Demo, data and code to train an assistant-style large language model with ~800k GPT-3.5-Turbo Generations
Clone this repository down and download the CPU quantized gpt4all model.
Place the quantized model in the chat
directory and start chatting by running:
./chat/gpt4all-lora-quantized-OSX-m1
on M1 Mac/OSX./chat/gpt4all-lora-quantized-linux-x86
on Windows/Linux
To compile for custom hardware, see our fork of the Alpaca C++ repo.
Note: the full model on GPU (16GB of RAM required) perform much better in our qualitative evaluations.
Trained LoRa Weights:
- gpt4all-lora: https://huggingface.co/nomic-ai/gpt4all-lora
- gpt4all-lora-epoch-2 https://huggingface.co/nomic-ai/gpt4all-lora-epoch-2
Raw Data:
We are not distributing a LLaMa 7B checkpoint.
You can reproduce our trained model by doing the following:
Clone the repo
git clone --recurse-submodules [email protected]:nomic-ai/gpt4all.git
git submodule configure && git submodule update
Setup the environment
python -m pip install -r requirements.txt
cd transformers
pip install -e .
cd ../peft
pip install -e .
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.json train.py --config configs/train/finetune-7b.yaml
python generate.py --config configs/generate/generate.yaml --prompt "Write a script to reverse a string in Python
If you utilize this reposistory, 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}},
}