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Fix loading condition for universal llama tokenizer (#2753)

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ThisIsPIRI 2023-06-18 21:14:06 +00:00 committed by GitHub
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modules/models.py
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@ -86,7 +86,7 @@ def load_tokenizer(model_name, model):
tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/")) tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
elif type(model) is transformers.LlamaForCausalLM or "LlamaGPTQForCausalLM" in str(type(model)): elif type(model) is transformers.LlamaForCausalLM or "LlamaGPTQForCausalLM" in str(type(model)):
# Try to load an universal LLaMA tokenizer # Try to load an universal LLaMA tokenizer
if any(s in shared.model_name.lower() for s in ['llava', 'oasst']): if not any(s in shared.model_name.lower() for s in ['llava', 'oasst']):
for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]: for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
if p.exists(): if p.exists():
logger.info(f"Loading the universal LLaMA tokenizer from {p}...") logger.info(f"Loading the universal LLaMA tokenizer from {p}...")