qwen-think 0.1.1

The canonical Python library for managing Qwen3.6's thinking state across sessions, backends, and frameworks.

qwen-think

The Thinking Session Manager for Qwen3.6 — SDK + Router + Bug Fixes

The canonical Python library for managing Qwen3.6's thinking state across sessions, backends, and frameworks.

Colab Notebook — end-to-end test harness (unit tests + live GPU tests)

What It Solves

1. Backend Normalization — Qwen3.6's enable_thinking flag has three different invocation patterns across backends. This library normalizes them into a single API.

2. Sampling Parameter Swap — Qwen3.6 requires different sampling parameters for thinking vs. non-thinking mode. A router that flips enable_thinking without also swapping params produces silently degraded output.

3. Context Budget Guard — Qwen3.6 advises maintaining at least 128K tokens of context to preserve thinking capabilities. This library tracks and guards against silent degradation.

Installation

pip install qwen-think
# With OpenAI client support:
pip install qwen-think[openai]

Quick Start

from openai import OpenAI
from qwen_think import ThinkingSession

client = OpenAI(base_url="http://localhost:8000/v1", api_key="...")
session = ThinkingSession(client, backend="vllm", budget=200_000)

# Auto-routes: detects complexity, sets mode, swaps sampling
response = session.chat("refactor this module", preserve=True)

The Three Backend Patterns

Backend	Flag Format	Notes
vLLM / SGLang	extra_body={"chat_template_kwargs": {"enable_thinking": False}}	Nested
DashScope	extra_body={"enable_thinking": False}	Top-level
llama.cpp	--chat-template-kwargs '{"enable_thinking": false}'	Server-side only

Bug Fixes Included

vLLM Semantic Router (#858)

When use_reasoning: false is configured, the router removes the field instead of explicitly setting enable_thinking: false. Since Qwen3.6 thinks by default, removing the field has no effect.

Fix: This library always explicitly sets the boolean — never omits it.

Ray Serve (#52979)

enable_thinking: false in the HTTP body doesn't propagate to the model, and thinking output continues appearing.

Fix: The normalized payload always includes the explicit flag.

Sampling Parameters

Qwen3.6 requires different sampling params depending on thinking mode:

Thinking mode:

temperature=1.0, top_p=0.95, top_k=20, min_p=0.0,
presence_penalty=1.5, repetition_penalty=1.0

Instruct / non-thinking mode:

temperature=0.7, top_p=0.80, top_k=20, min_p=0.0,
presence_penalty=1.5

A router that flips enable_thinking without atomically swapping these params produces silently degraded output — not incorrect, just suboptimal in ways that don't surface as errors.

Context Budget

Qwen3.6 advises maintaining a context length of at least 128K tokens to preserve thinking capabilities. The BudgetManager tracks usage and:

• Warns when approaching the threshold
• Auto-compresses older messages when running low
• Refuses to continue when below the minimum

session = ThinkingSession(client, budget=200_000, min_context=128_000)
status = session.budget_status
# BudgetStatus(total_tokens=200000, used_tokens=45000, available_tokens=155000,
#              action=BudgetAction.OK, message="Context usage: 22.5%...")

Thinking Preservation

Qwen3.6 introduces preserve_thinking — a feature that retains thinking context across conversation history, improving reasoning quality for iterative development.

session = ThinkingSession(client, preserve_thinking=True)
# Thinking content is cached and included in subsequent turns

Complexity Router

The router classifies query complexity and selects the appropriate mode:

Complexity	Mode	Preserved	Use Case
SIMPLE	NO_THINK	No	"What is X?"
MODERATE	THINK	No	Multi-sentence reasoning
COMPLEX	THINK + preserve	Yes	Coding, debugging, refactoring

from qwen_think import ComplexityRouter

router = ComplexityRouter()
decision = router.route("implement a REST API with authentication")
# RouterDecision(complexity=COMPLEX, mode=THINK, preserve_thinking=True)

API Reference

ThinkingSession

session = ThinkingSession(
    client,                # OpenAI-compatible client
    backend="vllm",       # or "sglang", "dashscope", "llamacpp"
    model="Qwen/Qwen3.6-35B-A3B",
    budget=200_000,        # Total context budget
    min_context=128_000,   # 128K minimum for thinking
    preserve_thinking=True,
    auto_route=True,       # Auto-classify complexity
)

Manual Mode Control

# Force a specific mode
session.chat("quick answer", mode=ThinkingMode.NO_THINK)

# Let the router decide
session.chat("refactor this module")

# Check current state
session.thinking_mode  # → ThinkingMode.THINK
session.budget_status  # → BudgetStatus(...)

License

Apache-2.0