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CPU Details

Details that do not depend upon whether running on CPU for Linux, Windows, or MAC.

LLaMa.cpp

Default llama.cpp model is LLaMa2 GPTQ model from TheBloke:

  • Run LLaMa.cpp LLaMa2 model:

    With documents in user_path folder, run:

    # if don't have wget, download to repo folder using below link
wget https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf
python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path

For another llama.cpp model:

  • Choose from TheBloke, then with documents in user_path folder, run: 
     python generate.py --base_model=llama --model_path_llama=https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf --score_model=None --langchain_mode='UserData' --user_path=user_path
    For llama.cpp based models on CPU, for computers with low system RAM or slow CPUs, we recommend running: 
     python generate.py --base_model=llama --model_path_llama=https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q6_K.gguf --llamacpp_dict="{'use_mlock':False,'n_batch':256}" --max_seq_len=512 --score_model=None --langchain_mode='UserData' --user_path=user_path

GPT4ALL

  • Choose Model from GPT4All Model explorer GPT4All-J compatible model. One does not need to download manually, the GPT4ALL package will download at runtime and put it into .cache like Hugging Face would.

  • With documents in user_path folder, run:

     python generate.py --base_model=gptj --model_path_gptj=ggml-gpt4all-j-v1.3-groovy.bin --score_model=None --langchain_mode='UserData' --user_path=user_path

or

 python generate.py --base_model=gpt4all_llama --model_name_gpt4all_llama=ggml-wizardLM-7B.q4_2.bin --score_model=None --langchain_mode='UserData' --user_path=user_path

However, gpjt model often gives no output, even outside h2oGPT. See GPT4All for details on installation instructions if any issues encountered.

Low-memory

See Low Memory for more information about low-memory recommendations.