LoRA微调实战：基于Jupyter Notebook的训练流程

在大语言模型微调实践中，LoRA（Low-Rank Adaptation）作为一种高效且资源友好的方法，正被广泛采用。本文将通过Jupyter Notebook展示完整的LoRA微调流程。

环境准备

!pip install transformers datasets accelerate peft

数据加载与预处理

from datasets import load_dataset
from transformers import AutoTokenizer

# 加载数据集
train_dataset = load_dataset("csv", data_files="train.csv")

# 加载tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize_function(examples):
    return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=128)

train_dataset = train_dataset.map(tokenize_function, batched=True)

模型与LoRA配置

from transformers import AutoModelForSequenceClassification
from peft import get_peft_model, LoraConfig, TaskType

# 加载基础模型
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# 配置LoRA参数
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["query", "value"],
    lora_dropout=0.1,
    bias="none",
    task_type=TaskType.SEQ_CLS
)

# 应用LoRA适配器
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()

训练设置与执行

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./lora_finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
    save_strategy="epoch"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset["train"],
    eval_dataset=train_dataset["test"]
)

trainer.train()

模型保存与加载

# 保存微调后的模型
model.save_pretrained("./final_lora_model")
tokenizer.save_pretrained("./final_lora_model")

# 加载微调模型
from peft import PeftModel
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
model = PeftModel.from_pretrained(model, "./final_lora_model")

通过上述步骤，即可完成基于LoRA的高效微调流程。该方案显著降低了训练资源需求，同时保持了良好的性能表现。