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README.html

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<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
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<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />
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<script src="_static/documentation_options.js?v=0c96e96c"></script>
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<script src="_static/documentation_options.js?v=9a529362"></script>
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<script src="_static/doctools.js?v=9a2dae69"></script>
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<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
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<script src="_static/clipboard.min.js?v=a7894cd8"></script>
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<link rel="search" title="Search" href="search.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docsearch:language" content="en"/>
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<meta name="docsearch:version" content="0.4.3.post4" />
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<meta name="docsearch:version" content="0.4.4" />
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<meta name="docbuild:last-update" content="Mar 13, 2025"/>
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</head>
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_sources/backend/function_calling.ipynb

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_sources/backend/native_api.ipynb

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_sources/backend/offline_engine_api.ipynb

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_sources/backend/openai_api_completions.ipynb

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_sources/backend/openai_api_embeddings.ipynb

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_sources/backend/openai_api_vision.ipynb

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_sources/backend/send_request.ipynb

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_sources/backend/separate_reasoning.ipynb

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_sources/backend/speculative_decoding.ipynb

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_sources/backend/structured_outputs.ipynb

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_sources/developer/setup_github_runner.md

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# Nvidia
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docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-devel-ubuntu22.04 /bin/bash
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# AMD
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docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.3.post4-rocm630 /bin/bash
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docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.4-rocm630 /bin/bash
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# AMD just the last 2 GPUs
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docker run --rm --device=/dev/kfd --device=/dev/dri/renderD176 --device=/dev/dri/renderD184 --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.3.post4-rocm630 /bin/bash
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docker run --rm --device=/dev/kfd --device=/dev/dri/renderD176 --device=/dev/dri/renderD184 --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.4-rocm630 /bin/bash
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```
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### Step 2: Configure the runner by `config.sh`

_sources/frontend/frontend.ipynb

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_sources/start/install.md

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```bash
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pip install --upgrade pip
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pip install uv
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uv pip install "sglang[all]>=0.4.3.post4" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
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uv pip install "sglang[all]>=0.4.4" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
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```
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**Quick Fixes to Common Problems**
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## Method 2: From source
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```
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# Use the last release branch
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git clone -b v0.4.3.post4 https://github.com/sgl-project/sglang.git
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git clone -b v0.4.4 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
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```
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# Use the last release branch
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git clone -b v0.4.3.post4 https://github.com/sgl-project/sglang.git
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git clone -b v0.4.4 https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
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Note: For AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
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```bash
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docker build --build-arg SGL_BRANCH=v0.4.3.post4 -t v0.4.3.post4-rocm630 -f Dockerfile.rocm .
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docker build --build-arg SGL_BRANCH=v0.4.4 -t v0.4.4-rocm630 -f Dockerfile.rocm .
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alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
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--shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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drun -p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<secret>" \
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v0.4.3.post4-rocm630 \
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v0.4.4-rocm630 \
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python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
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drun v0.4.3.post4-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
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drun v0.4.4-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
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```
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## Method 4: Using docker compose

_static/documentation_options.js

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const DOCUMENTATION_OPTIONS = {
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VERSION: '0.4.3.post4',
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VERSION: '0.4.4',
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LANGUAGE: 'en',
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COLLAPSE_INDEX: false,
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BUILDER: 'html',

backend/custom_chat_template.html

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<link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
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<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />
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<script src="../_static/documentation_options.js?v=0c96e96c"></script>
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<script src="../_static/documentation_options.js?v=9a529362"></script>
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<script src="../_static/doctools.js?v=9a2dae69"></script>
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<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
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<script src="../_static/clipboard.min.js?v=a7894cd8"></script>
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<link rel="prev" title="Reasoning Parser" href="separate_reasoning.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docsearch:language" content="en"/>
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<meta name="docsearch:version" content="0.4.3.post4" />
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<meta name="docsearch:version" content="0.4.4" />
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<meta name="docbuild:last-update" content="Mar 13, 2025"/>
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</head>
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