Update convert_hf_to_gguf_update.py
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Where data meets intelligence
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1 changed files with 29 additions and 59 deletions
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@ -2,7 +2,7 @@
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# -*- coding: utf-8 -*-
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# This script downloads the tokenizer models of the specified models from Huggingface and
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# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
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# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
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#
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# This is necessary in order to analyze the type of pre-tokenizer used by the model and
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# provide the necessary information to llama.cpp via the GGUF header in order to implement
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@ -15,9 +15,9 @@
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# - Add a new model to the "models" list
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# - Run the script with your huggingface token:
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#
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# python3 convert_hf_to_gguf_update.py <huggingface_token>
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# python3 convert-hf-to-gguf-update.py <huggingface_token>
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#
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# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py
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# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
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# - Update llama.cpp with the new pre-tokenizer if necessary
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#
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# TODO: generate tokenizer tests for llama.cpp
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@ -27,6 +27,7 @@ import logging
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import os
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import pathlib
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import re
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import time
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import requests
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import sys
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@ -37,15 +38,17 @@ from enum import IntEnum, auto
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from transformers import AutoTokenizer
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger("convert_hf_to_gguf_update")
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logger = logging.getLogger("convert-hf-to-gguf-update")
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sess = requests.Session()
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# User input for new model
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new_name = input("Enter the name of the new model: ")
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new_url = input("Enter the URL of the new model: ")
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class TOKENIZER_TYPE(IntEnum):
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SPM = auto()
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BPE = auto()
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WPM = auto()
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UGM = auto()
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# TODO: this string has to exercise as much pre-tokenizer functionality as possible
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@ -56,42 +59,21 @@ if len(sys.argv) == 2:
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token = sys.argv[1]
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if not token.startswith("hf_"):
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logger.info("Huggingface token seems invalid")
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logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
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logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
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sys.exit(1)
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else:
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logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
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logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
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sys.exit(1)
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# TODO: add models here, base models preferred
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models = [
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{"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
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{"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
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{"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
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{"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
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{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
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{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
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{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
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{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
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{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
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{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
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{"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
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{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
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{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
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{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
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{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
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{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
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{"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
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{"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
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{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
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{"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
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{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
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{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
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{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
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{"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
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{"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
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{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
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{"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
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]
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models = []
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# Construct new entry and add to models list
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new_entry = {"name": new_name, "tokt": TOKENIZER_TYPE.BPE, "repo": new_url}
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models.append(new_entry)
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print('Model added...')
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print(models)
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time.sleep(15)
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def download_file_with_auth(url, token, save_path):
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@ -112,13 +94,9 @@ def download_model(model):
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os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
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files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
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if tokt == TOKENIZER_TYPE.SPM:
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files.append("tokenizer.model")
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if tokt == TOKENIZER_TYPE.UGM:
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files.append("spiece.model")
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for file in files:
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save_path = f"models/tokenizers/{name}/{file}"
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if os.path.isfile(save_path):
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@ -134,14 +112,14 @@ for model in models:
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logger.error(f"Failed to download model {model['name']}. Error: {e}")
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# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
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# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
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src_ifs = ""
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for model in models:
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name = model["name"]
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tokt = model["tokt"]
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if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
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if tokt == TOKENIZER_TYPE.SPM:
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continue
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# Skip if the tokenizer folder does not exist or there are other download issues previously
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@ -151,10 +129,7 @@ for model in models:
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# create the tokenizer
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try:
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if name == "t5":
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
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else:
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
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except OSError as e:
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logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
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continue # Skip to the next model if the tokenizer can't be loaded
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@ -201,7 +176,7 @@ src_func = f"""
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res = None
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# NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
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# NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
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# or pull the latest version of the model from Huggingface
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# don't edit the hashes manually!
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{src_ifs}
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@ -210,9 +185,9 @@ src_func = f"""
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logger.warning("**************************************************************************************")
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logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
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logger.warning("** There are 2 possible reasons for this:")
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logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet")
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logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet")
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logger.warning("** - the pre-tokenization config has changed upstream")
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logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
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logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
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logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920")
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logger.warning("**")
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logger.warning(f"** chkhsh: {{chkhsh}}")
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@ -226,7 +201,7 @@ src_func = f"""
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return res
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"""
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convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
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convert_py_pth = pathlib.Path("convert-hf-to-gguf.py")
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convert_py = convert_py_pth.read_text(encoding="utf-8")
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convert_py = re.sub(
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r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
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@ -237,7 +212,7 @@ convert_py = re.sub(
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convert_py_pth.write_text(convert_py, encoding="utf-8")
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logger.info("+++ convert_hf_to_gguf.py was updated")
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logger.info("+++ convert-hf-to-gguf.py was updated")
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# generate tests for each tokenizer model
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@ -275,7 +250,6 @@ tests = [
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"\n =",
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"' era",
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"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
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"!!!!!!",
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"3",
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"33",
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"333",
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@ -285,8 +259,7 @@ tests = [
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"3333333",
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"33333333",
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"333333333",
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"Cửa Việt", # llama-bpe fails on this
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" discards",
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# "Cửa Việt", # llama-bpe fails on this
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chktxt,
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]
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@ -314,10 +287,7 @@ for model in models:
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# create the tokenizer
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try:
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if name == "t5":
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
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else:
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
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tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}",)
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except OSError as e:
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logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
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continue # Skip this model and continue with the next one in the loop
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@ -343,6 +313,6 @@ logger.info("\nRun the following commands to generate the vocab files for testin
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for model in models:
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name = model["name"]
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print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
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print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
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logger.info("\n")
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