metadata

datasets:
  - PowerInfer/QWQ-LONGCOT-500K
base_model:
  - Qwen/Qwen2.5-3B-Instruct

SmallThinker-3B-preview

We introduce SmallThinker-3B-preview, a new model fine-tuned from the Qwen2.5-3b-Instruct model.

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

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.

Intended Use Cases

SmallThinker is designed for the following use cases:

Edge Deployment: Its small size makes it ideal for deployment on resource-constrained devices.
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).

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.
Limited Knowledge: Due to limited SFT data and the model's relatively small scale, its reasoning capabilities are constrained by its knowledge base.
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.