#模型下载from modelscope import snapshot_downloadmodel_dir = snapshot_download('Qwen/Qwen2.5-7B-Instruct', cache_dir='/root/autodl-tmp', revision='master')

from modelscope import AutoModelForCausalLM, AutoTokenizer
# 指定要加载的模型名称model_name = "/root/autodl-tmp/Qwen/Qwen2___5-7B-Instruct"
# 加载因果语言模型model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",# 自动确定 PyTorch 的数据类型device_map="auto"# 自动确定设备映射)
# 加载分词器tokenizer = AutoTokenizer.from_pretrained(model_name)

# 设置提示文本prompt = "Give me a short introduction to artificial intelligence."# 构建包含系统角色和用户角色的消息列表messages = [{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},{"role": "user", "content": prompt}]
# 将消息列表应用聊天模板进行处理，不进行分词操作且添加生成提示text = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)text

# 将处理后的文本转换为模型输入张量，并移动到模型所在设备上model_inputs = tokenizer([text], return_tensors="pt").to(model.device)model_inputs

# 生成文本，设置最大新生成的标记数为 512generated_ids = model.generate(**model_inputs,max_new_tokens=512)
# 从生成的标记中去除输入部分的标记generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]generated_ids

# 将生成的标记解码为文本，并跳过特殊标记response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]response

'Certainly! Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn, and perform tasks that typically require human cognition. This includes activities such as visual perception, speech recognition, decision-making, and language translation.\n\nKey components of AI include:\n\n1. **Machine Learning**: A subset of AI where systems can automatically learn and improve from experience without being explicitly programmed.\n2. **Deep Learning**: A more advanced form of machine learning that uses neural networks with many layers to model and solve complex problems.\n3. **Natural Language Processing (NLP)**: The ability of computers to understand, interpret, and generate human language.\n4. **Computer Vision**: Enabling machines to interpret and understand the visual world, similar to how humans would.\n5. **Robotics**: Combining AI with robotics to create machines that can perform tasks autonomously or semi-autonomously.\n\nAI has numerous applications across various fields, including healthcare, finance, transportation, entertainment, and more. It continues to evolve rapidly, transforming industries and societies in profound ways.'