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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reasoning Parser\n",
"\n",
"SGLang supports parsing reasoning content out from \"normal\" content for reasoning models such as [DeepSeek R1](https://huggingface.co/deepseek-ai/DeepSeek-R1).\n",
"\n",
"## Supported Models & Parsers\n",
"\n",
"| Model | Reasoning tags | Parser | Notes |\n",
"|---------|-----------------------------|------------------|-------|\n",
"| [DeepSeek‑R1 series](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) | `<think>` … `</think>` | `deepseek-r1` | Supports all variants (R1, R1-0528, R1-Distill) |\n",
"| [DeepSeek‑V3 series](https://huggingface.co/deepseek-ai/DeepSeek-V3.1) | `<think>` … `</think>` | `deepseek-v3` | Including [DeepSeek‑V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp). Supports `thinking` parameter |\n",
"| [Standard Qwen3 models](https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f) | `<think>` … `</think>` | `qwen3` | Supports `enable_thinking` parameter |\n",
"| [Qwen3-Thinking models](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | `<think>` … `</think>` | `qwen3` or `qwen3-thinking` | Always generates thinking content |\n",
"| [Kimi K2 Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) | `◁think▷` … `◁/think▷` | `kimi_k2` | Uses special thinking delimiters. Also requires `--tool-call-parser kimi_k2` for tool use. |\n",
"| [GPT OSS](https://huggingface.co/openai/gpt-oss-120b) | `<\\|channel\\|>analysis<\\|message\\|>` … `<\\|end\\|>` | `gpt-oss` | N/A |\n",
"### Model-Specific Behaviors\n",
"\n",
"**DeepSeek-R1 Family:**\n",
"- DeepSeek-R1: No `<think>` start tag, jumps directly to thinking content\n",
"- DeepSeek-R1-0528: Generates both `<think>` start and `</think>` end tags\n",
"- Both are handled by the same `deepseek-r1` parser\n",
"\n",
"**DeepSeek-V3 Family:**\n",
"- DeepSeek-V3.1/V3.2: Hybrid model supporting both thinking and non-thinking modes, use the `deepseek-v3` parser and `thinking` parameter (NOTE: not `enable_thinking`)\n",
"\n",
"**Qwen3 Family:**\n",
"- Standard Qwen3 (e.g., Qwen3-2507): Use `qwen3` parser, supports `enable_thinking` in chat templates\n",
"- Qwen3-Thinking (e.g., Qwen3-235B-A22B-Thinking-2507): Use `qwen3` or `qwen3-thinking` parser, always thinks\n",
"\n",
"**Kimi K2:**\n",
"- Kimi K2 Thinking: Uses special `◁think▷` and `◁/think▷` tags. For agentic tool use, also specify `--tool-call-parser kimi_k2`.\n",
"\n",
"**GPT OSS:**\n",
"- GPT OSS: Uses special `<|channel|>analysis<|message|>` and `<|end|>` tags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage\n",
"\n",
"### Launching the Server"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specify the `--reasoning-parser` option."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"from openai import OpenAI\n",
"from sglang.test.doc_patch import launch_server_cmd\n",
"from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
"\n",
"server_process, port = launch_server_cmd(\n",
" \"python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --host 0.0.0.0 --reasoning-parser deepseek-r1 --log-level warning\"\n",
")\n",
"\n",
"wait_for_server(f\"http://localhost:{port}\", process=server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that `--reasoning-parser` defines the parser used to interpret responses."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OpenAI Compatible API\n",
"\n",
"Using the OpenAI compatible API, the contract follows the [DeepSeek API design](https://api-docs.deepseek.com/guides/reasoning_model) established with the release of DeepSeek-R1:\n",
"\n",
"- `reasoning_content`: The content of the CoT.\n",
"- `content`: The content of the final answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Initialize OpenAI-like client\n",
"client = OpenAI(api_key=\"None\", base_url=f\"http://0.0.0.0:{port}/v1\")\n",
"model_name = client.models.list().data[0].id\n",
"\n",
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"What is 1+3?\",\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Non-Streaming Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response_non_stream = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=messages,\n",
" temperature=0.6,\n",
" top_p=0.95,\n",
" stream=False, # Non-streaming\n",
" extra_body={\"separate_reasoning\": True},\n",
")\n",
"print_highlight(\"==== Reasoning ====\")\n",
"print_highlight(response_non_stream.choices[0].message.reasoning_content)\n",
"\n",
"print_highlight(\"==== Text ====\")\n",
"print_highlight(response_non_stream.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Streaming Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response_stream = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=messages,\n",
" temperature=0.6,\n",
" top_p=0.95,\n",
" stream=True, # Non-streaming\n",
" extra_body={\"separate_reasoning\": True},\n",
")\n",
"\n",
"reasoning_content = \"\"\n",
"content = \"\"\n",
"for chunk in response_stream:\n",
" if chunk.choices[0].delta.content:\n",
" content += chunk.choices[0].delta.content\n",
" if chunk.choices[0].delta.reasoning_content:\n",
" reasoning_content += chunk.choices[0].delta.reasoning_content\n",
"\n",
"print_highlight(\"==== Reasoning ====\")\n",
"print_highlight(reasoning_content)\n",
"\n",
"print_highlight(\"==== Text ====\")\n",
"print_highlight(content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optionally, you can buffer the reasoning content to the last reasoning chunk (or the first chunk after the reasoning content)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response_stream = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=messages,\n",
" temperature=0.6,\n",
" top_p=0.95,\n",
" stream=True, # Non-streaming\n",
" extra_body={\"separate_reasoning\": True, \"stream_reasoning\": False},\n",
")\n",
"\n",
"reasoning_content = \"\"\n",
"content = \"\"\n",
"for chunk in response_stream:\n",
" if chunk.choices[0].delta.content:\n",
" content += chunk.choices[0].delta.content\n",
" if chunk.choices[0].delta.reasoning_content:\n",
" reasoning_content += chunk.choices[0].delta.reasoning_content\n",
"\n",
"print_highlight(\"==== Reasoning ====\")\n",
"print_highlight(reasoning_content)\n",
"\n",
"print_highlight(\"==== Text ====\")\n",
"print_highlight(content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The reasoning separation is enable by default when specify . \n",
"**To disable it, set the `separate_reasoning` option to `False` in request.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response_non_stream = client.chat.completions.create(\n",
" model=model_name,\n",
" messages=messages,\n",
" temperature=0.6,\n",
" top_p=0.95,\n",
" stream=False, # Non-streaming\n",
" extra_body={\"separate_reasoning\": False},\n",
")\n",
"\n",
"print_highlight(\"==== Original Output ====\")\n",
"print_highlight(response_non_stream.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SGLang Native API "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoTokenizer\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\")\n",
"input = tokenizer.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True, return_dict=False\n",
")\n",
"\n",
"gen_url = f\"http://localhost:{port}/generate\"\n",
"gen_data = {\n",
" \"text\": input,\n",
" \"sampling_params\": {\n",
" \"skip_special_tokens\": False,\n",
" \"max_new_tokens\": 1024,\n",
" \"temperature\": 0.6,\n",
" \"top_p\": 0.95,\n",
" },\n",
"}\n",
"gen_response = requests.post(gen_url, json=gen_data).json()[\"text\"]\n",
"\n",
"print_highlight(\"==== Original Output ====\")\n",
"print_highlight(gen_response)\n",
"\n",
"parse_url = f\"http://localhost:{port}/separate_reasoning\"\n",
"separate_reasoning_data = {\n",
" \"text\": gen_response,\n",
" \"reasoning_parser\": \"deepseek-r1\",\n",
"}\n",
"separate_reasoning_response_json = requests.post(\n",
" parse_url, json=separate_reasoning_data\n",
").json()\n",
"print_highlight(\"==== Reasoning ====\")\n",
"print_highlight(separate_reasoning_response_json[\"reasoning_text\"])\n",
"print_highlight(\"==== Text ====\")\n",
"print_highlight(separate_reasoning_response_json[\"text\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Offline Engine API"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sglang as sgl\n",
"from sglang.srt.parser.reasoning_parser import ReasoningParser\n",
"from sglang.utils import print_highlight\n",
"\n",
"llm = sgl.Engine(model_path=\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\")\n",
"tokenizer = AutoTokenizer.from_pretrained(\"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\")\n",
"input = tokenizer.apply_chat_template(\n",
" messages, tokenize=False, add_generation_prompt=True, return_dict=False\n",
")\n",
"sampling_params = {\n",
" \"max_new_tokens\": 1024,\n",
" \"skip_special_tokens\": False,\n",
" \"temperature\": 0.6,\n",
" \"top_p\": 0.95,\n",
"}\n",
"result = llm.generate(prompt=input, sampling_params=sampling_params)\n",
"\n",
"generated_text = result[\"text\"] # Assume there is only one prompt\n",
"\n",
"print_highlight(\"==== Original Output ====\")\n",
"print_highlight(generated_text)\n",
"\n",
"parser = ReasoningParser(\"deepseek-r1\")\n",
"reasoning_text, text = parser.parse_non_stream(generated_text)\n",
"print_highlight(\"==== Reasoning ====\")\n",
"print_highlight(reasoning_text)\n",
"print_highlight(\"==== Text ====\")\n",
"print_highlight(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm.shutdown()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Supporting New Reasoning Model Schemas\n",
"\n",
"For future reasoning models, you can implement the reasoning parser as a subclass of `BaseReasoningFormatDetector` in `python/sglang/srt/reasoning_parser.py` and specify the reasoning parser for new reasoning model schemas accordingly."
]
}
],
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"name": "python",
"nbconvert_exporter": "python",
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