书生模型实战L1---8G 显存玩转书生大模型 Demo(什么是分词器？AutoTokenizer, AutoModelForCausalLM)

书生模型实战系列文章目录

第一章 入门岛L0（Linux）
第二章 入门岛L0（python）
第三章 入门岛L0（Git）
提示：以上内容可以看往期文章
第四章 基础岛L1（Demo）

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作业

提交作业

作业1–基础任务

作业2–进阶任务

一、执行代码

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name_or_path = "/root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b"
# 分词器加载
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, device_map='cuda:0')
#模型加载
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)

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提示：以下是对上述执行代码的一些解读

一、分词器tokenizer 是什么？

直接来看一个案例：

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

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

tokenizer =