.venv/transformers/docs/source/zh/tiktoken.md

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# Transformers与Tiktonken的互操作性

在🤗 transformers中，当使用`from_pretrained`方法从Hub加载模型时，如果模型包含tiktoken格式的`tokenizer.model`文件，框架可以无缝支持tiktoken模型文件，并自动将其转换为我们的[快速词符化器](https://huggingface.co/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast)。

### 已知包含`tiktoken.model`文件发布的模型：
    - gpt2
    - llama3

## 使用示例

为了在transformers中正确加载`tiktoken`文件，请确保`tiktoken.model`文件是tiktoken格式的，并且会在加载`from_pretrained`时自动加载。以下展示如何从同一个文件中加载词符化器(tokenizer)和模型：

```py
from transformers import AutoTokenizer

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="original") 
```
## 创建tiktoken词符化器(tokenizer)

`tokenizer.model`文件中不包含任何额外的词符(token)或模式字符串(pattern strings)的信息。如果这些信息很重要，需要将词符化器(tokenizer)转换为适用于[`PreTrainedTokenizerFast`]类的`tokenizer.json`格式。

使用[tiktoken.get_encoding](https://github.com/openai/tiktoken/blob/63527649963def8c759b0f91f2eb69a40934e468/tiktoken/registry.py#L63)生成`tokenizer.model`文件，再使用[`convert_tiktoken_to_fast`]函数将其转换为`tokenizer.json`文件。

```py

from transformers.integrations.tiktoken import convert_tiktoken_to_fast
from tiktoken import get_encoding

# You can load your custom encoding or the one provided by OpenAI
encoding = get_encoding("gpt2")
convert_tiktoken_to_fast(encoding, "config/save/dir")
```

生成的`tokenizer.json`文件将被保存到指定的目录，并且可以通过[`PreTrainedTokenizerFast`]类来加载。

```py
tokenizer = PreTrainedTokenizerFast.from_pretrained("config/save/dir")
```