xiangmin/Qwen2.5-1.5B-Open-R1-Distill
xiangmin/Qwen2.5-1.5B-Open-R1-Distill is a 1.5 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. Developed by xiangmin, this model specializes in mathematical reasoning tasks, having been trained on the OpenR1-Math-220k dataset. It features a 32768-token context length and is optimized for performance in mathematical problem-solving.
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Model Overview
xiangmin/Qwen2.5-1.5B-Open-R1-Distill is a specialized 1.5 billion parameter language model derived from the Qwen/Qwen2.5-1.5B-Instruct architecture. This model has undergone supervised fine-tuning (SFT) using the TRL framework, specifically leveraging the open-r1/OpenR1-Math-220k dataset. Its training focuses on enhancing capabilities related to mathematical reasoning and problem-solving.
Key Capabilities
- Mathematical Reasoning: Optimized for tasks requiring mathematical understanding and computation due to its fine-tuning on a dedicated math dataset.
- Instruction Following: Inherits instruction-following abilities from its base Qwen2.5-1.5B-Instruct model.
- Efficient Performance: As a 1.5 billion parameter model, it offers a balance between performance and computational efficiency.
Good For
- Mathematical Applications: Ideal for use cases involving mathematical question answering, problem-solving, and generating mathematical explanations.
- Resource-Constrained Environments: Suitable for deployment where larger models might be impractical, offering specialized performance in a smaller footprint.
- Research and Development: Provides a fine-tuned base for further experimentation in mathematical AI or domain-specific applications.