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import re
from datasets import Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
base_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# 加载数据
raw_data_path = r"data/CoTdata.txt"
with open(raw_data_path, "r", encoding="utf-8") as f:
raw_lines = f.readlines()
# 处理每一行数据，解析出关键词、思维链和诗歌内容
def process_line(line):
# 使用 [：:] 同时匹配中文和英文冒号
pattern = r"^(.*?)<think>(.*?)</think>[：:](.*)$"
match = re.match(pattern, line.strip())
if match:
keywords = match.group(1).strip()
cot = match.group(2).strip()
poem = match.group(3).strip()
# 构造训练实例：输入部分给出提示和关键词，输出部分包含完整思维链及答案
training_text = (
f"【输入】：根据以下关键词生成一首歌词，歌词中包含多个句子，确保句子通顺、诗意、格式正确。"
f"让我们一步一步的思考（思考过程包含在<think>和</think>之间）：{keywords}\n\n"
f"【输出】：<think>{cot}</think>\n{poem}"
)
return training_text
else:
# 如果格式不符，输出提示并返回 None
print("跳过格式错误的行：", line.strip())
return None
# 解析所有数据行
processed_samples = []
for line in raw_lines:
result = process_line(line)
if result:
processed_samples.append(result)
# 构建 Hugging Face 数据集
dataset = Dataset.from_dict({"text": processed_samples})
# 加载基础模型和 LoRA 模型
model = PeftModel.from_pretrained(base_model, r"D:\GoodMusicV3.0\3_24_LoRA").to("cuda") # 替换为你的 LoRA 路径
tokenizer.pad_token = tokenizer.eos_token
lora_config = LoraConfig(
r=8, # 低秩矩阵的秩，常取 8、16 或 32
lora_alpha=32, # 缩放因子，控制 LoRA 影响
target_modules=["q_proj", "k_proj", "v_proj"], # 应用 LoRA 的模块，通常是注意力层的投影
lora_dropout=0.1, # Dropout 概率，防止过拟合
bias="none", # 通常设为 "none"
task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
model.cuda()
# 分词函数：对文本进行分词，并构造 labels
def tokenize_function(examples):
# 此处的文本已经包含了输入和输出的完整内容
tokenized = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
tokenized["labels"] = tokenized["input_ids"].copy()
return tokenized
# 对数据集进行映射处理
tokenized_dataset = dataset.map(tokenize_function, batched=True)
# 设置训练参数
training_args = TrainingArguments(
output_dir="./lora",
num_train_epochs=1000,
per_device_train_batch_size=16,
learning_rate=2e-5,
weight_decay=0.01,
logging_steps=10000,
save_steps=15000,
fp16=True,
)
# 构造 Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_dataset,
tokenizer=tokenizer,
)
# 开始训练
trainer.train()
# 推理示例
generation_config = {
"max_new_tokens": 1024,
"temperature": 1.0,
"top_p": 0.9,
"top_k": 40,
"repetition_penalty": 1.2,
"do_sample": True,
"encoder_no_repeat_ngram_size": 4,
}
if True:
prompt = "根据以下关键词生成一首歌词，歌词中包含多个句子，句子与句子之间使用/隔开，让我们一步一步的思考（思考过程包含在<think>和</think>之间）：温柔，轮廓，洒脱："
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(input_ids, **generation_config)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=False)
print(decoded)
# 保存模型
model.save_pretrained("4_2_LoRA_3")
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