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# Introducing SmallThinker-3B: A Lightweight Model Fine-tuned on QwQ Synthetic Data |
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We introduce **SmallThinker-3B**, 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). |
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## Benchmark Performance |
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| Model | AMPS_Hard Score | |
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| SmallThinker | 58.0 | |
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| GPT-4o (2024-08-06) | 54.0 | |
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| Qwen2.5-3B-Instruct | 44.0 | |
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## Intended Use Cases |
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SmallThinker is designed for the following use cases: |
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1. **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices. |
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2. **Draft Model for QwQ-32B-Preview:** QwQ can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. |
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## Limitations & Disclaimer |
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Please be aware of the following limitations: |
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* **Language Limitation:** The model has only been trained on English-language datasets, hence its capabilities in other languages are still lacking. |
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* **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. |
