diff --git a/README_en.md b/README_en.md
index 52deef92efd996d3dcd9743c630e914bf3d13fa0..8c18309c6aaaa7e0e913d98fbbbaaf7f5fd60434 100644
--- a/README_en.md
+++ b/README_en.md
@@ -51,7 +51,7 @@ > In the tests of ChatGLM3-6B-Base, BBH used a 3-shot test, GSM8K and MATH that require inference used a 0-shot CoT test, MBPP used a 0-shot generation followed by running test cases to calculate Pass@1, and other multiple-choice type datasets all used a 0-shot test.
We have conducted manual evaluation tests on ChatGLM3-6B-32K in multiple long-text application scenarios. Compared with the second-generation model, its effect has improved by more than 50% on average. In applications such as paper reading, document summarization, and financial report analysis, this improvement is particularly significant. In addition, we also tested the model on the LongBench evaluation set, and the specific results are shown in the table below.-| Model | 平均 | Summary | Single-Doc QA | Multi-Doc QA | Code | Few-shot | Synthetic | +| Model | Average | Summary | Single-Doc QA | Multi-Doc QA | Code | Few-shot | Synthetic | |----------------------|:-----:|:----:|:----:|:----:|:------:|:-----:|:-----:| | ChatGLM2-6B-32K | 41.5 | 24.8 | 37.6 | 34.7 | 52.8 | 51.3 | 47.7 | | ChatGLM3-6B-32K | 50.2 | 26.6 | 45.8 | 46.1 | 56.2 | 61.2 | 65 |
@@ -82,9 +82,9 @@ 
- Code Interpreter: Code interpreter mode, where the model can execute code in a Jupyter environment and obtain results to complete complex tasks. -### 代码调用 +### Usage -可以通过如下代码调用 ChatGLM 模型来生成对话：+The ChatGLM model can be called to start a conversation using the following code: ```python >>> from transformers import AutoTokenizer, AutoModel
