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--- |
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license: other |
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language: |
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- en |
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pipeline_tag: text-generation |
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inference: false |
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tags: |
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- transformers |
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- gguf |
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- imatrix |
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- SmallThinker-3B-Preview |
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--- |
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Quantizations of https://huggingface.co./PowerInfer/SmallThinker-3B-Preview |
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### Inference Clients/UIs |
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* [llama.cpp](https://github.com/ggerganov/llama.cpp) |
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* [KoboldCPP](https://github.com/LostRuins/koboldcpp) |
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* [ollama](https://github.com/ollama/ollama) |
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui) |
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* [jan](https://github.com/janhq/jan) |
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* [GPT4All](https://github.com/nomic-ai/gpt4all) |
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--- |
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# From original readme |
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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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Now you can directly deploy SmallThinker On your phones with [PowerServe](https://github.com/powerserve-project/PowerServe). |
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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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Colab Link: [Colab](https://colab.research.google.com/drive/182q600at0sVw7uX0SXFp6bQI7pyjWXQ2?usp=sharing) |
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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). |
