基于PyTorch Lightning的微调实践分享

基于PyTorch Lightning的微调实践分享

最近在尝试用PyTorch Lightning做LLM微调，踩了不少坑，记录一下。我的目标是用LoRA方案微调Qwen模型。

环境准备

pip install pytorch-lightning transformers accelerate datasets peft

核心代码实现

import pytorch_lightning as pl
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

class LLMFineTune(pl.LightningModule):
    def __init__(self, model_name="Qwen/Qwen2-7B", lora_r=8, lora_alpha=32):
        super().__init__()
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        
        # LoRA配置
        lora_config = LoraConfig(
            r=lora_r,
            lora_alpha=lora_alpha,
            target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
            lora_dropout=0.1,
            bias="none",
            task_type="CAUSAL_LM"
        )
        
        self.model = get_peft_model(self.model, lora_config)
        self.save_hyperparameters()

    def training_step(self, batch, batch_idx):
        outputs = self.model(**batch)
        loss = outputs.loss
        self.log("train_loss", loss)
        return loss

关键踩坑点

1. 显存问题：使用gradient_checkpointing和low_cpu_mem_usage参数
2. LoRA适配层：必须指定正确的target_modules，否则微调效果差
3. 数据预处理：注意tokenize时的padding和truncation设置

运行命令

python train.py --gpus 4 --precision 16 --gradient_accumulation_steps 2

这套方案在实际项目中确实有效，推荐给有类似需求的同学。