## 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:
   ```bash
   # if don't have wget, download to repo folder using below link
   wget https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/resolve/main/llama-2-7b-chat.ggmlv3.q8_0.bin
   python generate.py --base_model='llama' --prompt_type=llama2 --score_model=None --langchain_mode='UserData' --user_path=user_path
   ```

For another llama.cpp model:

* Download from [TheBloke](https://huggingface.co/TheBloke).  For example, [13B WizardLM Quantized](https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML) or [7B WizardLM Quantized](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML).  TheBloke has a variety of model types, quantization bit depths, and memory consumption.  Choose what is best for your system's specs.  For 7B case, download [WizardLM-7B-uncensored.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML/resolve/main/WizardLM-7B-uncensored.ggmlv3.q8_0.bin) into local path:
   ```bash
   wget https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML/resolve/main/WizardLM-7B-uncensored.ggmlv3.q8_0.bin
   ```
* With documents in `user_path` folder, run:
  ```bash
   python generate.py --base_model=llama --model_path_llama=WizardLM-7B-uncensored.ggmlv3.q8_0.bin --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:
  ```bash
   python generate.py --base_model=llama --model_path_llama=WizardLM-7B-uncensored.ggmlv3.q8_0.bin --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](https://gpt4all.io/index.html). 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:
  ```bash
   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
  ```bash
   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](FAQ.md#gpt4all-not-producing-output), even outside h2oGPT.  See [GPT4All](https://github.com/nomic-ai/gpt4all) for details on installation instructions if any issues encountered.

### Low-memory

See [Low Memory](FAQ.md#low-memory-mode) for more information about low-memory recommendations.