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ReasoningTool callingAgenticLong context

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Setup

To get started, sign into Baseten with Truss and then install the OpenAI SDK.
Sign in to Baseten
uvx truss login --browser
Install the OpenAI SDK
uv pip install openai
Pick the model you want to deploy. Each tab is a self-contained recipe.
Qwen/Qwen3.6-27B is a 27B-parameter dense model with up to 256K context.This preset serves Qwen3.6-27B on H100:4 with MTP speculative decoding, optimized for low time-to-first-token on interactive chat and agent workflows.

Hardware

H100 × 4

Engine

vLLM 0.20.0

Context

256K

Concurrency

64

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Write the config

Create and move into the project directory:
mkdir qwen3.6-27b-latency && cd qwen3.6-27b-latency
Then create a file named config.yaml and paste the following:
config.yaml
model_name: "model:qwen3.6-27b preset:latency"

model_metadata:
  example_model_input:
    model: "Qwen/Qwen3.6-27B"
    messages:
      - role: user
        content: "What is the capital of France?"
    stream: true
    max_tokens: 512
    temperature: 1.0
    top_p: 0.95
  tags:
    - openai-compatible

base_image:
  image: vllm/vllm-openai:v0.20.0

weights:
  - source: "hf://Qwen/Qwen3.6-27B@main"
    mount_location: "/app/checkpoint/qwen3.6-27b"
    auth_secret_name: "hf_access_token"

resources:
  accelerator: H100:4
  use_gpu: true

runtime:
  predict_concurrency: 64

environment_variables:
  HF_HUB_ENABLE_HF_TRANSFER: "1"
  VLLM_LOGGING_LEVEL: WARNING

secrets:
  hf_access_token: null

docker_server:
  start_command: >-
    sh -c "vllm serve /app/checkpoint/qwen3.6-27b
    --served-model-name Qwen/Qwen3.6-27B
    --host 0.0.0.0
    --port 8000
    --trust-remote-code
    --tensor-parallel-size 4
    --max-model-len 262144
    --language-model-only
    --reasoning-parser qwen3
    --enable-auto-tool-choice
    --tool-call-parser qwen3_coder
    --speculative_config.method mtp
    --speculative_config.num_speculative_tokens 2"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000

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Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:
FlagValueWhat it does
--trust-remote-code(no value)Execute model-specific Python from the checkpoint (required for many Qwen, Phi, and custom architectures).
--tensor-parallel-size4Number of GPUs to shard the model across.
--max-model-len262144Maximum context length (tokens) the server accepts per request.
--language-model-only(no value)Disable the multimodal path; text-only serving. Remove to enable image/video inputs.
--reasoning-parserqwen3Server-side parser that separates reasoning output into reasoning_content. qwen3: Qwen3-family thinking format (used by Qwen3, Qwen3.5, and Qwen3.6).
--enable-auto-tool-choice(no value)Let the model choose when to call tools without requiring tool_choice: "required".
--tool-call-parserqwen3_coderServer-side parser that emits structured tool_calls on the response. qwen3_coder: Qwen3-Coder tool format.
--speculative_config.methodmtpSpeculative decoding method. mtp: Multi-token prediction head speculation.
--speculative_config.num_speculative_tokens2Number of tokens the draft speculator proposes per step.

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Deploy

Push the config to Baseten:
uvx truss push
You should see output similar to:
✨ Model qwen3.6-27b-latency was successfully pushed ✨
🪵 View logs for your deployment at https://app.baseten.co/models/abcd1234/logs/wxyz5678
Your model ID is the string after /models/ in the logs URL (abcd1234 in the example). Use it wherever you see {model_id} in the next section.

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Call the model

Your deployment serves an OpenAI-compatible API. Replace {model_id} with your model ID and make sure BASETEN_API_KEY is set.Now call your deployment to run inference:
main.py
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BASETEN_API_KEY"],
    base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)

response = client.chat.completions.create(
    model="Qwen/Qwen3.6-27B",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
)

print(response.choices[0].message.content)
To access the model’s chain of thought, enable thinking mode. The server parses the reasoning output into a separate reasoning_content field on the response:
response = client.chat.completions.create(
    model="Qwen/Qwen3.6-27B",
    messages=[
        {"role": "user", "content": "How many r's in strawberry?"}
    ],
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
print(response.choices[0].message.reasoning_content)  # chain of thought
print(response.choices[0].message.content)            # final answer
To let the model call tools, pass a tools array. The server returns structured tool_calls on the response:
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
        },
    },
}]

response = client.chat.completions.create(
    model="Qwen/Qwen3.6-27B",
    messages=[
        {"role": "user", "content": "What's the weather in Paris?"}
    ],
    tools=tools,
)
print(response.choices[0].message.tool_calls)