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, zero-overhead CPU scheduler, continuous batching, token attention (paged attention), speculative decoding, tensor parallelism, chunked prefill, structured outputs, and quantization (FP8/INT4/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, mcdse) 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: Installation start/install.md .. toctree:: :maxdepth: 1 :caption: Backend Tutorial references/deepseek backend/send_request.ipynb 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/server_arguments.md backend/sampling_params.md backend/hyperparameter_tuning.md .. toctree:: :maxdepth: 1 :caption: Advanced Features backend/speculative_decoding.ipynb backend/structured_outputs.ipynb backend/function_calling.ipynb backend/separate_reasoning.ipynb backend/custom_chat_template.md backend/quantization.md .. toctree:: :maxdepth: 1 :caption: Frontend Tutorial frontend/frontend.ipynb frontend/choices_methods.md .. toctree:: :maxdepth: 1 :caption: SGLang Router router/router.md .. toctree:: :maxdepth: 1 :caption: References references/general references/hardware references/advanced_deploy references/performance_tuning