Refactor autotokenizer format to reusable function

llama_cpp/llama_chat_format.py

@@ -1,7 +1,9 @@
 from __future__ import annotations
 
+import os
 import dataclasses
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, Protocol
+
 from . import llama_types
 from . import llama
 
@@ -327,6 +329,26 @@ def get_chat_format(name: str):
         )
 
 
+def hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path: Union[str, os.PathLike[str]]) -> ChatFormatter:
+    # https://huggingface.co/docs/transformers/main/chat_templating
+    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
+    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
+
+    def format_autotokenizer(
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        **kwargs: Any,
+    ) -> ChatFormatterResponse:
+        tokenizer.use_default_system_prompt = False
+        _prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+        # Return formatted prompt and eos token by default
+        return ChatFormatterResponse(prompt=_prompt, stop=tokenizer.eos_token)
+
+    return format_autotokenizer
+
+
 # see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
 # system prompt is "embedded" in the first message
 @register_chat_format("llama-2")
@@ -510,26 +532,6 @@ def format_chatml(
     _prompt = _format_chatml(system_message, _messages, _sep)
     return ChatFormatterResponse(prompt=_prompt)
 
-# eg, export HF_MODEL=mistralai/Mistral-7B-Instruct-v0.1
-@register_chat_format("autotokenizer")
-def format_autotokenizer(
-    messages: List[llama_types.ChatCompletionRequestMessage],
-    **kwargs: Any,
-) -> ChatFormatterResponse:
-    # https://huggingface.co/docs/transformers/main/chat_templating
-    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
-    # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
-    import os
-    from transformers import AutoTokenizer
-    huggingFaceModel = os.getenv("HF_MODEL") # eg, mistralai/Mistral-7B-Instruct-v0.1
-    print(huggingFaceModel)
-    if not huggingFaceModel:
-        raise Exception("HF_MODEL needs to be set in env to use chat format 'autotokenizer'")
-    tokenizer = AutoTokenizer.from_pretrained(huggingFaceModel)
-    tokenizer.use_default_system_prompt = False
-    _prompt = tokenizer.apply_chat_template(messages, tokenize=False)
-    # Return formatted prompt and eos token by default
-    return ChatFormatterResponse(prompt=_prompt, stop=tokenizer.eos_token)
 
 @register_chat_completion_handler("functionary")
 def functionary_chat_handler(