LoRA微调实战教程：从环境搭建到模型训练全过程

LoRA微调实战教程：从环境搭建到模型训练全过程

环境准备

首先创建虚拟环境并安装必要依赖：

conda create -n lora-finetune python=3.8
conda activate lora-finetune
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers accelerate peft datasets

数据准备

使用HuggingFace datasets加载数据集：

from datasets import load_dataset

dataset = load_dataset("json", data_files="train.json")
# 预处理函数
def preprocess(example):
    return {"text": example["prompt"] + " " + example["completion"]}

dataset = dataset.map(preprocess, batched=False)

LoRA配置与训练

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer

# 加载基础模型
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# LoRA配置
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.01,
    bias="none",
    task_type="CAUSAL_LM"
)

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

# 训练参数
training_args = TrainingArguments(
    output_dir="./lora-llama2",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=1e-4,

    num_train_epochs=3,
    logging_dir="./logs",
    save_strategy="epoch"
)

# 开始训练
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    tokenizer=tokenizer
)

trainer.train()

模型保存与推理

# 保存微调后的模型
model.save_pretrained("./fine-tuned-lora")

# 加载并推理
loaded_model = AutoModelForCausalLM.from_pretrained("./fine-tuned-lora")

该流程实现了完整的LoRA微调，适用于特定领域文本生成任务的定制化训练。