Delete test_model.py

test_model.py

@@ -1,70 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-# 加载基础模型和分词器
-model_name = "microsoft/DialoGPT-small"
-base_model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.float32,
-    low_cpu_mem_usage=True,
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-tokenizer.pad_token = tokenizer.eos_token
-
-# 加载LoRA适配器
-model = PeftModel.from_pretrained(base_model, "./dialogpt-small-lora")
-
-# 测试函数
-def test_model(instruction, input_text):
-    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:\n"
-    
-    # 编码输入
-    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
-    
-    # 生成响应
-    with torch.no_grad():
-        outputs = model.generate(
-            input_ids=inputs['input_ids'],
-            attention_mask=inputs['attention_mask'],
-            max_new_tokens=50,  # 限制新生成的token数量
-            num_return_sequences=1,
-            temperature=0.3,  # 降低温度获得更确定的输出
-            do_sample=True,
-            pad_token_id=tokenizer.eos_token_id,
-            eos_token_id=tokenizer.eos_token_id,
-            repetition_penalty=1.1  # 减少重复
-        )
-    
-    # 解码输出
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    # 提取生成的部分
-    generated_text = response[len(prompt):].strip()
-    
-    return generated_text
-
-# 测试示例
-if __name__ == "__main__":
-    print("测试微调后的模型...")
-    print("="*50)
-    
-    # 测试1：生成用户JSON对象
-    instruction = "根据以下信息，生成一个用户JSON对象。"
-    input_text = "用户ID是999，用户名是test_user，邮箱是test@example.com"
-    
-    result = test_model(instruction, input_text)
-    print(f"指令: {instruction}")
-    print(f"输入: {input_text}")
-    print(f"输出: {result}")
-    print("="*50)
-    
-    # 测试2：另一个示例
-    instruction = "根据以下信息，生成一个用户JSON对象。"
-    input_text = "用户ID是888，用户名是admin，邮箱是admin@company.com"
-    
-    result = test_model(instruction, input_text)
-    print(f"指令: {instruction}")
-    print(f"输入: {input_text}")
-    print(f"输出: {result}")
-    print("="*50)