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datasets: |
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- PowerInfer/QWQ-LONGCOT-500K |
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# SmallThinker-3B-preview |
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We introduce **SmallThinker-3B-preview**, a new model fine-tuned from the [Qwen2.5-3b-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model. |
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## Benchmark Performance |
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| Model | AIME24 | AMC23 | GAOKAO2024_I | GAOKAO2024_II | MMLU_STEM | AMPS_Hard | math_comp | |
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|---------|--------|-------|--------------|---------------|-----------|-----------|-----------| |
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| Qwen2.5-3B-Instruct | 6.67 | 45 | 50 | 35.8 | 59.8 | - | - | |
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| SmallThinker | 16.667 | 57.5 | 64.2 | 57.1 | 68.2 | 70 | 46.8 | |
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| GPT-4o | 9.3 | - | - | - | 64.2 | 57 | 50 | |
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Limitation: Due to SmallThinker's current limitations in instruction following, for math_comp we adopt a more lenient evaluation method where only correct answers are required, without constraining responses to follow the specified AAAAA format. |
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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:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s). |
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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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* **Limited Knowledge:** Due to limited SFT data and the model's relatively small scale, its reasoning capabilities are constrained by its knowledge base. |
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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. |
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* **Repetition Issue:** The model tends to repeat itself when answering high-difficulty questions. Please increase the `repetition_penalty` to mitigate this issue. |
