案例分享：某金融领域LoRA微调项目实施经验

案例分享：某金融领域LoRA微调项目实施经验

在金融场景下，我们对Qwen-7B模型进行了LoRA微调以提升其在信贷风险评估任务上的表现。以下为具体实施步骤。

环境准备

pip install transformers accelerate peft datasets

数据准备与预处理

from datasets import load_dataset
import pandas as pd

dataset = load_dataset('json', data_files='financial_data.json')
# 假设数据包含'input_text'和'label'字段
train_dataset = dataset['train'].map(lambda x: {
    'prompt': f"请判断以下贷款申请的风险等级：{x['input_text']}",
    'response': x['label']
})

LoRA微调配置

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

model = QwenForCausalLM.from_pretrained("Qwen/Qwen-7B", trust_remote_code=True)
peft_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["c_attn", "c_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()

训练设置

training_args = TrainingArguments(
    output_dir="./finetuned_lora",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=3,
    learning_rate=2e-4,
    logging_steps=10,
    save_steps=100,
    evaluation_strategy="no"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    tokenizer=tokenizer
)
trainer.train()

模型评估

# 评估微调后的模型表现
model.eval()
with torch.no_grad():
    outputs = model.generate(input_ids=input_ids, max_length=50)
    print(tokenizer.decode(outputs[0]))

该方案在金融数据集上取得了显著提升，验证了LoRA微调在专业领域应用的有效性。