一、Qwen2 Lora 微调
Qwen是阿里巴巴集团Qwen团队研发的大语言模型和大型多模态模型系列。Qwen2 是 Qwen1.5 的重大升级。无论是语言模型还是多模态模型，均在大规模多语言和多模态数据上进行预训练，并通过高质量数据进行后期微调以贴近人类偏好。Qwen具备自然语言理解、文本生成、视觉理解、音频理解、工具使用、角色扮演、作为AI Agent进行互动等多种能力。
Qwen2有以下特点：
- •
5种模型规模，包括0.5B、1.5B、7B、57B-A14B和72B； - • 针对每种尺寸提供基础模型和指令微调模型，并确保指令微调模型按照人类偏好进行校准；
- • 基础模型和指令微调模型的多语言支持；
- • 所有模型均稳定支持
32K长度上下文；Qwen2-7B-Instruct与Qwen2-72B-Instruct可支持128K上下文（需额外配置） - • 支持工具调用、
RAG（检索增强文本生成）、角色扮演、AI Agent等；
更多详细的介绍可以参考官方文档：
https://qwen.readthedocs.io/zh-cn/latest/
下面实验所使用的核心依赖版本如下：
torch==1.13.1+cu116
peft==0.12.0
transformers==4.37.0
tensorboard==2.17.1
二、构建 Qwen2-1.5B Lora 模型
LoRA 微调技术的思想很简单，在原始 PLM (Pre-trained Language Model) 增加一个旁路，一般是在 transformer 层，做一个降维再升维的操作，模型的输入输出维度不变，来模拟 intrinsic rank，如下图的 A 和 B。训练时冻结 PLM 的参数，只训练 A 和 B ，，输出时将旁路输出与 PLM 的参数叠加，进而影响原始模型的效果。该方式，可以大大降低训练的参数量，而性能可以优于其它参数高效微调方法，甚至和全参数微调（Fine-Tuning）持平甚至超过。
对于 A 和 B 参数的初始化，A 使用随机高斯分布，B 使用 0 矩阵，这样在最初时可以保证旁路为一个 0 矩阵，最开始时使用原始模型的能力。
在构建 Qwen2-1.5B Lora 结构模型前，先了解下现在 Qwen2-1.5B 的结构：
这里直接使用 PyTorch 的模型打印方式，主要看模型的组成：
from transformers import AutoModelForCausalLM
model_path = "model/Qwen2-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
print(model)
输出结果：
Qwen2ForCausalLM(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 1536)
(layers): ModuleList(
(0): Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): Linear(in_features=1536, out_features=1536, bias=True)
(k_proj): Linear(in_features=1536, out_features=256, bias=True)
(v_proj): Linear(in_features=1536, out_features=256, bias=True)
(o_proj): Linear(in_features=1536, out_features=1536, bias=False)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): Linear(in_features=1536, out_features=8960, bias=False)
(up_proj): Linear(in_features=1536, out_features=8960, bias=False)
(down_proj): Linear(in_features=8960, out_features=1536, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm()
(post_attention_layernorm): Qwen2RMSNorm()
)
.
. 省略中间结构
.
(27): Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): Linear(in_features=1536, out_features=1536, bias=True)
(k_proj): Linear(in_features=1536, out_features=256, bias=True)
(v_proj): Linear(in_features=1536, out_features=256, bias=True)
(o_proj): Linear(in_features=1536, out_features=1536, bias=False)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): Linear(in_features=1536, out_features=8960, bias=False)
(up_proj): Linear(in_features=1536, out_features=8960, bias=False)
(down_proj): Linear(in_features=8960, out_features=1536, bias=False)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm()
(post_attention_layernorm): Qwen2RMSNorm()
)
)
(norm): Qwen2RMSNorm()
)
(lm_head): Linear(in_features=1536, out_features=151936, bias=False)
)
从上面的结构可以看出 Qwen2-1.5B 的结构其实并不复杂，由 27 层 DecoderLayer 构成，每个 Decoder 主要的核心是 self_attention 和 mlp，因此可以尝试在 q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj 层添加 Lora 结构，下面使用 PEFT 库实现，这里 r 使用 8，lora_alpha 使用 32 ：
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType
model_path = "model/Qwen2-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
print(model)
输出结果：
trainable params: 9,232,384 || all params: 1,552,946,688 || trainable%: 0.5945
PeftModelForCausalLM(
(base_model): LoraModel(
(model): Qwen2ForCausalLM(
(model): Qwen2Model(
(embed_tokens): Embedding(151936, 1536)
(layers): ModuleList(
(0): Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=1536, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=256, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=256, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=256, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=256, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=1536, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=8960, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=8960, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=8960, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=8960, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=8960, out_features=1536, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=8960, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm()
(post_attention_layernorm): Qwen2RMSNorm()
)
.
. 省略中间结构
.
(27): Qwen2DecoderLayer(
(self_attn): Qwen2Attention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=1536, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=256, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=256, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=256, bias=True)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=256, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=1536, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(rotary_emb): Qwen2RotaryEmbedding()
)
(mlp): Qwen2MLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=8960, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=8960, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=1536, out_features=8960, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=1536, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=8960, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=8960, out_features=1536, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=8960, out_features=8, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=8, out_features=1536, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(lora_magnitude_vector): ModuleDict()
)
(act_fn): SiLU()
)
(input_layernorm): Qwen2RMSNorm()
(post_attention_layernorm): Qwen2RMSNorm()
)
)
(norm): Qwen2RMSNorm()
)
(lm_head): Linear(in_features=1536, out_features=151936, bias=False)
)
)
)
从结果可以看出，Lora 之后在每一层都增加了一个 lora_A 和 lora_B 结构来实现降维升维的作用。
三、准备训练数据集
数据集采用 GitHub 上的 Chinese-medical-dialogue-data 中文医疗对话数据集。
GitHub 地址如下：
https://github.com/Toyhom/Chinese-medical-dialogue-data
数据分了 6 个科目类型：
数据格式如下所示：
其中 ask 为病症的问题描述，answer 为病症的回答。
由于整体数据比较多，这里为了演示效果，选取 内科、肿瘤科、儿科、外科 四个科目的数据进行实验，并且每个科目取前 10000 条数据进行训练、2000 条数据进行验证。
首先将数据集转为 json 格式方便后续读取：
import json
import pandas as pd
data_path = [
"./data/Chinese-medical-dialogue-data-master/Data_数据/IM_内科/内科5000-33000.csv",
"./data/Chinese-medical-dialogue-data-master/Data_数据/Oncology_肿瘤科/肿瘤科5-10000.csv",
"./data/Chinese-medical-dialogue-data-master/Data_数据/Pediatric_儿科/儿科5-14000.csv",
"./data/Chinese-medical-dialogue-data-master/Data_数据/Surgical_外科/外科5-14000.csv",
]
train_json_path = "./data/train.json"
val_json_path = "./data/val.json"
# 每个数据取 10000 条作为训练
train_size = 10000
# 每个数据取 2000 条作为验证
val_size = 2000
def main():
train_f = open(train_json_path, "a", encoding='utf-8')
val_f = open(val_json_path, "a", encoding='utf-8')
for path in data_path:
data = pd.read_csv(path, encoding='ANSI')
train_count = 0
val_count = 0
for index, row in data.iterrows():
question = row["ask"]
answer = row["answer"]
line = {
"question": question,
"answer": answer
}
line = json.dumps(line, ensure_ascii=False)
if train_count < train_size:
train_f.write(line + "\n")
train_count = train_count + 1
elif val_count < val_size:
val_f.write(line + "\n")
val_count = val_count + 1
else:
break
print("数据处理完毕！")
train_f.close()
val_f.close()
if __name__ == '__main__':
main()
处理之后可以看到两个生成的文件：
四、微调训练
解析数据，构建 Dataset 数据集
qa_dataset.py：
# -*- coding: utf-8 -*-
from torch.utils.data import Dataset
import torch
import json
import numpy as np
class QADataset(Dataset):
def __init__(self, data_path, tokenizer, max_source_length, max_target_length) -> None:
super().__init__()
self.tokenizer = tokenizer
self.max_source_length = max_source_length
self.max_target_length = max_target_length
self.max_seq_length = self.max_source_length + self.max_target_length
self.data = []
if data_path:
with open(data_path, "r", encoding='utf-8') as f:
for line in f:
if not line or line == "":
continue
json_line = json.loads(line)
question = json_line["question"]
answer = json_line["answer"]
self.data.append({
"question": question,
"answer": answer
})
print("data load ， size：", len(self.data))
def preprocess(self, question, answer):
messages = [
{"role": "system", "content": "你是一个医疗方面的专家，可以根据患者的问题进行解答。"},
{"role": "user", "content": question}
]
prompt = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
instruction = self.tokenizer(prompt, add_special_tokens=False, max_length=self.max_source_length)
response = self.tokenizer(answer, add_special_tokens=False, max_length=self.max_target_length)
input_ids = instruction["input_ids"] + response["input_ids"] + [self.tokenizer.pad_token_id]
attention_mask = (instruction["attention_mask"] + response["attention_mask"] + [1])
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [self.tokenizer.pad_token_id]
if len(input_ids) > self.max_seq_length:
input_ids = input_ids[:self.max_seq_length]
attention_mask = attention_mask[:self.max_seq_length]
labels = labels[:self.max_seq_length]
return input_ids, attention_mask, labels
def __getitem__(self, index):
item_data = self.data[index]
input_ids, attention_mask, labels = self.preprocess(**item_data)
return {
"input_ids": torch.LongTensor(np.array(input_ids)),
"attention_mask": torch.LongTensor(np.array(attention_mask)),
"labels": torch.LongTensor(np.array(labels))
}
def __len__(self):
return len(self.data)
训练：
# -*- coding: utf-8 -*-
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
import pandas as pd
from qa_dataset import QADataset
from tqdm import tqdm
import os, time, sys
def train_model(model, train_loader, val_loader, optimizer, gradient_accumulation_steps,
device, num_epochs, model_output_dir, writer):
batch_step = 0
for epoch in range(num_epochs):
time1 = time.time()
model.train()
for index, data in enumerate(tqdm(train_loader, file=sys.stdout, desc="Train Epoch: " + str(epoch))):
input_ids = data['input_ids'].to(device, dtype=torch.long)
attention_mask = data['attention_mask'].to(device, dtype=torch.long)
labels = data['labels'].to(device, dtype=torch.long)
# 前向传播
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
)
loss = outputs.loss
# 反向传播，计算当前梯度
loss.backward()
# 梯度累积步数
if (index % gradient_accumulation_steps == 0 and index != 0) or index == len(train_loader) - 1:
# 更新网络参数
optimizer.step()
# 清空过往梯度
optimizer.zero_grad()
writer.add_scalar('Loss/train', loss, batch_step)
batch_step += 1
# 100轮打印一次 loss
if index % 100 == 0 or index == len(train_loader) - 1:
time2 = time.time()
tqdm.write(
f"{index}, epoch: {epoch} -loss: {str(loss)} ; each step's time spent: {(str(float(time2 - time1) / float(index + 0.0001)))}")
# 验证
model.eval()
val_loss = validate_model(model, val_loader, device)
writer.add_scalar('Loss/val', val_loss, epoch)
print(f"val loss: {val_loss} , epoch: {epoch}")
print("Save Model To ", model_output_dir)
model.save_pretrained(model_output_dir)
def validate_model(model, device, val_loader):
running_loss = 0.0
with torch.no_grad():
for _, data in enumerate(tqdm(val_loader, file=sys.stdout, desc="Validation Data")):
input_ids = data['input_ids'].to(device, dtype=torch.long)
attention_mask = data['attention_mask'].to(device, dtype=torch.long)
labels = data['labels'].to(device, dtype=torch.long)
outputs = model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels,
)
loss = outputs.loss
running_loss += loss.item()
return running_loss / len(val_loader)
def main():
# 基础模型位置
model_name = "model/Qwen2-1.5B-Instruct"
# 训练集
train_json_path = "./data/train.json"
# 验证集
val_json_path = "./data/val.json"
max_source_length = 128
max_target_length = 256
epochs = 10
batch_size = 1
lr = 1e-4
gradient_accumulation_steps = 16
lora_rank = 8
lora_alpha = 32
model_output_dir = "output"
logs_dir = "logs"
# 设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 加载分词器和模型
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
# setup peft
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
inference_mode=False,
r=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=0.1
)
model = get_peft_model(model, peft_config)
model.is_parallelizable = True
model.model_parallel = True
model.print_trainable_parameters()
print("Start Load Train Data...")
train_params = {
"batch_size": batch_size,
"shuffle": True,
"num_workers": 0,
}
training_set = QADataset(train_json_path, tokenizer, max_source_length, max_target_length)
training_loader = DataLoader(training_set, **train_params)
print("Start Load Validation Data...")
val_params = {
"batch_size": batch_size,
"shuffle": False,
"num_workers": 0,
}
val_set = QADataset(val_json_path, tokenizer, max_source_length, max_target_length)
val_loader = DataLoader(val_set, **val_params)
# 日志记录
writer = SummaryWriter(logs_dir)
# 优化器
optimizer = torch.optim.AdamW(params=model.parameters(), lr=lr)
model = model.to(device)
# 开始训练
print("Start Training...")
train_model(
model=model,
train_loader=training_loader,
val_loader=val_loader,
optimizer=optimizer,
gradient_accumulation_steps=gradient_accumulation_steps,
device=device,
num_epochs=epochs,
model_output_dir=model_output_dir,
writer=writer
)
writer.close()
if __name__ == '__main__':
main()
训练过程：
训练结束后，可以在 output 中看到 lora 模型：
五、模型测试
# -*- coding: utf-8 -*-
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
model_path = "model/Qwen2-1.5B-Instruct"
lora_dir = "output"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = PeftModel.from_pretrained(model, lora_dir)
model.to(device)
prompt = """
5月至今上腹靠右隐痛，右背隐痛带酸，便秘，喜睡，时有腹痛，头痛，腰酸症状？
"""
messages = [
{"role": "system", "content": "你是一个医疗方面的专家，可以根据患者的问题进行解答。"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
print(text)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=258)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
模型回答：根据你的叙述，胃炎胆汁反流性胃炎的可能性大，建议口服奥美拉唑，吗丁啉救治，清淡易消化饮食，忌辛辣打击食物，留意歇息，不要加班除了正规救治胃痛外，患者还需要有看重护理方面，比如恰当饮食，始终保持心情愉快。与此同时患者还要留意决定一家专业医院诊病，这样才能获得良好的治疗效果。
六、模型合并
上面测试还是分开加载的基础模型和lora模型，可以将两个合并为一个，方便后续部署：
# -*- coding: utf-8 -*-
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_path = "model/Qwen2-1.5B-Instruct"
lora_dir = "output"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
model = PeftModel.from_pretrained(model, lora_dir).to(device)
print(model)
# 合并model, 同时保存 token
model = model.merge_and_unload()
model.save_pretrained("lora_output")
tokenizer.save_pretrained("lora_output")
合并后的结构：
后面就不需要再通过 PeftModel 直接加载模型既可使用：
# -*- coding: utf-8 -*-
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_path = "lora_output"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.to(device)
prompt = """
5月至今上腹靠右隐痛，右背隐痛带酸，便秘，喜睡，时有腹痛，头痛，腰酸症状？
"""
messages = [
{"role": "system", "content": "你是一个医疗方面的专家，可以根据患者的问题进行解答。"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
print(text)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=258)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
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