Demo, data and code to train an assistant-style large language model
 # Try it yourself You can download pre-compiled LLaMa C++ Interactive Chat binaries here: - [OSX](https://s3.amazonaws.com/static.nomic.ai/gpt4all/models/gpt4all-lora-quantized-OSX-m1) - [Intel/Windows]() and the model - [gpt4all-quantized]() # Reproducibility You can find trained LoRa model weights at: - gpt4all-lora https://huggingface.co/nomic-ai/gpt4all-lora We are not distributing a LLaMa 7B checkpoint. You can reproduce our trained model by doing the following: ## Setup Clone the repo `git clone --recurse-submodules git@github.com: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 . ``` ## Generate ```bash python generate.py --config configs/generate/generate.yaml --prompt "Write a script to reverse a string in Python ``` ## Train ```bash 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 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}}, } ```