vbatts/llama.cpp
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proto: Add experimental vocab pre-tokenizer regular expressions

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teleprint-me 2024-05-21 00:14:26 -04:00
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1 changed files with 20 additions and 2 deletions
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gguf-py/gguf/constants.py
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@ -973,6 +973,12 @@ class LLaMaModelType(IntEnum):
SFT = auto() # SafeTensor file type
# NOTE: Tokenizers defaults to OpenAI GPT-2 Byte Level Reg-Exp
# The pattern uses perl, is grammatical, and splits are technically arbitrary.
# https://github.com/openai/gpt-2/blob/master/src/encoder.py#L53
# https://github.com/huggingface/tokenizers/blob/main/tokenizers/src/pre_tokenizers/byte_level.rs#L40-L42
LLAMA_TOKENIZER_DEFAULT_PRE = "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+"
#
# HuggingFace Model Map
#
@ -983,8 +989,20 @@ class LLaMaModelType(IntEnum):
# - Possible algorithms are WordLevel, BPE, WordPiece, or Unigram
# - Possible LLaMa tokenizer model types are: None, SPM, BPE, or WPM
HF_MODEL_MAP = (
{"model_arch": MODEL_ARCH.LLAMA, "vocab_type": LLaMaVocabType.SPM, "repo": "meta-llama/Llama-2-7b-hf", },
{"model_arch": MODEL_ARCH.LLAMA, "vocab_type": LLaMaVocabType.BPE, "repo": "meta-llama/Meta-Llama-3-8B", },
{
"model_repo": "meta-llama/Llama-2-7b-hf",
"model_arch": MODEL_ARCH.LLAMA,
"vocab_type": LLaMaVocabType.SPM,
"vocab_pre": [],
},
{
"model_arch": MODEL_ARCH.LLAMA,
"vocab_type": LLaMaVocabType.BPE,
"repo": "meta-llama/Meta-Llama-3-8B",
"vocab_pre": [
"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
],
},
{"model_arch": MODEL_ARCH.PHI3, "vocab_type": LLaMaVocabType.SPM, "repo": "microsoft/Phi-3-mini-4k-instruct", },
{"model_arch": MODEL_ARCH.LLAMA, "vocab_type": LLaMaVocabType.BPE, "repo": "deepseek-ai/deepseek-llm-7b-base", },
{"model_arch": MODEL_ARCH.LLAMA, "vocab_type": LLaMaVocabType.BPE, "repo": "deepseek-ai/deepseek-coder-6.7b-base", },