初始化项目，由ModelHub XC社区提供模型

Model: Karlzhy/Content_Review_Model
Source: Original Platform

2.py

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+import os
+import torch
+import inspect
+from datasets import load_from_disk
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    TrainingArguments,
+    Trainer
+)
+from peft import LoraConfig, get_peft_model, TaskType
+from sklearn.metrics import accuracy_score, f1_score
+
+# ✅ Hugging Face Token
+hf_token = "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"
+
+# ✅ 检查 TrainingArguments 来源
+from transformers import TrainingArguments
+print("🧠 当前 TrainingArguments 来源:", inspect.getfile(TrainingArguments))
+
+# ✅ 模型与 LoRA 配置
+base_model = "Qwen/Qwen2-0.5B-Instruct"
+output_dir = "./qwen_lora_checkpoint"
+
+lora_config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    lora_dropout=0.05,
+    bias="none",
+    task_type=TaskType.SEQ_CLS,
+    target_modules=["q_proj", "v_proj"]
+)
+
+# ✅ 加载 tokenizer，并设置 pad_token
+tokenizer = AutoTokenizer.from_pretrained(
+    base_model,
+    token=hf_token,
+    trust_remote_code=True
+)
+tokenizer.pad_token = tokenizer.eos_token
+pad_token_id = tokenizer.pad_token_id
+
+# ✅ 加载模型（只加载一次），并设置 pad_token_id
+base = AutoModelForSequenceClassification.from_pretrained(
+    base_model,
+    token=hf_token,
+    trust_remote_code=True,
+    num_labels=2
+)
+base.config.pad_token_id = pad_token_id
+
+# ✅ 应用 LoRA
+model = get_peft_model(base, lora_config)
+
+# ✅ 加载数据
+dataset = load_from_disk("./qwen_classification_dataset")
+
+def preprocess(example):
+    return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
+
+tokenized_dataset = dataset.map(preprocess, batched=True)
+tokenized_dataset = tokenized_dataset.rename_column("label", "labels")
+tokenized_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])
+
+# ✅ 训练参数（自动使用 GPU / fp16）
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    learning_rate=2e-5,
+    num_train_epochs=3,
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    logging_dir=f"{output_dir}/logs",
+    save_total_limit=2,
+    load_best_model_at_end=True,
+    metric_for_best_model="accuracy",
+    remove_unused_columns=False,
+    report_to="none",
+    fp16=torch.cuda.is_available(),  # 自动开启 fp16
+    gradient_accumulation_steps=2,
+    dataloader_pin_memory=True,
+)
+
+# ✅ 评估函数
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    preds = torch.argmax(torch.tensor(logits), dim=1)
+    acc = accuracy_score(labels, preds)
+    f1 = f1_score(labels, preds)
+    return {"accuracy": acc, "f1": f1}
+
+# ✅ 构建 Trainer
+trainer = Trainer(
+    model=model,
+    tokenizer=tokenizer,
+    args=training_args,
+    train_dataset=tokenized_dataset["train"],
+    eval_dataset=tokenized_dataset["validation"],
+    compute_metrics=compute_metrics,
+)
+
+# ✅ 开始训练
+trainer.train()
+
+# ✅ 保存模型和 tokenizer
+model.save_pretrained(output_dir)
+tokenizer.save_pretrained(output_dir)
+
+print(f"✅ 微调完成，模型保存在 {output_dir}")