recall value: 0.9401197604790419 - name: F1 type: f1 value: 0.9348716040193524 - name: Accuracy type: accuracy value: 0.9435483870967742 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv3-finetuned-cord_100 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset. It achieves the following results on the evaluation set: - Loss: 0.2908 - Precision: 0.9297 - Recall: 0.9401 - F1: 0.9349 - Accuracy: 0.9435 ## 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: 5 - eval_batch_size: 5 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 4.17 \| 250 \| 1.0995 \| 0.6869 \| 0.7635 \| 0.7231 \| 0.7789 \| \| 1.4568 \| 8.33 \| 500 \| 0.5676 \| 0.8382 \| 0.8765 \| 0.8569 \| 0.8773 \| \| 1.4568 \| 12.5 \| 750 \| 0.4044 \| 0.8920 \| 0.9147 \| 0.9032 \| 0.9202 \| \| 0.3562 \| 16.67 \| 1000 \| 0.3518 \| 0.9086 \| 0.9229 \| 0.9157 \| 0.9270 \| \| 0.3562 \| 20.83 \| 1250 \| 0.3060 \| 0.9245 \| 0.9349 \| 0.9297 \| 0.9372 \| \| 0.1509 \| 25.0 \| 1500 \| 0.3032 \| 0.9261 \| 0.9379 \| 0.9319 \| 0.9419 \| \| 0.1509 \| 29.17 \| 1750 \| 0.2980 \| 0.9261 \| 0.9386 \| 0.9323 \| 0.9368 \| \| 0.0848 \| 33.33 \| 2000 \| 0.2996 \| 0.9226 \| 0.9371 \| 0.9298 \| 0.9385 \| \| 0.0848 \| 37.5 \| 2250 \| 0.2924 \| 0.9276 \| 0.9394 \| 0.9334 \| 0.9440 \| \| 0.0619 \| 41.67 \| 2500 \| 0.2908 \| 0.9297 \| 0.9401 \| 0.9349 \| 0.9435 \| ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu118 - Datasets 2.13.1 - Tokenizers 0.13.3
ByteExplorer/rl_course_vizdoom_health_gathering_supreme	ByteExplorer	2023-07-21T16:53:47Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-07-20T19:48:03Z	--- 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: 9.71 +/- 4.60 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 ByteExplorer/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 --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 --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.
leopuv/cats_vs_dogs_classifier	leopuv	2023-07-21T16:45:22Z	84	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "dataset:lewtun/dog_food", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-07-20T16:19:13Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: leopuv/cats_vs_dogs_classifier results: [] datasets: - lewtun/dog_food --- <!-- 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. --> # leopuv/cats_vs_dogs_classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0285 - Train Accuracy: 0.9865 - Validation Loss: 0.0340 - Validation Accuracy: 0.9865 - 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': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 80000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 0.1739 \| 0.9715 \| 0.0787 \| 0.9715 \| 0 \| \| 0.0744 \| 0.984 \| 0.0432 \| 0.9840 \| 1 \| \| 0.0543 \| 0.9895 \| 0.0365 \| 0.9895 \| 2 \| \| 0.0420 \| 0.9885 \| 0.0346 \| 0.9885 \| 3 \| \| 0.0402 \| 0.9855 \| 0.0414 \| 0.9855 \| 4 \| \| 0.0378 \| 0.9885 \| 0.0307 \| 0.9885 \| 5 \| \| 0.0306 \| 0.9855 \| 0.0375 \| 0.9855 \| 6 \| \| 0.0343 \| 0.987 \| 0.0402 \| 0.9870 \| 7 \| \| 0.0283 \| 0.9875 \| 0.0381 \| 0.9875 \| 8 \| \| 0.0285 \| 0.9865 \| 0.0340 \| 0.9865 \| 9 \| ### Framework versions - Transformers 4.31.0 - TensorFlow 2.12.0 - Datasets 2.13.1 - Tokenizers 0.13.3
jphme/vicuna-13b-v1.3-ger-GGML	jphme	2023-07-21T16:40:44Z	0	0	transformers	[ "transformers", "text-generation", "de", "en", "license:cc-by-nc-sa-4.0", "region:us" ]	text-generation	2023-07-11T11:03:11Z	--- inference: false license: cc-by-nc-sa-4.0 language: - de - en library_name: transformers pipeline_tag: text-generation --- # Vicuna 13b v1.3 German GGML These files are GGML format model files for [Vicuna 13b v1.3 German](https://huggingface.co/jphme/vicuna-13b-v1.3-ger). Please find all information about the model in the original repository. GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as: * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [KoboldCpp](https://github.com/LostRuins/koboldcpp) * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) * [ctransformers](https://github.com/marella/ctransformers) ## Prompt template: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hello!</s> USER: How are you? ASSISTANT: I am good.</s> ``` ## Compatibility ### `q4_0` + `q5_1` So far, I only quantized a `q4_0` and `q5_1` version for my own use. Please let me know if there is demand for other quantizations. These should be compatbile with any UIs, tools and libraries released since late May. ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| vicuna-13b-v1.3-ger.ggmlv3.q4_0.bin \| q4_0 \| 4 \| 7.37 GB \| ~9.8 GB \| Original llama.cpp quant method, 4-bit. \| \| vicuna-13b-v1.3-ger.ggmlv3.q5_1.bin \| q5_1 \| 5 \| 9.78 GB \| ~12.3 GB \| Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 32 -m vicuna-13b-v1.3-ger.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are an story writing assistant who writes very long, detailed and interesting stories\n\nUser:\nWrite a story about llamas\nAssistant:\n" ``` If you're able to use full GPU offloading, you should use `-t 1` to get best performance. If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). ## Thanks Special thanks to [LMSYS](https://huggingface.co/lmsys) for the great Orca Mini base model and [TheBloke](https://huggingface.co/TheBloke) for his great work quantizing billions of models (and for his template for this README).
jphme/vicuna-13b-v1.3-ger	jphme	2023-07-21T16:36:09Z	10	8	transformers	[ "transformers", "pytorch", "llama", "text-generation", "de", "en", "arxiv:2302.13971", "arxiv:2306.05685", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2023-07-11T11:02:48Z	--- language: - de - en pipeline_tag: text-generation inference: false --- # Vicuna 13b v1.3 German vicuna-13b-v1.3-ger is a variant of [LMSYS](https://huggingface.co/lmsys)´s [Vicuna 13b v1.3](https://huggingface.co/lmsys/vicuna-13b-v1.3) model, finetuned on an additional dataset in German language. The original model has been trained on explain tuned datasets, created using instructions and input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches. This model is optimized for German text, providing proficiency in understanding, generating, and interacting with German language content. However the model is not yet fully optimized for German language, as it has been trained on a small, experimental dataset and has limited capabilities due to the small parameter count. Some of the fineunting data is also targeted towards factual retrieval (only answer questions from information in the context and refuse to hallucinate) and the model should perform better for these tasks than original Vicuna. I am working on improving the model´s capabilities and will update the model if there is sufficient interest. A quantized GGML version for use with llama.cpp, kobold.cpp and other GUIs for CPU inference can be found [here](https://huggingface.co/jphme/vicuna-13b-v1.3-ger-GGML). ## Prompt Template ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hello!</s> USER: How are you? ASSISTANT: I am good.</s> ``` ## Results I did only evaluate the output on a small, handcrafted sample on test prompts in German, confirming that the model's ability to understand and generate German text is above the base model in many situations. ## Problems There might be inconsistencies in multi-turn chat applications, as there was a small problem with the <eos> tokens during preparation of the finetuning dataset. Please report any problems so I can fix this for the next version. --------------------------- # Original Vicuna Model Card ## Model Details Vicuna is a chat assistant trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. - Developed by: [LMSYS](https://lmsys.org/) - Model type: An auto-regressive language model based on the transformer architecture. - License: Non-commercial license - Finetuned from model: [LLaMA](https://arxiv.org/abs/2302.13971). ### Model Sources - Repository: https://github.com/lm-sys/FastChat - Blog: https://lmsys.org/blog/2023-03-30-vicuna/ - Paper: https://arxiv.org/abs/2306.05685 - Demo: https://chat.lmsys.org/ ## Uses The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence. ## How to Get Started with the Model - Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights. - APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api. ## Training Details Vicuna v1.3 is fine-tuned from LLaMA with supervised instruction fine-tuning. The training data is around 140K conversations collected from ShareGPT.com. See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf). ## Evaluation Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard). ## Difference between different versions of Vicuna See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
Darisian/taxi-v3	Darisian	2023-07-21T16:35:55Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2023-07-21T16:35:52Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="Darisian/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"]) ```
OPERFIND/step2	OPERFIND	2023-07-21T16:34:52Z	8	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-07-20T18:19:04Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### step2 Dreambooth model trained by OPERFIND with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:
tyzp-INC/few-mjwong	tyzp-INC	2023-07-21T16:29:00Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "xlm-roberta", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-07-21T16:25:39Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # tyzp-INC/few-mjwong This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Usage To use this model for inference, first install the SetFit library: ```bash python -m pip install setfit ``` You can then run inference as follows: ```python from setfit import SetFitModel # Download from Hub and run inference model = SetFitModel.from_pretrained("tyzp-INC/few-mjwong") # Run inference preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"]) ``` ## BibTeX entry and citation info ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```
michaelfeil/ct2fast-Llama-2-13b-chat-hf	michaelfeil	2023-07-21T16:19:27Z	6	4	transformers	[ "transformers", "llama", "text-generation", "ctranslate2", "int8", "float16", "facebook", "meta", "pytorch", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "region:us" ]	text-generation	2023-07-18T21:22:27Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - ctranslate2 - int8 - float16 - facebook - meta - pytorch - llama - llama-2 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-Llama-2-13b-chat-hf" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-21 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
michaelfeil/ct2fast-Llama-2-13b-hf	michaelfeil	2023-07-21T16:17:17Z	6	1	transformers	[ "transformers", "llama", "text-generation", "ctranslate2", "int8", "float16", "facebook", "meta", "pytorch", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "region:us" ]	text-generation	2023-07-18T21:56:56Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false tags: - ctranslate2 - int8 - float16 - facebook - meta - pytorch - llama - llama-2 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-Llama-2-13b-hf" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-21 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|
michaelfeil/ct2fast-Llama-2-7b-chat-hf	michaelfeil	2023-07-21T16:14:52Z	13	4	transformers	[ "transformers", "llama", "text-generation", "ctranslate2", "int8", "float16", "facebook", "meta", "pytorch", "llama-2", "en", "arxiv:2307.09288", "autotrain_compatible", "region:us" ]	text-generation	2023-07-18T20:39:17Z	--- extra_gated_heading: Access Llama 2 on Hugging Face extra_gated_description: >- This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days. extra_gated_prompt: "Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved." extra_gated_button_content: Submit extra_gated_fields: I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox language: - en pipeline_tag: text-generation inference: false arxiv: 2307.09288 tags: - ctranslate2 - int8 - float16 - facebook - meta - pytorch - llama - llama-2 --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) ```bash pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1 ``` ```python # from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-Llama-2-7b-chat-hf" from hf_hub_ctranslate2 import GeneratorCT2fromHfHub model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` Checkpoint compatible to [ctranslate2>=3.17.1](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` Converted on 2023-07-21 using ``` LLama-2 -> removed <pad> token. ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # Llama 2 Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. Input Models input text only. Output Models generate text only. Model Architecture Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. \|\|Training Data\|Params\|Content Length\|GQA\|Tokens\|LR\| \|---\|---\|---\|---\|---\|---\|---\| \|Llama 2\|A new mix of publicly available online data\|7B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|13B\|4k\|✗\|2.0T\|3.0 x 10<sup>-4</sup>\| \|Llama 2\|A new mix of publicly available online data\|70B\|4k\|✔\|2.0T\|1.5 x 10<sup>-4</sup>\| Llama 2 family of models. Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. Model Dates Llama 2 was trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use Intended Use Cases Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212). Out-of-scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software Training Factors We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. \|\|Time (GPU hours)\|Power Consumption (W)\|Carbon Emitted(tCO<sub>2</sub>eq)\| \|---\|---\|---\|---\| \|Llama 2 7B\|184320\|400\|31.22\| \|Llama 2 13B\|368640\|400\|62.44\| \|Llama 2 70B\|1720320\|400\|291.42\| \|Total\|3311616\|\|539.00\| CO<sub>2</sub> emissions during pretraining. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. \|Model\|Size\|Code\|Commonsense Reasoning\|World Knowledge\|Reading Comprehension\|Math\|MMLU\|BBH\|AGI Eval\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|Llama 1\|7B\|14.1\|60.8\|46.2\|58.5\|6.95\|35.1\|30.3\|23.9\| \|Llama 1\|13B\|18.9\|66.1\|52.6\|62.3\|10.9\|46.9\|37.0\|33.9\| \|Llama 1\|33B\|26.0\|70.0\|58.4\|67.6\|21.4\|57.8\|39.8\|41.7\| \|Llama 1\|65B\|30.7\|70.7\|60.5\|68.6\|30.8\|63.4\|43.5\|47.6\| \|Llama 2\|7B\|16.8\|63.9\|48.9\|61.3\|14.6\|45.3\|32.6\|29.3\| \|Llama 2\|13B\|24.5\|66.9\|55.4\|65.8\|28.7\|54.8\|39.4\|39.1\| \|Llama 2\|70B\|37.5\|71.9\|63.6\|69.4\|35.2\|68.9\|51.2\|54.2\| Overall performance on grouped academic benchmarks. Code: We report the average pass@1 scores of our models on HumanEval and MBPP. Commonsense Reasoning: We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. World Knowledge: We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. Reading Comprehension: For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. MATH: We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama 1\|7B\|27.42\|23.00\| \|Llama 1\|13B\|41.74\|23.08\| \|Llama 1\|33B\|44.19\|22.57\| \|Llama 1\|65B\|48.71\|21.77\| \|Llama 2\|7B\|33.29\|21.25\| \|Llama 2\|13B\|41.86\|26.10\| \|Llama 2\|70B\|50.18\|24.60\| Evaluation of pretrained LLMs on automatic safety benchmarks. For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). \|\|\|TruthfulQA\|Toxigen\| \|---\|---\|---\|---\| \|Llama-2-Chat\|7B\|57.04\|0.00\| \|Llama-2-Chat\|13B\|62.18\|0.00\| \|Llama-2-Chat\|70B\|64.14\|0.01\| Evaluation of fine-tuned LLMs on different safety datasets. Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index \|Model\|Llama2\|Llama2-hf\|Llama2-chat\|Llama2-chat-hf\| \|---\|---\|---\|---\|---\| \|7B\| [Link](https://huggingface.co/llamaste/Llama-2-7b) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)\| \|13B\| [Link](https://huggingface.co/llamaste/Llama-2-13b) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)\| \|70B\| [Link](https://huggingface.co/llamaste/Llama-2-70b) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) \| [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)\|

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