微调前的准备工作

确保硬件环境满足需求（如GPU显存≥24GB），安装Python 3.8+、PyTorch 1.12+和Transformers库。准备行业专属数据集，需包含至少10万条高质量文本，格式为JSON或CSV，涵盖领域内常见问答、术语及场景。

数据预处理

清洗数据去除噪声（如特殊字符、乱码），标准化文本格式（统一标点、大小写）。使用分词工具（如Jieba）对中文文本分词，构建词汇表。划分训练集（80%）、验证集（10%）和测试集（10%），确保数据分布均衡。

模型加载与配置

从Hugging Face Hub加载ChatGLM-B基础模型：

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()

配置训练参数：学习率设为2e-5，批次大小（batch size）为4，启用梯度裁剪（max_grad_norm=1.0）。

微调训练

使用LoRA（Low-Rank Adaptation）高效微调技术，仅训练部分参数以减少显存占用：

from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["query_key_value"],
    lora_dropout=0.1,
    bias="none"
)
model = get_peft_model(model, lora_config)

启动训练循环，每500步验证一次损失，早停（early stopping）阈值设为3次验证损失未下降。

模型评估与部署

在测试集上计算困惑度（Perplexity）和BLEU分数，对比基线模型效果。使用FlashAttention优化推理速度，导出模型为ONNX格式：

torch.onnx.export(model, inputs, "chatglm_finetuned.onnx", opset_version=13)

部署时通过FastAPI封装API接口，支持实时行业问答。

持续优化策略

定期增量训练新数据，使用A/B测试对比模型版本效果。监控线上日志，针对性补充低频场景数据，迭代提升准确率。

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