Update README.md

README.md

@@ -36,7 +36,7 @@ It is impratical for us to manually set specific configurations for each fine-tu
 Therefore, OpenCSG racked their brains to provide a relatively fair method to compare the fine-tuned models on the HumanEval benchmark.
 To simplify the comparison, we chosed the Pass@1 metric for the Python language, but our fine-tuning dataset includes samples in multiple languages.
 
-**For fairness, we evaluated the original and fine-tuned CodeLlama models based only on the prompts from the original cases, without including any other instructions.**
+**For fairness, we evaluated the original and fine-tuned StarCoder models based only on the prompts from the original cases, without including any other instructions.**
 
 **Otherwise, we use the greedy decoding method for each model during evaluation.**
 
@@ -142,7 +142,7 @@ HumanEval 是评估模型在代码生成方面性能的最常见的基准，尤
 因此，OpenCSG 提供了一个相对公平的方法来在 HumanEval 基准上比较各微调模型。
 方便起见，我们选择了Python语言Pass@1指标，但要注意的是，我们的微调数据集是包含多种编程语言。
 
-**为了公平起见，我们仅根据原始问题的提示来评估原始和微调过的 CodeLlama 模型，不包含任何其他说明。**
+**为了公平起见，我们仅根据原始问题的提示来评估原始和微调过的 StarCoder 模型，不包含任何其他说明。**
 
 **除此之外，我们在评估过程中对每个模型都使用贪婪解码方法。**