1.来源

https://github.com/InternLM/Tutorial/blob/camp3/docs/L1/Demo/task.md

2.过程

1. 在 /root/share/pre_envs 中配置好了预置环境 icamp3_demo

conda activate /root/share/pre_envs/icamp3_demo

2. 创建一个目录，用于存放我们的代码。并创建一个 cli_demo.py

mkdir -p /root/demo
touch /root/demo/cli_demo.py

3. 然后，我们将下面的代码复制到 cli_demo.py 中。【/root/share/new_models下存放部署代码】

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM


model_name_or_path = "/root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, device_map='cuda:0')
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='cuda:0')
model = model.eval()

system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""

messages = [(system_prompt, '')]

print("=============Welcome to InternLM chatbot, type 'exit' to exit.=============")

while True:
    input_text = input("\nUser  >>> ")
    input_text = input_text.replace(' ', '')
    if input_text == "exit":
        break

    length = 0
    for response, _ in model.stream_chat(tokenizer, input_text, messages):
        if response is not None:
            print(response[length:], flush=True, end="")
            length = len(response)

启动py文件后：

4. 进行代理，使其在本地能够进行访问：
   

5. 浏览本地服务器：
   
   

推理过程资源消耗如图所示：