lora微调大模型实战

### LoRA 微调大模型实战教程 #### 准备工作环境 为了进行LoRA微调，需先安装必要的Python包。假设已经具备基本的PyTorch环境： ```bash pip install transformers peft datasets torch accelerate bitsandbytes ``` #### 加载预训练模型与数据集 选择一个合适的预训练模型并加载相应的Tokenizer和Model实例。这里以`distilbert-base-uncased`为例[^3]。 ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification model_name_or_path = "distilbert-base-uncased" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForSequenceClassification.from_pretrained( model_name_or_path, num_labels=2 # 假设是一个二分类任务 ) ``` #### 配置LoRA适配层 通过引入PeFT库来实现对原有Transformer架构的有效调整，在不改变原结构的基础上增加少量可学习参数完成特定下游任务的学习过程。 ```python from peft import LoraConfig, get_peft_model lora_config = LoraConfig( r=8, # 秩因子 lora_alpha=16, # 扩展比例系数 target_modules=["q", "v"], # 应用于哪些模块，默认为查询Q和值V矩阵 lora_dropout=0.05 # Dropout概率 ) peft_model = get_peft_model(model, lora_config) ``` #### 数据处理与Dataloader构建 准备适合输入给定模型的数据格式，并创建DataLoader对象以便后续迭代读取批次样本。 ```python import datasets from torch.utils.data import DataLoader dataset = datasets.load_dataset('glue', 'mrpc') # 使用GLUE benchmark中的MRPC子集作为示范 train_encodings = tokenizer(dataset['train']['sentence1'], dataset['train']['sentence2'], truncation=True, padding=True, max_length=128) val_encodings = tokenizer(dataset['validation']['sentence1'], dataset['validation']['sentence2'], truncation=True, padding=True, max_length=128) class MRPCDataset(torch.utils.data.Dataset): def __init__(self, encodings, labels): self.encodings = encodings self.labels = labels def __getitem__(self, idx): item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()} item['labels'] = torch.tensor(self.labels[idx]) return item def __len__(self): return len(self.labels) train_dataset = MRPCDataset(train_encodings, dataset['train']['label']) val_dataset = MRPCDataset(val_encodings, dataset['validation']['label']) train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True) eval_loader = DataLoader(val_dataset, batch_size=16) ``` #### 定义优化器与训练循环 设置AdamW优化算法以及线性的warmup策略帮助网络更快收敛；接着编写简单的训练逻辑控制流程。 ```python optimizer = torch.optim.AdamW(peft_model.parameters(), lr=5e-5) device = "cuda" if torch.cuda.is_available() else "cpu" peft_model.to(device) for epoch in range(epochs): for step, batch in enumerate(train_loader): optimizer.zero_grad() input_ids = batch["input_ids"].to(device) attention_mask = batch["attention_mask"].to(device) labels = batch["labels"].to(device) outputs = peft_model(input_ids=input_ids, attention_mask=attention_mask, labels=labels) loss = outputs.loss loss.backward() optimizer.step() if (step + 1) % log_interval == 0 or (step + 1) == len(train_loader): print(f'Epoch [{epoch+1}/{num_epochs}], Step [{step+1}/{total_steps}], Loss: {loss.item():.4f}') evaluate_on_validation_set(eval_loader, device=device) ``` 上述代码片段展示了如何利用LoRA技术针对大型语言模型执行高效而有效的迁移学习实践案例[^1]。