veritasium/qwen2.5-3b-math-sft-clean-v2
The veritasium/qwen2.5-3b-math-sft-clean-v2 model is a 3.1 billion parameter Qwen2.5-3B-Instruct checkpoint, conservatively fine-tuned for concise integer-answer mathematics. It specializes in short mathematical derivations where the final answer is a single integer, typically formatted as \boxed{integer}. This model is intended for research into mathematical reasoning and aims to provide accurate, concise integer solutions.
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Overview
This model, veritasium/qwen2.5-3b-math-sft-clean-v2, is a 3.1 billion parameter Qwen2.5-3B-Instruct checkpoint specifically fine-tuned for concise integer-answer mathematics. It was developed to address regressions observed in an earlier SFT run, focusing on improving semantic correctness and output quality for mathematical tasks.
Key Capabilities
- Specialized Mathematical Reasoning: Optimized for short mathematical derivations culminating in a single integer answer.
- Concise Output: Designed to produce completions ending with
\boxed{integer}. - Improved Accuracy: Aims to provide more semantically correct mathematical reasoning compared to its predecessor, with a focus on avoiding invalid arithmetic or logic.
- Small Footprint: Based on a 3.1B parameter model, offering a balance between performance and computational efficiency.
Training and Evaluation
The model was trained on a carefully audited dataset of 190 examples, ensuring each completion contained exactly one integer answer box and no answer-forcing phrases. It underwent a paired n=1 evaluation against the base Qwen/Qwen2.5-3B-Instruct model on 831 questions. While the clean v2 model showed a slight improvement (+0.76pp) in accuracy on the clean usable slice (excluding calibration IDs), this difference was not statistically significant in the single-seed experiment. The training configuration used LoRA with rank 8 and alpha 16, targeting q_proj and v_proj modules, for 2 epochs.
Good For
- Research in Mathematical LLMs: Ideal for researchers exploring fine-tuning techniques for mathematical reasoning in LLMs.
- Integer-Answer Math Problems: Best suited for use cases requiring concise, integer-based solutions to mathematical problems.
- Diagnostic Context: Useful for understanding the impact of data auditing and targeted SFT on mathematical performance.