AGI4Good/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:

Open-source Model (7B)	GSM8k
MetaMath-Mistral-7B	77.7
MetaMath-7B-V1.0	66.5
HSPMATH-7B	64.3
Llemma-7B (SFT)	58.7
WizardMath-7B	54.9
RFT-7B	50.3
Qwen-7b	47.84
Mistral-7b	37.83
Yi-6b	32.6
ChatGLM-6B	32.4
LLaMA2-7b	12.96

Close-source Model	GSM8k
GPT-3.5	57.1
PaLM-540B	56.5
Minerva-540B	58.8
Minerva-62B	52.4
Chinchilla-70B	43.7

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%.