通义千问Qwen2-7b-instruct部署

news2024/11/15 19:25:19

前言

https://www.modelscope.cn/models/qwen/Qwen2-7B-Instruct
其实完全可以按照这个介绍来装，不过容易遇到一些问题，新学习，也是记录一下

环境

python 3.10

在自己电脑上用conda创建一个新环境
找租用的服务器租一台，3090一小时也就一两块钱

我个人是租的服务器来运行的

具体步骤

安装依赖

pip install transformers==4.37.0 accelerate tiktoken einops scipy transformers_stream_generator==0.0.4 peft deepspeed

pip install modelscope

假如说，依赖的包找不到，可以从网上找到zip或者tar，通过绝对地址来进行安装

pip install 包的绝对路径

模型下载

我没有预先下载模型，完全通过代码自动下载的，快慢主要受限于网速，我在服务器上下载模型很快，十分钟左右就下好了。

from modelscope import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "qwen/Qwen2-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)

生成tokenizer

tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen2-7B-Instruct")

设置提示词

prompt = "给我讲一个故事。"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

模型初始角色试system，通过content中的提示词You are a helpful assistant.，这时候模型的角色就会变成了assistant，此时用户再进行对话Give me a short introduction to large language model.，模型就会给出回应

使用tokenizer分词

model_inputs = tokenizer([text], return_tensors="pt").to(device)

代码运行结果

在这里插入图片描述

完整代码

from modelscope import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "qwen/Qwen2-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("qwen/Qwen2-7B-Instruct")

prompt = "给我讲一个故事。"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)