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Remove legacy Sphinx docs/ and finish the Mintlify cutover (#28964)

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zijiexia
2026-07-13 15:06:08 -07:00
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docs_new/docs/references/custom_chat_template.mdx
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@@ -3,7 +3,7 @@ title: "Custom Chat Template"
metatags:
description: "SGLang custom chat templates: JSON and Jinja formats for OpenAI-compatible API server. Override tokenizer defaults."
---
**NOTE**: There are two chat template systems in SGLang project. This document is about setting a custom chat template for the OpenAI-compatible API server (defined at [conversation.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/conversation.py)). It is NOT related to the chat template used in the SGLang language frontend (defined at [chat_template.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/lang/chat_template.py)).
**NOTE**: There are two chat template systems in SGLang project. This document is about setting a custom chat template for the OpenAI-compatible API server (defined at [conversation.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/parser/conversation.py)). It is NOT related to the chat template used in the SGLang language frontend (defined at [chat_template.py](https://github.com/sgl-project/sglang/blob/main/python/sglang/lang/chat_template.py)).
By default, the server uses the chat template specified in the model tokenizer from Hugging Face.
It should just work for most official models such as Llama-2/Llama-3.
docs_new/docs/references/frontend/frontend_index.rst
-9
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@@ -1,9 +0,0 @@
Frontend Language
=================
.. toctree::
:maxdepth: 1
:caption: Frontend Language
frontend_tutorial.ipynb
choices_methods.md
docs_new/docs/references/frontend/frontend_tutorial.ipynb
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@@ -1,456 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SGLang Frontend Language"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SGLang frontend language can be used to define simple and easy prompts in a convenient, structured way."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch A Server\n",
"\n",
"Launch the server in your terminal and wait for it to initialize."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sglang import assistant_begin, assistant_end\n",
"from sglang import assistant, function, gen, system, user\n",
"from sglang import image\n",
"from sglang import RuntimeEndpoint\n",
"from sglang.lang.api import set_default_backend\n",
"from sglang.srt.utils import load_image\n",
"from sglang.test.doc_patch import launch_server_cmd\n",
"from sglang.utils import print_highlight, terminate_process, wait_for_server\n",
"\n",
"server_process, port = launch_server_cmd(\n",
" \"python -m sglang.launch_server --model-path Qwen/Qwen2.5-7B-Instruct --host 0.0.0.0 --log-level warning\"\n",
")\n",
"\n",
"wait_for_server(f\"http://localhost:{port}\", process=server_process)\n",
"print(f\"Server started on http://localhost:{port}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the default backend. Note: Besides the local server, you may use also `OpenAI` or other API endpoints."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"set_default_backend(RuntimeEndpoint(f\"http://localhost:{port}\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Usage\n",
"\n",
"The most simple way of using SGLang frontend language is a simple question answer dialog between a user and an assistant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def basic_qa(s, question):\n",
" s += system(f\"You are a helpful assistant than can answer questions.\")\n",
" s += user(question)\n",
" s += assistant(gen(\"answer\", max_tokens=512))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"state = basic_qa(\"List 3 countries and their capitals.\")\n",
"print_highlight(state[\"answer\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-turn Dialog\n",
"\n",
"SGLang frontend language can also be used to define multi-turn dialogs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def multi_turn_qa(s):\n",
" s += system(f\"You are a helpful assistant than can answer questions.\")\n",
" s += user(\"Please give me a list of 3 countries and their capitals.\")\n",
" s += assistant(gen(\"first_answer\", max_tokens=512))\n",
" s += user(\"Please give me another list of 3 countries and their capitals.\")\n",
" s += assistant(gen(\"second_answer\", max_tokens=512))\n",
" return s\n",
"\n",
"\n",
"state = multi_turn_qa()\n",
"print_highlight(state[\"first_answer\"])\n",
"print_highlight(state[\"second_answer\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Control flow\n",
"\n",
"You may use any Python code within the function to define more complex control flows."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def tool_use(s, question):\n",
" s += assistant(\n",
" \"To answer this question: \"\n",
" + question\n",
" + \". I need to use a \"\n",
" + gen(\"tool\", choices=[\"calculator\", \"search engine\"])\n",
" + \". \"\n",
" )\n",
"\n",
" if s[\"tool\"] == \"calculator\":\n",
" s += assistant(\"The math expression is: \" + gen(\"expression\"))\n",
" elif s[\"tool\"] == \"search engine\":\n",
" s += assistant(\"The key word to search is: \" + gen(\"word\"))\n",
"\n",
"\n",
"state = tool_use(\"What is 2 * 2?\")\n",
"print_highlight(state[\"tool\"])\n",
"print_highlight(state[\"expression\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parallelism\n",
"\n",
"Use `fork` to launch parallel prompts. Because `sgl.gen` is non-blocking, the for loop below issues two generation calls in parallel."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def tip_suggestion(s):\n",
" s += assistant(\n",
" \"Here are two tips for staying healthy: \"\n",
" \"1. Balanced Diet. 2. Regular Exercise.\\n\\n\"\n",
" )\n",
"\n",
" forks = s.fork(2)\n",
" for i, f in enumerate(forks):\n",
" f += assistant(\n",
" f\"Now, expand tip {i+1} into a paragraph:\\n\"\n",
" + gen(\"detailed_tip\", max_tokens=256, stop=\"\\n\\n\")\n",
" )\n",
"\n",
" s += assistant(\"Tip 1:\" + forks[0][\"detailed_tip\"] + \"\\n\")\n",
" s += assistant(\"Tip 2:\" + forks[1][\"detailed_tip\"] + \"\\n\")\n",
" s += assistant(\n",
" \"To summarize the above two tips, I can say:\\n\" + gen(\"summary\", max_tokens=512)\n",
" )\n",
"\n",
"\n",
"state = tip_suggestion()\n",
"print_highlight(state[\"summary\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constrained Decoding\n",
"\n",
"Use `regex` to specify a regular expression as a decoding constraint. This is only supported for local models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def regular_expression_gen(s):\n",
" s += user(\"What is the IP address of the Google DNS servers?\")\n",
" s += assistant(\n",
" gen(\n",
" \"answer\",\n",
" temperature=0,\n",
" regex=r\"((25[0-5]|2[0-4]\\d|[01]?\\d\\d?).){3}(25[0-5]|2[0-4]\\d|[01]?\\d\\d?)\",\n",
" )\n",
" )\n",
"\n",
"\n",
"state = regular_expression_gen()\n",
"print_highlight(state[\"answer\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `regex` to define a `JSON` decoding schema."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"character_regex = (\n",
" r\"\"\"\\{\\n\"\"\"\n",
" + r\"\"\" \"name\": \"[\\w\\d\\s]{1,16}\",\\n\"\"\"\n",
" + r\"\"\" \"house\": \"(Gryffindor|Slytherin|Ravenclaw|Hufflepuff)\",\\n\"\"\"\n",
" + r\"\"\" \"blood status\": \"(Pure-blood|Half-blood|Muggle-born)\",\\n\"\"\"\n",
" + r\"\"\" \"occupation\": \"(student|teacher|auror|ministry of magic|death eater|order of the phoenix)\",\\n\"\"\"\n",
" + r\"\"\" \"wand\": \\{\\n\"\"\"\n",
" + r\"\"\" \"wood\": \"[\\w\\d\\s]{1,16}\",\\n\"\"\"\n",
" + r\"\"\" \"core\": \"[\\w\\d\\s]{1,16}\",\\n\"\"\"\n",
" + r\"\"\" \"length\": [0-9]{1,2}\\.[0-9]{0,2}\\n\"\"\"\n",
" + r\"\"\" \\},\\n\"\"\"\n",
" + r\"\"\" \"alive\": \"(Alive|Deceased)\",\\n\"\"\"\n",
" + r\"\"\" \"patronus\": \"[\\w\\d\\s]{1,16}\",\\n\"\"\"\n",
" + r\"\"\" \"bogart\": \"[\\w\\d\\s]{1,16}\"\\n\"\"\"\n",
" + r\"\"\"\\}\"\"\"\n",
")\n",
"\n",
"\n",
"@function\n",
"def character_gen(s, name):\n",
" s += user(\n",
" f\"{name} is a character in Harry Potter. Please fill in the following information about this character.\"\n",
" )\n",
" s += assistant(gen(\"json_output\", max_tokens=256, regex=character_regex))\n",
"\n",
"\n",
"state = character_gen(\"Harry Potter\")\n",
"print_highlight(state[\"json_output\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Batching \n",
"\n",
"Use `run_batch` to run a batch of prompts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def text_qa(s, question):\n",
" s += user(question)\n",
" s += assistant(gen(\"answer\", stop=\"\\n\"))\n",
"\n",
"\n",
"states = text_qa.run_batch(\n",
" [\n",
" {\"question\": \"What is the capital of the United Kingdom?\"},\n",
" {\"question\": \"What is the capital of France?\"},\n",
" {\"question\": \"What is the capital of Japan?\"},\n",
" ],\n",
" progress_bar=True,\n",
")\n",
"\n",
"for i, state in enumerate(states):\n",
" print_highlight(f\"Answer {i+1}: {states[i]['answer']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Streaming \n",
"\n",
"Use `stream` to stream the output to the user."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def text_qa(s, question):\n",
" s += user(question)\n",
" s += assistant(gen(\"answer\", stop=\"\\n\"))\n",
"\n",
"\n",
"state = text_qa.run(\n",
" question=\"What is the capital of France?\", temperature=0.1, stream=True\n",
")\n",
"\n",
"for out in state.text_iter():\n",
" print(out, end=\"\", flush=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Complex Prompts\n",
"\n",
"You may use `{system|user|assistant}_{begin|end}` to define complex prompts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def chat_example(s):\n",
" s += system(\"You are a helpful assistant.\")\n",
" # Same as: s += s.system(\"You are a helpful assistant.\")\n",
"\n",
" with s.user():\n",
" s += \"Question: What is the capital of France?\"\n",
"\n",
" s += assistant_begin()\n",
" s += \"Answer: \" + gen(\"answer\", max_tokens=100, stop=\"\\n\")\n",
" s += assistant_end()\n",
"\n",
"\n",
"state = chat_example()\n",
"print_highlight(state[\"answer\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-modal Generation\n",
"\n",
"You may use SGLang frontend language to define multi-modal prompts.\n",
"See [here](https://docs.sglang.io/supported_models/text_generation/multimodal_language_models.html) for supported models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"server_process, port = launch_server_cmd(\n",
" \"python -m sglang.launch_server --model-path Qwen/Qwen2.5-VL-7B-Instruct --host 0.0.0.0 --log-level warning\"\n",
")\n",
"\n",
"wait_for_server(f\"http://localhost:{port}\", process=server_process)\n",
"print(f\"Server started on http://localhost:{port}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"set_default_backend(RuntimeEndpoint(f\"http://localhost:{port}\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ask a question about an image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"@function\n",
"def image_qa(s, image_file, question):\n",
" s += user(image(image_file) + question)\n",
" s += assistant(gen(\"answer\", max_tokens=256))\n",
"\n",
"\n",
"image_url = \"https://raw.githubusercontent.com/sgl-project/sglang/main/examples/assets/example_image.png\"\n",
"image_bytes, _ = load_image(image_url)\n",
"state = image_qa(image_bytes, \"What is in the image?\")\n",
"print_highlight(state[\"answer\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
docs_new/docs/references/multi_node_deployment/deploy_on_k8s.mdx
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@@ -17,7 +17,7 @@ Here we take the deployment of DeepSeek-R1 as an example.
1. At least two Kubernetes nodes, each with two H20 systems and eight GPUs, are required.
2. Make sure your K8S cluster has LWS correctly installed. If it hasn't been set up yet, please follow the [installation instructions](https://github.com/kubernetes-sigs/lws/blob/main/site/content/en/docs/installation/_index). **Note:** For LWS versions ≤0.5.x, you must use the Downward API to obtain `LWS_WORKER_INDEX`, as native support for this feature was introduced in v0.6.0.
2. Make sure your K8S cluster has LWS correctly installed. If it hasn't been set up yet, please follow the [installation instructions](https://lws.sigs.k8s.io/docs/installation/). **Note:** For LWS versions ≤0.5.x, you must use the Downward API to obtain `LWS_WORKER_INDEX`, as native support for this feature was introduced in v0.6.0.
## Basic example
@@ -267,7 +267,7 @@ This should resolve most NCCL-related issues.
#### RoCE scenario
* Please make sure that RDMA devices are available in the cluster environment.
* Please make sure that the nodes in the cluster have Mellanox NICs with RoCE. In this example, we use Mellanox ConnectX 5 model NICs, and the proper OFED driver has been installed. If not, please refer to the document [Install OFED Driver](https://docs.nvidia.com/networking/display/mlnxofedv461000/installing+mellanox+ofed) to install the driver.
* Please make sure that the nodes in the cluster have Mellanox NICs with RoCE. In this example, we use Mellanox ConnectX 5 model NICs, and the proper OFED driver has been installed. If not, please refer to the document [Install OFED Driver](https://docs.nvidia.com/networking/display/mlnxofedv24102180lts/installing+the+driver) to install the driver.
* Check your env:
```shell Command
docs_new/docs/references/multi_node_deployment/multi_node_index.rst
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@@ -1,14 +0,0 @@
Multi-Node Deployment
=====================
.. toctree::
:maxdepth: 1
:caption: Multi-Node Deployment
multi_node.md
deploy_on_k8s.md
lws_pd/lws_pd_deploy.md
rbg_pd/deepseekv32_pd.md
- `Deploying DeepSeek with PD Disaggregation and Large-Scale Expert Parallelism on 96 H100 GPUs <https://lmsys.org/blog/2025-05-05-large-scale-ep/>`_
- `Deploying Kimi K2 with PD Disaggregation and Large-Scale Expert Parallelism on 128 H200 GPUs <https://lmsys.org/blog/2025-07-20-k2-large-scale-ep/>`_