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SGLang Documentation
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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:
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- **Fast Backend Runtime** : Provides efficient serving with RadixAttention for prefix caching, jump-forward constrained decoding, overhead-free CPU scheduler, continuous batching, token attention (paged attention), tensor parallelism, FlashInfer kernels, chunked prefill, and quantization (FP8/INT4/AWQ/GPTQ).
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- **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.
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- **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.
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- **Active Community** : SGLang is open-source and backed by an active community with industry adoption.
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.. toctree ::
:maxdepth: 1
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:caption: Getting Started
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start/install.md
start/send_request.ipynb
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.. toctree ::
:maxdepth: 1
:caption: Backend Tutorial
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backend/openai_api_completions.ipynb
backend/openai_api_vision.ipynb
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backend/openai_api_embeddings.ipynb
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backend/native_api.ipynb
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backend/offline_engine_api.ipynb
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backend/backend.md
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.. toctree ::
:maxdepth: 1
:caption: Frontend Tutorial
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frontend/frontend.md
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frontend/choices_methods.md
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.. toctree ::
:maxdepth: 1
:caption: SGLang Router
router/router.md
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.. toctree ::
:maxdepth: 1
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:caption: References
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references/supported_models.md
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references/sampling_params.md
references/hyperparameter_tuning.md
references/benchmark_and_profiling.md
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references/custom_chat_template.md
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references/contributor_guide.md
references/troubleshooting.md
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references/faq.md
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references/learn_more.md