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MACOS

Supports CPU and MPS (Metal M1/M2).

Install

  • Download and Install Miniconda for Python 3.10.
  • Run Miniconda
  • Setup environment with Conda Rust: 
    conda create -n h2ogpt python=3.10 rust
conda activate h2ogpt
  • Install dependencies: 
    git clone https://github.com/h2oai/h2ogpt.git
cd h2ogpt

# fix any bad env
pip uninstall -y pandoc pypandoc pypandoc-binary

# Install Torch:
pip install -r requirements.txt --extra-index https://download.pytorch.org/whl/cpu
  • Install document question-answer dependencies: 
    # Required for Doc Q/A: LangChain:
pip install -r reqs_optional/requirements_optional_langchain.txt
# Required for CPU: LLaMa/GPT4All:
pip install -r reqs_optional/requirements_optional_gpt4all.txt
# Optional: PyMuPDF/ArXiv:
pip install -r reqs_optional/requirements_optional_langchain.gpllike.txt
# Optional: Selenium/PlayWright:
pip install -r reqs_optional/requirements_optional_langchain.urls.txt
# Optional: for supporting unstructured package
python -m nltk.downloader all
  • For supporting Word and Excel documents, download libreoffice: https://www.libreoffice.org/download/download-libreoffice/ .
  • To support OCR, install Tesseract Documentation: 
    brew install libmagic
brew link libmagic
brew install poppler
brew install tesseract
brew install tesseract-lang
  • Metal M1/M2 Only: Verify whether torch uses MPS, run below python script: 
     import torch
 if torch.backends.mps.is_available():
     mps_device = torch.device("mps")
     x = torch.ones(1, device=mps_device)
     print (x)
 else:
     print ("MPS device not found.")
    Output 
    tensor([1.], device='mps:0')
  • Metal M1/M2 Only: Install and setup GPU-specific dependencies to support LLaMa.cpp on GPU:
  • GGUF: See https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases for other releases, try to stick to same version. One roughly follows: 
    pip uninstall -y llama-cpp-python llama-cpp-python-cuda
# GGUF:
pip install https://github.com/jllllll/llama-cpp-python-cuBLAS-wheels/releases/download/metal/llama_cpp_python-0.2.10-cp310-cp310-macosx_11_0_arm64.whl
    • Pass difference value of --model_path_llama if download a different GGUF model from TheBloke, or pass URL/path in UI. The default model can be downloaded here and placed in repo folder or give this URL.
  • GGMLv3 (NOT RECOMMENDED) then compile: 
    pip uninstall llama-cpp-python -y
CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python==0.1.78 --no-cache-dir
    • Note Only supports v3 ggml 4 bit quantized models for MPS, so use llama models ends with ggmlv3 & q4_x.bin.
  • vLLM support 
    pip install https://h2o-release.s3.amazonaws.com/h2ogpt/openvllm-0.28.1-py3-none-any.whl

Run

  • To run LLaMa.cpp model in CPU or GPU mode (NOTE: if you haven't compiled llama-cpp-python for M1/M2 as mentioned above you can simply run without --llamacpp_dict arg, which will run on CPU):

    • CPU Mode: To run in CPU mode, specify the 'n_gpu_layers':0 in --llamacpp_dict arg, for GGUF installed do: 
      python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path --model_path_llama=https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf --max_seq_len=4096 --llamacpp_dict="{'n_gpu_layers':0,'n_batch':128}"
    • GPU Mode for GGUF: To run in GPU mode, specify the number of gpus needed to be used 'n_gpu_layers: 2' in --llamacpp_dict arg, by default it is set to higher value to use all the available gpus 
      python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path --model_path_llama=https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf --max_seq_len=4096

Ignore CLI output showing 0.0.0.0, and instead go to http://localhost:7860 or the public live URL printed by the server (disable shared link with --share=False).

  • Full Hugging Face Model -- slower than GGUF/GGML in general:

    python generate.py --base_model=h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b --score_model=None --langchain_mode='UserData' --user_path=user_path

  • CLI mode with GGUF:

    python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path --cli=True --model_path_llama=https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf --max_seq_len=4096

See CPU and GPU for some other general aspects about using h2oGPT on CPU or GPU, such as which models to try.

Issues

  • If you see ld: library not found for -lSystem then ensure you do below and then retry from scratch to do pip install commands:

    export LDFLAGS=-L/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/lib`

  • If conda Rust has issus, you can download and install [Native Rust]((https://www.geeksforgeeks.org/how-to-install-rust-in-macos/):

    curl –proto ‘=https’ –tlsv1.2 -sSf https://sh.rustup.rs | sh
# enter new shell and test:
rustc --version

  • When running a Mac with Intel hardware (not M1), you may run into

    _clang: error: the clang compiler does not support '-march=native'_

    during pip install. If so, set your archflags during pip install. E.g.

    ARCHFLAGS="-arch x86_64" pip install -r requirements.txt

  • If you encounter an error while building a wheel during the pip install process, you may need to install a C++ compiler on your computer.