AmirMohseni/reasoning-router-0.6b

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.

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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.

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🚀 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

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📚 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.

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⚠️ 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.

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🔧 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.

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✅ 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.