Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Plugin for LLM adding support for the GPT4All collection of models
| Date | Stars |
|---|---|
| 2026-07-31 | 265 |
| 2026-08-03 | 265 |
| 2026-08-06 | 265 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# llm-gpt4all
[](https://pypi.org/project/llm-gpt4all/)
[](https://github.com/simonw/llm-gpt4all/releases)
[](https://github.com/simonw/llm-gpt4all/actions?query=workflow%3ATest)
[](https://github.com/simonw/llm-gpt4all/blob/main/LICENSE)
Plugin for [LLM](https://llm.datasette.io/) adding support for the [GPT4All](https://gpt4all.io/) collection of models.
## Installation
Install this plugin in the same environment as LLM.
```bash
llm install llm-gpt4all
```
After installing the plugin you can see a new list of available models like this:
```bash
llm models list
```
The output will include something like this:
```
gpt4all: all-MiniLM-L6-v2-f16 - SBert, 43.76MB download, needs 1GB RAM (installed)
gpt4all: orca-mini-3b-gguf2-q4_0 - Mini Orca (Small), 1.84GB download, needs 4GB RAM (installed)
gpt4all: mistral-7b-instruct-v0 - Mistral Instruct, 3.83GB download, needs 8GB RAM (installed)
gpt4all: mistral-7b-openorca - Mistral OpenOrca, 3.83GB download, needs 8GB RAM (installed)
gpt4all: Meta-Llama-3-8B-Instruct - Llama 3 Instruct, 4.34GB download, needs 8GB RAM (installed) (aliases: l3)
gpt4all: all-MiniLM-L6-v2 - SBert, 43.82MB download, needs 1GB RAM
gpt4all: nomic-embed-text-v1 - Nomic Embed Text v1, 261.58MB download, needs 1GB RAM
gpt4all: nomic-embed-text-v1 - Nomic Embed Text v1.5, 261.58MB download, needs 1GB RAM
gpt4all: replit-code-v1_5-3b-newbpe-q4_0 - Replit, 1.82GB download, needs 4GB RAM
gpt4all: mpt-7b-chat - MPT Chat, 3.54GB download, needs 8GB RAM
gpt4all: orca-2-7b - Orca 2 (Medium), 3.56GB download, needs 8GB RAM
gpt4all: rift-coder-v0-7b-q4_0 - Rift coder, 3.56GB download, needs 8GB RAM
gpt4all: mpt-7b-chat-newbpe-q4_0 - MPT Chat, 3.64GB download, needs 8GB RAM
gpt4all: em_german_mistral_v01 - EM German Mistral, 3.83GB download, needs 8GB RAM
gpt4all: ghost-7b-v0 - Ghost 7B v0.9.1, 3.83GB download, needs 8GB RAM
gpt4all: Nous-Hermes-2-Mistral-7B-DPO - Nous Hermes 2 Mistral DPO, 3.83GB download, needs 8GB RAM
gpt4all: gpt4all-falcon-newbpe-q4_0 - GPT4All Falcon, 3.92GB download, needs 8GB RAM
gpt4all: gpt4all-13b-snoozy-q4_0 - Snoozy, 6.86GB download, needs 16GB RAM
gpt4all: wizardlm-13b-v1 - Wizard v1.2, 6.86GB download, needs 16GB RAM
gpt4all: orca-2-13b - Orca 2 (Full), 6.86GB download, needs 16GB RAM
gpt4all: nous-hermes-llama2-13b - Hermes, 6.86GB download, needs 16GB RAM
gpt4all: starcoder-newbpe-q4_0 - Starcoder, 8.37GB download, needs 4GB RAM
```
Further details on these models can be found [in this Observable notebook](https://observablehq.com/@simonw/gpt4all-models).
## Usage
### Model Installation and Prompting
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.
```bash
llm -m orca-mini-3b-gguf2-q4_0 '3 names for a pet cow'
```
The first time you run this you will see a progress bar for the model file download:
```
31%|█████████▋ | 1.16G/3.79G [00:26<01:02, 42.0MiB/s]
```
On subsequent uses the model output will be displayed immediately.
Note that the models will be downloaded to `~/.cache/gpt4all`.
### Model options
Run `llm models --options` for a list of available model options, which should include:
```
gpt4all: mistral-7b-instruct-v0 - Mistral Instruct, 3.83GB download, needs 8GB RAM (installed)
max_tokens: int
The maximum number of tokens to generate.
temp: float
The model temperature. Larger values increase creativity but decrease
factuality.
top_k: int
Randomly sample from the top_k most likely tokens at each generation
step. Set this to 1 for greedy decoding.
top_p: float
Randomly sample at each generation step from the top moExcerpt of 5,710 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:47c0381b0a8adff9, llm:Repository description: 'Plugin for LLM adding support for the GPT4All collection of models' (simonw/llm-gpt4all).
matched fp:47c0381b0a8adff9, llm:Repository description: 'Plugin for LLM adding support for the GPT4All collection of models' (simonw/llm-gpt4all).
matched fp:47c0381b0a8adff9, llm:Repository description: 'Plugin for LLM adding support for the GPT4All collection of models' (simonw/llm-gpt4all).