Upload inference.py with huggingface_hub

inference.py

@@ -0,0 +1,67 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 使用你本地的检查点路径
+model_path = "/root/Qwen2.5-7B-Instruct-R1-forfinance/"
+
+# 加载模型和分词器
+print("正在加载模型...")
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,  # 根据config.json中的torch_dtype
+    device_map="auto",
+    trust_remote_code=True  # 如果需要的话
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_path,
+    trust_remote_code=True
+)
+
+print("模型加载完成！")
+
+# 准备输入
+prompt = "假设你是一位金融行业专家，请回答下列问题。\n在宏观分析中，描述在既定利率水平下产品市场达到均衡状态的曲线是什么？\n请一步步思考。"
+
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+# 应用聊天模板
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+print("输入文本:")
+print(text)
+print("\n" + "="*50 + "\n")
+
+# 编码输入
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# 生成回答
+print("正在生成回答...")
+with torch.no_grad():  # 节省显存
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=2048,  # 适当减少避免太长
+        do_sample=True,
+        temperature=0.7,
+        top_p=0.8,
+        repetition_penalty=1.05,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+# 解码生成的tokens
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+# 输出结果
+print("模型回答:")
+print(response)