GPT4All

Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa

GPT4All is made possible by our compute partner Paperspace.

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

Run on an M1 Mac (not sped up!)

Chat Client

Run any GPT4All model natively on your home desktop with the auto-updating desktop chat client. See website for exaustive list of models.

GPT4All Website

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

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

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}},
}

a935855375/gpt4all