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# Introducing SmallThinker-3B-alpha: A Small Model Fine-tuned on QwQ Synthetic Data

We introduce **SmallThinker-3B-alpha**, a new model fine-tuned from the [Qwen2.5-3b-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model using synthetic data generated by [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview). 

## Benchmark Performance

| Model | AIME24 | AMC23 | GAOKAO2024_I | GAOKAO2024_II | MMLU_STEM | AMPS_Hard | math_comp |
|---------|--------|-------|--------------|---------------|-----------|-----------|-----------|
| Qwen2.5-3B-Instruct | 6.67 | 45 | 50 | 35.8 | 59.8 | - | - |
| SmallThinker | 16.667 | 57.5 | 64.2 | 57.1 | 68.2 | 70 | 46.8 |
| GPT-4o | 9.3 | - | - | - | 64.2 | 57 | 50 |



## Intended Use Cases

SmallThinker is designed for the following use cases:

1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
2.  **Draft Model for QwQ-32B-Preview:** SmallThinker 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.
*   **Repetition Issue:** The model tends to repeat itself when answering high-difficulty questions. Please increase the `repetition_penalty` to mitigate this issue.