llm-gpt4all 0.1.1

Plugin for LLM adding support for GPT4ALL models

llm-gpt4all

Plugin for LLM adding support for the GPT4All collection of models.

Installation

Install this plugin in the same environment as LLM.

llm install llm-gpt4all

After installing the plugin you can see a new list of available models like this:

llm models list

The output will include something like this:

gpt4all: orca-mini-3b - Orca (Small), 1.80GB download, needs 4GB RAM (installed)
gpt4all: ggml-gpt4all-j-v1 - Groovy, 3.53GB download, needs 8GB RAM (installed)
gpt4all: nous-hermes-13b - Hermes, 7.58GB download, needs 16GB RAM (installed)
gpt4all: orca-mini-7b - Orca, 3.53GB download, needs 8GB RAM
gpt4all: ggml-model-gpt4all-falcon-q4_0 - GPT4All Falcon, 3.78GB download, needs 8GB RAM
gpt4all: ggml-vicuna-7b-1 - Vicuna, 3.92GB download, needs 8GB RAM
gpt4all: ggml-wizardLM-7B - Wizard, 3.92GB download, needs 8GB RAM
gpt4all: ggml-mpt-7b-base - MPT Base, 4.52GB download, needs 8GB RAM
gpt4all: ggml-mpt-7b-instruct - MPT Instruct, 4.52GB download, needs 8GB RAM
gpt4all: ggml-mpt-7b-chat - MPT Chat, 4.52GB download, needs 8GB RAM
gpt4all: ggml-replit-code-v1-3b - Replit, 4.84GB download, needs 4GB RAM
gpt4all: orca-mini-13b - Orca (Large), 6.82GB download, needs 16GB RAM
gpt4all: GPT4All-13B-snoozy - Snoozy, 7.58GB download, needs 16GB RAM
gpt4all: ggml-vicuna-13b-1 - Vicuna (large), 7.58GB download, needs 16GB RAM
gpt4all: ggml-nous-gpt4-vicuna-13b - Nous Vicuna, 7.58GB download, needs 16GB RAM
gpt4all: ggml-stable-vicuna-13B - Stable Vicuna, 7.58GB download, needs 16GB RAM
gpt4all: wizardLM-13B-Uncensored - Wizard Uncensored, 7.58GB download, needs 16GB RAM

Further details on these models can be found in this Observable notebook.

Usage

You can execute a model using the name displayed in the llm models list output. The model file will be downloaded the first time you attempt to run it.

llm -m orca-mini-7b '3 names for a pet cow'

The first time you run this you will see a progress bar:

 31%|█████████▋                        | 1.16G/3.79G [00:26<01:02, 42.0MiB/s]

On subsequent uses the model output will be displayed immediately.

Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-gpt4all
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

pip install -e '.[test]'

To run the tests:

pytest