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training_chatglm_demo.py
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training_chatglm_demo.py
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# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description:
"""
import argparse
import sys
from loguru import logger
sys.path.append('../..')
from textgen import GptModel
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--train_file', default='../data/sharegpt_zh_100_format.jsonl', type=str, help='Train file')
parser.add_argument('--test_file', default='../data/sharegpt_zh_100_format.jsonl', type=str, help='Test file')
parser.add_argument('--model_type', default='chatglm', type=str, help='Transformers model type')
parser.add_argument('--model_name', default='THUDM/chatglm-6b', type=str,
help='Transformers model or path')
parser.add_argument('--do_train', action='store_true', help='Whether to run training.')
parser.add_argument('--do_predict', action='store_true', help='Whether to run predict.')
parser.add_argument('--bf16', action='store_true', help='Whether to use bf16 mixed precision training.')
parser.add_argument('--output_dir', default='./outputs-chatglm-demo/', type=str, help='Model output directory')
parser.add_argument('--prompt_template_name', default='vicuna', type=str, help='Prompt template name')
parser.add_argument('--max_seq_length', default=128, type=int, help='Input max sequence length')
parser.add_argument('--max_length', default=128, type=int, help='Output max sequence length')
parser.add_argument('--num_epochs', default=0.2, type=float, help='Number of training epochs')
parser.add_argument('--batch_size', default=8, type=int, help='Batch size')
parser.add_argument('--eval_steps', default=50, type=int, help='Eval every X steps')
parser.add_argument('--save_steps', default=50, type=int, help='Save checkpoint every X steps')
parser.add_argument("--local_rank", type=int, help="Used by dist launchers")
args = parser.parse_args()
logger.info(args)
model = None
# fine-tune Llama model
if args.do_train:
logger.info('Loading data...')
model_args = {
"use_peft": True,
"overwrite_output_dir": True,
"reprocess_input_data": True,
"max_seq_length": args.max_seq_length,
"max_length": args.max_length,
"per_device_train_batch_size": args.batch_size,
"eval_batch_size": args.batch_size,
"num_train_epochs": args.num_epochs,
"output_dir": args.output_dir,
"resume_from_checkpoint": args.output_dir,
"eval_steps": args.eval_steps,
"save_steps": args.save_steps,
"bf16": args.bf16,
"prompt_template_name": args.prompt_template_name,
}
model = GptModel(args.model_type, args.model_name, args=model_args)
model.train_model(args.train_file)
if args.do_predict:
if model is None:
model = GptModel(
args.model_type, args.model_name,
peft_name=args.output_dir,
args={'use_peft': True, 'eval_batch_size': args.batch_size, "max_length": args.max_length, }
)
response = model.predict(["介绍下北京", "介绍下南京", "给出5个必去武汉的理由"])
print(response)
# Chat model with multi turns conversation
response, history = model.chat("简单介绍下北京", history=None)
print(response, history)
response, history = model.chat('继续', history=history)
print(response)
if __name__ == '__main__':
main()