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* [feat] extra parameters are all passed directly via http payload now, or in extra_body if using openai client * [fix] delete ci test case for enable_thinking * [fix] add reasoning_parser when server starts * [doc] update docs related to metadata * [fix] fix ci consistency test error with reasoning parser * [fix] cancel enable_thinking default value
84 lines
2.8 KiB
Markdown
84 lines
2.8 KiB
Markdown
# 思考链内容
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思考模型在输出中返回 `reasoning_content` 字段，表示思考链内容,即得出最终结论的思考步骤.
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##目前支持思考链的模型
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| 模型名称 | 解析器名称 | 默认开启思考链 |
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|---------------|-------------|---------|
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| baidu/ERNIE-4.5-VL-424B-A47B-Paddle | ernie-45-vl | ✓ |
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| baidu/ERNIE-4.5-VL-28B-A3B-Paddle | ernie-45-vl | ✓ |
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思考模型需要指定解析器,以便于对思考内容进行解析. 通过 `"enable_thinking": false` 参数可以关闭模型思考模式.
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可以支持思考模式开关的接口:
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1. OpenAI 服务中 `/v1/chat/completions` 请求.
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2. OpenAI Python客户端中 `/v1/chat/completions` 请求.
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3. Offline 接口中 `llm.chat`请求.
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同时在思考模型中，支持通过 `reasoning_max_tokens` 控制思考内容的长度，在请求中添加 `"reasoning_max_tokens": 1024` 即可。
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## 快速使用
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在启动模型服务时, 通过 `--reasoning-parser` 参数指定解析器名称.
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该解析器会解析思考模型的输出, 提取 `reasoning_content` 字段.
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```bash
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python -m fastdeploy.entrypoints.openai.api_server \
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--model /path/to/your/model \
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--enable-mm \
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--tensor-parallel-size 8 \
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--port 8192 \
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--quantization wint4 \
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--reasoning-parser ernie-45-vl
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```
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接下来, 向模型发送 `chat completion` 请求
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```bash
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curl -X POST "http://0.0.0.0:8192/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": [
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{"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
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{"type": "text", "text": "图中的文物属于哪个年代"}
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]}
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],
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"enable_thinking": true,
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"reasoning_max_tokens": 1024
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}'
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```
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字段 `reasoning_content` 包含得出最终结论的思考步骤，而 `content` 字段包含最终结论。
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### 流式会话
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在流式会话中, `reasoning_content` 字段会可以在 `chat completion response chunks` 中的 `delta` 中获取
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```python
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from openai import OpenAI
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# Set OpenAI's API key and API base to use vLLM's API server.
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openai_api_key = "EMPTY"
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openai_api_base = "http://localhost:8192/v1"
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client = OpenAI(
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api_key=openai_api_key,
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base_url=openai_api_base,
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)
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chat_response = client.chat.completions.create(
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messages=[
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{"role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg"}},
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{"type": "text", "text": "图中的文物属于哪个年代"}]}
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],
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model="vl",
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stream=True,
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extra_body={
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"enable_thinking": True,
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"reasoning_max_tokens": 1024
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}
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)
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for chunk in chat_response:
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if chunk.choices[0].delta is not None:
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print(chunk.choices[0].delta, end='')
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print("\n")
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```
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