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Introducing SmallThinker-3B: A Lightweight Model Fine-tuned on QwQ Synthetic Data
We introduce SmallThinker-3B, a new model fine-tuned from the Qwen2.5-3b-Instruct model using synthetic data generated by QwQ-32B-Preview.
Benchmark Performance
| Model | AMPS_Hard Score |
|---|---|
| SmallThinker | 58.0 |
| GPT-4o (2024-08-06) | 54.0 |
| Qwen2.5-3B-Instruct | 44.0 |
Intended Use Cases
SmallThinker is designed for the following use cases:
- Edge Deployment: Its small size makes it ideal for deployment on resource-constrained devices.
- Draft Model for QwQ-32B-Preview: QwQ can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model.
Limitations & Disclaimer
Please be aware of the following limitations:
- Language Limitation: The model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking.
- Unpredictable Outputs: The model may produce unexpected outputs due to its size and probabilistic generation paradigm. Users should exercise caution and validate the model's responses.