Sayram/DeepSeek-R1-Distill-Qwen-1.5B-GRPO is a 1.5 billion parameter language model fine-tuned from deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B. It specializes in mathematical reasoning, having been trained with the GRPO method on the OpenR1-Math-220k dataset. This model is optimized for complex mathematical problem-solving and quantitative tasks, leveraging its specialized training for enhanced accuracy in this domain. Its 131072 token context length supports processing extensive mathematical problems and related textual information.
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Model Overview
Sayram/DeepSeek-R1-Distill-Qwen-1.5B-GRPO is a 1.5 billion parameter language model derived from the deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B base model. Its primary distinction lies in its specialized training for mathematical reasoning, utilizing the open-r1/OpenR1-Math-220k dataset.
Key Capabilities
- Enhanced Mathematical Reasoning: The model was fine-tuned using the GRPO (Guided Reasoning Policy Optimization) method, as introduced in the DeepSeekMath paper. This method is designed to push the limits of mathematical problem-solving in language models.
- Specialized Training: Its training on a dedicated mathematical dataset makes it particularly adept at understanding and generating responses for quantitative problems.
- High Context Length: Features a substantial context window of 131072 tokens, allowing it to process and reason over lengthy mathematical descriptions and complex problem statements.
When to Use This Model
This model is particularly well-suited for applications requiring strong mathematical reasoning capabilities, such as:
- Solving complex math problems.
- Generating explanations for mathematical concepts.
- Assisting in educational tools focused on mathematics.
- Any use case where robust quantitative analysis and logical deduction are paramount.