update readme

README.md

@@ -43,9 +43,9 @@ For more details about the open-source model of Qwen-7B, please refer to the [Gi
 
 ## 依赖项（Dependency）
 
-运行Qwen-7B-Chat，请确保机器环境pytorch版本不低于1.12，再执行以下pip命令安装依赖库
+运行Qwen-7B-Chat，请确保满足上述要求，再执行以下pip命令安装依赖库
 
-To run Qwen-7B-Chat, please make sure that pytorch version is not lower than 1.12, and then execute the following pip commands to install the dependent libraries.
+To run Qwen-7B-Chat, please make sure you meet the above requirements, and then execute the following pip commands to install the dependent libraries.
 
 ```bash
 pip install transformers==4.31.0 accelerate tiktoken einops
@@ -292,9 +292,9 @@ Qwen-7B-Chat also has the capability to be used as a [HuggingFace Agent](https:/
 
 ## 量化（Quantization）
 
-如希望使用更低精度的量化模型，如4比特和8比特的模型，我们提供了简单的示例来说明如何快速使用量化模型。在开始前，确保你已经安装了`bitsandbytes`。请注意：`bitsandbytes`的安装要求是：
+如希望使用更低精度的量化模型，如4比特和8比特的模型，我们提供了简单的示例来说明如何快速使用量化模型。在开始前，确保你已经安装了`bitsandbytes`。请注意，`bitsandbytes`的安装要求是：
 
-We provide examples to show how to load models in `NF4` and `Int8`. For starters, make sure you have implemented `bitsandbytes`. Note that the requirements for `bitsandbytes` is:
+We provide examples to show how to load models in `NF4` and `Int8`. For starters, make sure you have implemented `bitsandbytes`. Note that the requirements for `bitsandbytes` are:
 
 ```
 **Requirements** Python >=3.8. Linux distribution (Ubuntu, MacOS, etc.) + CUDA > 10.0.
@@ -309,7 +309,7 @@ Windows users should find another option, which might be [bitsandbytes-windows-w
 Then you only need to add your quantization configuration to `AutoModelForCausalLM.from_pretrained`. See the example below:
 
 ```python
-from transformers import BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig
 
 # quantization configuration for NF4 (4 bits)
 quantization_config = BitsAndBytesConfig(