阿里云平台ChatGLM-6B搭建_你好 !我是人工智能助手智谱清言(chatglm),很高兴见到你,欢迎问我任何问题

任务一ChatGLM-6B搭建

平台：阿里云（免费A10算力）PAI DSW

首先创建实例，选择环境为

然后打开环境

首先先下载源码

git clone https://github.com/THUDM/ChatGLM-6B
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需要一段时间下载，然后安装相关的依赖

cd ChatGLM-6B
# 其中 transformers版本用的是4.3.0（因为没有4.2.7）
pip install -r requirements.txt
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然后下载模型，因为hungingface无法打开，所以使用的是阿里云提供的模型

import os
dsw_region = os.environ.get("dsw_region")
url_link = {
    "cn-shanghai": "https://atp-modelzoo-sh.oss-cn-shanghai-internal.aliyuncs.com/release/tutorials/chatGLM/ChatGLM-6B-main.tar.gz",
    "cn-hangzhou": "https://atp-modelzoo.oss-cn-hangzhou-internal.aliyuncs.com/release/tutorials/chatGLM/ChatGLM-6B-main.tar.gz",
    "cn-shenzhen": "https://atp-modelzoo-sz.oss-cn-shenzhen-internal.aliyuncs.com/release/tutorials/chatGLM/ChatGLM-6B-main.tar.gz",
    "cn-beijing": "https://atp-modelzoo-bj.oss-cn-beijing-internal.aliyuncs.com/release/tutorials/chatGLM/ChatGLM-6B-main.tar.gz", 
}
path = url_link[dsw_region]
os.environ['LINK_CHAT'] = path
!wget $LINK_CHAT
!tar -xvf ChatGLM-6B-main.tar.gz
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下载完成之后，模型在ChatGLM-6B-main/punting/chatglm-6b中，而且文件中缺少一个test_modeling_chatglm.py

我们建立一个py文件，名为test_modeling_chatglm.py，代码如下

import datetime
import math
import unittest
import torch
import random

from transformers import AutoTokenizer, AutoModel
from transformers.testing_utils import require_torch, slow, torch_device


def set_random_seed(seed):
    import random

    random.seed(seed)

    # pytorch RNGs
    import torch

    torch.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

    # numpy RNG
    import numpy as np

    np.random.seed(seed)



def ids_tensor(shape, vocab_size):
    #  Creates a random int32 tensor of the shape within the vocab size
    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(random.randint(0, vocab_size - 1))

    return torch.tensor(data=values, dtype=torch.long, device=torch_device).view(shape).contiguous()


def get_model_and_tokenizer():
    model = AutoModel.from_pretrained("/mnt/vepfs/workspace/zxdu/chatglm_6b", trust_remote_code=True).half()
    model.to(torch_device)
    model.eval()
    tokenizer = AutoTokenizer.from_pretrained("/mnt/vepfs/workspace/zxdu/chatglm_6b", trust_remote_code=True)
    return model, tokenizer


@require_torch
class ChatGLMGenerationTest(unittest.TestCase):
    def get_generation_kwargs(self):
        pass

    def test_chat(self):
        model, tokenizer = get_model_and_tokenizer()
        prompts = ["你好", "介绍一下清华大学", "它创建于哪一年"]
        history = []
        set_random_seed(42)
        expected_responses = [
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