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* use existing TikTokenConverter to read tiktoken tokenizer.model file * del test file * create titktoken integration file * adding tiktoken llama test * ALTNATIVE IMPLEMENTATION: supports llama 405B * fix one char * remove redundant line * small fix * rm unused import * flag for converting from tiktokeng * remove unneeded file * ruff * remove llamatiktokenconverter, stick to general converter * tiktoken support v2 * update test * remove stale changes * udpate doc * protect import * use is_protobuf_available * add templateprocessor in tiktokenconverter * reverting templateprocessor from tiktoken support * update test * add require_tiktoken * dev-ci * trigger build * trigger build again * dev-ci * [build-ci-image] tiktoken * dev-ci * dev-ci * dev-ci * dev-ci * change tiktoken file name * feedback review * feedback rev * applying feedback, removing tiktoken converters * conform test * adding docs for review * add doc file for review * add doc file for review * add doc file for review * support loading model without config.json file * Revert "support loading model without config.json file" This reverts commit 2753602e51c34cef2f184eb11f36d2ad1b02babb. * remove dev var * updating docs * safely import protobuf * fix protobuf import error * fix protobuf import error * trying isort to fix ruff error * fix ruff error * try to fix ruff again * try to fix ruff again * try to fix ruff again * doc table of contents * add fix for consistency.dockerfile torchaudio * ruff * applying feedback * minor typo * merging with push-ci-image * clean up imports * revert dockerfile consistency
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Tiktoken and interaction with Transformers
Support for tiktoken model files is seamlessly integrated in 🤗 transformers when loading models
from_pretrained with a tokenizer.model tiktoken file on the Hub, which is automatically converted into our
fast tokenizer.
Known models that were released with a tiktoken.model:
- gpt2
- llama3
Example usage
In order to load tiktoken files in transformers, ensure that the tokenizer.model file is a tiktoken file and it
will automatically be loaded when loading from_pretrained. Here is how one would load a tokenizer and a model, which
can be loaded from the exact same file:
from transformers import AutoTokenizer
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, subfolder="original")