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1from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
2
3# 加载模型和分词器
4model_name = "your-username/meeting-room-expert-model" # 请替换为实际模型ID
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForCausalLM.from_pretrained(model_name)
7
8# 创建生成管道
9generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
10
11# 示例：询问会议室设备配置
12prompt = "请为一个50平米的中型会议室设计一套视频会议系统方案，包括："
13response = generator(prompt, max_length=500, do_sample=True, temperature=0.7)
14print(response[0]['generated_text'])
15
16# 示例：生成会议管理制度
17prompt = "请制定一份公司会议室使用管理制度，包括预约流程、使用规范和设备维护要求："
18response = generator(prompt, max_length=800, do_sample=True, temperature=0.5)
19print(response[0]['generated_text'])