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* add chat_template_kwagrs and update params docs * add chat_template_kwagrs and update params docs * update enable_thinking * pre-commit * update test case --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
82 lines
3.2 KiB
Markdown
82 lines
3.2 KiB
Markdown
# Reasoning Outputs
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Reasoning models return an additional `reasoning_content` field in their output, which contains the reasoning steps that led to the final conclusion.
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## Supported Models
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| Model Name | Parser Name | Eable_thinking by Default |
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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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The reasoning model requires a specified parser to extract reasoning content. The reasoning mode can be disabled by setting the `"enable_thinking": false` parameter.
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Interfaces that support toggling the reasoning mode:
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1. `/v1/chat/completions` requests in OpenAI services.
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2. `/v1/chat/completions` requests in the OpenAI Python client.
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3. `llm.chat` requests in Offline interfaces.
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For reasoning models, the length of the reasoning content can be controlled via `reasoning_max_tokens`. Add `"reasoning_max_tokens": 1024` to the request.
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### Quick Start
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When launching the model service, specify the parser name using the `--reasoning-parser` argument.
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This parser will process the model's output and extract the `reasoning_content` field.
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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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Next, make a request to the model that should return the reasoning content in the response.
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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": "Which era does the cultural relic in the picture belong to"}
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]}
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],
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"chat_template_kwargs":{"enable_thinking": true},
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"reasoning_max_tokens": 1024
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}'
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```
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The `reasoning_content` field contains the reasoning steps to reach the final conclusion, while the `content` field holds the conclusion itself.
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### Streaming chat completions
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Streaming chat completions are also supported for reasoning models. The `reasoning_content` field is available in the `delta` field in `chat completion response chunks`
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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": "Which era does the cultural relic in the picture belong to"}]}
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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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"chat_template_kwargs":{"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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