watch -n 1 nvidia-smi

1、数据处理代码

import asttrain_dataset = []# 定义合法的字段列表
valid_fields = ["id", "conversations"]with open('train.json', 'r', encoding="utf-8") as f:train_data = f.readlines()
for i, item in enumerate(train_data):json_data = ast.literal_eval(item)hh = {"id": i, "conversations": [{"from": "user", "value": json_data['instruction'].replace("'", "").replace('"', "") },{"from": "assistant", "value": json_data['output'].replace("'", "").replace('"', "") }]}train_dataset.append(hh)with open("data.json", "w", encoding='utf-8') as f:f.write(",\n".join(str(x) for x in train_dataset))# nohup bash finetune/finetune_lora_single_gpu.sh > /home/wangyp/Big_Model/train.log 2>&1 &

2、微调代码

https://github.com/QwenLM/

3、调用微调模型进行推理代码 

 CUDA_VISIBLE_DEVICES=0 python call_finetune.py

"""
调用微调以后的模型代码
"""from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfigmodel = AutoPeftModelForCausalLM.from_pretrained('/home/wangyp/Big_Model/qwen/output_qwen', # path to the output directorydevice_map="auto",trust_remote_code=True
).eval()
merged_model = model.merge_and_unload()
merged_model.save_pretrained('/home/wangyp/Big_Model/qwen/qwen72b_sft', max_shard_size="2048MB", safe_serialization=True)tokenizer = AutoTokenizer.from_pretrained("/home/wangyp/Big_Model/qwen/output_qwen", trust_remote_code=True)
tokenizer.save_pretrained("/home/wangyp/Big_Model/qwen/qwen72b_sft")
print("tikenizer 保存成功！")query = "一名年龄在70岁的女性，出现了晕厥、不自主颤抖、情绪不稳等症状，请详细说明其手术治疗和术前准备。"tokenizer = AutoTokenizer.from_pretrained("/home/wangyp/Big_Model/qwen/qwen72b_sft", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("/home/wangyp/Big_Model/qwen/qwen72b_sft", device_map="auto",trust_remote_code=True).eval()
response, history = model.chat(tokenizer, query, history=None)
print("微调后qwen答：" + response)tokenizer = AutoTokenizer.from_pretrained("/mnt/sdd/Qwen-7B-Chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("/mnt/sdd/Qwen-7B-Chat", device_map="auto",trust_remote_code=True).eval()
response, history = model.chat(tokenizer, query, history=None)
print("官方qwen答：" + response)