CHATGLM3应用指南（三）——模型微调

CHATGLM3的本地部署可以见博客：CHATGLM3应用指南（一）——本地部署_chatglm3需要多大内存-CSDN博客

一、微调数据集制作

数据集的形式如下图所示：

 可使用下面代码对数据集格式调整

#! /usr/bin/env python
print('!!!!!')
import json
from collections import Counter
from argparse import ArgumentParser
import os

parser = ArgumentParser()
parser.add_argument("--path", type=str, required=True)

args = parser.parse_args()
print(args.path)
print('!!!!!')
with open(args.path ,encoding="utf-8") as f:
    data = [json.loads(line) for line in f]

train_examples = [{
    "prompt": x['content'],
    "response": x['summary'],
} for x in data]

os.makedirs("formatted_data", exist_ok=True)

with open("formatted_data/EE_term_define_2.jsonl", "w",encoding="utf-8") as f:
    for e in train_examples:
        f.write(json.dumps(e, ensure_ascii=False) + "\n")

二、微调模型的训练

运行finetune_pt.sh文件，使用命令

sh finetune_pt.sh

二、微调模型的推理

（1）在终端输入“jupyter notebook”

跳转到浏览器的jupyter 

（2）创建以下的.ipynb文件

import argparse
from transformers import AutoConfig, AutoModel, AutoTokenizer
import torch
import os

# parser = argparse.ArgumentParser()
# parser.add_argument("--pt-checkpoint", type=str, default=r"D:\Jupyter_file\ChatGLM3\chatgalm3-6b\finetune_demo\output\advertise_gen_pt-20231206-201809-128-2e-2\checkpoint-1000", help="The checkpoint path")
# parser.add_argument("--model", type=str, default=r"D:\Jupyter_file\ChatGLM3\chatgalm3-6b", help="main model weights")
# parser.add_argument("--tokenizer", type=str, default=None, help="main model weights")
# parser.add_argument("--pt-pre-seq-len", type=int, default=128, help="The pre-seq-len used in p-tuning")
# parser.add_argument("--device", type=str, default="cuda")
# parser.add_argument("--max-new-tokens", type=int, default=128)


args={
    'pt_checkpoint':r"D:\Project\ChatGLM3\finetune_demo\output\advertise_gen_pt-20231206-201809-128-2e-2\checkpoint-1000",
    'model':r"D:\Project\LLM\Model\llm\chatglm3",
    'tokenizer':None,
    'pt-pre-seq-len':128,
    'device':"cuda",
    'max_new_tokens':128
     }
if args['tokenizer'] is None:
    args['tokenizer'] = args['model']

if args['pt_checkpoint']:
    tokenizer = AutoTokenizer.from_pretrained(args['tokenizer'], trust_remote_code=True)
    config = AutoConfig.from_pretrained(args['model'], trust_remote_code=True, pre_seq_len=128)
    model = AutoModel.from_pretrained(args['model'], config=config, trust_remote_code=True)
    prefix_state_dict = torch.load(os.path.join(args['pt_checkpoint'], "pytorch_model.bin"))
    new_prefix_state_dict = {}
    for k, v in prefix_state_dict.items():
        if k.startswith("transformer.prefix_encoder."):
            new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
    model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
else:
    tokenizer = AutoTokenizer.from_pretrained(args['tokenizer'], trust_remote_code=True)
    model = AutoModel.from_pretrained(args['model'], trust_remote_code=True)

model = model.to(args['device'])

while True:
    prompt = input("Prompt:")
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = inputs.to(args['device'])
    response = model.generate(input_ids=inputs["input_ids"], max_length=inputs["input_ids"].shape[-1] + args['max_new_tokens'])
    response = response[0, inputs["input_ids"].shape[-1]:]
    print("Response:", tokenizer.decode(response, skip_special_tokens=True))

（3）修改“pt_checkpoint”为自己训练好的微调模型的“output”文件夹，修改“model”为chatglm3—6b底座模型的存放路径。

（4）确认所使用的内核，如果不对应，可以点击“内核” —>“更换内核”（5）点击运行，等待一段时间后，在prompt输入提示词，即可出现回答。