InfiniAILab/OpenR1-Qwen-7B-SFT-Instruct
OpenR1-Qwen-7B-SFT-Instruct is a 7.6 billion parameter language model developed by InfiniAILab, fine-tuned from Qwen/Qwen2.5-7B-Instruct. It was specifically trained using Supervised Fine-Tuning (SFT) on the OpenR1-Math-220k dataset, giving it enhanced capabilities in mathematical reasoning and problem-solving. With a context length of 32768 tokens, this model is primarily designed for applications requiring strong mathematical understanding and accurate numerical processing.
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OpenR1-Qwen-7B-SFT-Instruct Overview
OpenR1-Qwen-7B-SFT-Instruct is a specialized language model developed by InfiniAILab, built upon the robust Qwen2.5-7B-Instruct architecture. This 7.6 billion parameter model has undergone Supervised Fine-Tuning (SFT) using the open-r1/OpenR1-Math-220k dataset, which is specifically curated for mathematical tasks. This targeted training enhances its ability to understand and generate responses related to mathematical problems and reasoning.
Key Capabilities
- Enhanced Mathematical Reasoning: Specialized training on a math-focused dataset improves performance on numerical and logical problems.
- Instruction Following: Retains the strong instruction-following capabilities of its base Qwen2.5-7B-Instruct model.
- Large Context Window: Supports a context length of 32768 tokens, allowing for processing longer prompts and complex problem descriptions.
Good for
- Mathematical Problem Solving: Ideal for applications requiring accurate solutions to math questions, from basic arithmetic to more complex algebraic or logical challenges.
- Educational Tools: Can be integrated into platforms for tutoring, homework assistance, or generating math-related content.
- Research and Development: Useful for researchers exploring the capabilities of LLMs in quantitative domains.