import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel base_model_name = "hfl/llama-3-chinese-8b-instruct-v3" adapter_model_name = "tiankuo1111/LLAMA3-TEST" # 加载 tokenizer tokenizer = AutoTokenizer.from_pretrained(base_model_name) # 加载基础模型到 CPU base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float32, device_map=None) # 加载 LoRA 适配器 model = PeftModel.from_pretrained(base_model, adapter_model_name) # 运行测试 def chat_with_model(prompt): inputs = tokenizer(prompt, return_tensors="pt").to("cuda") with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=100) return tokenizer.decode(output[0], skip_special_tokens=True) iface = gr.Interface(fn=chat_with_model, inputs="text", outputs="text", title="LoRA Model Chatbot") iface.launch()