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README.md
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##
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1.
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---
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datasets:
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- PowerInfer/QWQ-LONGCOT-500K
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- PowerInfer/LONGCOT-Refine-500K
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/SmallThinker-3B-Preview-GGUF
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This is quantized version of [PowerInfer/SmallThinker-3B-Preview](https://huggingface.co/PowerInfer/SmallThinker-3B-Preview) created using llama.cpp
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# Original Model Card
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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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## Training Details
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The model was trained using 8 H100 GPUs with a global batch size of 16. The specific configuration is as follows:
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```
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neat_packing: true
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cutoff_len: 16384
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per_device_train_batch_size: 2
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gradient_accumulation_steps: 1
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learning_rate: 1.0e-5
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num_train_epochs: 3
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lr_scheduler_type: cosine
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warmup_ratio: 0.02
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bf16: true
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ddp_timeout: 180000000
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weight_decay: 0.0
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```
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The SFT (Supervised Fine-Tuning) process was conducted in two phases:
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1. First Phase:
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- Used only the PowerInfer/QWQ-LONGCOT-500K dataset
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- Trained for 1.5 epochs
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2. Second Phase:
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- Combined training with PowerInfer/QWQ-LONGCOT-500K and PowerInfer/LONGCOT-Refine datasets
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- Continued training for an additional 2 epochs
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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.
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