Update README.md

README.md

@@ -10,7 +10,7 @@ https://meta-math.github.io/
 
 ## Model Details
 
-MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the very strong Mistral-7B model. It is glad to see using MetaMathQA datasets and change the base model from llama-2-7B to Mistral-7b can boost the GSM8K performance from 66.5 to 77.7.
+MetaMath-Mistral-7B is fully fine-tuned on the MetaMathQA datasets and based on the very strong Mistral-7B model. It is glad to see using MetaMathQA datasets and change the base model from llama-2-7B to Mistral-7b can boost the GSM8K performance from 66.5 to **77.7**.
 
 For everyone who wants to fine-tune Mistral-7B, I would suggest using a smaller learning rate(usually 1/5 to 1/10 of the lr for LlaMa-2-7B) and staying other training args unchanged.
 More training details and scripts can be seen at https://github.com/meta-math/MetaMath
@@ -42,8 +42,8 @@ prompting template:
 
 where you need to use your query question to replace the {instruction} 
 
-There are another interesting repo about Arithmo-Mistral-7B in https://huggingface.co/akjindal53244/Arithmo-Mistral-7B, where they combine our MetaMathQA dataset and MathInstruct datasets to train a powerful model. Thanks agian for their contributions.
-We would also try to train the combination of **MetaMathQA** and **MathInstruct** datasets, and also open all the results and training detalis.
+There is another interesting repo about Arithmo-Mistral-7B in https://huggingface.co/akjindal53244/Arithmo-Mistral-7B, where they combine our MetaMathQA dataset and MathInstruct datasets to train a powerful model. Thanks agian for their contributions.
+We would also try to train the combination of **MetaMathQA** and **MathInstruct** datasets, and also open all the results and training details.
 
 ## Experiments