1 大模型及 InternLM 模型简介
1.1 什么是大模型？
大模型=大语料+大算力+大模型参数
大模型的优势在于其能够捕捉和理解数据中更为复杂、抽象的特征和关系。书读三遍，其义自见
大模型的应用和发展也需要在性能、成本和道德等多个方面进行权衡和考量。
1.2 InternLM 模型全链条开源
InternLM： 开源的轻量级训练框架。
InternLM-7B 和 InternLM-20B：基于 InternLM 训练框架，上海人工智能实验室已经发布的两个开源的预训练模型。
Lagent：轻量级、开源的基于大语言模型的智能体（agent）框架。
浦语·灵笔：基于书生·浦语大语言模型研发的视觉-语言大模型，能够实现图像到文本、文本到图像的双向转换。
2 InternLM-Chat-7B 智能对话 Demo
2.1 环境准备
进入 conda 环境之后，使用以下命令从本地克隆一个已有的 pytorch 2.0.1 的环境
bash # 请每次使用 jupyter lab 打开终端时务必先执行 bash 命令进入 bash 中 bash /root/share/install_conda_env_internlm_base.sh internlm-demo # 执行该脚本文件来安装项目实验环境
屏幕输出：
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----------
start create the new conda env: internlm-demo
Source: /share/conda_envs/internlm-base
Destination: /root/.conda/envs/internlm-demo
Packages: 96
Files: 0
Downloading and Extracting Packages:
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate internlm-demo
#
# To deactivate an active environment, use
#
# $ conda deactivate
Finised!
Now you can use your environment by typing:
conda activate internlm-demo
(base) root@intern-studio-069640:~#
View Code
然后使用以下命令激活环境
conda activate internlm-demo
屏幕输出：
(base) root@intern-studio-069640:~# conda activate internlm-demo (internlm-demo) root@intern-studio-069640:~#View Code
并在环境中安装运行 demo 所需要的依赖。
# 升级pip python -m pip install --upgrade pip pip install modelscope==1.9.5 pip install transformers==4.35.2 pip install streamlit==1.24.0 pip install sentencepiece==0.1.99 pip install accelerate==0.24.1
屏幕输出：
(internlm-demo) root@intern-studio-069640:~# # 升级pip
(internlm-demo) root@intern-studio-069640:~# python -m pip install --upgrade pip
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
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Installing collected packages: pip
Attempting uninstall: pip
Found existing installation: pip 23.3.1
Uninstalling pip-23.3.1:
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~#
(internlm-demo) root@intern-studio-069640:~# pip install modelscope==1.9.5
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Building wheels for collected packages: oss2, aliyun-python-sdk-core, crcmod
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Successfully built oss2 aliyun-python-sdk-core crcmod
Installing collected packages: sortedcontainers, pytz, crcmod, addict, zipp, xxhash, tzdata, tqdm, tomli, six, simplejson, scipy, pyyaml, pycryptodome, pyarrow, platformdirs, packaging, multidict, jmespath, gast, fsspec, frozenlist, einops, dill, attrs, async-timeout, yarl, python-dateutil, multiprocess, importlib-metadata, huggingface-hub, aiosignal, yapf, pandas, aliyun-python-sdk-core, aiohttp, aliyun-python-sdk-kms, oss2, datasets, modelscope
Successfully installed addict-2.4.0 aiohttp-3.9.3 aiosignal-1.3.1 aliyun-python-sdk-core-2.14.0 aliyun-python-sdk-kms-2.16.2 async-timeout-4.0.3 attrs-23.2.0 crcmod-1.7 datasets-2.13.0 dill-0.3.6 einops-0.7.0 frozenlist-1.4.1 fsspec-2024.2.0 gast-0.5.4 huggingface-hub-0.20.3 importlib-metadata-7.0.1 jmespath-0.10.0 modelscope-1.9.5 multidict-6.0.5 multiprocess-0.70.14 oss2-2.18.4 packaging-23.2 pandas-2.2.0 platformdirs-4.2.0 pyarrow-15.0.0 pycryptodome-3.20.0 python-dateutil-2.8.2 pytz-2024.1 pyyaml-6.0.1 scipy-1.12.0 simplejson-3.19.2 six-1.16.0 sortedcontainers-2.4.0 tomli-2.0.1 tqdm-4.66.2 tzdata-2024.1 xxhash-3.4.1 yapf-0.40.2 yarl-1.9.4 zipp-3.17.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~# pip install transformers==4.35.2
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting transformers==4.35.2
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/12/dd/f17b11a93a9ca27728e12512d167eb1281c151c4c6881d3ab59eb58f4127/transformers-4.35.2-py3-none-any.whl (7.9 MB)
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Installing collected packages: safetensors, regex, tokenizers, transformers
Successfully installed regex-2023.12.25 safetensors-0.4.2 tokenizers-0.15.2 transformers-4.35.2
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~# pip install streamlit==1.24.0
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting streamlit==1.24.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/3d/5f/1716c66c58a8b080f9673e7ca569044b1b8ac6a5cd8519fe4b1afa89abf8/streamlit-1.24.0-py2.py3-none-any.whl (8.9 MB)
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Installing collected packages: watchdog, validators, tornado, toolz, toml, tenacity, smmap, rpds-py, pytz-deprecation-shim, pympler, pygments, protobuf, pillow, mdurl, importlib-metadata, click, cachetools, blinker, tzlocal, referencing, pydeck, markdown-it-py, gitdb, rich, jsonschema-specifications, gitpython, jsonschema, altair, streamlit
Attempting uninstall: pillow
Found existing installation: Pillow 10.0.1
Uninstalling Pillow-10.0.1:
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Attempting uninstall: importlib-metadata
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Successfully installed altair-5.2.0 blinker-1.7.0 cachetools-5.3.2 click-8.1.7 gitdb-4.0.11 gitpython-3.1.41 importlib-metadata-6.11.0 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 markdown-it-py-3.0.0 mdurl-0.1.2 pillow-9.5.0 protobuf-4.25.2 pydeck-0.8.1b0 pygments-2.17.2 pympler-1.0.1 pytz-deprecation-shim-0.1.0.post0 referencing-0.33.0 rich-13.7.0 rpds-py-0.17.1 smmap-5.0.1 streamlit-1.24.0 tenacity-8.2.3 toml-0.10.2 toolz-0.12.1 tornado-6.4 tzlocal-4.3.1 validators-0.22.0 watchdog-4.0.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~# pip install sentencepiece==0.1.99
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting sentencepiece==0.1.99
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7f/e5/323dc813b3e1339305f888d035e2f3725084fc4dcf051995b366dd26cc90/sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
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Installing collected packages: sentencepiece
Successfully installed sentencepiece-0.1.99
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~# pip install accelerate==0.24.1
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting accelerate==0.24.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/13/9e/ee987874058f2d93006961f6ff49e0bcb60ab9c26709ebe06bfa8707a4d8/accelerate-0.24.1-py3-none-any.whl (261 kB)
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Installing collected packages: psutil, accelerate
Successfully installed accelerate-0.24.1 psutil-5.9.8
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
(internlm-demo) root@intern-studio-069640:~#
View Code
2.2 模型下载
share 目录下已经为我们准备了全系列的 InternLM 模型，所以我们可以直接复制即可。使用如下命令复制：
mkdir -p /root/model/Shanghai_AI_Laboratory cp -r /root/share/temp/model_repos/internlm-chat-7b /root/model/Shanghai_AI_Laboratory
屏幕输出：
(internlm-demo) root@intern-studio-069640:~# mkdir -p /root/model/Shanghai_AI_Laboratory (internlm-demo) root@intern-studio-069640:~# cp -r /root/share/temp/model_repos/internlm-chat-7b /root/model/Shanghai_AI_LaboratoryView Code
2.3 代码准备
首先 clone 代码，在 /root 路径下新建 code 目录，然后切换路径, clone 代码.
mkdir code
cd /root/code git clone https://gitee.com/internlm/InternLM.git
屏幕输出：
(internlm-demo) root@intern-studio-069640:~# mkdir code (internlm-demo) root@intern-studio-069640:~# cd /root/code (internlm-demo) root@intern-studio-069640:~/code# git clone https://gitee.com/internlm/InternLM.git Cloning into 'InternLM'... remote: Enumerating objects: 2604, done. remote: Counting objects: 100% (592/592), done. remote: Compressing objects: 100% (264/264), done. remote: Total 2604 (delta 323), reused 581 (delta 318), pack-reused 2012 Receiving objects: 100% (2604/2604), 4.87 MiB | 1.08 MiB/s, done. Resolving deltas: 100% (1607/1607), done. (internlm-demo) root@intern-studio-069640:~/code#View Code
切换 commit 版本，与教程 commit 版本保持一致：
cd InternLM git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17
屏幕输出：
(internlm-demo) root@intern-studio-069640:~/code# cd InternLM (internlm-demo) root@intern-studio-069640:~/code/InternLM# git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17 Note: switching to '3028f07cb79e5b1d7342f4ad8d11efad3fd13d17'. You are in 'detached HEAD' state. You can look around, make experimental changes and commit them, and you can discard any commits you make in this state without impacting any branches by switching back to a branch. If you want to create a new branch to retain commits you create, you may do so (now or later) by using -c with the switch command. Example: git switch -c <new-branch-name> Or undo this operation with: git switch - Turn off this advice by setting config variable advice.detachedHead to false HEAD is now at 3028f07 fix(readme): update README with original weight download link (#460) (internlm-demo) root@intern-studio-069640:~/code/InternLM#View Code
将 /root/code/InternLM/web_demo.py 中 29 行和 33 行的模型更换为本地的 /root/model/Shanghai_AI_Laboratory/internlm-chat-7b。
2.4 终端运行
在 /root/code/InternLM 目录下新建一个 cli_demo.py 文件，将以下代码填入其中：
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name_or_path = "/root/model/Shanghai_AI_Laboratory/internlm-chat-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
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("User >>> ")
input_text = input_text.replace(' ', '')
if input_text == "exit":
break
response, history = model.chat(tokenizer, input_text, history=messages)
messages.append((input_text, response))
print(f"robot >>> {response}")
然后在终端运行以下命令开始体验：
python /root/code/InternLM/cli_demo.py
屏幕输出：
(internlm-demo) root@intern-studio-069640:~/code/InternLM# vim cli_demo.py (internlm-demo) root@intern-studio-069640:~/code/InternLM# python /root/code/InternLM/cli_demo.py Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:42<00:00, 5.36s/it] =============Welcome to InternLM chatbot, type 'exit' to exit.============= User >>> robot >>> 您好！我能帮助您处理任何问题。请问您有什么需要我帮忙的？ User >>> robot >>> 您好！请问您需要了解什么方面的知识呢？我将尽我所能为您提供帮助。View Code
2.5 web demo 运行
截屏：
