Python 医疗领域大模型微调，LLMs在医疗领域的lora微调，医疗领域问答大模型微调训练，大模型在医疗领域的SFT，基于glm-4-9b-chat在医疗领域的指令微调（一）

一、项目介绍

本项目基于THUDM/glm-4-9b-chat模型进行垂域的微调。主要设计医疗领域问答对的lora微调。下面是半精度微调，下一遍是4bit微调，占用资源更小

二、代码实战

1.半精度微调

导入依赖包

from datasets import Dataset, load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer

加载数据

dataset = load_dataset("csv", data_files="./问答.csv", split="train")
dataset = dataset.filter(lambda x: x["answer"] is not None)
print(dataset)

datasets = dataset.train_test_split(test_size=0.1)
print(datasets)

print(datasets['train'][:2])

数据预处理

tokenizer = AutoTokenizer.from_pretrained("./glm-4-9b-chat", trust_remote_code=True)
print(tokenizer)

def process_func(example):
    MAX_LENGTH = 256
    input_ids, attention_mask, labels = [], [], []
    instruction = example["question"].strip()     # query
    instruction = tokenizer.apply_chat_template([{"role": "user", "content": instruction}],
                                       add_generation_prompt=True,
                                       tokenize=True,
                                       return_tensors="pt",
                                       return_dict=True
                                       )      # '[gMASK] <sop> <|user|> \nquery <|assistant|>'
    
    response = tokenizer("\n" + example["answer"], add_special_tokens=False)        # \n response, 缺少eos token
    input_ids = instruction["input_ids"][0].numpy().tolist() + response["input_ids"] + [tokenizer.eos_token_id]
    attention_mask = instruction["attention_mask"][0].numpy().tolist() + response["attention_mask"] + [1]
    labels = [-100] * len(instruction["input_ids"][0].numpy().tolist()) + response["input_ids"] + [tokenizer.eos_token_id]
    if len(input_ids) > MAX_LENGTH:
        input_ids = input_ids[:MAX_LENGTH]
        attention_mask = attention_mask[:MAX_LENGTH]
        labels = labels[:MAX_LENGTH]
    return {
        "input_ids": input_ids,
        "attention_mask": attention_mask,
        "labels": labels
    }

tokenized_ds = datasets['train'].map(process_func, remove_columns=['id', 'question', 'answer'])
tokenized_ts = datasets['test'].map(process_func, remove_columns=['id', 'question', 'answer'])
print(tokenized_ds)

print(tokenizer.decode(tokenized_ds[1]["input_ids"]))
print(tokenizer.decode(list(filter(lambda x: x != -100, tokenized_ds[1]["labels"]))))

创建模型实例

import torch
model = AutoModelForCausalLM.from_pretrained("./glm-4-9b-chat", trust_remote_code=True, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16, device_map="auto")

for name, param in model.named_parameters():
    print(name)

配置文件

from peft import LoraConfig, TaskType, get_peft_model, PeftModel

config = LoraConfig(target_modules=["query_key_value"], modules_to_save=["post_attention_layernorm"])
print(config)

model = get_peft_model(model, config)
print(config)

for name, parameter in model.named_parameters():
    print(name)

print(model.print_trainable_parameters())
print(model)

 

配置训练参数，创建训练器，进行训练

args = TrainingArguments(
    output_dir="./chatbot",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,
    gradient_checkpointing=True,
    logging_steps=100,
    num_train_epochs=10,
    learning_rate=1e-4,
    remove_unused_columns=False,
    save_strategy="epoch"
)

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=tokenized_ds.select(range(10000)),
    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)

trainer.train()