GPT4All

Open-source assistant-style large language models that run locally on CPU

GPT4All Website

Discord

📗 Technical Report 3: GPT4All Snoozy and Groovy

📗 Technical Report 2: GPT4All-J

📗 Technical Report 1: GPT4All

🐍 Official Python Bindings

💻 Official Typescript Bindings

💬 Official Chat Interface

💬 Official Web Chat Interface

🦜️🔗 Official Langchain Backend

GPT4All is made possible by our compute partner Paperspace.

Run on an M1 Mac (not sped up!)

GPT4All: An ecosystem of open-source on-edge large language models.

GTP4All is an ecosystem to train and deploy powerful and customized large language models that run locally on consumer grade CPUs.

The goal is simple - be the best instruction tuned assistant-style language model that any person or enterprise can freely use, distribute and build on.

A GPT4All model is a 3GB - 8GB file that you can download and plug into the GPT4All open-source ecosystem software. Nomic AI supports and maintains this software ecosystem to enforce quality and security alongside spearheading the effort to allow any person or enterprise to easily train and deploy their own on-edge large language models.

Chat Client

Run any GPT4All model natively on your home desktop with the auto-updating desktop chat client. See GPT4All Website for a full list of open-source models you can run with this powerful desktop application.

Direct Installer Links:

• Mac/OSX

• Windows

• Ubuntu

If you have older hardware that only supports avx and not avx2 you can use these.

• Mac/OSX - avx-only

• Windows - avx-only

• Ubuntu - avx-only

Find the most up-to-date information on the GPT4All Website

Bindings

• 🐍 Official Python Bindings
• 💻 Official Typescript Bindings

Training GPT4All-J

Please see GPT4All-J Technical Report for details.

GPT4All-J Training Data

• We are releasing the curated training data for anyone to replicate GPT4All-J here: GPT4All-J Training Data
  • Atlas Map of Prompts
  • Atlas Map of Responses

We have released updated versions of our GPT4All-J model and training data.

• v1.0: The original model trained on the v1.0 dataset
• v1.1-breezy: Trained on a filtered dataset where we removed all instances of AI language model
• v1.2-jazzy: Trained on a filtered dataset where we also removed instances like I'm sorry, I can't answer... and AI language model

The models and data versions can be specified by passing a revision argument.

For example, to load the v1.2-jazzy model and dataset, run:

from datasets import load_dataset
from transformers import AutoModelForCausalLM

dataset = load_dataset("nomic-ai/gpt4all-j-prompt-generations", revision="v1.2-jazzy")
model = AutoModelForCausalLM.from_pretrained("nomic-ai/gpt4all-j-prompt-generations", revision="v1.2-jazzy")

GPT4All-J Training Instructions

accelerate launch --dynamo_backend=inductor --num_processes=8 --num_machines=1 --machine_rank=0 --deepspeed_multinode_launcher standard --mixed_precision=bf16  --use_deepspeed --deepspeed_config_file=configs/deepspeed/ds_config_gptj.json train.py --config configs/train/finetune_gptj.yaml

Contributing

GPT4All welcomes contribution, involvment, and discussion from the open source community! Please see CONTRIBUTING.md and follow the issue, bug report, and PR markdown templates.

Check project discord, with project owners, or through existing issues/PRs to avoid duplicate work. Please make sure to tag all of the above with relevant project identifiers or your contribution could potentially get lost. Example tags: backend, bindings, python-bindings, documentation, etc.

Citation

If you utilize this repository, models or data in a downstream project, please consider citing it with:

@misc{gpt4all,
  author = {Yuvanesh Anand and Zach Nussbaum and Brandon Duderstadt and Benjamin Schmidt and Andriy Mulyar},
  title = {GPT4All: Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/nomic-ai/gpt4all}},
}