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sglang/docs/index.rst
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SGLang Documentation
====================================
SGLang is a fast serving framework for large language models and vision language models.
It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
The core features include:
- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (INT4/FP8/AWQ/GPTQ).
- **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
- **Extensive Model Support**: Supports a wide range of generative models (Llama, Gemma, Mistral, QWen, DeepSeek, LLaVA, etc.), embedding models (e5-mistral, gte) and reward models (Skywork), with easy extensibility for integrating new models.
- **Active Community**: SGLang is open-source and backed by an active community with industry adoption.
.. toctree::
:maxdepth: 1
:caption: Getting Started
start/install.md
start/send_request.ipynb
.. toctree::
:maxdepth: 1
:caption: Backend Tutorial
backend/openai_api_completions.ipynb
backend/openai_api_vision.ipynb
backend/openai_api_embeddings.ipynb
backend/native_api.ipynb
backend/offline_engine_api.ipynb
backend/backend.md
.. toctree::
:maxdepth: 1
:caption: Frontend Tutorial
frontend/frontend.md
frontend/choices_methods.md
.. toctree::
:maxdepth: 1
:caption: References
references/supported_models.md
references/sampling_params.md
references/hyperparameter_tuning.md
references/benchmark_and_profiling.md
references/custom_chat_template.md
references/contributor_guide.md
references/troubleshooting.md
references/faq.md
references/learn_more.md