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<h1 align="center">GPT4All</h1> <h1 align="center">GPT4All</h1>
<p align="center">Data and code to train an assistant-style LLM</p> <p align="center">Data and code to train an assistant-style large language model</p>
# Try it yourself # Try it yourself
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# Reproducibility # Reproducibility
To reproduce our trained LoRa model, do the following: To reproduce our trained assistant model with LoRA, do the following:
## Setup ## Setup
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## Train ## Train
`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` `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`
If you utilize this reposistory, models or data in a downstream project, please consider citing it with:
```
@misc{gpt4all,
author = {Yuvanesh Anand and Zachary 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}},
}
```