Update README.md
Browse files
README.md
CHANGED
|
@@ -7,6 +7,8 @@ We have released HSPMATH-7B, a supervised fine-tuning model for MATH.
|
|
| 7 |
We constructed a supervised fine-tuning dataset of 75k samples through a simple yet effective method based on the MetaMathQA dataset.
|
| 8 |
After supervised fine-tuning the Llemma-7B model, we achieved a strong performance of 64.3% on the GSM8K dataset.
|
| 9 |
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).
|
|
|
|
|
|
|
| 10 |
A comparison of performances with methods of similar model sizes (7B) is shown in the table below:
|
| 11 |
|
| 12 |
|
|
|
|
| 7 |
We constructed a supervised fine-tuning dataset of 75k samples through a simple yet effective method based on the MetaMathQA dataset.
|
| 8 |
After supervised fine-tuning the Llemma-7B model, we achieved a strong performance of 64.3% on the GSM8K dataset.
|
| 9 |
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).
|
| 10 |
+
|
| 11 |
+
|
| 12 |
A comparison of performances with methods of similar model sizes (7B) is shown in the table below:
|
| 13 |
|
| 14 |
|