58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
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import argparse
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import os
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from configs.model_config import *
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# Additional argparse types
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def path(string):
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if not string:
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return ''
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s = os.path.expanduser(string)
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if not os.path.exists(s):
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raise argparse.ArgumentTypeError(f'No such file or directory: "{string}"')
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return s
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def file_path(string):
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if not string:
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return ''
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s = os.path.expanduser(string)
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if not os.path.isfile(s):
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raise argparse.ArgumentTypeError(f'No such file: "{string}"')
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return s
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def dir_path(string):
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if not string:
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return ''
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s = os.path.expanduser(string)
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if not os.path.isdir(s):
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raise argparse.ArgumentTypeError(f'No such directory: "{string}"')
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return s
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parser = argparse.ArgumentParser(prog='langchain-ChatGLM',
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description='About langchain-ChatGLM, local knowledge based ChatGLM with langchain ｜ '
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'基于本地知识库的 ChatGLM 问答')
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parser.add_argument('--no-remote-model', action='store_true', help='remote in the model on '
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'loader checkpoint, '
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'if your load local '
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'model to add the ` '
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'--no-remote-model`')
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parser.add_argument('--model-name', type=str, default=LLM_MODEL, help='Name of the model to load by default.')
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parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
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parser.add_argument("--lora-dir", type=str, default=LORA_DIR, help="Path to directory with all the loras")
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parser.add_argument('--use-ptuning-v2',type=str,default=USE_PTUNING_V2,help="whether use ptuning-v2 checkpoint")
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parser.add_argument("--ptuning-dir",type=str,default=PTUNING_DIR,help="the dir of ptuning-v2 checkpoint")
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# Accelerate/transformers
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parser.add_argument('--load-in-8bit', action='store_true', default=LOAD_IN_8BIT,
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help='Load the model with 8-bit precision.')
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parser.add_argument('--bf16', action='store_true', default=BF16,
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help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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args = parser.parse_args([])
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# Generares dict with a default value for each argument
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DEFAULT_ARGS = vars(args)
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