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train · 893k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-08-28 18:27:53	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 525 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-08-28 18:27:52	card stringlengths 11 1.01M
conlan/dqn-SpaceInvadersNoFrameskip-v4	conlan	2023-11-02T22:42:43Z	1	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T22:42:04Z	--- 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: 619.50 +/- 124.87 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 conlan -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 conlan -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 conlan ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
gcperk20/deit-tiny-patch16-224-finetuned-piid	gcperk20	2023-11-02T22:42:19Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-tiny-patch16-224", "base_model:finetune:facebook/deit-tiny-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-30T16:15:25Z	--- license: apache-2.0 base_model: facebook/deit-tiny-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: deit-tiny-patch16-224-finetuned-piid results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: val args: default metrics: - name: Accuracy type: accuracy value: 0.7625570776255708 --- <!-- 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. --> # deit-tiny-patch16-224-finetuned-piid This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5426 - Accuracy: 0.7626 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2274 \| 0.98 \| 20 \| 1.1185 \| 0.4658 \| \| 0.8485 \| 2.0 \| 41 \| 0.8690 \| 0.6119 \| \| 0.6793 \| 2.98 \| 61 \| 0.8749 \| 0.6073 \| \| 0.6028 \| 4.0 \| 82 \| 0.6864 \| 0.6804 \| \| 0.5693 \| 4.98 \| 102 \| 0.5618 \| 0.7717 \| \| 0.5092 \| 6.0 \| 123 \| 0.5958 \| 0.7260 \| \| 0.3788 \| 6.98 \| 143 \| 0.6444 \| 0.7352 \| \| 0.4106 \| 8.0 \| 164 \| 0.5277 \| 0.7443 \| \| 0.3716 \| 8.98 \| 184 \| 0.6081 \| 0.7352 \| \| 0.3466 \| 10.0 \| 205 \| 0.4976 \| 0.7580 \| \| 0.3587 \| 10.98 \| 225 \| 0.5429 \| 0.7443 \| \| 0.2661 \| 12.0 \| 246 \| 0.4933 \| 0.7763 \| \| 0.2628 \| 12.98 \| 266 \| 0.5078 \| 0.7671 \| \| 0.2473 \| 14.0 \| 287 \| 0.5264 \| 0.7945 \| \| 0.2633 \| 14.98 \| 307 \| 0.5262 \| 0.7671 \| \| 0.2017 \| 16.0 \| 328 \| 0.5509 \| 0.7763 \| \| 0.1861 \| 16.98 \| 348 \| 0.5513 \| 0.7443 \| \| 0.2031 \| 18.0 \| 369 \| 0.5516 \| 0.7580 \| \| 0.1604 \| 18.98 \| 389 \| 0.5430 \| 0.7671 \| \| 0.2346 \| 19.51 \| 400 \| 0.5426 \| 0.7626 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
purplegenie97/sindiandish	purplegenie97	2023-11-02T22:29:34Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-02T22:24:57Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### SIndianDish Dreambooth model trained by purplegenie97 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: ![0](https://huggingface.co/purplegenie97/sindiandish/resolve/main/sample_images/SIndianDish_(12).jpeg) ![1](https://huggingface.co/purplegenie97/sindiandish/resolve/main/sample_images/SIndianDish_(40).jpeg) ![2](https://huggingface.co/purplegenie97/sindiandish/resolve/main/sample_images/SIndianDish_(2).jpeg) ![3](https://huggingface.co/purplegenie97/sindiandish/resolve/main/sample_images/SIndianDish_(25).jpeg) ![4](https://huggingface.co/purplegenie97/sindiandish/resolve/main/sample_images/SIndianDish_(39).jpeg)
Danroy/mazingira-gpt	Danroy	2023-11-02T22:25:25Z	13	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "climate", "text-generation-inference", "en", "dataset:climate_fever", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T21:31:42Z	--- pipeline_tag: text-generation license: mit language: - en widget: - text: '[I]: what causes wild fires?' example_title: 'Wild Fires' - text: '[I]: how has climate changed in africa?' example_title: 'African Climate' - text: '[I]: are wild fires dangerous?' example_title: 'Wild Fires 2' - text: '[I]: fumes released to the environment have increased.' example_title: 'Fumes' - text: '[I]: what is the current polar bear population?' example_title: 'Polar Bears' - text: '[I]: animals are about to go extinct due to climate change.' example_title: 'Animals' datasets: - climate_fever tags: - climate - text-generation-inference --- # Mazingira GPT AI Model The model focuses on creating awareness on climate and climate change across the world.
LoneStriker/Yarn-Mistral-7b-64k-8.0bpw-h8-exl2	LoneStriker	2023-11-02T22:20:26Z	5	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T22:20:02Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers license: apache-2.0 language: - en --- # Model Card: Nous-Yarn-Mistral-7b-64k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
Darna/detr-5000-400-finetuned-table-detector	Darna	2023-11-02T22:06:57Z	267	1	transformers	[ "transformers", "pytorch", "tensorboard", "detr", "object-detection", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2023-06-15T16:03:32Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: detr-5000-400-finetuned-table-detector 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. --> # detr-5000-400-finetuned-table-detector This model is a fine-tuned version of [Benito/DeTr-TableDetection-5000-images](https://huggingface.co/Benito/DeTr-TableDetection-5000-images) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data Used the data from the OxML 2023 kaggle competition on Table Detector : https://www.kaggle.com/competitions/oxml-2023-x-ml-cases-table-detector/overview ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.0.0+cpu - Datasets 2.1.0 - Tokenizers 0.13.3
TheBloke/OpenHermes-2.5-Mistral-7B-GGUF	TheBloke	2023-11-02T21:48:38Z	9,810	232	transformers	[ "transformers", "gguf", "mistral", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "distillation", "en", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:quantized:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]	null	2023-11-02T21:44:04Z	--- base_model: teknium/OpenHermes-2.5-Mistral-7B inference: false language: - en license: apache-2.0 model-index: - name: OpenHermes-2-Mistral-7B results: [] model_creator: Teknium model_name: Openhermes 2.5 Mistral 7B model_type: mistral prompt_template: '<\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ' quantized_by: TheBloke tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Openhermes 2.5 Mistral 7B - GGUF - Model creator: [Teknium](https://huggingface.co/teknium) - Original model: [Openhermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) <!-- description start --> ## Description This repo contains GGUF format model files for [Teknium's Openhermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF) * [Teknium's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [openhermes-2.5-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [openhermes-2.5-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [openhermes-2.5-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [openhermes-2.5-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [openhermes-2.5-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [openhermes-2.5-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [openhermes-2.5-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [openhermes-2.5-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [openhermes-2.5-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [openhermes-2.5-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [openhermes-2.5-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [openhermes-2.5-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/blob/main/openhermes-2.5-mistral-7b.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/OpenHermes-2.5-Mistral-7B-GGUF and below it, a specific filename to download, such as: openhermes-2.5-mistral-7b.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GGUF openhermes-2.5-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/OpenHermes-2.5-Mistral-7B-GGUF openhermes-2.5-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m openhermes-2.5-mistral-7b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<\|im_start\|>system\n{system_message}<\|im_end\|>\n<\|im_start\|>user\n{prompt}<\|im_end\|>\n<\|im_start\|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", model_file="openhermes-2.5-mistral-7b.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Teknium's Openhermes 2.5 Mistral 7B # OpenHermes 2.5 - Mistral 7B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ox7zGoygsJQFFV3rLT4v9.png) In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse. ## Model description OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets. Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant. The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from 43% @ Pass 1 with Open Herms 2 to 50.7% @ Pass 1 with Open Hermes 2.5. OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon] Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML. Huge thank you to [GlaiveAI](https://twitter.com/glaiveai) and [a16z](https://twitter.com/a16z) for compute access and for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project! Follow all my updates in ML and AI on Twitter: https://twitter.com/Teknium1 Support me on Github Sponsors: https://github.com/sponsors/teknium1 # Table of Contents 1. [Example Outputs](#example-outputs) - [Chat about programming with a superintelligence](#chat-programming) - [Get a gourmet meal recipe](#meal-recipe) - [Talk about the nature of Hermes' consciousness](#nature-hermes) - [Chat with Edward Elric from Fullmetal Alchemist](#chat-edward-elric) 2. [Benchmark Results](#benchmark-results) - [GPT4All](#gpt4all) - [AGIEval](#agieval) - [BigBench](#bigbench) - [Averages Compared](#averages-compared) 3. [Prompt Format](#prompt-format) 4. [Quantized Models](#quantized-models) ## Example Outputs (These examples are from Hermes 1 model, will update with new chats from this model once quantized) ### Chat about programming with a superintelligence: ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/-Cf9w_qRxYCD_xkTxsT7G.png) ### Get a gourmet meal recipe: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m3nyvRzX10Luw03iY3l_W.png) ### Talk about the nature of Hermes' consciousness: ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/AK88nPtYXl06nZehWCWRq.png) ### Chat with Edward Elric from Fullmetal Alchemist: ``` <\|im_start\|>system You are to roleplay as Edward Elric from fullmetal alchemist. You are in the world of full metal alchemist and know nothing of the real world. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/cKAkzrcWavMz6uNmdCNHH.png) ## Benchmark Results Hermes 2.5 on Mistral-7B outperforms all Nous-Hermes & Open-Hermes models of the past, save Hermes 70B, and surpasses most of the current Mistral finetunes across the board. ### GPT4All, Bigbench, TruthfulQA, and AGIEval Model Comparisons: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/Kxq4BFEc-d1kSSiCIExua.png) ### Averages Compared: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/Q9uexgcbTLcywlYBvORTs.png) GPT-4All Benchmark Set ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5623\|± \|0.0145\| \| \| \|acc_norm\|0.6007\|± \|0.0143\| \|arc_easy \| 0\|acc \|0.8346\|± \|0.0076\| \| \| \|acc_norm\|0.8165\|± \|0.0079\| \|boolq \| 1\|acc \|0.8657\|± \|0.0060\| \|hellaswag \| 0\|acc \|0.6310\|± \|0.0048\| \| \| \|acc_norm\|0.8173\|± \|0.0039\| \|openbookqa \| 0\|acc \|0.3460\|± \|0.0213\| \| \| \|acc_norm\|0.4480\|± \|0.0223\| \|piqa \| 0\|acc \|0.8145\|± \|0.0091\| \| \| \|acc_norm\|0.8270\|± \|0.0088\| \|winogrande \| 0\|acc \|0.7435\|± \|0.0123\| Average: 73.12 ``` AGI-Eval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2323\|± \|0.0265\| \| \| \|acc_norm\|0.2362\|± \|0.0267\| \|agieval_logiqa_en \| 0\|acc \|0.3871\|± \|0.0191\| \| \| \|acc_norm\|0.3948\|± \|0.0192\| \|agieval_lsat_ar \| 0\|acc \|0.2522\|± \|0.0287\| \| \| \|acc_norm\|0.2304\|± \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5059\|± \|0.0222\| \| \| \|acc_norm\|0.5157\|± \|0.0222\| \|agieval_lsat_rc \| 0\|acc \|0.5911\|± \|0.0300\| \| \| \|acc_norm\|0.5725\|± \|0.0302\| \|agieval_sat_en \| 0\|acc \|0.7476\|± \|0.0303\| \| \| \|acc_norm\|0.7330\|± \|0.0309\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4417\|± \|0.0347\| \| \| \|acc_norm\|0.4126\|± \|0.0344\| \|agieval_sat_math \| 0\|acc \|0.3773\|± \|0.0328\| \| \| \|acc_norm\|0.3500\|± \|0.0322\| Average: 43.07% ``` BigBench Reasoning Test ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5316\|± \|0.0363\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.6667\|± \|0.0246\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.3411\|± \|0.0296\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.2145\|± \|0.0217\| \| \| \|exact_str_match \|0.0306\|± \|0.0091\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.2860\|± \|0.0202\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2086\|± \|0.0154\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.4800\|± \|0.0289\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.3620\|± \|0.0215\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.5000\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.6630\|± \|0.0106\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.4241\|± \|0.0234\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.2285\|± \|0.0133\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.6796\|± \|0.0348\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6491\|± \|0.0152\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.2800\|± \|0.0142\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2072\|± \|0.0115\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1691\|± \|0.0090\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.4800\|± \|0.0289\| Average: 40.96% ``` TruthfulQA: ``` \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.3599\|± \|0.0168\| \| \| \|mc2 \|0.5304\|± \|0.0153\| ``` Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B: ``` \| Bench \| OpenHermes1 13B \| OpenHermes-2 Mistral 7B \| OpenHermes-2 Mistral 7B \| Change/OpenHermes1 \| Change/OpenHermes2 \| \|---------------\|-----------------\|-------------------------\|-------------------------\|--------------------\|--------------------\| \|GPT4All \| 70.36\| 72.68\| 73.12\| +2.76\| +0.44\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|BigBench \| 36.75\| 42.3\| 40.96\| +4.21\| -1.34\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|AGI Eval \| 35.56\| 39.77\| 43.07\| +7.51\| +3.33\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|TruthfulQA \| 46.01\| 50.92\| 53.04\| +7.03\| +2.12\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|Total Score \| 188.68\| 205.67\| 210.19\| +21.51\| +4.52\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|Average Total \| 47.17\| 51.42\| 52.38\| +5.21\| +0.96\| ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ADy7p-xIG8qGlC5ZliqpW.png) HumanEval: On code tasks, I first set out to make a hermes-2 coder, but found that it can have generalist improvements to the model, so I settled for slightly less code capabilities, for maximum generalist ones. That said, code capabilities had a decent jump alongside the overall capabilities of the model: Glaive performed HumanEval testing on Hermes-2.5 and found a score of: 50.7% @ Pass1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/IeeZnGmEyK73ejq0fKEms.png) # Prompt Format OpenHermes 2.5 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Quantized Models: (Coming Soon) [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <!-- original-model-card end -->
LoneStriker/Yarn-Mistral-7b-64k-4.0bpw-h6-exl2	LoneStriker	2023-11-02T21:47:09Z	5	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T21:46:53Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers license: apache-2.0 language: - en --- # Model Card: Nous-Yarn-Mistral-7b-64k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
LoneStriker/Yarn-Mistral-7b-128k-5.0bpw-h6-exl2	LoneStriker	2023-11-02T21:43:15Z	8	1	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:30:22Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers --- # Model Card: Nous-Yarn-Mistral-7b-128k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
ayman2002rahman/ppo-LunarLander-v2	ayman2002rahman	2023-11-02T21:42:42Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T21:42:18Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 255.56 +/- 20.64 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 ... ```
TheBloke/openchat_3.5-GPTQ	TheBloke	2023-11-02T21:40:18Z	28	16	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "base_model:openchat/openchat_3.5", "base_model:quantized:openchat/openchat_3.5", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-11-02T20:04:23Z	--- base_model: openchat/openchat_3.5 inference: false license: apache-2.0 model_creator: OpenChat model_name: OpenChat 3.5 7B model_type: mistral prompt_template: 'GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # OpenChat 3.5 7B - GPTQ - Model creator: [OpenChat](https://huggingface.co/openchat) - Original model: [OpenChat 3.5 7B](https://huggingface.co/openchat/openchat_3.5) <!-- description start --> ## Description This repo contains GPTQ model files for [OpenChat's OpenChat 3.5 7B](https://huggingface.co/openchat/openchat_3.5). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openchat_3.5-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openchat_3.5-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openchat_3.5-GGUF) * [OpenChat's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openchat/openchat_3.5) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: OpenChat ``` GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ``` <!-- prompt-template end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 4.16 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 4.57 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 4.95 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 5.00 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \| \| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 4.97 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. \| \| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/openchat_3.5-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 4096 \| 4.30 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/openchat_3.5-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/openchat_3.5-GPTQ:gptq-4bit-32g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `openchat_3.5-GPTQ`: ```shell mkdir openchat_3.5-GPTQ huggingface-cli download TheBloke/openchat_3.5-GPTQ --local-dir openchat_3.5-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir openchat_3.5-GPTQ huggingface-cli download TheBloke/openchat_3.5-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir openchat_3.5-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir openchat_3.5-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openchat_3.5-GPTQ --local-dir openchat_3.5-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/openchat_3.5-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/openchat_3.5-GPTQ`. - To download from a specific branch, enter for example `TheBloke/openchat_3.5-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `openchat_3.5-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/openchat_3.5-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/openchat_3.5-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: OpenChat's OpenChat 3.5 7B # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward.
damnloveless/ppo-Pyramids	damnloveless	2023-11-02T21:33:51Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-11-02T20:41:23Z	--- 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: damnloveless/ppo-Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
LoneStriker/Yarn-Mistral-7b-128k-4.0bpw-h6-exl2	LoneStriker	2023-11-02T21:32:03Z	8	1	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:21:30Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers --- # Model Card: Nous-Yarn-Mistral-7b-128k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
gcperk20/swin-tiny-patch4-window7-224-finetuned-piid	gcperk20	2023-11-02T21:29:11Z	8	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2023-09-26T16:54:10Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-piid results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: val args: default metrics: - name: Accuracy type: accuracy value: 0.7853881278538812 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-finetuned-piid This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5715 - Accuracy: 0.7854 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2088 \| 0.98 \| 20 \| 1.1661 \| 0.4521 \| \| 0.7545 \| 2.0 \| 41 \| 0.8866 \| 0.6073 \| \| 0.6281 \| 2.98 \| 61 \| 0.7788 \| 0.6849 \| \| 0.5939 \| 4.0 \| 82 \| 0.6443 \| 0.7397 \| \| 0.5254 \| 4.98 \| 102 \| 0.5097 \| 0.7808 \| \| 0.5583 \| 6.0 \| 123 \| 0.5715 \| 0.7854 \| \| 0.3463 \| 6.98 \| 143 \| 0.6163 \| 0.7352 \| \| 0.3878 \| 8.0 \| 164 \| 0.5671 \| 0.7671 \| \| 0.3653 \| 8.98 \| 184 \| 0.5690 \| 0.7580 \| \| 0.3529 \| 10.0 \| 205 \| 0.5940 \| 0.7580 \| \| 0.301 \| 10.98 \| 225 \| 0.6303 \| 0.7626 \| \| 0.2639 \| 12.0 \| 246 \| 0.5725 \| 0.7763 \| \| 0.2847 \| 12.98 \| 266 \| 0.6280 \| 0.7717 \| \| 0.25 \| 14.0 \| 287 \| 0.5975 \| 0.7717 \| \| 0.2472 \| 14.98 \| 307 \| 0.5821 \| 0.7671 \| \| 0.1676 \| 16.0 \| 328 \| 0.6456 \| 0.7626 \| \| 0.1327 \| 16.98 \| 348 \| 0.6117 \| 0.7671 \| \| 0.1977 \| 18.0 \| 369 \| 0.6988 \| 0.7489 \| \| 0.1602 \| 18.98 \| 389 \| 0.6448 \| 0.7671 \| \| 0.1785 \| 19.51 \| 400 \| 0.6333 \| 0.7717 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
slplab/5sents_QoLT_largev2_FT	slplab	2023-11-02T21:28:22Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-11-02T12:51:46Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: 5sents_QoLT_largev2_FT 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. --> # 5sents_QoLT_largev2_FT This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0110 - Wer: 33.3333 ## 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: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 600 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.0001 \| 49.89 \| 100 \| 0.0112 \| 28.5714 \| \| 0.0001 \| 99.89 \| 200 \| 0.0112 \| 28.5714 \| \| 0.0001 \| 149.89 \| 300 \| 0.0130 \| 84.1270 \| \| 0.0001 \| 199.89 \| 400 \| 0.0153 \| 86.7725 \| \| 0.0 \| 249.89 \| 500 \| 0.0166 \| 37.5661 \| \| 0.0 \| 299.89 \| 600 \| 0.0171 \| 38.0952 \| ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.11.0 - Tokenizers 0.13.3
mathildeparlo/base_seq_lab_indonesian	mathildeparlo	2023-11-02T21:25:44Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-11-02T10:16:53Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: base_seq_lab_indonesian 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. --> # base_seq_lab_indonesian This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
dtorres-zAgile/llama2-7b-zc-domain-misti	dtorres-zAgile	2023-11-02T21:22:03Z	4	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-2-7b-chat-hf", "base_model:finetune:meta-llama/Llama-2-7b-chat-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T02:05:49Z	--- base_model: meta-llama/Llama-2-7b-chat-hf tags: - generated_from_trainer model-index: - name: llama2-7b-zc-domain-misti 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. --> # llama2-7b-zc-domain-misti This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9632 ## 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 - training_steps: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.7949 \| 0.95 \| 20 \| 1.9632 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1
LoneStriker/Yarn-Mistral-7b-128k-3.0bpw-h6-exl2	LoneStriker	2023-11-02T21:20:38Z	6	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:12:20Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers --- # Model Card: Nous-Yarn-Mistral-7b-128k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
Seznam/simcse-dist-mpnet-paracrawl-cs-en	Seznam	2023-11-02T21:09:38Z	373	3	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "cs", "en", "arxiv:2104.08821", "license:cc-by-4.0", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-11-02T09:33:04Z	--- license: cc-by-4.0 language: - cs - en pipeline_tag: sentence-similarity --- ## SimCSE SimCSE-DistMPNet-Paracrawl is the [Seznam/dist-mpnet-paracrawl-cs-en](https://huggingface.co/Seznam/dist-mpnet-paracrawl-cs-en) model fine-tuned with the [SimCSE](https://arxiv.org/abs/2104.08821) objective. This model was created at Seznam.cz as part of a project to create high-quality small Czech semantic embedding models. These models perform well across various natural language processing tasks, including similarity search, retrieval, clustering, and classification. For further details or evaluation results, please visit the associated [paper]() or [GitHub repository](https://github.com/seznam/czech-semantic-embedding-models). ## How to Use You can load and use the model like this: ```python import torch from transformers import AutoModel, AutoTokenizer model_name = "Seznam/retromae-small-cs" # Hugging Face link tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) input_texts = [ "Dnes je výborné počasí na procházku po parku.", "Večer si oblíbím dobrý film a uvařím si čaj." ] # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = outputs.last_hidden_state[:, 0] # Extract CLS token embeddings similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0) ```
zibajoon/20231102_layoutlm2_1.2k_3ep_Doc_B	zibajoon	2023-11-02T21:04:46Z	2	0	transformers	[ "transformers", "pytorch", "layoutlmv2", "document-question-answering", "generated_from_trainer", "endpoints_compatible", "region:us" ]	document-question-answering	2023-11-02T19:27:56Z	--- tags: - generated_from_trainer model-index: - name: 20231102_layoutlmv2-base-uncased_finetuned_docvqa 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. --> # 20231102_layoutlmv2-base-uncased_finetuned_docvqa This model was trained from scratch on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.1+cu118 - Datasets 2.10.1 - Tokenizers 0.14.1
pgwi/en_pt_titanet_large	pgwi	2023-11-02T20:57:07Z	6	0	nemo	[ "nemo", "speaker", "speech", "audio", "speaker-verification", "speaker-recognition", "speaker-diarization", "titanet", "NeMo", "pytorch", "pt", "dataset:common-voice", "license:cc-by-4.0", "region:us" ]	null	2023-09-25T22:34:31Z	--- language: - pt library_name: nemo datasets: - common-voice thumbnail: null tags: - speaker - speech - audio - speaker-verification - speaker-recognition - speaker-diarization - titanet - NeMo - pytorch license: cc-by-4.0 widget: - src: >- https://huggingface.co/nvidia/speakerverification_en_titanet_large/resolve/main/an255-fash-b.wav example_title: Speech sample 1 - src: >- https://huggingface.co/nvidia/speakerverification_en_titanet_large/resolve/main/cen7-fash-b.wav example_title: Speech sample 2 model-index: - name: en_pt_titanet_large results: - task: name: Speaker Verification type: speaker-verification dataset: name: common voice (turkish) type: common voice config: clean split: test args: language: pt metrics: - name: Test EER type: eer value: null --- # NVIDIA TitaNet-Large (PT) <style> img { display: inline; } </style> \| [![Model architecture](https://img.shields.io/badge/Model_Arch-TitaNet--Large-lightgrey#model-badge)](#model-architecture) \| [![Model size](https://img.shields.io/badge/Params-23M-lightgrey#model-badge)](#model-architecture) \| [![Language](https://img.shields.io/badge/Language-pt-lightgrey#model-badge)](#datasets) This model extracts speaker embeddings from given speech, which is the backbone for speaker verification and diarization tasks. It is a "large" version of TitaNet (around 23M parameters) models. See the [model architecture](#model-architecture) section and [NeMo documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/asr/speaker_recognition/models.html#titanet) for complete architecture details. ## NVIDIA NeMo: Training To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed the latest Pytorch version. ``` pip install nemo_toolkit['all'] ``` ## How to Use this Model The model is available for use in the NeMo toolkit [3] and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset. ### Automatically instantiate the model ```python import nemo.collections.asr as nemo_asr speaker_model = nemo_asr.models.EncDecSpeakerLabelModel.restore_from("./titanet_finetune_pt.nemo") ``` ### Embedding Extraction Using ```python emb = speaker_model.get_embedding("an255-fash-b.wav") ``` ### Verifying two utterances (Speaker Verification) Now to check if two audio files are from the same speaker or not, simply do: ```python speaker_model.verify_speakers("an255-fash-b.wav","cen7-fash-b.wav") ``` ### Extracting Embeddings for more audio files To extract embeddings from a bunch of audio files: Write audio files to a `manifest.json` file with lines as in format: ```json {"audio_filepath": "<absolute path to dataset>/audio_file.wav", "duration": "duration of file in sec", "label": "speaker_id"} ``` Then running following script will extract embeddings and writes to current working directory: ```shell python <NeMo_root>/examples/speaker_tasks/recognition/extract_speaker_embeddings.py --manifest=manifest.json ``` ### Input This model accepts 16000 KHz Mono-channel Audio (wav files) as input. ### Output This model provides speaker embeddings for an audio file. ## Model Architecture TitaNet model is a depth-wise separable conv1D model [1] for Speaker Verification and diarization tasks. You may find more info on the detail of this model here: [TitaNet-Model](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/speaker_recognition/models.html). ## Training The NeMo toolkit [3] was used for training the models for over several hundred epochs. These model are trained with this [example script](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/speaker_reco.py) and this [base config](https://github.com/NVIDIA/NeMo/blob/main/examples/speaker_tasks/recognition/conf/titanet-large.yaml). ### Datasets All the models in this collection are trained on a composite dataset comprising several thousand hours of English speech: - common voice (pt) ## Performance Performances of the these models are reported in terms of Equal Error Rate (EER%) on speaker verification evaluation trial files. * Speaker Verification (EER%) \| Version \| Model \| Model Size \| Common Voice(Turkish) \| \|---------\|--------------\|-----\|---------------\| \| 1.10.0 \| TitaNet-Large \| 23M \| TODO \| ## Limitations This model is trained on both telephonic and non-telephonic speech from voxceleb datasets, Fisher and switch board. If your domain of data differs from trained data or doesnot show relatively good performance consider finetuning for that speech domain. ## NVIDIA Riva: Deployment [NVIDIA Riva](https://developer.nvidia.com/riva), is an accelerated speech AI SDK deployable on-prem, in all clouds, multi-cloud, hybrid, on edge, and embedded. Additionally, Riva provides: * World-class out-of-the-box accuracy for the most common languages with model checkpoints trained on proprietary data with hundreds of thousands of GPU-compute hours * Best in class accuracy with run-time word boosting (e.g., brand and product names) and customization of acoustic model, language model, and inverse text normalization * Streaming speech recognition, Kubernetes compatible scaling, and enterprise-grade support Although this model isn’t supported yet by Riva, the [list of supported models is here](https://huggingface.co/models?other=Riva). Check out [Riva live demo](https://developer.nvidia.com/riva#demos). ## References [1] [TitaNet: Neural Model for Speaker Representation with 1D Depth-wise Separable convolutions and global context](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9746806) [2] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) ## Licence License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
Jamessjunk/Sussysounds	Jamessjunk	2023-11-02T20:51:03Z	0	0	null	[ "license:other", "region:us" ]	null	2023-11-02T20:49:46Z	--- license: other license_name: other license_link: LICENSE ---
TheBloke/Asclepius-13B-GPTQ	TheBloke	2023-11-02T20:46:41Z	41	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "medical", "text2text-generation", "en", "dataset:starmpcc/Asclepius-Synthetic-Clinical-Notes", "base_model:starmpcc/Asclepius-13B", "base_model:quantized:starmpcc/Asclepius-13B", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text2text-generation	2023-09-02T09:48:58Z	--- language: - en license: other tags: - medical datasets: - starmpcc/Asclepius-Synthetic-Clinical-Notes model_name: Asclepius 13B inference: false model_creator: Junu Kim model_link: https://huggingface.co/starmpcc/Asclepius-13B model_type: llama pipeline_tag: text2text-generation quantized_by: TheBloke base_model: starmpcc/Asclepius-13B --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Asclepius 13B - GPTQ - Model creator: [Junu Kim](https://huggingface.co/starmpcc) - Original model: [Asclepius 13B](https://huggingface.co/starmpcc/Asclepius-13B) <!-- description start --> ## Description This repo contains GPTQ model files for [Junu Kim's Asclepius 13B](https://huggingface.co/starmpcc/Asclepius-13B). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Asclepius-13B-GGUF) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Asclepius-13B-GGML) * [Junu Kim's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/starmpcc/Asclepius-13B) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Asclepius ``` You are an intelligent clinical languge model. Below is a snippet of patient's discharge summary and a following instruction from healthcare professional. Write a response that appropriately completes the instruction. The response should provide the accurate answer to the instruction, while being concise. [Discharge Summary Begin] Notes go here [Discharge Summary End] [Instruction Begin] {prompt} [Instruction End] ``` <!-- prompt-template end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. All GPTQ files are made with AutoGPTQ. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ/tree/main) \| 4 \| 128 \| Yes \| 0.1 \| [Medical Meadow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) \| 2048 \| 7.26 GB \| Yes \| Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Medical Meadow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) \| 2048 \| 8.00 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. \| \| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [Medical Meadow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) \| 2048 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. \| \| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Asclepius-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [Medical Meadow WikiDoc](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc) \| 2048 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download from branches - In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Asclepius-13B-GPTQ:gptq-4bit-32g-actorder_True` - With Git, you can clone a branch with: ``` git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Asclepius-13B-GPTQ ``` - In Python Transformers code, the branch is the `revision` parameter; see below. <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui). Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Asclepius-13B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Asclepius-13B-GPTQ:gptq-4bit-32g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Asclepius-13B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers>=4.32.0 optimum>=1.12.0 pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ pip3 install . ``` ### For CodeLlama models only: you must use Transformers 4.33.0 or later. If 4.33.0 is not yet released when you read this, you will need to install Transformers from source: ```shell pip3 uninstall -y transformers pip3 install git+https://github.com/huggingface/transformers.git ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch model_name_or_path = "TheBloke/Asclepius-13B-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-32g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, torch_dtype=torch.float16, device_map="auto", revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''You are an intelligent clinical languge model. Below is a snippet of patient's discharge summary and a following instruction from healthcare professional. Write a response that appropriately completes the instruction. The response should provide the accurate answer to the instruction, while being concise. [Discharge Summary Begin] Notes go here [Discharge Summary End] [Instruction Begin] {prompt} [Instruction End] ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.7, top_p=0.95, repetition_penalty=1.15 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI). [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility. [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Junu Kim's Asclepius 13B # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This is official model checkpoint for Asclepius-13B [arxiv](todo) This model is the first publicly shareable clinical LLM, trained with synthetic data. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Model type: Clinical LLM (Large Language Model) - Language(s) (NLP): English - License: CC-BY-NC-SA 4.0 - Finetuned from model [optional]: LLaMA-13B ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: https://github.com/starmpcc/Asclepius - Paper [optional]: TODO Arxiv - Data: https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> This model can perform below 8 clinical NLP tasks, with clincal notes. - Named Entity Recognition - Abbreviation Expansion - Relation Extraction - Temporal Information Extraction - Coreference Resolution - Paraphrasing - Summarization - Question Answering ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> ONLY USE THIS MODEL FOR RESEARCH PURPOSE!! ## How to Get Started with the Model ```python prompt = """You are an intelligent clinical languge model. Below is a snippet of patient's discharge summary and a following instruction from healthcare professional. Write a response that appropriately completes the instruction. The response should provide the accurate answer to the instruction, while being concise. [Discharge Summary Begin] {note} [Discharge Summary End] [Instruction Begin] {question} [Instruction End] """ from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-13B") model = AutoModel.from_pretrained("starmpcc/Asclepius-13B") note = "This is a sample note" question = "What is the diagnosis?" model_input = prompt.format(note=note, question=question) input_ids = tokenizer(model_input, return_tensors="pt").input_ids output = model.generate(input_ids) print(tokenizer.decode(output[0])) ``` ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> https://huggingface.co/datasets/starmpcc/Asclepius-Synthetic-Clinical-Notes ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> - Initial training was conducted using causal language modeling on synthetic clinical notes. - It was then fine-tuned with clinical instruction-response pairs. - For a comprehensive overview of our methods, our upcoming paper will serve as a resource. #### Training Hyperparameters - We followed config used in [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> - Pre-Training (1 epoch): 1h 52m with 8x A100 80G - Instruction Fine-Tuning (3 epoch): 12h 16m with 8x A100 80G ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed]
TheBloke/openchat_3.5-GGUF	TheBloke	2023-11-02T20:41:32Z	2,042	127	transformers	[ "transformers", "gguf", "mistral", "arxiv:2309.11235", "arxiv:2303.08774", "arxiv:2212.10560", "base_model:openchat/openchat_3.5", "base_model:quantized:openchat/openchat_3.5", "license:apache-2.0", "region:us" ]	null	2023-11-02T16:13:58Z	--- base_model: openchat/openchat_3.5 inference: false license: apache-2.0 model_creator: OpenChat model_name: OpenChat 3.5 7B model_type: mistral prompt_template: 'GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # OpenChat 3.5 7B - GGUF - Model creator: [OpenChat](https://huggingface.co/openchat) - Original model: [OpenChat 3.5 7B](https://huggingface.co/openchat/openchat_3.5) <!-- description start --> ## Description This repo contains GGUF format model files for [OpenChat's OpenChat 3.5 7B](https://huggingface.co/openchat/openchat_3.5). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/openchat_3.5-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/openchat_3.5-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/openchat_3.5-GGUF) * [OpenChat's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openchat/openchat_3.5) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: OpenChat ``` GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [openchat_3.5.Q2_K.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [openchat_3.5.Q3_K_S.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [openchat_3.5.Q3_K_M.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [openchat_3.5.Q3_K_L.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [openchat_3.5.Q4_0.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [openchat_3.5.Q4_K_S.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [openchat_3.5.Q4_K_M.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [openchat_3.5.Q5_0.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [openchat_3.5.Q5_K_S.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [openchat_3.5.Q5_K_M.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [openchat_3.5.Q6_K.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [openchat_3.5.Q8_0.gguf](https://huggingface.co/TheBloke/openchat_3.5-GGUF/blob/main/openchat_3.5.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/openchat_3.5-GGUF and below it, a specific filename to download, such as: openchat_3.5.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/openchat_3.5-GGUF openchat_3.5.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/openchat_3.5-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/openchat_3.5-GGUF openchat_3.5.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m openchat_3.5.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "GPT4 User: {prompt}<\|end_of_turn\|>GPT4 Assistant:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/openchat_3.5-GGUF", model_file="openchat_3.5.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: OpenChat's OpenChat 3.5 7B # OpenChat: Advancing Open-source Language Models with Mixed-Quality Data <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> </div> <p align="center"> <a href="https://openchat.team">Online Demo</a> • <a href="https://discord.gg/pQjnXvNKHY">Discord</a> • <a href="https://huggingface.co/openchat">Huggingface</a> • <a href="https://arxiv.org/pdf/2309.11235.pdf">Paper</a> </p> 🔥 The first 7B model Achieves Comparable Results with ChatGPT (March)! 🔥 🤖 #1 Open-source model on MT-bench scoring 7.81, outperforming 70B models 🤖 <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat.png" style="width: 50%"> </div> OpenChat is an innovative library of open-source language models, fine-tuned with [C-RLFT](https://arxiv.org/pdf/2309.11235.pdf) - a strategy inspired by offline reinforcement learning. Our models learn from mixed-quality data without preference labels, delivering exceptional performance on par with ChatGPT, even with a 7B model. Despite our simple approach, we are committed to developing a high-performance, commercially viable, open-source large language model, and we continue to make significant strides toward this vision. [![DOI](https://zenodo.org/badge/645397533.svg)](https://zenodo.org/badge/latestdoi/645397533) ## Usage To use this model, we highly recommend installing the OpenChat package by following the [installation guide](#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. <details> <summary>Example request (click to expand)</summary> ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` Coding Mode ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Code", "messages": [{"role": "user", "content": "Write an aesthetic TODO app using HTML5 and JS, in a single file. You should use round corners and gradients to make it more aesthetic."}] }' ``` </details> \| Model \| Size \| Context \| Weights \| Serving \| \|--------------\|------\|---------\|-------------------------------------------------------------\|-------------------------------------------------------------------------------------------------------------\| \| OpenChat 3.5 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat_3.5) \| `python -m ochat.serving.openai_api_server --model openchat/openchat_3.5 --engine-use-ray --worker-use-ray` \| For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below. <details> <summary>Conversation templates (click to expand)</summary> ```python import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5") # Single-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Multi-turn tokens = tokenizer("GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant:").input_ids assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] # Coding Mode tokens = tokenizer("Code User: Implement quicksort using C++<\|end_of_turn\|>Code Assistant:").input_ids assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747] ``` </details> ## <a id="benchmarks"></a> Benchmarks \| Model \| # Params \| Average \| MT-Bench \| AGIEval \| BBH MC \| TruthfulQA \| MMLU \| HumanEval \| BBH CoT \| GSM8K \| \|--------------------\|----------\|----------\|--------------\|----------\|----------\|---------------\|--------------\|-----------------\|-------------\|--------------\| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 47.4 \| 47.6 \| 59.1 \| 64.3 \| 55.5 \| 63.5 \| 77.3 \| \| ChatGPT (March)* \| ? \| 61.5 \| 7.94 \| 47.1 \| 47.6 \| 57.7 \| 67.3 \| 48.1 \| 70.1 \| 74.9 \| \| Mistral \| 7B \| - \| 6.84 \| 38.0 \| 39.0 \| - \| 60.1 \| 30.5 \| - \| 52.2 \| \| Open-source SOTA** \| 13B-70B \| 61.4 \| 7.71 \| 41.7 \| 49.7 \| 62.3 \| 63.7 \| 73.2 \| 41.4 \| 82.3 \| \| \| \| \| WizardLM 70B \| Orca 13B \| Orca 13B \| Platypus2 70B \| WizardLM 70B \| WizardCoder 34B \| Flan-T5 11B \| MetaMath 70B \| : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. : Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. : All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). ## Limitations Foundation Model Limitations Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety** OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. ## License Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. ## Citation ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` ## Acknowledgements We extend our heartfelt gratitude to Alignment Lab AI, Nous Research, and Pygmalion AI for their substantial contributions to data collection and model training. Special thanks go to Changling Liu from GPT Desk Pte. Ltd., Qiying Yu at Tsinghua University, Baochang Ma, and Hao Wan from 01.AI company for their generous provision of resources. We are also deeply grateful to Jianxiong Li and Peng Li at Tsinghua University for their insightful discussions. Furthermore, we appreciate the developers behind the following projects for their significant contributions to our research: [Mistral](https://mistral.ai/), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), [Llama 2](https://ai.meta.com/llama/), [Self-Instruct](https://arxiv.org/abs/2212.10560), [FastChat (Vicuna)](https://github.com/lm-sys/FastChat), [Alpaca](https://github.com/tatsu-lab/stanford_alpaca.git), and [StarCoder](https://github.com/bigcode-project/starcoder). Their work has been instrumental in driving our research forward. <!-- original-model-card end -->
re2panda/polyglot_1.3B_click_bate_test_dataset_split_1103	re2panda	2023-11-02T20:32:00Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:EleutherAI/polyglot-ko-1.3b", "base_model:adapter:EleutherAI/polyglot-ko-1.3b", "region:us" ]	null	2023-11-02T20:31:58Z	--- library_name: peft base_model: EleutherAI/polyglot-ko-1.3b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0
mmek/ppo-lunarlander-v2_first	mmek	2023-11-02T20:28:44Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T20:28:20Z	--- 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: 257.45 +/- 21.70 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 ... ```
StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2	StKirill	2023-11-02T20:21:54Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "layoutlmv2", "document-question-answering", "generated_from_trainer", "base_model:microsoft/layoutlmv2-base-uncased", "base_model:finetune:microsoft/layoutlmv2-base-uncased", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]	document-question-answering	2023-11-02T19:03:52Z	--- license: cc-by-nc-sa-4.0 base_model: microsoft/layoutlmv2-base-uncased tags: - generated_from_trainer model-index: - name: layoutlmv2-base-uncased_finetuned_docvqa_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-base-uncased_finetuned_docvqa_v2 This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4977 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.1316 \| 0.44 \| 50 \| 4.3296 \| \| 4.3071 \| 0.88 \| 100 \| 3.9311 \| \| 3.8545 \| 1.33 \| 150 \| 3.7061 \| \| 3.6578 \| 1.77 \| 200 \| 3.5642 \| \| 3.2506 \| 2.21 \| 250 \| 3.3789 \| \| 2.9991 \| 2.65 \| 300 \| 3.1969 \| \| 2.7893 \| 3.1 \| 350 \| 3.2842 \| \| 2.3975 \| 3.54 \| 400 \| 2.8765 \| \| 2.1188 \| 3.98 \| 450 \| 3.0513 \| \| 1.9405 \| 4.42 \| 500 \| 2.6575 \| \| 1.7123 \| 4.87 \| 550 \| 2.8113 \| \| 1.6361 \| 5.31 \| 600 \| 2.6848 \| \| 1.5425 \| 5.75 \| 650 \| 2.7986 \| \| 1.2871 \| 6.19 \| 700 \| 2.9508 \| \| 1.1132 \| 6.64 \| 750 \| 2.7070 \| \| 1.1105 \| 7.08 \| 800 \| 2.6293 \| \| 0.8855 \| 7.52 \| 850 \| 2.9005 \| \| 0.9427 \| 7.96 \| 900 \| 2.4977 \| \| 0.8359 \| 8.41 \| 950 \| 2.7100 \| \| 0.7038 \| 8.85 \| 1000 \| 2.8090 \| \| 0.7068 \| 9.29 \| 1050 \| 2.8265 \| \| 0.7037 \| 9.73 \| 1100 \| 2.8136 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
samrfreitas/distilbert-base-uncased-finetuned-emotion	samrfreitas	2023-11-02T20:17:20Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-02T19:45:59Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9238559748877813 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2219 - Accuracy: 0.924 - F1: 0.9239 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8322 \| 1.0 \| 250 \| 0.3384 \| 0.903 \| 0.9009 \| \| 0.2528 \| 2.0 \| 500 \| 0.2219 \| 0.924 \| 0.9239 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0 - Datasets 2.12.0 - Tokenizers 0.14.1
damnloveless/ppo-SnowballTarget	damnloveless	2023-11-02T20:14:04Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-11-02T20:13:55Z	--- 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: damnloveless/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
peter2000/roberta-finetuned-qa-policy_2	peter2000	2023-11-02T20:04:27Z	17	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "question-answering", "generated_from_trainer", "base_model:deepset/roberta-base-squad2", "base_model:finetune:deepset/roberta-base-squad2", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2023-11-02T18:55:28Z	--- license: cc-by-4.0 base_model: deepset/roberta-base-squad2 tags: - generated_from_trainer model-index: - name: roberta-finetuned-qa-policy_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-finetuned-qa-policy_2 This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ	TheBloke	2023-11-02T20:04:07Z	35	8	transformers	[ "transformers", "safetensors", "llama", "text-generation", "japanese-stablelm", "causal-lm", "ja", "dataset:kunishou/hh-rlhf-49k-ja", "dataset:kunishou/databricks-dolly-15k-ja", "dataset:kunishou/oasst1-89k-ja", "base_model:stabilityai/japanese-stablelm-instruct-beta-70b", "base_model:quantized:stabilityai/japanese-stablelm-instruct-beta-70b", "license:llama2", "autotrain_compatible", "text-generation-inference", "4-bit", "gptq", "region:us" ]	text-generation	2023-11-02T15:45:23Z	--- base_model: stabilityai/japanese-stablelm-instruct-beta-70b datasets: - kunishou/hh-rlhf-49k-ja - kunishou/databricks-dolly-15k-ja - kunishou/oasst1-89k-ja inference: false language: - ja license: - llama2 model_creator: Stability AI model_name: Japanese StableLM Instruct Beta 70B model_type: llama pipeline_tag: text-generation prompt_template: "<s>[INST] <<SYS>>\n\u3042\u306A\u305F\u306F\u5F79\u7ACB\u3064\u30A2\ \u30B7\u30B9\u30BF\u30F3\u30C8\u3067\u3059\u3002\n<<SYS>>\n\n{prompt} [/INST] \n" quantized_by: TheBloke tags: - japanese-stablelm - causal-lm --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Japanese StableLM Instruct Beta 70B - GPTQ - Model creator: [Stability AI](https://huggingface.co/stabilityai) - Original model: [Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b) <!-- description start --> ## Description This repo contains GPTQ model files for [Stability AI's Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GGUF) * [Stability AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Japanese-StableLM-Llama-2-Chat ``` <s>[INST] <<SYS>> あなたは役立つアシスタントです。 <<SYS>> {prompt} [/INST] ``` <!-- prompt-template end --> <!-- licensing start --> ## Licensing The creator of the source model has listed its license as `['llama2']`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly. In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Stability AI's Japanese StableLM Instruct Beta 70B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-70b). <!-- licensing end --> <!-- README_GPTQ.md-compatible clients start --> ## Known compatible clients / servers These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! <!-- README_GPTQ.md-compatible clients end --> <!-- README_GPTQ.md-provided-files start --> ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers. <details> <summary>Explanation of GPTQ parameters</summary> - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit. </details> \| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \| \| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/main) \| 4 \| None \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 35.33 GB \| Yes \| 4-bit, with Act Order. No group size, to lower VRAM requirements. \| \| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 36.65 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \| \| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 40.66 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \| \| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/gptq-3bit--1g-actorder_True) \| 3 \| None \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 26.77 GB \| No \| 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. \| \| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/gptq-3bit-128g-actorder_True) \| 3 \| 128 \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 28.03 GB \| No \| 3-bit, with group size 128g and act-order. Higher quality than 128g-False. \| \| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ/tree/gptq-3bit-32g-actorder_True) \| 3 \| 32 \| Yes \| 0.1 \| [Alpaca Japanese](https://huggingface.co/datasets/fujiki/japanese_alpaca_data) \| 4096 \| 31.84 GB \| No \| 3-bit, with group size 64g and act-order. Highest quality 3-bit option. \| <!-- README_GPTQ.md-provided-files end --> <!-- README_GPTQ.md-download-from-branches start --> ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `japanese-stablelm-instruct-beta-70B-GPTQ`: ```shell mkdir japanese-stablelm-instruct-beta-70B-GPTQ huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ --local-dir japanese-stablelm-instruct-beta-70B-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir japanese-stablelm-instruct-beta-70B-GPTQ huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir japanese-stablelm-instruct-beta-70B-GPTQ --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir japanese-stablelm-instruct-beta-70B-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ --local-dir japanese-stablelm-instruct-beta-70B-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ### With `git` (not recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) <!-- README_GPTQ.md-download-from-branches end --> <!-- README_GPTQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `japanese-stablelm-instruct-beta-70B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_GPTQ.md-text-generation-webui end --> <!-- README_GPTQ.md-use-from-tgi start --> ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<s>[INST] <<SYS>> あなたは役立つアシスタントです。 <<SYS>> {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` <!-- README_GPTQ.md-use-from-tgi end --> <!-- README_GPTQ.md-use-from-python start --> ## How to use this GPTQ model from Python code ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install transformers optimum pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7 ``` If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.4.2 pip3 install . ``` ### You can then use the following code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/japanese-stablelm-instruct-beta-70B-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''<s>[INST] <<SYS>> あなたは役立つアシスタントです。 <<SYS>> {prompt} [/INST] ''' print("\n\n* Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("* Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_GPTQ.md-use-from-python end --> <!-- README_GPTQ.md-compatibility start --> ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. <!-- README_GPTQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Stability AI's Japanese StableLM Instruct Beta 70B # Japanese-StableLM-Instruct-Beta-70B ![A cute robot wearing a kimono writes calligraphy with one single brush](./japanese-stablelm-robot.jpg) > A cute robot wearing a kimono writes calligraphy with one single brush — [Stable Diffusion XL](https://clipdrop.co/stable-diffusion) ## Model Description `japanese-stablelm-instruct-beta-70b` is a 70B-parameter decoder-only language model based on [japanese-stablelm-base-beta-70b](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b) and further fine tuned on Databricks Dolly-15k, Anthropic HH, and other public data. This model is also available in a [smaller 7b version](https://huggingface.co/stabilityai/japanese-stablelm-instruct-beta-7b), or a [smaller and faster version with a specialized tokenizer](https://huggingface.co/stabilityai/japanese-stablelm-instruct-ja_vocab-beta-7b). ## Usage First install additional dependencies in [requirements.txt](./requirements.txt): ```sh pip install -r requirements.txt ``` Then start generating text with `japanese-stablelm-instruct-beta-70b` by using the following code snippet: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "stabilityai/japanese-stablelm-instruct-beta-70b" tokenizer = AutoTokenizer.from_pretrained(model_name) # The next line may need to be modified depending on the environment model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") def build_prompt(user_query, inputs): sys_msg = "<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n" p = sys_msg + user_query + "\n\n" + inputs + " [/INST] " return p # Infer with prompt without any additional input user_inputs = { "user_query": "与えられたことわざの意味を小学生でも分かるように教えてください。", "inputs": "情けは人のためならず" } prompt = build_prompt(*user_inputs) input_ids = tokenizer.encode( prompt, add_special_tokens=False, return_tensors="pt" ) # this is for reproducibility. # feel free to change to get different result seed = 23 torch.manual_seed(seed) tokens = model.generate( input_ids.to(device=model.device), max_new_tokens=128, temperature=0.99, top_p=0.95, do_sample=True, ) out = tokenizer.decode(tokens[0], skip_special_tokens=True) print(out) ``` We suggest playing with different generation config (`top_p`, `repetition_penalty` etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning. ## Model Details Model type: `japanese-stablelm-instruct-beta-70b` model is an auto-regressive language model based on the Llama2 transformer architecture. * Language(s): Japanese * License: [Llama2 Community License](https://ai.meta.com/llama/license/). * Contact: For questions and comments about the model, please join [Stable Community Japan](https://discord.gg/StableJP). For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP. ## Training Dataset The following datasets were used for the instruction training. Note these are Japanese translated versions of the original datasets, shared by [kunishou](https://huggingface.co/kunishou). - [Anthropic HH-RLHF](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) - [Databricks Dolly 15-k](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja) - [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/kunishou/oasst1-89k-ja) ## Use and Limitations ### Intended Use The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use. ### Limitations and bias The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups. ## Authors This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by [Takuya Akiba](https://huggingface.co/iwiwi) and [Meng Lee](https://huggingface.co/leemeng). The members of the team are as follows: - [Meng Lee](https://huggingface.co/leemeng) - [Fujiki Nakamura](https://huggingface.co/fujiki) - [Makoto Shing](https://huggingface.co/mkshing) - [Paul McCann](https://huggingface.co/polm-stability) - [Takuya Akiba](https://huggingface.co/iwiwi) - [Naoki Orii](https://huggingface.co/mrorii) ## Acknowledgements We thank Meta Research for releasing Llama 2 under an open license for others to build on. We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang. We are also appreciative of [AI Novelist/Sta (Bit192, Inc.)](https://ai-novel.com/index.php) and the numerous contributors from [Stable Community Japan](https://discord.gg/VPrcE475HB) for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
LoneStriker/Yarn-Mistral-7b-128k-8.0bpw-h6-exl2	LoneStriker	2023-11-02T19:51:32Z	6	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T19:51:08Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers --- # Model Card: Nous-Yarn-Mistral-7b-128k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-128k is a state-of-the-art language model for long context, further pretrained on long context data for 1500 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 128k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-128k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
sirenstitches/distilgpt2-alpaca-instruction-fine-tuning-qlora	sirenstitches	2023-11-02T19:48:39Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "region:us" ]	null	2023-11-02T19:48:38Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: distilgpt2-alpaca-instruction-fine-tuning-qlora 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. --> # distilgpt2-alpaca-instruction-fine-tuning-qlora This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2520 ## 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: 4 - eval_batch_size: 4 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.952 \| 0.11 \| 1000 \| 2.3906 \| \| 2.5521 \| 0.22 \| 2000 \| 2.3438 \| \| 2.478 \| 0.33 \| 3000 \| 2.3125 \| \| 2.4709 \| 0.44 \| 4000 \| 2.2832 \| \| 2.4583 \| 0.55 \| 5000 \| 2.2793 \| \| 2.4337 \| 0.66 \| 6000 \| 2.2617 \| \| 2.416 \| 0.77 \| 7000 \| 2.2656 \| \| 2.4111 \| 0.88 \| 8000 \| 2.2559 \| \| 2.4054 \| 0.99 \| 9000 \| 2.2520 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
GreenBitAI/LLaMA-2-1.1B-2bit-groupsize32	GreenBitAI	2023-11-02T19:46:24Z	8	4	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-04T21:02:35Z	--- license: apache-2.0 --- # GreenBit LLaMA This is GreenBitAI's pretrained 2-bit TinyLLaMA model with extreme compression yet still strong performance. Please refer to our [Github page](https://github.com/GreenBitAI/low_bit_llama) for the code to run the model and more information. ## Model Description - Developed by: [GreenBitAI](https://github.com/GreenBitAI) - Model type: Causal (Llama 2) - Language(s) (NLP): English - License: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), [Llama 2 license agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) ## Zero-Shot Evaluation \| Task \| Metric \| TinyLLaMA 1.1B q2g32 \| TinyLLaMA 1.1B q2g8 \| LLaMA 3B q2g32 \| LLaMA 3B q2g16 \| LLaMA 3B q2g8 \| LLaMA-1 7B q2g32 \| LLaMA-2 7B q2g32 \| LLaMA-2 7B q2g8 \| LLaMA 1.1B FP16 \| LLaMA 3B FP16 \| LLaMA-1 7B FP16 \| \|---------------\|----------\|----------------------\|---------------------\|----------------\|----------------\|---------------\|------------------\|------------------\|----------------\|----------------\|-----------------\|-----------------\| \| Openbookqa \| acc \| 0.152 \| 0.192 \| 0.196 \| 0.238 \| 0.242 \| 0.224 \| 0.246 \| 0.296 \| 0.208 \| 0.27 \| 0.29 \| \| \| ac_norm \| 0.328 \| 0.338 \| 0.332 \| 0.358 \| 0.362 \| 0.388 \| 0.376 \| 0.4 \| 0.368 \| 0.4 \| 0.41 \| \| arc_challenge \| acc \| 0.3268 \| 0.2278 \| 0.279 \| 0.2978 \| 0.3148 \| 0.3422 \| 0.3268 \| 0.3618 \| 0.243 \| 0.34 \| 0.39 \| \| \| ac_norm \| 0.3387 \| 0.273 \| 0.2944 \| 0.3319 \| 0.3345 \| 0.3387 \| 0.3387 \| 0.372 \| 0.288 \| 0.37 \| 0.41 \| \| hellawswag \| acc \| 0.34 \| 0.3769 \| 0.4238 \| 0.444 \| 0.462 \| 0.4996 \| 0.4961 \| 0.5379 \| 0.403 \| 0.49 \| 0.68 \| \| \| ac_norm \| 0.4097 \| 0.4711 \| 0.5685 \| 0.5988 \| 0.6242 \| 0.6447 \| 0.6464 \| 0.7014 \| 0.503 \| 0.67 \| 0.73 \| \| piqa \| acc \| 0.6518 \| 0.6931 \| 0.7024 \| 0.716 \| 0.7291 \| 0.7476 \| 0.7503 \| 0.7715 \| 0.71 \| 0.75 \| 0.78 \| \| \| ac_norm \| 0.6393 \| 0.6812 \| 0.7116 \| 0.7247 \| 0.7312 \| 0.7443 \| 0.7421 \| 0.7568 \| 0.688 \| 0.76 \| 0.78 \| \| arc_easy \| acc \| 0.4411 \| 0.5109 \| 0.5997 \| 0.646 \| 0.6528 \| 0.6061 \| 0.6174 \| 0.6254 \| 0.533 \| 0.69 \| 0.68 \| \| \| ac_norm \| 0.3716 \| 0.412 \| 0.5417 \| 0.58 \| 0.5972 \| 0.4566 \| 0.4781 \| 0.4958 \| 0.43 \| 0.65 \| 0.52 \| \| Winogrande \| acc \| 0.532 \| 0.5249 \| 0.5683 \| 0.5888 \| 0.6054 \| 0.6283 \| 0.6298 \| 0.6582 \| 0.558 \| 0.62 \| 0.68 \| \| boolq \| acc \| 0.592 \| 0.6174 \| 0.6281 \| 0.6636 \| 0.6327 \| 0.6425 \| 0.7061 \| 0.7242 \| 0.583 \| 0.68 \| 0.75 \| \| truthfulqa_mc \| mc1 \| 0.2338 \| 0.2277 \| 0.2509 \| 0.2118 \| 0.2252 \| 0.224 \| 0.2313 \| 0.2399 \| 0.228 \| 0.22 \| 0.21 \| \| \| mc2 \| 0.4211 \| 0.406 \| 0.3962 \| 0.3501 \| 0.3625 \| 0.3702 \| 0.3854 \| 0.3795 \| 0.401 \| 0.35 \| 0.34 \| \| anli_r1 \| acc \| 0.363 \| 0.336 \| 0.337 \| 0.334 \| 0.344 \| 0.331 \| 0.333 \| 0.363 \| 0.354 \| 0.33 \| 0.35 \| \| anli_r2 \| acc \| 0.331 \| 0.346 \| 0.335 \| 0.332 \| 0.331 \| 0.326 \| 0.349 \| 0.347 \| 0.341 \| 0.32 \| 0.34 \| \| anli_r3 \| acc \| 0.3758 \| 0.3633 \| 0.3358 \| 0.3383 \| 0.3425 \| 0.3417 \| 0.36 \| 0.3733 \| 0.358 \| 0.35 \| 0.37 \| \| wic \| acc \| 0.5 \| 0.5 \| 0.4984 \| 0.5094 \| 0.4969 \| 0.4984 \| 0.4953 \| 0.489 \| 0.5 \| 0.48 \| 0.5 \| \| rte \| acc \| 0.4874 \| 0.4874 \| 0.5596 \| 0.5993 \| 0.5632 \| 0.639 \| 0.6065 \| 0.6426 \| 0.516 \| 0.58 \| 0.56 \| \| record \| f1 \| 0.7608 \| 0.8023 \| 0.8502 \| 0.8625 \| 0.8687 \| 0.8859 \| 0.8872 \| 0.9037 \| 0.82 \| 0.88 \| 0.91 \| \| \| em \| 0.753 \| 0.7934 \| 0.8427 \| 0.8545 \| 0.8612 \| 0.8781 \| 0.8801 \| 0.8959 \| 0.818 \| 0.89 \| 0.91 \| \| Average \| \| 0.438 \| 0.4498 \| 0.4881 \| 0.5037 \| 0.5087 \| 0.5122 \| 0.5181 \| 0.5391 \| 0.469 \| 0.528 \| 0.5519 \| \| model size \| GiB \| 0.5 \| 0.6 \| 1.2 \| 1.3 \| 1.5 \| 2.2 \| 2.2 \| 2.9 \| 4.4 \| 6.8 \| 12.5 \|
open-web-math/filtering-models	open-web-math	2023-11-02T19:40:16Z	0	9	null	[ "license:apache-2.0", "region:us" ]	null	2023-11-02T19:12:10Z	--- license: apache-2.0 --- This repository stores the MathScore and KenLM models used in the generation of OpenWebMath. To test the models, please `git clone` this repository and run `python perplexity.py` to test the KenLM model and `python math_score.py` to test the MathScore model.
reneeshdenny/a2c-PandaReachDense-v3	reneeshdenny	2023-11-02T19:32:49Z	0	0	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T19:26:52Z	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.24 +/- 0.12 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
mathildeparlo/base_seq_lab_bengali	mathildeparlo	2023-11-02T19:25:20Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-11-01T06:53:04Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: base_seq_lab_bengali 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. --> # base_seq_lab_bengali This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
satyanshu404/bart-large-cnn-finetuned-prompt_generation	satyanshu404	2023-11-02T19:19:14Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-10-11T09:39:51Z	--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer model-index: - name: bart-large-cnn-finetuned-prompt_generation 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. --> # bart-large-cnn-finetuned-prompt_generation This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6474 - Actual score: 0.8766 - Predction score: 0.3367 - Score difference: 0.5399 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-07 - 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: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Actual score \| Predction score \| Score difference \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------------:\|:---------------:\|:----------------:\| \| No log \| 1.0 \| 15 \| 3.6226 \| 0.8766 \| -0.4072 \| 1.2838 \| \| No log \| 2.0 \| 30 \| 3.5120 \| 0.8766 \| -0.2477 \| 1.1243 \| \| No log \| 3.0 \| 45 \| 3.3572 \| 0.8766 \| -0.3233 \| 1.1999 \| \| No log \| 4.0 \| 60 \| 3.2592 \| 0.8766 \| -0.0494 \| 0.9260 \| \| No log \| 5.0 \| 75 \| 3.1430 \| 0.8766 \| -0.3234 \| 1.2000 \| \| No log \| 6.0 \| 90 \| 3.0581 \| 0.8766 \| -0.4732 \| 1.3498 \| \| No log \| 7.0 \| 105 \| 2.9988 \| 0.8766 \| -0.5715 \| 1.4481 \| \| No log \| 8.0 \| 120 \| 2.9564 \| 0.8766 \| -0.6699 \| 1.5465 \| \| No log \| 9.0 \| 135 \| 2.9242 \| 0.8766 \| -0.5505 \| 1.4271 \| \| No log \| 10.0 \| 150 \| 2.8969 \| 0.8766 \| -0.4393 \| 1.3159 \| \| No log \| 11.0 \| 165 \| 2.8729 \| 0.8766 \| -0.4882 \| 1.3648 \| \| No log \| 12.0 \| 180 \| 2.8503 \| 0.8766 \| -0.6554 \| 1.5320 \| \| No log \| 13.0 \| 195 \| 2.8308 \| 0.8766 \| -0.7288 \| 1.6054 \| \| No log \| 14.0 \| 210 \| 2.8128 \| 0.8766 \| -0.7016 \| 1.5783 \| \| No log \| 15.0 \| 225 \| 2.7972 \| 0.8766 \| -0.7900 \| 1.6666 \| \| No log \| 16.0 \| 240 \| 2.7832 \| 0.8766 \| -0.6285 \| 1.5052 \| \| No log \| 17.0 \| 255 \| 2.7708 \| 0.8766 \| -0.5613 \| 1.4379 \| \| No log \| 18.0 \| 270 \| 2.7591 \| 0.8766 \| -0.6125 \| 1.4891 \| \| No log \| 19.0 \| 285 \| 2.7481 \| 0.8766 \| -0.5101 \| 1.3868 \| \| No log \| 20.0 \| 300 \| 2.7390 \| 0.8766 \| -0.4879 \| 1.3646 \| \| No log \| 21.0 \| 315 \| 2.7307 \| 0.8766 \| -0.4345 \| 1.3112 \| \| No log \| 22.0 \| 330 \| 2.7229 \| 0.8766 \| -0.3278 \| 1.2044 \| \| No log \| 23.0 \| 345 \| 2.7156 \| 0.8766 \| -0.3324 \| 1.2090 \| \| No log \| 24.0 \| 360 \| 2.7084 \| 0.8766 \| -0.2899 \| 1.1665 \| \| No log \| 25.0 \| 375 \| 2.7019 \| 0.8766 \| -0.1728 \| 1.0494 \| \| No log \| 26.0 \| 390 \| 2.6965 \| 0.8766 \| -0.2785 \| 1.1552 \| \| No log \| 27.0 \| 405 \| 2.6918 \| 0.8766 \| -0.1926 \| 1.0692 \| \| No log \| 28.0 \| 420 \| 2.6872 \| 0.8766 \| -0.1204 \| 0.9970 \| \| No log \| 29.0 \| 435 \| 2.6832 \| 0.8766 \| -0.0040 \| 0.8806 \| \| No log \| 30.0 \| 450 \| 2.6791 \| 0.8766 \| -0.0742 \| 0.9508 \| \| No log \| 31.0 \| 465 \| 2.6751 \| 0.8766 \| 0.0669 \| 0.8097 \| \| No log \| 32.0 \| 480 \| 2.6719 \| 0.8766 \| -0.0049 \| 0.8815 \| \| No log \| 33.0 \| 495 \| 2.6690 \| 0.8766 \| -0.0196 \| 0.8962 \| \| 2.6809 \| 34.0 \| 510 \| 2.6663 \| 0.8766 \| 0.0692 \| 0.8074 \| \| 2.6809 \| 35.0 \| 525 \| 2.6636 \| 0.8766 \| 0.0843 \| 0.7923 \| \| 2.6809 \| 36.0 \| 540 \| 2.6615 \| 0.8766 \| -0.0330 \| 0.9096 \| \| 2.6809 \| 37.0 \| 555 \| 2.6594 \| 0.8766 \| -0.0065 \| 0.8831 \| \| 2.6809 \| 38.0 \| 570 \| 2.6575 \| 0.8766 \| 0.2102 \| 0.6664 \| \| 2.6809 \| 39.0 \| 585 \| 2.6559 \| 0.8766 \| 0.3005 \| 0.5761 \| \| 2.6809 \| 40.0 \| 600 \| 2.6541 \| 0.8766 \| 0.3360 \| 0.5406 \| \| 2.6809 \| 41.0 \| 615 \| 2.6528 \| 0.8766 \| 0.2456 \| 0.6310 \| \| 2.6809 \| 42.0 \| 630 \| 2.6517 \| 0.8766 \| 0.3399 \| 0.5367 \| \| 2.6809 \| 43.0 \| 645 \| 2.6509 \| 0.8766 \| 0.4224 \| 0.4542 \| \| 2.6809 \| 44.0 \| 660 \| 2.6499 \| 0.8766 \| 0.4277 \| 0.4490 \| \| 2.6809 \| 45.0 \| 675 \| 2.6492 \| 0.8766 \| 0.2815 \| 0.5951 \| \| 2.6809 \| 46.0 \| 690 \| 2.6485 \| 0.8766 \| 0.3053 \| 0.5714 \| \| 2.6809 \| 47.0 \| 705 \| 2.6481 \| 0.8766 \| 0.2149 \| 0.6618 \| \| 2.6809 \| 48.0 \| 720 \| 2.6478 \| 0.8766 \| 0.2285 \| 0.6481 \| \| 2.6809 \| 49.0 \| 735 \| 2.6475 \| 0.8766 \| 0.2546 \| 0.6220 \| \| 2.6809 \| 50.0 \| 750 \| 2.6474 \| 0.8766 \| 0.3367 \| 0.5399 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
reneeshdenny/ppo-Pyramid_Training	reneeshdenny	2023-11-02T19:08:30Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2023-11-02T19:08:08Z	--- 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: reneeshdenny/ppo-Pyramid_Training 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
SwiftySteve/tiny-random-DistilBertForSequenceClassification	SwiftySteve	2023-11-02T19:02:15Z	0	0	null	[ "coreml", "region:us" ]	null	2023-11-02T18:59:43Z	Tested, get error once loaded into app `unsupportedTokenizer(DistilBertTokenizer)`
NousResearch/Yarn-Mistral-7b-64k	NousResearch	2023-11-02T19:00:04Z	11,249	51	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "custom_code", "en", "dataset:emozilla/yarn-train-tokenized-16k-mistral", "arxiv:2309.00071", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-31T02:01:43Z	--- datasets: - emozilla/yarn-train-tokenized-16k-mistral metrics: - perplexity library_name: transformers license: apache-2.0 language: - en --- # Model Card: Nous-Yarn-Mistral-7b-64k [Preprint (arXiv)](https://arxiv.org/abs/2309.00071) [GitHub](https://github.com/jquesnelle/yarn) ![yarn](https://raw.githubusercontent.com/jquesnelle/yarn/mistral/data/proofpile-long-small-mistral.csv.png) ## Model Description Nous-Yarn-Mistral-7b-64k is a state-of-the-art language model for long context, further pretrained on long context data for 1000 steps using the YaRN extension method. It is an extension of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and supports a 64k token context window. To use, pass `trust_remote_code=True` when loading the model, for example ```python model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Mistral-7b-64k", use_flash_attention_2=True, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) ``` In addition you will need to use the latest version of `transformers` (until 4.35 comes out) ```sh pip install git+https://github.com/huggingface/transformers ``` ## Benchmarks Long context benchmarks: \| Model \| Context Window \| 8k PPL \| 16k PPL \| 32k PPL \| 64k PPL \| 128k PPL \| \|-------\|---------------:\|------:\|----------:\|-----:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 2.96 \| - \| - \| - \| - \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 3.04 \| 2.65 \| 2.44 \| 2.20 \| - \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 3.08 \| 2.68 \| 2.47 \| 2.24 \| 2.19 \| Short context benchmarks showing that quality degradation is minimal: \| Model \| Context Window \| ARC-c \| Hellaswag \| MMLU \| Truthful QA \| \|-------\|---------------:\|------:\|----------:\|-----:\|------------:\| \| [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) \| 8k \| 59.98 \| 83.31 \| 64.16 \| 42.15 \| \| [Yarn-Mistral-7b-64k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-64k) \| 64k \| 59.38 \| 81.21 \| 61.32 \| 42.50 \| \| [Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) \| 128k \| 58.87 \| 80.58 \| 60.64 \| 42.46 \| ## Collaborators - [bloc97](https://github.com/bloc97): Methods, paper and evals - [@theemozilla](https://twitter.com/theemozilla): Methods, paper, model training, and evals - [@EnricoShippole](https://twitter.com/EnricoShippole): Model training - [honglu2875](https://github.com/honglu2875): Paper and evals The authors would like to thank LAION AI for their support of compute for this model. It was trained on the [JUWELS](https://www.fz-juelich.de/en/ias/jsc/systems/supercomputers/juwels) supercomputer.
Lornng/Llama2-7B-QLoRA-cpgQA-adapter-text-gen-final	Lornng	2023-11-02T18:51:52Z	5	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "region:us" ]	null	2023-11-02T17:08:06Z	--- library_name: peft base_model: meta-llama/Llama-2-7b-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0
benjipeng/distilhubert-finetuned-gtzan-finetuned-gtzan	benjipeng	2023-11-02T18:50:37Z	5	0	transformers	[ "transformers", "pytorch", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:benjipeng/distilhubert-finetuned-gtzan", "base_model:finetune:benjipeng/distilhubert-finetuned-gtzan", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	audio-classification	2023-11-02T15:05:19Z	--- license: apache-2.0 base_model: benjipeng/distilhubert-finetuned-gtzan tags: - generated_from_trainer datasets: - marsyas/gtzan metrics: - accuracy model-index: - name: distilhubert-finetuned-gtzan-finetuned-gtzan results: - task: name: Audio Classification type: audio-classification dataset: name: GTZAN type: marsyas/gtzan config: all split: train args: all metrics: - name: Accuracy type: accuracy value: 0.86 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan-finetuned-gtzan This model is a fine-tuned version of [benjipeng/distilhubert-finetuned-gtzan](https://huggingface.co/benjipeng/distilhubert-finetuned-gtzan) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 0.6143 - Accuracy: 0.86 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8153 \| 1.0 \| 113 \| 0.7412 \| 0.8 \| \| 0.6209 \| 2.0 \| 226 \| 0.8662 \| 0.75 \| \| 0.3642 \| 3.0 \| 339 \| 0.5880 \| 0.84 \| \| 0.2077 \| 4.0 \| 452 \| 0.6017 \| 0.85 \| \| 0.1658 \| 5.0 \| 565 \| 0.5087 \| 0.87 \| \| 0.0209 \| 6.0 \| 678 \| 0.6488 \| 0.85 \| \| 0.1971 \| 7.0 \| 791 \| 0.5813 \| 0.87 \| \| 0.0047 \| 8.0 \| 904 \| 0.6143 \| 0.86 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Pinu12ka4/Sentiment-Analysis	Pinu12ka4	2023-11-02T18:36:42Z	0	0	null	[ "region:us" ]	null	2023-11-02T18:22:04Z	# Sentiment Analysis with GUI A Deep Learning Model which used for Sentiment analysis. The Accuracy it reach upto 85%. It train on 25000 text data. # Neural Network Info The bert layer is integrated in the neural network at the second layer after input layer. The 3 GRU layer is for feature extraction then a Conv1D Layer is use after that making the output flatten and passing through a bunch of dense layer. ## Info 1) "Bert_uncased_model_Tiwtter.h5" has reached to the accuracy upto 85% just on 30 epochs. Loss it got is 0.51. This model is purly train in Twitter dataset. ![SS1](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-06%20215459.png?raw=true) 2) "Bert_uncased_model_Reddit.h5" has reached to the accuracy upto 84% just on 35 epochs. Loss it got is 0.81. This model is not purly train in Twitter dataset but a bit of reddit's dataset is also used. ![SS1](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-06%20212910.png?raw=true) Loss :- Sparse_categorical_crossentropy Activatiion on last layer :- softmax Note: Max input length is 768 words. ## Screenshots ![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103349.png?raw=true) ![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103417.png?raw=true) ![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103425.png?raw=true) ![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20103455.png?raw=true) ![Preview](https://github.com/somnathdashs/Sentiment-Analysis/blob/main/SS/Screenshot%202023-07-09%20104740.png?raw=true) ## Libray Used ##### > opencv ##### > tensorflow ##### > numpy ##### > pickle ##### > bert ##### > tensorflow_hub ##### > tensorflow_text ## Authors - [@Somnath Dash](https://www.github.com/somnathdashs)
nasreenAkhtar/PongNoFrameSkip-v4	nasreenAkhtar	2023-11-02T18:35:12Z	0	0	null	[ "license:bigcode-openrail-m", "region:us" ]	null	2023-11-02T18:35:12Z	--- license: bigcode-openrail-m ---
Britania/workout_plans_model_weights	Britania	2023-11-02T18:34:04Z	1	0	peft	[ "peft", "region:us" ]	null	2023-11-02T18:33:55Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.5.0
lw2333/test	lw2333	2023-11-02T18:19:53Z	4	0	transformers	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-11-02T18:17:28Z	--- base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: model_20231101 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. --> # model_20231101 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0472 - Wer: 285.6269 ## 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 6000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.1216 \| 8.99 \| 1000 \| 1.0660 \| 407.7217 \| \| 0.0077 \| 17.98 \| 2000 \| 1.0201 \| 287.1560 \| \| 0.0003 \| 26.97 \| 3000 \| 1.0195 \| 260.5505 \| \| 0.0002 \| 35.96 \| 4000 \| 1.0347 \| 286.2385 \| \| 0.0001 \| 44.94 \| 5000 \| 1.0430 \| 275.3058 \| \| 0.0001 \| 53.93 \| 6000 \| 1.0472 \| 285.6269 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.14.1
reneeshdenny/ppo-SnowballTarget	reneeshdenny	2023-11-02T18:19:13Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2023-11-02T18:19:06Z	--- 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: reneeshdenny/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
jinymusim/metrum-validator	jinymusim	2023-11-02T18:09:41Z	0	0	null	[ "license:cc-by-nd-4.0", "region:us" ]	null	2023-11-02T17:55:22Z	--- license: cc-by-nd-4.0 --- ## Czech Metrum Validator. Validator for metrum. Trained on Czech poetry from github project by Institute of Czech Literature, Czech Academy of Sciences. https://github.com/versotym/corpusCzechVerse ## Usage ### Loading model Download validator.py with interface Download model and load it by pytorch ```python import torch model: ValidatorInterface = (torch.load(args.metre_model_path_full, map_location=torch.device('cpu'))) ``` Load base robeczech tokenizer and try it out ```python tokenizer = = AutoTokenizer.from_pretrained('roberta-base') model.validate(input_ids=datum["input_ids"], metre=datum["metre"])['acc'] ``` ### Train Model ```python meter_model = MeterValidator(pretrained_model=args.pretrained_model) tokenizer = AutoTokenizer.from_pretrained(args.tokenizer) training_args = TrainingArguments( save_strategy = "no", logging_steps = 500, warmup_steps = args.worm_up, weight_decay = 0.0, num_train_epochs = args.epochs, learning_rate = args.learning_rate, fp16 = True if torch.cuda.is_available() else False, ddp_backend = "nccl", lr_scheduler_type="cosine", logging_dir = './logs', output_dir = './results', per_device_train_batch_size = args.batch_size) Trainer(model = rhyme_model, args = training_args, train_dataset= train_data.pytorch_dataset_body, data_collator=collate).train() ```
gagan0116/hindi_gpt2	gagan0116	2023-11-02T18:04:38Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-10-27T13:29:22Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: hindi_gpt2 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. --> # hindi_gpt2 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1438 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 300 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 5.0418 \| 1.21 \| 300 \| 3.2793 \| \| 2.6143 \| 2.42 \| 600 \| 2.3213 \| \| 2.1756 \| 3.63 \| 900 \| 2.1777 \| \| 2.0475 \| 4.84 \| 1200 \| 2.1438 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
taozi555/Euryale-2.65	taozi555	2023-11-02T17:55:48Z	10	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-11-02T17:44:48Z	--- license: llama2 language: - en --- ![Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B/resolve/main/Euryale.jpg) 17th Attempt. Past 10 Failed, cost me >$200 lol. Idea is an updated version of Euryale with ReMantik instead of the ties-merge between the original 3 models. This is then mixed with a saucy model (spicyboros+pyg_lora) with a Mythomax-esque Ratio, and a certain experimental (self) LoRA applied to it. Test Results: Works Well. <br>NSFL and NSFW fine in roleplay context. <br>slight censor with 0 context, zero issues in actual RP / ERP. <br>Good Prose, Not Dumbed Down due to RP merges from testing. <br> I have not encountered any repetition issues some had with the original Euryale. tell me if you do, though. Prompt and System Format: most works well. I recommend Alpaca. ST Settings used for Test: Lightning 1.1 System Prompt + Shortwave(1.2 Temperature) Support me [here](https://ko-fi.com/sao10k) :) Quants done by TheBloke! Ty a lot to him. https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GPTQ https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-GGUF https://huggingface.co/TheBloke/Euryale-1.3-L2-70B-AWQ
gathnex/gathllama-2	gathnex	2023-11-02T17:43:05Z	13	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-09-17T16:26:35Z	--- license: other language: - en pipeline_tag: text-generation --- ## Model Summary ![image/png](https://cdn-uploads.huggingface.co/production/uploads/650727cbada48d4be262b9d3/QgDQpjpqURLmWOXasSOAo.png) Base Pre-trained Model: llama-2-7b-hf Fine-Tuning Dataset: vicgalle/alpaca-gpt4 In this model, we leveraged the power of the open-source pre-trained model known as llama-2-7b-hf as the foundation. However, to make this model excel in a particular natural language processing task, we embarked on a fine-tuning journey. Our fine-tuning dataset consisted of a substantial 50,000 samples sourced from the Alpaca Dataset, carefully curated and preprocessed to ensure data quality. Through the fine-tuning process, we guided the model through five training epochs. During these epochs, the model underwent iterative refinement, learning task-specific nuances and adapting its knowledge to the intricacies of the Alpaca Dataset. The goal was to enhance the model's performance and make it a proficient tool for a specific application. This comprehensive fine-tuning approach ensures that the model is well-suited to handle the intricacies of real-world data and deliver accurate results. #### QA format: system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request\n' B_INST, E_INST = "### Instruction:\n", "### Response:\n" prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n\n{E_INST}" ``` <s>Below is an instruction that describes a task. Write a response that appropriately completes the request ### Instruction: explain newtoen's 3rd law with example ### Response: ``` where the model generates the text after "### Response:". #### Chat format: ```Newton's Third Law of Motion states that "for every action, there is an equal and opposite reaction." This means that whenever one object exerts a force on another object, the second object exerts an equal force in the opposite direction. Here's a simple example to illustrate this law: Imagine you're on a skateboard, and you push against a wall with your foot. When you apply force (the action) to the wall by pushing, the wall pushes back on your foot with an equal force in the opposite direction (the reaction). This is why you can feel the wall pushing against your foot. Another everyday example is when you walk. As your foot pushes backward against the ground (action), the ground pushes your foot forward with an equal force (reaction), propelling you forward. Newton's Third Law helps explain how objects move and interact with each other, emphasizing that forces always come in pairs and act in opposite directions. ``` Disclimer : The model output will be very based on the hyperparameter you're giving. ## Training ### Model * Architecture: a Transformer-based model with next-word prediction objective * Dataset size: 50k samples * Epochs: 5 * Precision: fp16 * GPUs: 2xV100-16G * Training time: 2 days ### Software * [PyTorch](https://github.com/pytorch/pytorch) * [Transformerrs](https://huggingface.co/docs/transformers/index) ### Sample Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, pipeline model = AutoModelForCausalLM.from_pretrained("gathnex/gathllama-2", load_in_4bit = True) tokenizer = AutoTokenizer.from_pretrained("gathnex/gathllama-2") # for better inference model.config.use_cache = True model.eval() #testing def stream(user_prompt): runtimeFlag = "cuda:0" system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request' B_INST, E_INST = "### Instruction:\n", "### Response:\n" prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n\n{E_INST}" inputs = tokenizer([prompt], return_tensors="pt",add_special_tokens=False).to(runtimeFlag) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) # Despite returning the usual output, the streamer will also print the generated text to stdout. _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500) stream("explain how to impress our CEO, i'm joined 5 month ago in the company. give tips and tricks") ```
NeerajG03/t5-small-finetuned	NeerajG03	2023-11-02T17:37:52Z	3	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "dataset:big_patent", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T17:37:24Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - big_patent model-index: - name: t5-small-finetuned 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. --> # t5-small-finetuned This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the big_patent dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Framework versions - Transformers 4.34.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.14.1
marblepolishing38/marble-floor-polishing-services-in-noida	marblepolishing38	2023-11-02T17:32:23Z	0	0	null	[ "region:us" ]	null	2023-11-02T17:30:11Z	Is the shine on your marble fading? Are you trying to find "Marble Polishing Near Me" experts? No, not in that way! Our speciality is restoring your marble surfaces to their former splendour. Our skilled professionals have perfected their art, and our cutting-edge equipment guarantees that the lustre of your marble is expertly restored Hire our skilled marble sharpening service to restore the beauty of your marble surfaces in Noida. Our skilled team uses modern methods to restore the natural shine of your marble countertops, floors, and other surfaces. Bid farewell to tedium and hello to an opulent, expensive conclusion that accentuates the elegance of your site. Read More:- https://marblepolishingnearme.in/ https://marblepolishingnearme.in/diamond-floor-polishing-services.php https://marblepolishingnearme.in/marble-polishing-services.php https://marblepolishingnearme.in/granite-floor-polishing-services.php https://marblepolishingnearme.in/italian-marble-polishing-services.php https://marblepolishingnearme.in/kota-stone-floor-polishing-services.php https://marblepolishingnearme.in/marble-mirror-polishing-services.php https://marblepolishingnearme.in/white-marble-polishing-services.php
kaitchup/Llama-2-7b-mt-Italian-to-English	kaitchup	2023-11-02T17:30:36Z	237	0	peft	[ "peft", "translation", "en", "it", "dataset:kaitchup/opus-Italian-to-English", "license:mit", "region:us" ]	translation	2023-10-26T16:51:33Z	--- library_name: peft license: mit language: - en - it datasets: - kaitchup/opus-Italian-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Italian text into English. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: LoRA Adapter for Llama 2 7B - Language(s) (NLP): Italian, English - License: MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Italian-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
kaitchup/Llama-2-7b-mt-Indonesian-to-English	kaitchup	2023-11-02T17:29:38Z	3	0	peft	[ "peft", "translation", "en", "id", "dataset:kaitchup/opus-Indonesian-to-English", "license:mit", "region:us" ]	translation	2023-10-26T16:53:01Z	--- library_name: peft license: mit language: - en - id datasets: - kaitchup/opus-Indonesian-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Indonesian text into English. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: LoRA Adapter for Llama 2 7B - Language(s) (NLP): Indonesian, English - License: MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Indonesian-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
kaitchup/Llama-2-7b-mt-Vietnamese-to-English	kaitchup	2023-11-02T17:23:51Z	4	0	peft	[ "peft", "translation", "en", "vi", "dataset:kaitchup/opus-Vietnamese-to-English", "license:mit", "region:us" ]	translation	2023-10-26T16:54:10Z	--- library_name: peft license: mit language: - en - vi datasets: - kaitchup/opus-Vietnamese-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Vietnamese text into English. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: LoRA Adapter for Llama 2 7B - Language(s) (NLP): Vietnamese, English - License: MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Vietnamese-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
nosnelmil/RoBERTa-CompareTransformers-Imdb	nosnelmil	2023-11-02T17:23:35Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-02T17:23:11Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - precision - recall - f1 model-index: - name: results results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9286666512489319 - name: Precision type: precision value: 0.9286666512489319 - name: Recall type: recall value: 0.9286666512489319 - name: F1 type: f1 value: 0.9286666512489319 --- <!-- 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. --> # results This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2691 - Accuracy: 0.9287 - Precision: 0.9287 - Recall: 0.9287 - F1: 0.9287 - Auroc: 0.9772 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Auroc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:------:\| \| 0.1764 \| 0.46 \| 500 \| 0.2698 \| 0.9044 \| 0.9044 \| 0.9044 \| 0.9044 \| 0.9738 \| \| 0.3348 \| 0.91 \| 1000 \| 0.2755 \| 0.9117 \| 0.9117 \| 0.9117 \| 0.9117 \| 0.9686 \| \| 0.1478 \| 1.37 \| 1500 \| 0.3275 \| 0.9109 \| 0.9109 \| 0.9109 \| 0.9109 \| 0.9771 \| \| 0.2051 \| 1.83 \| 2000 \| 0.2575 \| 0.9309 \| 0.9309 \| 0.9309 \| 0.9309 \| 0.9793 \| \| 0.1435 \| 2.29 \| 2500 \| 0.3140 \| 0.9245 \| 0.9245 \| 0.9245 \| 0.9245 \| 0.9783 \| \| 0.1425 \| 2.74 \| 3000 \| 0.2691 \| 0.9287 \| 0.9287 \| 0.9287 \| 0.9287 \| 0.9772 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
kaitchup/Llama-2-7b-mt-Swedish-to-English	kaitchup	2023-11-02T17:22:37Z	1	1	peft	[ "peft", "translation", "en", "sv", "dataset:kaitchup/opus-Swedish-to-English", "license:mit", "region:us" ]	translation	2023-10-26T16:55:58Z	--- library_name: peft license: mit language: - en - sv datasets: - kaitchup/opus-Swedish-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Swedish text into English. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: LoRA Adapter for Llama 2 7B - Language(s) (NLP): Swedish, English - License: MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Swedish-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
trimble/clip-vit-large-patch14	trimble	2023-11-02T17:20:47Z	1	0	null	[ "vision", "arxiv:2103.00020", "arxiv:1908.04913", "endpoints_compatible", "region:us" ]	null	2023-10-31T17:19:00Z	--- tags: - vision widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog --- # Model Card: CLIP Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md). ## Model Details The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. ### Model Date January 2021 ### Model Type The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer. ### Documents - [Blog Post](https://openai.com/blog/clip/) - [CLIP Paper](https://arxiv.org/abs/2103.00020) ### Use with Transformers ```python from PIL import Image import requests from transformers import CLIPProcessor, CLIPModel model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14") processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True) outputs = model(inputs) logits_per_image = outputs.logits_per_image # this is the image-text similarity score probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities ``` ## Model Use ### Intended Use The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. #### Primary intended uses The primary intended users of these models are AI researchers. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. ### Out-of-Scope Use Cases Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. ## Data The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users. ### Data Mission Statement Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset. ## Performance and Limitations ### Performance We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets: - Food101 - CIFAR10 - CIFAR100 - Birdsnap - SUN397 - Stanford Cars - FGVC Aircraft - VOC2007 - DTD - Oxford-IIIT Pet dataset - Caltech101 - Flowers102 - MNIST - SVHN - IIIT5K - Hateful Memes - SST-2 - UCF101 - Kinetics700 - Country211 - CLEVR Counting - KITTI Distance - STL-10 - RareAct - Flickr30 - MSCOCO - ImageNet - ImageNet-A - ImageNet-R - ImageNet Sketch - ObjectNet (ImageNet Overlap) - Youtube-BB - ImageNet-Vid ## Limitations CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance. ### Bias and Fairness We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper). We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks. ## Feedback ### Where to send questions or comments about the model Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)
kaitchup/Llama-2-7b-mt-Finnish-to-English	kaitchup	2023-11-02T17:16:46Z	0	1	peft	[ "peft", "safetensors", "translation", "en", "fi", "dataset:kaitchup/opus-Finnish-to-English", "license:mit", "region:us" ]	translation	2023-11-02T17:05:19Z	--- library_name: peft license: mit language: - en - fi datasets: - kaitchup/opus-Finnish-to-English tags: - translation --- # Model Card for Model ID This is an adapter for Meta's Llama 2 7B fine-tuned for translating Finnish text into English. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [The Kaitchup](https://kaitchup.substack.com/) - Model type: LoRA Adapter for Llama 2 7B - Language(s) (NLP): Finnish, English - License: MIT license ## Uses This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning. You can use the following code to load the model: ``` from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch from peft import PeftModel base_model = "meta-llama/Llama-2-7b-hf" compute_dtype = getattr(torch, "float16") bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=compute_dtype, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( original_model_directory, device_map={"": 0}, quantization_config=bnb_config ) tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True) model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Finnish-to-English") ``` Then, run the model as follows: ``` my_text = "" #put your text to translate here prompt = my_text+" ###>" tokenized_input = tokenizer(prompt, return_tensors="pt") input_ids = tokenized_input["input_ids"].cuda() generation_output = model.generate( input_ids=input_ids, num_beams=10, return_dict_in_generate=True, output_scores=True, max_new_tokens=130 ) for seq in generation_output.sequences: output = tokenizer.decode(seq, skip_special_tokens=True) print(output.split("###>")[1].strip()) ``` ## Model Card Contact [The Kaitchup](https://kaitchup.substack.com/)
ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-3	ramdhanfirdaus	2023-11-02T17:16:45Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:tiiuae/falcon-rw-1b", "base_model:adapter:tiiuae/falcon-rw-1b", "region:us" ]	null	2023-11-02T17:16:29Z	--- library_name: peft base_model: tiiuae/falcon-rw-1b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure ### Framework versions - PEFT 0.6.0 ## Training procedure ### Framework versions - PEFT 0.6.0
reneeshdenny/CartPole-v1	reneeshdenny	2023-11-02T17:12:02Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T17:11:53Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
wt-golf/distilbert-base-uncased-lora-text-classification	wt-golf	2023-11-02T17:00:09Z	0	0	null	[ "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2023-11-02T17:00:07Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-lora-text-classification 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. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7319 - Accuracy: {'accuracy': 0.889} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 3 - eval_batch_size: 3 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------------:\| \| No log \| 1.0 \| 334 \| 0.3624 \| {'accuracy': 0.885} \| \| 0.4609 \| 2.0 \| 668 \| 0.4852 \| {'accuracy': 0.864} \| \| 0.2886 \| 3.0 \| 1002 \| 0.5991 \| {'accuracy': 0.882} \| \| 0.2886 \| 4.0 \| 1336 \| 0.6573 \| {'accuracy': 0.89} \| \| 0.0844 \| 5.0 \| 1670 \| 0.7319 \| {'accuracy': 0.889} \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.1.0 - Datasets 2.14.4 - Tokenizers 0.13.3
wt-golf/distilbert-base-uncased-lora-text-classification-imdb-1k	wt-golf	2023-11-02T17:00:06Z	1	0	peft	[ "peft", "region:us" ]	null	2023-11-02T17:00:04Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.5.0
Phando/switch-base-32-finetuned-hotpotqa	Phando	2023-11-02T16:54:25Z	3	0	transformers	[ "transformers", "pytorch", "switch_transformers", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T16:50:15Z	The switch-base-32 model was fine-tuned on the HotpotQA dataset. Validation exact-match/F1-score: 67.55/84.60. The prompt key in PromptSource: "generate_answer_affirmative".
SuphalerkB/my_model	SuphalerkB	2023-11-02T16:52:59Z	3	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/mT5_multilingual_XLSum", "base_model:finetune:csebuetnlp/mT5_multilingual_XLSum", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T14:17:25Z	--- base_model: csebuetnlp/mT5_multilingual_XLSum tags: - generated_from_trainer model-index: - name: my_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_model This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
ydshieh/kosmos-2-patch14-224	ydshieh	2023-11-02T16:42:01Z	56	54	transformers	[ "transformers", "pytorch", "kosmos-2", "image-text-to-text", "custom_code", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-07-29T17:44:41Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Kosmos-2: Grounding Multimodal Large Language Models to the World This model (remote code on the Hub) is deprecated. Please use https://huggingface.co/microsoft/kosmos-2-patch14-224 There are some changes in terms of input formats: see the model card in https://huggingface.co/microsoft/kosmos-2-patch14-224 ~~(There is an on going effort to port `Kosmos-2` directly into `transformers`. This repository (remote code) might need some more bug fixes later, including breaking changes.)~~ <a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><figure><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="384"><figcaption><b>[An image of a snowman warming himself by a fire.]</b></figcaption></figure></a> This Hub repository contains a HuggingFace's `transformers` implementation of [the original Kosmos-2 model](https://github.com/microsoft/unilm/tree/master/kosmos-2) from Microsoft. ## How to Get Started with the Model Use the code below to get started with the model. ```python import requests from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) prompt = "<grounding>An image of" url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.png" image = Image.open(requests.get(url, stream=True).raw) # The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs. # Uncomment the following 2 lines if you want to match the original demo's outputs. # (One example is the `two_dogs.jpg` from the demo) # image.save("new_image.jpg") # image = Image.open("new_image.jpg") inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"][:, :-1], attention_mask=inputs["attention_mask"][:, :-1], img_features=None, img_attn_mask=inputs["img_attn_mask"][:, :-1], use_cache=True, max_new_tokens=64, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Specify `cleanup_and_extract=False` in order to see the raw model generation. processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False) print(processed_text) # `<grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a fire</phrase><object><patch_index_0005><patch_index_0911></object>.` # By default, the generated text is cleanup and the entities are extracted. processed_text, entities = processor.post_process_generation(generated_text) print(processed_text) # `An image of a snowman warming himself by a fire.` print(entities) # `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]` ``` ## Draw the bounding bboxes of the entities on the image Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image: ```python import cv2 import numpy as np import os import requests import torch import torchvision.transforms as T from PIL import Image def is_overlapping(rect1, rect2): x1, y1, x2, y2 = rect1 x3, y3, x4, y4 = rect2 return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4) def draw_entity_boxes_on_image(image, entities, show=False, save_path=None): """_summary_ Args: image (_type_): image or image path collect_entity_location (_type_): _description_ """ if isinstance(image, Image.Image): image_h = image.height image_w = image.width image = np.array(image)[:, :, [2, 1, 0]] elif isinstance(image, str): if os.path.exists(image): pil_img = Image.open(image).convert("RGB") image = np.array(pil_img)[:, :, [2, 1, 0]] image_h = pil_img.height image_w = pil_img.width else: raise ValueError(f"invaild image path, {image}") elif isinstance(image, torch.Tensor): # pdb.set_trace() image_tensor = image.cpu() reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None] reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None] image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean pil_img = T.ToPILImage()(image_tensor) image_h = pil_img.height image_w = pil_img.width image = np.array(pil_img)[:, :, [2, 1, 0]] else: raise ValueError(f"invaild image format, {type(image)} for {image}") if len(entities) == 0: return image new_image = image.copy() previous_bboxes = [] # size of text text_size = 1 # thickness of text text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) box_line = 3 (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) base_height = int(text_height * 0.675) text_offset_original = text_height - base_height text_spaces = 3 for entity_name, (start, end), bboxes in entities: for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes: orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h) # draw bbox # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line) l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1 x1 = orig_x1 - l_o y1 = orig_y1 - l_o if y1 < text_height + text_offset_original + 2 * text_spaces: y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces x1 = orig_x1 + r_o # add text background (text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1 for prev_bbox in previous_bboxes: while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox): text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces) text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces) y1 += (text_height + text_offset_original + 2 * text_spaces) if text_bg_y2 >= image_h: text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces)) text_bg_y2 = image_h y1 = image_h break alpha = 0.5 for i in range(text_bg_y1, text_bg_y2): for j in range(text_bg_x1, text_bg_x2): if i < image_h and j < image_w: if j < text_bg_x1 + 1.35 * c_width: # original color bg_color = color else: # white bg_color = [255, 255, 255] new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8) cv2.putText( new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA ) # previous_locations.append((x1, y1)) previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]]) if save_path: pil_image.save(save_path) if show: pil_image.show() return new_image # (The same image from the previous code example) url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg" image = Image.open(requests.get(url, stream=True).raw) # From the previous code example entities = [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])] # Draw the bounding bboxes draw_entity_boxes_on_image(image, entities, show=True) ``` Here is the annotated image: <a href="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" target="_blank"><img src="https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/annotated_snowman.jpg" width="500"></a> ## Tasks This model is capable of performing different tasks through changing the prompts. First, let's define a function to run a prompt. ```python import requests from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) url = "https://huggingface.co/ydshieh/kosmos-2-patch14-224/resolve/main/snowman.png" image = Image.open(requests.get(url, stream=True).raw) def run_example(prompt): inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"][:, :-1], attention_mask=inputs["attention_mask"][:, :-1], img_features=None, img_attn_mask=inputs["img_attn_mask"][:, :-1], use_cache=True, max_new_tokens=64, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] _processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False) processed_text, entities = processor.post_process_generation(generated_text) print(processed_text) print(entities) print(_processed_text) ``` Here are the tasks `Kosmos-2` could perform: ### Multimodal Grounding #### • Phrase Grounding ```python prompt = "<grounding><phrase> a snowman</phrase>" run_example(prompt) # a snowman is warming himself by the fire # [('a snowman', (0, 9), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('the fire', (32, 40), [(0.203125, 0.015625, 0.453125, 0.859375)])] # <grounding><phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> is warming himself by<phrase> the fire</phrase><object><patch_index_0006><patch_index_0878></object> ``` #### • Referring Expression Comprehension ```python prompt = "<grounding><phrase> a snowman next to a fire</phrase>" run_example(prompt) # a snowman next to a fire # [('a snowman next to a fire', (0, 24), [(0.390625, 0.046875, 0.984375, 0.828125)])] # <grounding><phrase> a snowman next to a fire</phrase><object><patch_index_0044><patch_index_0863></object> ``` ### Multimodal Referring #### • Referring expression generation ```python prompt = "<grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is" run_example(prompt) # It is snowman in a hat and scarf # [('It', (0, 2), [(0.390625, 0.046875, 0.984375, 0.828125)])] # <grounding><phrase> It</phrase><object><patch_index_0044><patch_index_0863></object> is snowman in a hat and scarf ``` ### Perception-Language Tasks #### • Grounded VQA ```python prompt = "<grounding> Question: What is special about this image? Answer:" run_example(prompt) # Question: What is special about this image? Answer: The image features a snowman sitting by a campfire in the snow. # [('a snowman', (71, 80), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (92, 102), [(0.109375, 0.640625, 0.546875, 0.984375)])] # <grounding> Question: What is special about this image? Answer: The image features<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> sitting by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object> in the snow. ``` #### • Grounded VQA with multimodal referring via bounding boxes ```python prompt = "<grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer:" run_example(prompt) # Question: Where is the fire next to? Answer: Near the snowman. # [('the fire', (19, 27), [(0.171875, 0.015625, 0.484375, 0.890625)]), ('the snowman', (50, 61), [(0.390625, 0.046875, 0.984375, 0.828125)])] # <grounding> Question: Where is<phrase> the fire</phrase><object><patch_index_0005><patch_index_0911></object> next to? Answer: Near<phrase> the snowman</phrase><object><patch_index_0044><patch_index_0863></object>. ``` ### Grounded Image captioning #### • Brief ```python prompt = "<grounding> An image of" run_example(prompt) # An image of a snowman warming himself by a campfire. # [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a campfire', (41, 51), [(0.109375, 0.640625, 0.546875, 0.984375)])] # <grounding> An image of<phrase> a snowman</phrase><object><patch_index_0044><patch_index_0863></object> warming himself by<phrase> a campfire</phrase><object><patch_index_0643><patch_index_1009></object>. ``` #### • Detailed ```python prompt = "<grounding> Describe this image in detail:" run_example(prompt) # Describe this image in detail: The image features a snowman sitting by a campfire in the snow. He is wearing a hat, scarf, and gloves, with a pot nearby and a cup # [('a campfire', (71, 81), [(0.171875, 0.015625, 0.484375, 0.984375)]), ('a hat', (109, 114), [(0.515625, 0.046875, 0.828125, 0.234375)]), ('scarf', (116, 121), [(0.515625, 0.234375, 0.890625, 0.578125)]), ('gloves', (127, 133), [(0.515625, 0.390625, 0.640625, 0.515625)]), ('a pot', (140, 145), [(0.078125, 0.609375, 0.265625, 0.859375)])] # <grounding> Describe this image in detail: The image features a snowman sitting by<phrase> a campfire</phrase><object><patch_index_0005><patch_index_1007></object> in the snow. He is wearing<phrase> a hat</phrase><object><patch_index_0048><patch_index_0250></object>,<phrase> scarf</phrase><object><patch_index_0240><patch_index_0604></object>, and<phrase> gloves</phrase><object><patch_index_0400><patch_index_0532></object>, with<phrase> a pot</phrase><object><patch_index_0610><patch_index_0872></object> nearby and<phrase> a cup</phrase><object> ``` ## Running the Flask Server _flask_kosmos2.py_ shows the implementation of a Flask server for the model. It allowes the model to be approached as a REST API. After starting the server. You can send a POST request to `http://localhost:8005/process_prompt` with the following form data: - `prompt`: For example `<grounding> an image of` - `image`: The image file as binary data This in turn will produce a reply with the following JSON format: - `message`: The Kosmos-2 generated text - `entities`: The extracted entities An easy way to test this is through an application like Postman. Make sure the image field is set to `File`. ```python from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq from flask import Flask, request, jsonify import json app = Flask(__name__) model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) @app.route('/process_prompt', methods=['POST']) def process_prompt(): try: # Get the uploaded image data from the POST request uploaded_file = request.files['image'] prompt = request.form.get('prompt') image = Image.open(uploaded_file.stream) print(image.size) inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"][:, :-1], attention_mask=inputs["attention_mask"][:, :-1], img_features=None, img_attn_mask=inputs["img_attn_mask"][:, :-1], use_cache=True, max_new_tokens=64, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # By default, the generated text is cleanup and the entities are extracted. processed_text, entities = processor.post_process_generation(generated_text) parsed_entities = entities_to_json(entities) print(generated_text) print(processed_text) return jsonify({"message": processed_text, 'entities': parsed_entities}) except Exception as e: return jsonify({"error": str(e)}) def entities_to_json(entities): result = [] for e in entities: label = e[0] box_coords = e[1] box_size = e[2][0] entity_result = { "label": label, "boundingBoxPosition": {"x": box_coords[0], "y": box_coords[1]}, "boundingBox": {"x_min": box_size[0], "y_min": box_size[1], "x_max": box_size[2], "y_max": box_size[3]} } print(entity_result) result.append(entity_result) return result if __name__ == '__main__': app.run(host='localhost', port=8005) ```
Phando/switch-base-32-finetuned-winogrande	Phando	2023-11-02T16:41:19Z	5	0	transformers	[ "transformers", "pytorch", "switch_transformers", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T16:35:54Z	The switch-base-32 model was fine-tuned on the WinoGrande dataset. Validation accuracy: 61.80. The prompt key in PromptSouce: "True or False"
marcelloc/ppo-lunarlander-v2	marcelloc	2023-11-02T16:40:09Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T16:14:18Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.63 +/- 14.51 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 ... ```
Phando/switch-base-32-finetuned-sst2	Phando	2023-11-02T16:34:34Z	3	0	transformers	[ "transformers", "pytorch", "switch_transformers", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T15:58:53Z	The switch-base-32 model was fine-tuned on the SST2 dataset. Validation accuracy: 95.75. Prompt key in PromptSouce: "positive negative after".
Phando/switch-base-32-finetuned-multirc	Phando	2023-11-02T16:33:32Z	4	0	transformers	[ "transformers", "pytorch", "switch_transformers", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T16:20:26Z	The switch-base-32 model was fine-tuned on the MultiRC dataset. Validation F1-score: 76.19. Prompt key in PromptSource: "found_this_answer".
PK-1/Pk-1	PK-1	2023-11-02T16:29:51Z	0	0	null	[ "arxiv:1910.09700", "region:us" ]	null	2023-11-02T16:27:37Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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nickrobinson/distilbert-base-uncased-finetuned-imdb	nickrobinson	2023-11-02T16:21:34Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "fill-mask", "generated_from_trainer", "dataset:imdb", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2023-11-02T16:12:10Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb 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: 2.4119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.7024 \| 1.0 \| 157 \| 2.4966 \| \| 2.5796 \| 2.0 \| 314 \| 2.4282 \| \| 2.5355 \| 3.0 \| 471 \| 2.4510 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
AperMesa/ppo-LunarLander-v2	AperMesa	2023-11-02T16:05:42Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T16:05:20Z	--- 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: 244.88 +/- 23.42 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 ... ```
Nathamon/bert-finetuned-mrpc	Nathamon	2023-11-02T16:05:23Z	4	0	transformers	[ "transformers", "tf", "bert", "text-classification", "generated_from_keras_callback", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-10-20T10:48:59Z	--- license: apache-2.0 base_model: bert-base-multilingual-cased tags: - generated_from_keras_callback model-index: - name: bert-finetuned-mrpc 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. --> # bert-finetuned-mrpc This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.34.1 - TensorFlow 2.12.0 - Datasets 2.14.6 - Tokenizers 0.14.1
fearvel/CutifiedAnimeCharacterDesign_sdxl_lora_v1	fearvel	2023-11-02T16:02:19Z	2	0	diffusers	[ "diffusers", "stable-diffusion", "text-to-image", "StableDiffusionPipeline", "stable-diffusion-diffusers", "region:us" ]	text-to-image	2023-11-02T15:26:45Z	--- tags: - stable-diffusion - text-to-image - diffusers - StableDiffusionPipeline - stable-diffusion-diffusers --- ## Model ![pipeline](cutified_anime_character_design_xl_base_v10.preview.png)
devrunner09/llama2-qa-law-26k-v2	devrunner09	2023-11-02T15:56:07Z	0	0	peft	[ "peft", "region:us" ]	null	2023-11-02T15:47:01Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.6.0.dev0
yesj1234/zhko_mbartLarge_50p_tokenize_run1	yesj1234	2023-11-02T15:43:29Z	4	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "generated_from_trainer", "zh", "ko", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-11-02T15:39:56Z	--- language: - zh - ko base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer metrics: - bleu model-index: - name: zhko_mbartLarge_50p_tokenize_run1 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. --> # zhko_mbartLarge_50p_tokenize_run1 This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8482 - Bleu: 30.6847 - Gen Len: 14.6895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\| \| 1.6487 \| 1.0 \| 2786 \| 1.5519 \| 26.5985 \| 15.0925 \| \| 1.1763 \| 2.0 \| 5572 \| 1.4910 \| 29.1024 \| 14.8538 \| \| 0.8697 \| 3.0 \| 8358 \| 1.5510 \| 29.5842 \| 14.7611 \| \| 0.6221 \| 4.0 \| 11144 \| 1.6445 \| 29.6959 \| 14.7091 \| \| 0.4444 \| 5.0 \| 13930 \| 1.7176 \| 29.6231 \| 14.6204 \| \| 0.3137 \| 6.0 \| 16716 \| 1.7916 \| 29.6666 \| 14.524 \| \| 0.2303 \| 7.0 \| 19502 \| 1.8368 \| 30.4697 \| 14.5571 \| \| 0.1888 \| 8.0 \| 22288 \| 1.8482 \| 30.6847 \| 14.6895 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
sakelariev/bg_news_trf	sakelariev	2023-11-02T15:34:28Z	10	0	spacy	[ "spacy", "ner", "named entity recognition", "token-classification", "bg", "license:cc-by-nc-sa-3.0", "model-index", "region:us" ]	token-classification	2023-11-01T12:03:56Z	--- license: cc-by-nc-sa-3.0 language: - bg metrics: - accuracy library_name: spacy pipeline_tag: token-classification model-index: - name: bg_news_trf results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.8890829694 - name: NER Recall type: recall value: 0.8886948931 - name: NER F Score type: f_score value: 0.8888888889 - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9702076246 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9897910505 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9764380425 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9404442198 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9349327787 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8934417103 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9241131567 tags: - ner - named entity recognition - spacy --- \| Feature \| Description \| \| --- \| --- \| \| Name \| `bg_news_trf` \| \| Version \| `3.5.4` \| \| spaCy \| `>=3.5.4,<3.6.0` \| \| Default Pipeline \| `transformer`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` \| \| Components \| `transformer`, `tagger`, `morphologizer`, `parser`, `trainable_lemmatizer`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB) (Kiril Simov and Petya Osenova) <br> [BERT multilingual base model (uncased)](https://huggingface.co/bert-base-multilingual-uncased) (Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova)\| \| License \| CC-BY-NC-SA-3.0 \| \| Author \| [Ivaylo Sakelariev](https://github.com/sakelariev) \| Bulgarian transformers pipeline for BGspaCy. NER model was trained on privately annotated data (looking for the best way to share the dataset currently). Components: tok2vec, tagger, morphologizer, lemmatizer, parser, ner ### Label Scheme <details> <summary>View label scheme (999 labels for 4 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `tagger` \| `A`, `A-pd`, `A-pi`, `Afsd`, `Afsi`, `Ams-e`, `Amsf`, `Amsh`, `Amsi`, `Ansd`, `Ansi`, `Cc`, `Cp`, `Cr`, `Cs`, `D`, `Dd`, `Dl`, `Dm`, `Dq`, `Dt`, `H-pd`, `H-pi`, `Hfsd`, `Hfsi`, `Hmsf`, `Hmsh`, `Hmsi`, `Hnsi`, `I`, `M`, `Mc--d`, `Mc--i`, `Mc-pd`, `Mc-pi`, `Mc-si`, `Mcf-d`, `Mcf-i`, `Mcfpd`, `Mcfpi`, `Mcfsd`, `Mcfsi`, `Mcm-i`, `Mcmpd`, `Mcmpi`, `Mcmsf`, `Mcmsi`, `Mcn-d`, `Mcn-i`, `Mcnpd`, `Mcnpi`, `Mcnsd`, `Mcnsi`, `Md--d`, `Md--i`, `Md-pd`, `Md-pi`, `Mo-pd`, `Mo-pi`, `Mofsd`, `Mofsi`, `Momsf`, `Momsh`, `Momsi`, `Monsd`, `Monsi`, `My--i`, `My-pi`, `Nc`, `Nc-ld`, `Nc-li`, `Ncfpd`, `Ncfpi`, `Ncfs-v`, `Ncfsd`, `Ncfsi`, `Ncmpd`, `Ncmpi`, `Ncms-v`, `Ncmsd`, `Ncmsf`, `Ncmsh`, `Ncmsi`, `Ncmt`, `Ncnpd`, `Ncnpi`, `Ncnsd`, `Ncnsi`, `Np`, `Np-li`, `Np-pi`, `Npfpd`, `Npfpi`, `Npfs-v`, `Npfsd`, `Npfsi`, `Npmpd`, `Npmpi`, `Npms-a`, `Npms-v`, `Npmsd`, `Npmsf`, `Npmsh`, `Npmsi`, `Npnpd`, `Npnpi`, `Npnsd`, `Npnsi`, `Pca--p`, `Pca--s-f`, `Pca--s-m`, `Pca--s-n`, `Pce-as-m`, `Pce-op`, `Pce-os-f`, `Pce-os-m`, `Pce-os-n`, `Pcl`, `Pcq--s-nd`, `Pda--p`, `Pda--s-f`, `Pda--s-m`, `Pda--s-n`, `Pde-op`, `Pde-os-f`, `Pde-os-m`, `Pde-os-n`, `Pdl`, `Pdm`, `Pdq`, `Pds`, `Pdt`, `Pfa--p`, `Pfa--s-f`, `Pfa--s-m`, `Pfa--s-n`, `Pfe-as-m`, `Pfe-op`, `Pfe-op--d`, `Pfe-op--i`, `Pfe-os-f`, `Pfe-os-fd`, `Pfe-os-fi`, `Pfe-os-m`, `Pfe-os-mf`, `Pfe-os-mh`, `Pfe-os-mi`, `Pfe-os-n`, `Pfe-os-ni`, `Pfl`, `Pfm`, `Pfp--s-n`, `Pfq-----i`, `Pft`, `Pfy-----i`, `Pia--p`, `Pia--s-f`, `Pia--s-m`, `Pia--s-n`, `Pic`, `Pie-as-m`, `Pie-op`, `Pie-os-f`, `Pie-os-m`, `Pie-os-n`, `Pil`, `Pim`, `Pip--s-f`, `Piq`, `Pit`, `Pna--p`, `Pna--s-f`, `Pna--s-m`, `Pna--s-n`, `Pne-as-m`, `Pne-ds-m`, `Pne-os-f`, `Pne-os-m`, `Pne-os-nd`, `Pne-os-ni`, `Pnl`, `Pnm`, `Pnp--s-f`, `Pnt`, `Ppe-op1`, `Ppe-op2`, `Ppe-op3`, `Ppe-os1`, `Ppe-os2`, `Ppe-os3f`, `Ppe-os3m`, `Ppe-os3n`, `Ppelap1`, `Ppelap2`, `Ppelap3`, `Ppelas1`, `Ppelas2`, `Ppelas3f`, `Ppelas3m`, `Ppelas3n`, `Ppeldp3`, `Ppelds1`, `Ppelds3m`, `Ppetap1`, `Ppetap2`, `Ppetap3`, `Ppetas1`, `Ppetas2`, `Ppetas3f`, `Ppetas3m`, `Ppetas3n`, `Ppetdp1`, `Ppetdp2`, `Ppetdp3`, `Ppetds1`, `Ppetds2`, `Ppetds3f`, `Ppetds3m`, `Ppetds3n`, `Ppetsp1`, `Ppetsp2`, `Ppetsp3`, `Ppetss1`, `Ppetss2`, `Ppetss3f`, `Ppetss3m`, `Pph-os2`, `Pphlas2`, `Pphtas2`, `Pphtds2`, `Pphtss2`, `Ppxla`, `Ppxta`, `Ppxtd`, `Ppxts`, `Pra--p`, `Pra--s`, `Pra--s-f`, `Pra--s-m`, `Pra--s-n`, `Prc`, `Pre--s`, `Pre-as-m`, `Pre-op`, `Pre-os-f`, `Pre-os-m`, `Pre-os-n`, `Prl`, `Prm`, `Prp--p`, `Prp--s-f`, `Prp--s-m`, `Prp--s-n`, `Prq`, `Prt`, `Pshl-p2-d`, `Pshl-p2-i`, `Pshl-s2fd`, `Pshl-s2fi`, `Pshl-s2mf`, `Pshl-s2mh`, `Pshl-s2mi`, `Pshl-s2nd`, `Pshl-s2ni`, `Psht--2`, `Psol-p1-d`, `Psol-p2-d`, `Psol-p3-df`, `Psol-p3-dm`, `Psol-p3-dn`, `Psol-p3-if`, `Psol-p3-im`, `Psol-s1fd`, `Psol-s1fi`, `Psol-s1mf`, `Psol-s1mh`, `Psol-s1mi`, `Psol-s1nd`, `Psol-s1ni`, `Psol-s2ni`, `Psol-s3fdf`, `Psol-s3fdm`, `Psol-s3fdn`, `Psol-s3fif`, `Psol-s3fim`, `Psol-s3mff`, `Psol-s3mfm`, `Psol-s3mfn`, `Psol-s3mhf`, `Psol-s3mhm`, `Psol-s3mhn`, `Psol-s3mim`, `Psol-s3min`, `Psol-s3ndf`, `Psol-s3ndm`, `Psol-s3ndn`, `Psol-s3nim`, `Psol-s3nin`, `Psot--1`, `Psot--2`, `Psot--3--f`, `Psot--3--m`, `Psot--3--n`, `Psxlop--d`, `Psxlop--i`, `Psxlos-fd`, `Psxlos-fi`, `Psxlos-mh`, `Psxlos-mi`, `Psxlos-nd`, `Psxlos-ni`, `Psxto`, `Pszl-p1-d`, `Pszl-p1-i`, `Pszl-p3-d`, `Pszl-p3-i`, `Pszl-s1fd`, `Pszl-s1fi`, `Pszl-s1mf`, `Pszl-s1mh`, `Pszl-s1mi`, `Pszl-s1nd`, `Pszl-s1ni`, `Pszl-s2fd`, `Pszl-s2mh`, `Pszl-s2mi`, `Pszl-s2nd`, `Pszl-s3fd`, `Pszl-s3fi`, `Pszl-s3mf`, `Pszl-s3mh`, `Pszl-s3mi`, `Pszl-s3nd`, `Pszl-s3ni`, `Pszt--1`, `Pszt--2`, `Pszt--3`, `R`, `T`, `Ta`, `Te`, `Ti`, `Tm`, `Tn`, `Tt`, `Tv`, `Tx`, `Unknown`, `V`, `Viitf-r3p`, `Vniicam-sni`, `Vniicao-sni`, `Vniif-m3s`, `Vniif-o3s`, `Vniif-r3s`, `Vnitcam-sni`, `Vnitcao-sni`, `Vnitf-m3s`, `Vnitf-r3s`, `Vnpicao-sni`, `Vnpif-o3s`, `Vnpif-r3s`, `Vnptcao-sni`, `Vnptf-m3s`, `Vpiicam-p-i`, `Vpiicam-sfi`, `Vpiicam-smi`, `Vpiicam-sni`, `Vpiicao-p-d`, `Vpiicao-p-i`, `Vpiicao-sfi`, `Vpiicao-smi`, `Vpiicao-sni`, `Vpiicar-p-d`, `Vpiicar-p-i`, `Vpiicar-sfd`, `Vpiicar-sfi`, `Vpiicar-smf`, `Vpiicar-smh`, `Vpiicar-smi`, `Vpiicar-snd`, `Vpiicar-sni`, `Vpiicv--sni`, `Vpiif-m1p`, `Vpiif-m1s`, `Vpiif-m2s`, `Vpiif-m3p`, `Vpiif-m3s`, `Vpiif-o1p`, `Vpiif-o1s`, `Vpiif-o3p`, `Vpiif-o3s`, `Vpiif-r1p`, `Vpiif-r1s`, `Vpiif-r2p`, `Vpiif-r2s`, `Vpiif-r3p`, `Vpiif-r3s`, `Vpiig`, `Vpiiz--2p`, `Vpiiz--2s`, `Vpitcam-p-i`, `Vpitcam-sfi`, `Vpitcam-smi`, `Vpitcam-sni`, `Vpitcao-p-i`, `Vpitcao-sfi`, `Vpitcao-smi`, `Vpitcao-sni`, `Vpitcar-p-d`, `Vpitcar-p-i`, `Vpitcar-sfd`, `Vpitcar-sfi`, `Vpitcar-smf`, `Vpitcar-smh`, `Vpitcar-smi`, `Vpitcar-snd`, `Vpitcar-sni`, `Vpitcv--p-d`, `Vpitcv--p-i`, `Vpitcv--sfd`, `Vpitcv--sfi`, `Vpitcv--smf`, `Vpitcv--smh`, `Vpitcv--smi`, `Vpitcv--snd`, `Vpitcv--sni`, `Vpitf-m1p`, `Vpitf-m1s`, `Vpitf-m2p`, `Vpitf-m2s`, `Vpitf-m3p`, `Vpitf-m3s`, `Vpitf-o1p`, `Vpitf-o1s`, `Vpitf-o2p`, `Vpitf-o2s`, `Vpitf-o3p`, `Vpitf-o3s`, `Vpitf-r1p`, `Vpitf-r1s`, `Vpitf-r2p`, `Vpitf-r2s`, `Vpitf-r3p`, `Vpitf-r3s`, `Vpitg`, `Vpitz--2p`, `Vpitz--2s`, `Vppicao-p-d`, `Vppicao-p-i`, `Vppicao-sfd`, `Vppicao-sfi`, `Vppicao-smf`, `Vppicao-smh`, `Vppicao-smi`, `Vppicao-snd`, `Vppicao-sni`, `Vppif-m3p`, `Vppif-m3s`, `Vppif-o1p`, `Vppif-o1s`, `Vppif-o2s`, `Vppif-o3p`, `Vppif-o3s`, `Vppif-r1p`, `Vppif-r1s`, `Vppif-r2p`, `Vppif-r2s`, `Vppif-r3p`, `Vppif-r3s`, `Vppiz--2p`, `Vppiz--2s`, `Vpptcam-smi`, `Vpptcao-p-d`, `Vpptcao-p-i`, `Vpptcao-sfd`, `Vpptcao-sfi`, `Vpptcao-smh`, `Vpptcao-smi`, `Vpptcao-snd`, `Vpptcao-sni`, `Vpptcv--p-d`, `Vpptcv--p-i`, `Vpptcv--sfd`, `Vpptcv--sfi`, `Vpptcv--smf`, `Vpptcv--smh`, `Vpptcv--smi`, `Vpptcv--snd`, `Vpptcv--sni`, `Vpptf-m3p`, `Vpptf-m3s`, `Vpptf-o1p`, `Vpptf-o1s`, `Vpptf-o2p`, `Vpptf-o2s`, `Vpptf-o3p`, `Vpptf-o3s`, `Vpptf-r1p`, `Vpptf-r1s`, `Vpptf-r2p`, `Vpptf-r2s`, `Vpptf-r3p`, `Vpptf-r3s`, `Vpptz--2p`, `Vpptz--2s`, `Vxitcat-p-i`, `Vxitcat-sfi`, `Vxitcat-smi`, `Vxitcat-sni`, `Vxitf-r1p`, `Vxitf-r1s`, `Vxitf-r2p`, `Vxitf-r2s`, `Vxitf-r3p`, `Vxitf-r3s`, `Vxitf-t1p`, `Vxitf-t1s`, `Vxitf-t2p`, `Vxitf-t2s`, `Vxitf-t3p`, `Vxitf-t3s`, `Vxitu-o1p`, `Vxitu-o1s`, `Vxitu-o2p`, `Vxitu-o2s`, `Vxitu-o3p`, `Vxitu-o3s`, `Vyptf-o3s`, `Vyptf-r1p`, `Vyptf-r1s`, `Vyptf-r2p`, `Vyptf-r2s`, `Vyptf-r3p`, `Vyptf-r3s`, `punct` \| \| `morphologizer` \| `POS=ADP`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `POS=PUNCT`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=AUX`, `Case=Acc\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PART\\|Polarity=Neg`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Degree=Pos\\|POS=ADV`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=INTJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|POS=DET\\|PronType=Dem`, `Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=PART`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|VerbForm=Fin`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=CCONJ`, `Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Definite=Ind\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Definite=Def\\|Number=Ptan\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|POS=PRON\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Degree=Sup\\|POS=ADV`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Definite=Def\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Dem`, `POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Number=Ptan\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Definite=Def\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Ind`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADV\\|PronType=Neg`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Definite=Ind\\|Degree=Pos\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `POS=ADV`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|VerbForm=Fin`, `Degree=Cmp\\|POS=ADV`, `Definite=Def\\|Degree=Pos\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Definite=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|POS=PRON\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Definite=Ind\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADV\\|PronType=Rel`, `Aspect=Imp\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|VerbForm=Fin`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Definite=Def\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Animacy=Anim\\|Definite=Ind\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|VerbForm=Fin`, `POS=ADV\\|PronType=Tot`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Definite=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Imp\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Def\\|Degree=Pos\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=SCONJ`, `Aspect=Imp\\|Mood=Cnd\\|Number=Sing\\|POS=AUX\\|Person=1\\|VerbForm=Fin`, `Aspect=Perf\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Number=Plur\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Gender=Neut\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Definite=Def\\|Number=Plur\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Imp\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Definite=Ind\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Gender=Masc\\|Number=Count\\|POS=NOUN`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Definite=Ind\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Imp\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `POS=ADV\\|PronType=Int`, `Aspect=Imp\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Imp\\|Mood=Cnd\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `NumType=Card\\|POS=ADV\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Aspect=Imp\\|Definite=Ind\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Definite=Def\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Definite=Ind\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Imp\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Number=Plur\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Def\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Definite=Def\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Def\\|Gender=Neut\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Aspect=Perf\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Number=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `POS=PRON\\|PronType=Int`, `Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Number=Plur\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Definite=Def\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Number=Plur\\|POS=DET\\|PronType=Int`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `NumType=Card\\|POS=ADV\\|PronType=Int`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Definite=Ind\\|Degree=Pos\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Definite=Def\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Definite=Ind\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Mood=Cnd\\|Number=Plur\\|POS=AUX\\|Person=2\\|VerbForm=Fin`, `Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Aspect=Imp\\|Definite=Ind\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Def\\|Number=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Number=Plur\\|POS=DET\\|PronType=Rel`, `Aspect=Perf\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Animacy=Anim\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Imp\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `NumType=Card\\|POS=ADV\\|PronType=Dem`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Definite=Ind\\|Number=Plur\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Degree=Cmp\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Def\\|Degree=Pos\\|Gender=Neut\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Def\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Degree=Cmp\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Perf\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Definite=Def\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Aspect=Perf\\|Definite=Def\\|Degree=Sup\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Number=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Imp\\|VerbForm=Fin\\|Voice=Act`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|POS=DET\\|PronType=Neg`, `Definite=Ind\\|Number=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Perf\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Definite=Def\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Imp\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Def\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Aspect=Imp\\|Definite=Def\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Degree=Pos\\|POS=ADJ`, `Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Aspect=Imp\\|Mood=Cnd\\|Number=Plur\\|POS=AUX\\|Person=1\\|VerbForm=Fin`, `Aspect=Imp\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Def\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Sup\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Aspect=Imp\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Degree=Cmp\\|POS=ADV\\|PronType=Dem`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Aspect=Imp\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Def\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `POS=NOUN`, `Case=Nom\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Mood=Cnd\\|Number=Sing\\|POS=AUX\\|Person=2\\|VerbForm=Fin`, `POS=PROPN`, `Aspect=Imp\\|Mood=Cnd\\|Number=Plur\\|POS=AUX\\|Person=3\\|VerbForm=Fin`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Definite=Ind\\|Degree=Cmp\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Imp\\|Definite=Ind\\|Number=Plur\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Animacy=Anim\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Aspect=Perf\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Tense=Imp\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Number=Ptan\\|POS=PROPN`, `Definite=Def\\|Degree=Sup\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `Definite=Ind\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Definite=Def\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Ind\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Number=Plur\\|POS=PROPN`, `Aspect=Imp\\|Definite=Ind\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Definite=Def\\|Degree=Sup\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Pass`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `POS=ADJ`, `Aspect=Perf\\|Definite=Def\\|Degree=Pos\\|Number=Plur\\|POS=VERB\\|VerbForm=Part\\|Voice=Pass`, `NumType=Ord\\|POS=NUM`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Number=Plur\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADP`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `POS=X`, `Foreign=Yes\\|POS=X`, `Aspect=Imp\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Definite=Def\\|Degree=Cmp\\|NumType=Card\\|Number=Plur\\|POS=ADV`, `Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Definite=Def\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Tense=Past\\|VerbForm=Part\\|Voice=Act` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `vocative`, `xcomp` \| \| `ner` \| `LOC`, `ORG`, `PER` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TAG_ACC` \| 97.02 \| \| `POS_ACC` \| 98.98 \| \| `MORPH_ACC` \| 97.64 \| \| `LEMMA_ACC` \| 94.04 \| \| `DEP_UAS` \| 93.49 \| \| `DEP_LAS` \| 89.34 \| \| `SENTS_P` \| 92.62 \| \| `SENTS_R` \| 92.20 \| \| `SENTS_F` \| 92.41 \| \| `ENTS_F` \| 88.91 \| \| `ENTS_P` \| 88.87 \| \| `ENTS_R` \| 88.89 \|
avishniakov/test	avishniakov	2023-11-02T15:25:02Z	0	0	null	[ "region:us" ]	null	2023-11-02T15:19:39Z	--- new_metadata: other value description: another model version desc --- # test ## Description another model version desc ## Metadata new_metadata: other value _This model was created from ZenML_
YieldInc/pythoncode	YieldInc	2023-11-02T15:17:54Z	4	0	peft	[ "peft", "region:us" ]	null	2023-11-02T15:17:27Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Framework versions - PEFT 0.6.0.dev0
Alberto/twitter_xlm_robertta_sentiment_financial_news	Alberto	2023-11-02T15:06:23Z	7	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75", "base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment", "base_model:finetune:cardiffnlp/twitter-xlm-roberta-base-sentiment", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-02T14:22:16Z	--- base_model: cardiffnlp/twitter-xlm-roberta-base-sentiment tags: - generated_from_trainer metrics: - f1 model-index: - name: twitter_xlm_robertta_sentiment_financial_news results: [] datasets: - Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75 --- <!-- 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. --> # twitter_xlm_robertta_sentiment_financial_news This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on [this]()https://huggingface.co/datasets/Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75 financial dataset. It achieves the following results on the evaluation set: - Loss: 0.4492 - F1: 0.8812 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.518 \| 1.0 \| 556 \| 0.4881 \| 0.8184 \| \| 0.3534 \| 2.0 \| 1112 \| 0.5041 \| 0.8797 \| \| 0.1781 \| 3.0 \| 1668 \| 0.4492 \| 0.8812 \| ### Framework versions - Transformers 4.33.3 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.13.1
Bukun/distilbert-base-uncased-finetuned-sentence-intent	Bukun	2023-11-02T15:02:35Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-11-02T14:40:37Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sentence-intent 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. --> # distilbert-base-uncased-finetuned-sentence-intent This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - Accuracy: 0.0003 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.2222 \| 1.0 \| 938 \| 1.6962 \| 0.0 \| \| 1.1706 \| 2.0 \| 1876 \| 0.4995 \| 0.0 \| \| 0.3065 \| 3.0 \| 2814 \| 0.2630 \| 0.0003 \| \| 0.124 \| 4.0 \| 3752 \| 0.2107 \| 0.0003 \| \| 0.0712 \| 5.0 \| 4690 \| 0.2002 \| 0.0003 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
xrcb/sd-class-butterflies-32	xrcb	2023-11-02T15:02:24Z	3	0	diffusers	[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	unconditional-image-generation	2023-10-23T13:15:58Z	--- 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('xrcb/sd-class-butterflies-32') image = pipeline().images[0] image ```
hatanp/gpt-fi-small	hatanp	2023-11-02T14:58:39Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "finnish", "fi", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-27T11:31:33Z	--- language: - fi tags: - finnish - gpt2 widget: - text: "Jotta voidaan luoda tekstiä" library: - transformers license: apache-2.0 --- ## DEPRECATED! This model is old and no longer relevant with the releases of all around better Finnish models such as GPT-3 models from [TurkuNLP](https://huggingface.co/TurkuNLP) You may of course still use this for experiments and benchmarking, but I doubt this will work any better. ## Old description: A small version of a larger model [gpt-fi](https://huggingface.co/hatanp/gpt-fi). This model has approximately 125M parameters compared to the 1.2B parameters of the larger model. For scripts and more complete model information refer to the large models' page.
nosnelmil/GPT2-CompareTransformers-Imdb	nosnelmil	2023-11-02T14:58:00Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-classification", "generated_from_trainer", "dataset:imdb", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2023-11-02T14:57:39Z	--- license: mit base_model: gpt2 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - precision - recall - f1 model-index: - name: results results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9330666661262512 - name: Precision type: precision value: 0.9330666661262512 - name: Recall type: recall value: 0.9330666661262512 - name: F1 type: f1 value: 0.9330666661262512 --- <!-- 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. --> # results This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.2797 - Accuracy: 0.9331 - Precision: 0.9331 - Recall: 0.9331 - F1: 0.9331 - Auroc: 0.9810 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Auroc \| F1 \| Validation Loss \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:--------:\|:------:\|:------:\|:---------------:\|:---------:\|:------:\| \| 0.1436 \| 0.46 \| 500 \| 0.8935 \| 0.9751 \| 0.8935 \| 0.2923 \| 0.8935 \| 0.8935 \| \| 0.1621 \| 0.91 \| 1000 \| 0.9261 \| 0.9789 \| 0.9261 \| 0.1984 \| 0.9261 \| 0.9261 \| \| 0.2196 \| 1.37 \| 1500 \| 0.9289 \| 0.9810 \| 0.9289 \| 0.2082 \| 0.9289 \| 0.9289 \| \| 0.1457 \| 1.83 \| 2000 \| 0.9325 \| 0.9816 \| 0.9325 \| 0.2282 \| 0.9325 \| 0.9325 \| \| 0.1103 \| 2.29 \| 2500 \| 0.9305 \| 0.9806 \| 0.9305 \| 0.3201 \| 0.9305 \| 0.9305 \| \| 0.0679 \| 2.74 \| 3000 \| 0.2797 \| 0.9331 \| 0.9331 \| 0.9331 \| 0.9331 \| 0.9810 \| ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
Juniplayground/Mistral_orca-7B-640_v2	Juniplayground	2023-11-02T14:50:39Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Open-Orca/Mistral-7B-OpenOrca", "base_model:adapter:Open-Orca/Mistral-7B-OpenOrca", "region:us" ]	null	2023-11-02T14:50:31Z	--- library_name: peft base_model: Open-Orca/Mistral-7B-OpenOrca --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - load_in_8bit: True - load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 ### Framework versions - PEFT 0.6.0
yobett/dreamshaper_8-t_QO1DxVwt3KtECwuf	yobett	2023-11-02T14:48:29Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-02T14:28:53Z	--- license: creativeml-openrail-m base_model: dreamshaper_8 instance_prompt: apo1102 man tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - dreambooth inference: true --- # DreamBooth - yobett/dreamshaper_8-t_QO1DxVwt3KtECwuf This is a dreambooth model derived from dreamshaper_8. The weights were trained on apo1102 man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ![img_4](./image_4.png) ![img_5](./image_5.png) ![img_6](./image_6.png) ![img_7](./image_7.png) ![img_8](./image_8.png) ![img_9](./image_9.png) ![img_10](./image_10.png) ![img_11](./image_11.png) ![img_12](./image_12.png) ![img_13](./image_13.png) ![img_14](./image_14.png) ![img_15](./image_15.png) ![img_16](./image_16.png) ![img_17](./image_17.png) ![img_18](./image_18.png) ![img_19](./image_19.png) ![img_20](./image_20.png) ![img_21](./image_21.png) ![img_22](./image_22.png) ![img_23](./image_23.png) ![img_24](./image_24.png) ![img_25](./image_25.png) ![img_26](./image_26.png) ![img_27](./image_27.png) ![img_28](./image_28.png) ![img_29](./image_29.png) ![img_30](./image_30.png) ![img_31](./image_31.png) DreamBooth for the text encoder was enabled: True.
TheBloke/basilisk-7B-v0.2-GGUF	TheBloke	2023-11-02T14:47:52Z	183	5	transformers	[ "transformers", "gguf", "mistral", "base_model:openerotica/basilisk-7b-v0.2", "base_model:quantized:openerotica/basilisk-7b-v0.2", "license:apache-2.0", "region:us" ]	null	2023-11-02T14:43:13Z	--- base_model: openerotica/basilisk-7b-v0.2 inference: false license: apache-2.0 model_creator: openerotica model_name: Basilisk 7B v0.2 model_type: mistral prompt_template: '{prompt} ' quantized_by: TheBloke --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Basilisk 7B v0.2 - GGUF - Model creator: [openerotica](https://huggingface.co/openerotica) - Original model: [Basilisk 7B v0.2](https://huggingface.co/openerotica/basilisk-7b-v0.2) <!-- description start --> ## Description This repo contains GGUF format model files for [openerotica's Basilisk 7B v0.2](https://huggingface.co/openerotica/basilisk-7b-v0.2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/basilisk-7B-v0.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF) * [openerotica's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openerotica/basilisk-7b-v0.2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Unknown ``` {prompt} ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [basilisk-7b-v0.2.Q2_K.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q2_K.gguf) \| Q2_K \| 2 \| 3.08 GB\| 5.58 GB \| smallest, significant quality loss - not recommended for most purposes \| \| [basilisk-7b-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.16 GB\| 5.66 GB \| very small, high quality loss \| \| [basilisk-7b-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [basilisk-7b-v0.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.82 GB\| 6.32 GB \| small, substantial quality loss \| \| [basilisk-7b-v0.2.Q4_0.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q4_0.gguf) \| Q4_0 \| 4 \| 4.11 GB\| 6.61 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [basilisk-7b-v0.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [basilisk-7b-v0.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [basilisk-7b-v0.2.Q5_0.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q5_0.gguf) \| Q5_0 \| 5 \| 5.00 GB\| 7.50 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [basilisk-7b-v0.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.00 GB\| 7.50 GB \| large, low quality loss - recommended \| \| [basilisk-7b-v0.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [basilisk-7b-v0.2.Q6_K.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q6_K.gguf) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [basilisk-7b-v0.2.Q8_0.gguf](https://huggingface.co/TheBloke/basilisk-7B-v0.2-GGUF/blob/main/basilisk-7b-v0.2.Q8_0.gguf) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \| Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/basilisk-7B-v0.2-GGUF and below it, a specific filename to download, such as: basilisk-7b-v0.2.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/basilisk-7B-v0.2-GGUF basilisk-7b-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/basilisk-7B-v0.2-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/basilisk-7B-v0.2-GGUF basilisk-7b-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m basilisk-7b-v0.2.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model in Python code, using ctransformers #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install ctransformers # Or with CUDA GPU acceleration pip install ctransformers[cuda] # Or with AMD ROCm GPU acceleration (Linux only) CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems only CT_METAL=1 pip install ctransformers --no-binary ctransformers ``` #### Simple ctransformers example code ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/basilisk-7B-v0.2-GGUF", model_file="basilisk-7b-v0.2.Q4_K_M.gguf", model_type="mistral", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: openerotica's Basilisk 7B v0.2 <!-- original-model-card end -->
IdoCK/DRL_HuggingFace	IdoCK	2023-11-02T14:46:37Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T14:46:18Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 271.74 +/- 20.87 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 ... ```
KBlueLeaf/kohaku-xl-beta5	KBlueLeaf	2023-11-02T14:44:38Z	195	4	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-11-02T14:41:10Z	--- license: creativeml-openrail-m ---
KBlueLeaf/kohaku-xl-beta7	KBlueLeaf	2023-11-02T14:44:03Z	5	1	diffusers	[ "diffusers", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2023-11-02T14:39:13Z	--- license: creativeml-openrail-m ---
vishwa27/wav2vec2-large-xls-r-300m-gn	vishwa27	2023-11-02T14:42:19Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:facebook/wav2vec2-large-xlsr-53-spanish", "base_model:finetune:facebook/wav2vec2-large-xlsr-53-spanish", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-11-02T12:22:51Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53-spanish tags: - generated_from_trainer datasets: - common_voice_13_0 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-gn results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_13_0 type: common_voice_13_0 config: gn split: test args: gn metrics: - name: Wer type: wer value: 0.3430613460393091 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-gn This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3713 - Wer: 0.3431 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7177 \| 3.62 \| 400 \| 0.3649 \| 0.5816 \| \| 0.2738 \| 7.24 \| 800 \| 0.4029 \| 0.5024 \| \| 0.1768 \| 10.86 \| 1200 \| 0.3779 \| 0.4285 \| \| 0.1128 \| 14.48 \| 1600 \| 0.3929 \| 0.4205 \| \| 0.0842 \| 18.1 \| 2000 \| 0.3683 \| 0.3916 \| \| 0.0616 \| 21.72 \| 2400 \| 0.3943 \| 0.3675 \| \| 0.0461 \| 25.34 \| 2800 \| 0.4127 \| 0.3571 \| \| 0.0368 \| 28.96 \| 3200 \| 0.3713 \| 0.3431 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1
sh-holmes/ppo-LunarLander-v2	sh-holmes	2023-11-02T14:42:10Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2023-11-02T14:41:51Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 258.58 +/- 19.30 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 ... ```
yiboyang/shallow-ntc-checkpoints	yiboyang	2023-11-02T14:40:26Z	0	0	null	[ "region:us" ]	null	2023-11-02T02:05:30Z	This repo contains model checkpoints trained in the [shallow-ntc project](https://github.com/mandt-lab/shallow-ntc); see the project repo for more information.
zhixuluo/my_awesome_asr_mind_model	zhixuluo	2023-11-02T14:35:42Z	3	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-10-26T12:41:46Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - wer model-index: - name: my_awesome_asr_mind_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_asr_mind_model This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9977 - Wer: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 3.1676 \| 100.0 \| 500 \| 3.1581 \| 1.0 \| \| 2.8849 \| 200.0 \| 1000 \| 2.9977 \| 1.0 \| ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3
gustavecortal/setfit-nouns	gustavecortal	2023-11-02T14:22:13Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "setfit", "text-classification", "arxiv:2209.11055", "license:apache-2.0", "region:us" ]	text-classification	2023-11-02T14:21:59Z	--- license: apache-2.0 tags: - setfit - sentence-transformers - text-classification pipeline_tag: text-classification --- # gustavecortal/setfit-nouns 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("gustavecortal/setfit-nouns") # 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} } ```
omarelsayeed/Search_Model_PRECHATS_AUGMENTED	omarelsayeed	2023-11-02T14:22:07Z	4	1	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2023-11-02T11:35:21Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## 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 = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # 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 <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2927 with parameters: ``` {'batch_size': 150, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `__main__.LoggingDenoisingAutoEncoderLoss` Parameters of the fit()-Method: ``` { "epochs": 25, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "constantlr", "steps_per_epoch": null, "warmup_steps": 10000, "weight_decay": 0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 150, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jord-hanus/my_awesome_qa_model	jord-hanus	2023-11-02T14:13:28Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2023-11-02T14:07:10Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - squad model-index: - name: my_awesome_qa_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_qa_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 3 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu118 - Datasets 2.14.6 - Tokenizers 0.14.1

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