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Remove universal llama tokenizer support
Instead replace it with a warning if the tokenizer files look off
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@ -12,13 +12,7 @@ This guide will cover usage through the official `transformers` implementation.
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* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
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* Direct download: https://huggingface.co/Neko-Institute-of-Science
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⚠️ The tokenizers for the Torrent source above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer:
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```
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python download-model.py oobabooga/llama-tokenizer
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```
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Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load.
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⚠️ The tokenizers for the Torrent source above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, in particular the files called `tokenizer_config.json` and `special_tokens_map.json`. Here you can find those files: https://huggingface.co/oobabooga/llama-tokenizer
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### Option 2: convert the weights yourself
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@ -3,6 +3,7 @@ import os
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import re
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import time
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from pathlib import Path
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import hashlib
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import torch
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import transformers
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@ -14,7 +15,6 @@ from transformers import (
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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LlamaTokenizer
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)
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import modules.shared as shared
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@ -91,30 +91,31 @@ def load_model(model_name, loader=None):
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def load_tokenizer(model_name, model):
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tokenizer = None
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path_to_model = Path(f"{shared.args.model_dir}/{model_name}/")
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if any(s in model_name.lower() for s in ['gpt-4chan', 'gpt4chan']) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
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elif model.__class__.__name__ in ['LlamaForCausalLM', 'LlamaGPTQForCausalLM', 'ExllamaHF']:
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# Try to load an universal LLaMA tokenizer
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if not any(s in shared.model_name.lower() for s in ['llava', 'oasst']):
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for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
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if p.exists():
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logger.info(f"Loading the universal LLaMA tokenizer from {p}...")
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tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True)
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return tokenizer
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elif path_to_model.exists():
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tokenizer = AutoTokenizer.from_pretrained(
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path_to_model,
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trust_remote_code=shared.args.trust_remote_code,
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use_fast=False
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)
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# Otherwise, load it from the model folder and hope that these
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# are not outdated tokenizer files.
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tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True)
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try:
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tokenizer.eos_token_id = 2
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tokenizer.bos_token_id = 1
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tokenizer.pad_token_id = 0
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except:
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pass
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else:
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path_to_model = Path(f"{shared.args.model_dir}/{model_name}/")
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if path_to_model.exists():
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tokenizer = AutoTokenizer.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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if tokenizer.__class__.__name__ == 'LlamaTokenizer':
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pairs = [
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['tokenizer_config.json', '516c6167c884793a738c440e29ccb80c15e1493ffc965affc69a1a8ddef4572a'],
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['special_tokens_map.json', 'ff3b4a612c4e447acb02d40071bddd989fe0da87eb5b7fe0dbadfc4f74de7531']
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]
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for pair in pairs:
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p = path_to_model / pair[0]
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if p.exists():
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with open(p, "rb") as f:
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bytes = f.read()
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file_hash = hashlib.sha256(bytes).hexdigest()
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if file_hash != pair[1]:
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logger.warning(f"{p} is different from the original LlamaTokenizer file. It is either customized or outdated.")
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return tokenizer
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