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fix typo

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JustinLin610
2023-08-05 23:59:59 +08:00
parent f63c1d1026
commit 6acf8f126f
2 changed files with 2 additions and 4 deletions
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README.md
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@@ -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(

2
README_CN.md
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@@ -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