# 切换环境conda activate PY3.12.2
# 安装依赖库pip install transformers datasets peft accelerate modelscope swanlab

git lfs installgit clone https://www.modelscope.cn/datasets/swift/zh_cls_fudan-news.git

zh_cls_fudan-news├── README.md├── dataset_infos.json├── test.jsonl└── train.jsonl

git lfs installgit clone https://www.modelscope.cn/qwen/Qwen2-0.5B.git

cd Qwen2-0.5Bgit lfs pull

# Qwen2-0.5B-train.pyimport jsonimport pandas as pdimport torchfrom datasets import Datasetfrom modelscope import AutoTokenizerfrom swanlab.integration.huggingface import SwanLabCallbackfrom peft import LoraConfig, TaskType, get_peft_modelfrom transformers import AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seqimport osimport swanlab
# 权重根目录BASE_DIR = 'D:\ModelSpace\Qwen2'
# 设备名称device = 'cuda' if torch.cuda.is_available() else 'cpu'# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 数据集处理函数，包括：训练数据集和测试数据集def dataset_jsonl_transfer(origin_path, new_path):"""将原始数据集转换为大模型微调所需数据格式的新数据集"""messages = []
# 读取原JSONL文件with open(origin_path, "r", encoding="utf-8") as file:for line in file:# 解析每一行原始数据（每一行均是一个JSON格式）data = json.loads(line)text = data["text"]catagory = data["category"]output = data["output"]message = {"input": f"文本:{text},分类选项列表:{catagory}","output": output,}messages.append(message)
# 保存处理后的JSONL文件，每行也是一个JSON格式with open(new_path, "w", encoding="utf-8") as file:for message in messages:file.write(json.dumps(message, ensure_ascii=False) + "\n")

# 在使用数据集训练大模型之前，对每行数据进行预处理def process_func(example):"""将数据集进行预处理"""MAX_LENGTH = 384input_ids, attention_mask, labels = [], [], []instruction = tokenizer(f"<|im_start|>system\n你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项列表，请输出文本内容的正确分类<|im_end|>\n<|im_start|>user\n{example['input']}<|im_end|>\n<|im_start|>assistant\n", add_special_tokens=False)# add_special_tokens 不在开头加 special_tokensresponse = tokenizer(f"{example['output']}", add_special_tokens=False)input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1]# 因为eos token咱们也是要关注的所以 补充为1labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH:# 做一个截断input_ids = input_ids[:MAX_LENGTH]attention_mask = attention_mask[:MAX_LENGTH]labels = labels[:MAX_LENGTH]
return {"input_ids": input_ids,"attention_mask": attention_mask,"labels": labels}

# 加载预训练模型和分词器model_dir = os.path.join(BASE_DIR, 'Qwen2-0.5B')tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(model_dir, device_map=device, torch_dtype=torch.bfloat16)model.enable_input_require_grads() # 开启梯度检查点时，要执行该方法
# 加载、处理数据集和测试集train_dataset_path = os.path.join(BASE_DIR, 'zh_cls_fudan-news', 'train.jsonl')test_dataset_path = os.path.join(BASE_DIR, 'zh_cls_fudan-news', 'test.jsonl')
train_jsonl_new_path = os.path.join(BASE_DIR, 'train.jsonl')test_jsonl_new_path = os.path.join(BASE_DIR, 'test.jsonl')
if not os.path.exists(train_jsonl_new_path):dataset_jsonl_transfer(train_dataset_path, train_jsonl_new_path)if not os.path.exists(test_jsonl_new_path):dataset_jsonl_transfer(test_dataset_path, test_jsonl_new_path)
# 得到微调数据集train_df = pd.read_json(train_jsonl_new_path, lines=True)train_ds = Dataset.from_pandas(train_df)train_dataset = train_ds.map(process_func, remove_columns=train_ds.column_names)
# 创建LoRA配置config = LoraConfig(task_type=TaskType.CAUSAL_LM,target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],inference_mode=False,# 训练模式r=8,# Lora 秩lora_alpha=32,# Lora alaph，具体作用参见 Lora 原理lora_dropout=0.1,# Dropout 比例)
# 将LoRA应用于模型model = get_peft_model(model, config)
# 创建微调参数args = TrainingArguments(output_dir=os.path.join(BASE_DIR, 'output', 'Qwen2-0.5B'),per_device_train_batch_size=4,gradient_accumulation_steps=4,logging_steps=10,num_train_epochs=2,save_steps=100,learning_rate=1e-4,save_on_each_node=True,gradient_checkpointing=True,report_to="none",)
# SwanLab微调过程回调数据swanlab_callback = SwanLabCallback(project="Qwen2-FineTuning", experiment_name="Qwen2-0.5B")
trainer = Trainer(model=model,args=args,train_dataset=train_dataset,data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),callbacks=[swanlab_callback],)
# 开始微调trainer.train()
# 模型结果结果评估def predict(messages, model, tokenizer):text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(model_inputs.input_ids,max_new_tokens=512)generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
# 模型评估：获取测试集的前10条测试数据test_df = pd.read_json(test_jsonl_new_path, lines=True)[:10]
test_text_list = []for index, row in test_df.iterrows():instruction = row['你是一个文本分类领域的专家，你会接收到一段文本和几个潜在的分类选项列表，请输出文本内容的正确分类']input_value = row['input']
messages = [{"role": "system", "content": f"{instruction}"},{"role": "user", "content": f"{input_value}"}]
response = predict(messages, model, tokenizer)messages.append({"role": "assistant", "content": f"{response}"})
result_text = f"{messages[0]}\n\n{messages[1]}\n\n{messages[2]}"test_text_list.append(swanlab.Text(result_text, caption=response))
swanlab.log({"Prediction": test_text_list})swanlab.finish()