# Table of Contents

- [Hugging Face - Documentation](#hugging-face-documentation)

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# Hugging Face - Documentation

Documentations
==============

Hub & Client Libraries
----------------------

[Hub\
\
Host Git-based models, datasets, and Spaces on the HF Hub](/docs/hub)
[Hub Python Library\
\
Python client to interact with the Hugging Face Hub](/docs/huggingface_hub)
[Huggingface.js\
\
JavaScript libraries for Hugging Face with built-in TS types](/docs/huggingface.js)
[Tasks\
\
Explore demos, models, and datasets for any ML tasks](/tasks)
[Dataset viewer\
\
API for metadata, stats, and content of HF Hub datasets](/docs/dataset-viewer)

Core ML Libraries
-----------------

[Transformers\
\
State-of-the-art ML for PyTorch, TensorFlow, JAX](/docs/transformers)
[Diffusers\
\
State-of-the-art Diffusion models in PyTorch](/docs/diffusers)
[Datasets\
\
Access & share datasets for any ML tasks](/docs/datasets)
[Transformers.js\
\
State-of-the-art ML running directly in your browser](/docs/transformers.js)
[Tokenizers\
\
Fast tokenizers optimized for research & production](/docs/tokenizers)
[Evaluate\
\
Evaluate and compare models performance](/docs/evaluate)
[timm\
\
State-of-the-art vision models: layers, optimizers, and utilities](/docs/timm)

Training & Optimization
-----------------------

[PEFT\
\
Parameter-efficient finetuning for large language models](/docs/peft)
[Accelerate\
\
Train PyTorch models with multi-GPU, TPU, mixed precision](/docs/accelerate)
[Optimum\
\
Optimize HF Transformers for faster training/inference](/docs/optimum)
[AWS Trainium & Inferentia\
\
Train/deploy Transformers/Diffusers on AWS](/docs/optimum-neuron)
[TRL\
\
Train transformers LMs with reinforcement learning](/docs/trl)
[Safetensors\
\
Safe way to store/distribute neural network weights](/docs/safetensors)
[Bitsandbytes\
\
Optimize and quantize models with bitsandbytes](/docs/bitsandbytes)
[Sentence Transformers\
\
Multilingual Sentence & Image Embeddings](https://sbert.net/)
[Lighteval\
\
All-in-one toolkit to evaluate LLMs across multiple backends](/docs/lighteval)

Deployment & Inference
----------------------

[Inference API (serverless)\
\
Experiment with 200k+ models via Serverless Inference](/docs/api-inference)
[Inference Endpoints (dedicated)\
\
Deploy models on dedicated & fully managed infrastructure](/docs/inference-endpoints)
[Amazon SageMaker\
\
Train/deploy Transformers models with SageMaker/HF DLCs](/docs/sagemaker)
[Text Generation Inference\
\
Serve language models with TGI optimized toolkit](/docs/text-generation-inference)
[Text Embeddings Inference\
\
Serve embeddings models with TEI optimized toolkit](/docs/text-embeddings-inference)

Collaboration & Extras
----------------------

[Gradio\
\
Build ML demos and web apps with a few lines of Python](https://www.gradio.app/docs/)
[smolagents\
\
Smol library to build great agents in Python](/docs/smolagents)
[AutoTrain\
\
AutoTrain API and UI for seamless model training](/docs/autotrain)
[Competitions\
\
Create and host ML competitions on Hugging Face](/docs/competitions)
[Chat UI\
\
Open source chat frontend powering HuggingChat](/docs/chat-ui)
[Leaderboards\
\
Create custom Leaderboards on Hugging Face](/docs/leaderboards)
[Argilla\
\
Collaboration tool for building high-quality datasets](https://argilla-io.github.io/argilla/)
[Distilabel\
\
Framework for synthetic data generation and AI feedback](https://distilabel.argilla.io/)
[Hugging Face Generative AI Services (HUGS)\
\
Zero-configuration optimized microservices for open models](/docs/hugs)

Community
---------

*   [Blog](/blog)
    
*   [Learn](/learn)
    
*   [Discord](/join/discord)
    
*   [Forum](https://discuss.huggingface.co/)
    
*   [Github](https://github.com/huggingface)

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