Demo, data, and code to train open-source assistant-style large language model based on GPT-J and LLaMa
📗 Technical Report 3: GPT4All Snoozy and Groovy
📗 Technical Report 2: GPT4All-J
💻 Official Typescript Bindings
🦜️🔗 Official Langchain Backend
GPT4All is made possible by our compute partner Paperspace.
Run on an M1 Mac (not sped up!)
Run any GPT4All model natively on your home desktop with the auto-updating desktop chat client. See website for exaustive list of models.
Direct Installer Links:
If you have older hardware that only supports avx and not avx2 you can use these.
Find the most up-to-date information on the GPT4All Website
Please see GPT4All-J Technical Report for details.
- We are releasing the curated training data for anyone to replicate GPT4All-J here: GPT4All-J Training Data
We have released updated versions of our GPT4All-J
model and training data.
v1.0
: The original model trained on the v1.0 datasetv1.1-breezy
: Trained on a filtered dataset where we removed all instances of AI language modelv1.2-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")
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
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}},
}