HSPMATH-7B

We have released HSPMATH-7B, a supervised fine-tuning model for MATH.

We constructed a supervised fine-tuning dataset of 75k samples through a simple yet effective method based on the MetaMathQA dataset. After supervised fine-tuning the Llemma-7B model, we achieved a strong performance of 64.3% on the GSM8K dataset. The dataset construction method involves introducing a hint before the solution. For details, refer to the paper: Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge.

A comparison of performances with methods of similar model sizes (7B) is shown in the table below:

Note:

• The MetaMath family models is fine-tuned on 400k samples, which is more than 5.3 times the size of our training set.
• Llemma-7B (SFT) and our model HSPMATH-7B are supervised fine-tuning (SFT) on the same dataset but without the Hint texts.
• We found that by introducing hints, the SFT model HSPMATH-7B improved by 5.6%.