Reasoning Router 0.6B
AmirMohseni/reasoning-router-0.6b is a fine-tuned reasoning router built on top of Qwen/Qwen3-0.6B. It classifies user prompts into two categories:
no_think→ The task does not require explicit reasoning.think→ The task benefits from a reasoning mode (e.g., math, multi-step analysis).
This router is designed for hybrid model systems, where it decides whether to route prompts to lightweight inference endpoints or to reasoning-enabled models such as the Qwen3 series or deepseek-ai/DeepSeek-V3.1.
Use Case
The reasoning router allows for efficient orchestration in model pipelines:
- Run cheap, fast inference for simple tasks.
- Switch to more powerful, expensive reasoning models only when needed.
This approach helps reduce costs, latency, and unnecessary compute in real-world deployments.
🚀 Quick Start
Example Usage
from transformers import pipeline
# Initialize the router pipeline
router = pipeline(
"text-classification",
model="AmirMohseni/reasoning-router-0.6b",
device_map="auto"
)
# Example prompt that requires reasoning
prompt = "What is the sum of the first 100 prime numbers?"
results = router(prompt)[0]
print('Label: ', results['label']) # Label: no_think
print('Probability Score: ', results['score']) # Probability Score: 0.6192409992218018
📚 Training Data
This model was trained on the AmirMohseni/reasoning-router-data-v2 dataset, which was curated from multiple instruction-following datasets. The dataset primarily contains:
- Math reasoning data → Derived from Big-Math-RL and AIME problems (1983–2024).
- General tasks → A mix of simple vs. reasoning-heavy queries to teach the model to distinguish between them.
⚠️ Limitations
- Language Coverage: The model is trained primarily on English. Performance on other languages may be weaker.
- Reasoning Coverage: For tasks labeled
think, the training data is heavily skewed towards mathematical reasoning. - No Coding Tasks: Programming or code-related reasoning tasks are not included in the current training data.
🔧 Model Details
- Base model:
Qwen/Qwen3-0.6B - Parameters: 0.6B
- Task: Binary classification (
no_think,think) - Intended use: Routing prompts for hybrid reasoning pipelines.
✅ Intended Use
- Routing user prompts in a multi-model reasoning system.
- Reducing compute costs by filtering out tasks that don’t require a dedicated reasoning model.