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README.md

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 license: llama2
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+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](https://arxiv.org/pdf/2402.14310.pdf).
+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%.