T1anyu/Qwen2.5-1.5B-Open-R1-Distill is a 1.5 billion parameter language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model specializes in mathematical reasoning, having been trained on the OpenR1-Math-220k dataset. It leverages the TRL framework for its training, making it particularly suitable for tasks requiring numerical and logical problem-solving capabilities.
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Model Overview
T1anyu/Qwen2.5-1.5B-Open-R1-Distill is a 1.5 billion parameter language model derived from the Qwen2.5-1.5B-Instruct architecture. Its primary distinction lies in its specialized fine-tuning on the OpenR1-Math-220k dataset, which focuses on mathematical problems.
Key Capabilities
- Mathematical Reasoning: Optimized for handling and solving mathematical queries and problems due to its specific training data.
- Instruction Following: Inherits instruction-following capabilities from its base Qwen2.5-1.5B-Instruct model.
- Efficient Deployment: With 1.5 billion parameters, it offers a balance between performance and computational efficiency, suitable for environments with resource constraints.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library. This approach allows for targeted optimization on specific datasets, enhancing its performance in the mathematical domain. The training process can be further explored via its Weights & Biases run.
Good for
- Applications requiring strong mathematical problem-solving.
- Educational tools focused on math assistance.
- Scenarios where a smaller, specialized model is preferred over larger, general-purpose LLMs for numerical tasks.