tergel/qwen2.5-math-1.5b-instruct-gsm8k-fs-gpt4o-bon
tergel/qwen2.5-math-1.5b-instruct-gsm8k-fs-gpt4o-bon is a 1.5 billion parameter Qwen2.5-Math-Instruct model developed by Tergel Munkhbat and KAIST AI. It is fine-tuned using self-training methods to generate concise reasoning paths for mathematical and reasoning tasks. This model specializes in maintaining accuracy while producing more efficient and direct reasoning outputs. Its primary use case is for applications requiring clear and succinct problem-solving steps.
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Model Overview
This model, developed by Tergel Munkhbat and KAIST AI, is a fine-tuned version of the Qwen2.5-Math-1.5B-Instruct architecture. It leverages novel self-training methods to enhance its reasoning capabilities, specifically focusing on generating concise reasoning paths for complex problems.
Key Capabilities
- Concise Reasoning: Optimized to produce shorter, more direct reasoning steps without compromising accuracy.
- Mathematical Task Proficiency: Built upon a math-instruct base, making it suitable for numerical and logical problem-solving.
- Self-Training Methodology: Utilizes advanced self-training techniques to improve reasoning efficiency.
Good For
- Applications requiring efficient and understandable step-by-step reasoning.
- Tasks where brevity in explanation is as important as correctness.
- Educational tools or automated systems that benefit from clear, succinct problem solutions.
For an in-depth understanding of the training methods, evaluation results, and technical specifications, refer to the associated paper.