MacBook Pro (15 英寸，2018) 运行 ChatGLM3-6B 大模型_macbook pro chatglm

接上篇 在 Mac 上加速 PyTorch 训练，准备完 MPS 环境之后，开始在本地体验 ChatGLM3-6B 模型。

一、下载本仓库：

(base) markvivv@MBP dev % git clone https://github.com/THUDM/ChatGLM3

Cloning into 'ChatGLM3'...
remote: Enumerating objects: 1053, done.
remote: Counting objects: 100% (634/634), done.
remote: Compressing objects: 100% (240/240), done.
remote: Total 1053 (delta 461), reused 466 (delta 393), pack-reused 419
Receiving objects: 100% (1053/1053), 17.22 MiB | 983.00 KiB/s, done.
Resolving deltas: 100% (608/608), done.

(base) markvivv@MBP dev % cd ChatGLM3
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二、然后使用 pip 安装依赖：

(base) markvivv@MBP ChatGLM3 % pip install -r requirements.txt
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Successfully built ffmpy greenlet sgmllib3k
Installing collected packages: sgmllib3k, sentencepiece, pytz, pydub, ffmpy, cpm_kernels, zipp, websockets, validators, urllib3, tzlocal, tzdata, tornado, toolz, tomlkit, toml, threadpoolctl, tenacity, sniffio, smmap, six, shellingham, semantic-version, scipy, safetensors, ruff, rpds-py, regex, python-multipart, pyparsing, pyjwt, pygments, pydantic-core, pyarrow, psutil, protobuf, packaging, orjson, mypy-extensions, multidict, mdurl, markdown, loguru, latex2mathml, kiwisolver, jsonpatch, joblib, importlib-resources, h11, greenlet, fsspec, frozenlist, fonttools, feedparser, cycler, contourpy, colorama, click, cachetools, blinker, attrs, annotated-types, aiofiles, yarl, uvicorn, typing-inspect, types-requests, typer, SQLAlchemy, scikit-learn, referencing, python-dateutil, pydeck, pydantic, nltk, mdtex2html, marshmallow, markdown-it-py, importlib-metadata, httpcore, gitdb, anyio, aiosignal, tiktoken, starlette, rich, pandas, matplotlib, langsmith, langchainhub, jsonschema-specifications, huggingface-hub, httpx, gitpython, dataclasses-json, arxiv, aiohttp, zhipuai, tokenizers, timm, sse-starlette, openai, langchain-core, jsonschema, gradio-client, fastapi, accelerate, transformers, langchain-community, altair, streamlit, sentence_transformers, langchain, gradio
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Successfully installed SQLAlchemy-2.0.25 accelerate-0.26.1 aiofiles-23.2.1 aiohttp-3.9.3 aiosignal-1.3.1 altair-5.2.0 annotated-types-0.6.0 anyio-4.2.0 arxiv-2.1.0 attrs-23.2.0 blinker-1.7.0 cachetools-5.3.2 click-8.1.7 colorama-0.4.6 contourpy-1.2.0 cpm_kernels-1.0.11 cycler-0.12.1 dataclasses-json-0.6.3 fastapi-0.109.0 feedparser-6.0.10 ffmpy-0.3.1 fonttools-4.47.2 frozenlist-1.4.1 fsspec-2023.12.2 gitdb-4.0.11 gitpython-3.1.41 gradio-4.16.0 gradio-client-0.8.1 greenlet-3.0.3 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 huggingface-hub-0.20.3 importlib-metadata-7.0.1 importlib-resources-6.1.1 joblib-1.3.2 jsonpatch-1.33 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 kiwisolver-1.4.5 langchain-0.1.4 langchain-community-0.0.16 langchain-core-0.1.17 langchainhub-0.1.14 langsmith-0.0.85 latex2mathml-3.77.0 loguru-0.7.2 markdown-3.5.2 markdown-it-py-3.0.0 marshmallow-3.20.2 matplotlib-3.8.2 mdtex2html-1.3.0 mdurl-0.1.2 multidict-6.0.4 mypy-extensions-1.0.0 nltk-3.8.1 openai-1.10.0 orjson-3.9.12 packaging-23.2 pandas-2.2.0 protobuf-4.25.2 psutil-5.9.8 pyarrow-15.0.0 pydantic-2.6.0 pydantic-core-2.16.1 pydeck-0.8.1b0 pydub-0.25.1 pygments-2.17.2 pyjwt-2.8.0 pyparsing-3.1.1 python-dateutil-2.8.2 python-multipart-0.0.6 pytz-2023.4 referencing-0.33.0 regex-2023.12.25 rich-13.7.0 rpds-py-0.17.1 ruff-0.1.15 safetensors-0.4.2 scikit-learn-1.4.0 scipy-1.12.0 semantic-version-2.10.0 sentence_transformers-2.3.1 sentencepiece-0.1.99 sgmllib3k-1.0.0 shellingham-1.5.4 six-1.16.0 smmap-5.0.1 sniffio-1.3.0 sse-starlette-2.0.0 starlette-0.35.1 streamlit-1.30.0 tenacity-8.2.3 threadpoolctl-3.2.0 tiktoken-0.5.2 timm-0.9.12 tokenizers-0.15.1 toml-0.10.2 tomlkit-0.12.0 toolz-0.12.1 tornado-6.4 transformers-4.37.2 typer-0.9.0 types-requests-2.31.0.20240125 typing-inspect-0.9.0 tzdata-2023.4 tzlocal-5.2 urllib3-2.2.0 uvicorn-0.27.0.post1 validators-0.22.0 websockets-11.0.3 yarl-1.9.4 zhipuai-2.0.1 zipp-3.17.0
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三、进入 base_demo，准备启动 streamlit 示例程序

(base) markvivv@MBP ChatGLM3 % cd base_demo
1

本人笔记本带独立显卡，4G 显存，16G 内存，默认参数可以启动示例程序，但是对大模型提任何问题都会提示如下错误：

RuntimeError: MPS backend out of memory (MPS allocated: 5.44 GB, other allocations: 1.17 GB, max allowed: 6.80 GB). Tried to allocate 428.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure).
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按 Ctrl + C 结束正在运行的模型，冒着机器可能突然崩溃的风险，按照提示信息执行命令取消内存限制

(base) markvivv@MBP basic_demo % PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
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内存限制错误提示信息如下：

(base) markvivv@MBP basic_demo % streamlit run web_demo_streamlit.py 

      
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