fix typo

README.md

@@ -186,7 +186,7 @@ print(f'Response: {response}')
 
 ## Quantization
 
-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.
@@ -197,7 +197,7 @@ 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(

README_CN.md

@@ -188,14 +188,12 @@ print(f'Response: {response}')
 
 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：
 
-
 ```
 **Requirements** Python >=3.8. Linux distribution (Ubuntu, MacOS, etc.) + CUDA > 10.0.
 ```
 
 Windows用户需安装特定版本的`bitsandbytes`，可选项包括[bitsandbytes-windows-webui](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
 
-
 你只需要在`AutoModelForCausalLM.from_pretrained`中添加你的量化配置，即可使用量化模型。如下所示：
 
 ```python