baalajimaestro/llama.cpp
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#717: Add support for Huggingface Autotokenizer (#790)

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Co-authored-by: Andrei <abetlen@gmail.com>
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Joe 2023-11-05 17:06:36 -06:00 committed by GitHub
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commit 4ff8def4d0
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llama_cpp/llama_chat_format.py
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@ -510,6 +510,26 @@ def format_chatml(
_prompt = _format_chatml(system_message, _messages, _sep) _prompt = _format_chatml(system_message, _messages, _sep)
return ChatFormatterResponse(prompt=_prompt) 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") @register_chat_completion_handler("functionary")
def functionary_chat_handler( def functionary_chat_handler(