32 lines
1.4 KiB
ReStructuredText
32 lines
1.4 KiB
ReStructuredText
SGLang Documentation
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SGLang is a fast serving framework for large language models and vision language models.
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It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
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The core features include:
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- **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).
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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 3, Gemma 2, Mistral, QWen, DeepSeek, LLaVA, etc.) and embedding models (e5-mistral), 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::
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:maxdepth: 1
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:caption: Getting Started
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install.md
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backend.md
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frontend.md
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.. toctree::
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:maxdepth: 1
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:caption: References
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sampling_params.md
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hyperparameter_tuning.md
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model_support.md
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contributor_guide.md
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choices_methods.md
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benchmark_and_profiling.md
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troubleshooting.md
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