simple-llm-finetuner VS AlpacaDataCleaned

Just the appropriate reddit groups and follow folks on twitter, plus use a search engine.

1. Learn to run a model, checkout llama.cpp Tons of free models on huggingface.com

2. Learn to finetune a model - https://github.com/lxe/simple-llm-finetuner

3. Learn to train one. PyTorch, TensorFlow, HuggingFace libraries, etc.

Good luck.

So as far as set up goes, you just need to: “”” Git clone https://github.com/lxe/simple-llama-finetuner Cd simple-llama-finetuner Pip install -r requirements.txt Python app.py ## if you’re on a remote machine (Paperspace is my go to) then you may need to edit the last line of this script to set ‘share=True’ in the launch args “””

oobabooga's textgen webui has a tab for fine tuning now. You only need a single consumer GPU to fine tune up to 33B parameter models at a rate of about 200 epochs per hour, per GPU.

There are also one-click finetuning projects which run on free Google Colab GPUs like https://github.com/lxe/simple-llama-finetuner

It's easy and not complex at all.

for a single 4090, easiest way to get started and simple to use: https://github.com/lxe/simple-llama-finetuner

I tried using the default alpaca_data_cleaned.json training dataset as mentioned here: https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json. Does anyone know why I could be getting this error? The file must be in correct format since it is the default file they have shown in their example.

This cleaned alpaca dataset gives a good idea of how data is formatted for the standard alpaca json format. Personally, I'd handle making your own datasets by using gpt4 to format the data into a dataset. You can do it by hand or use a llama model, but I've personally just found using chatgpt to be the most efficient way to get the highest possible output. I'm trying to go for quality over quantity.

I created a dataset merge based on the following very high quality datasets:

That dataset is licensed under CC BY NC 4.0, which is not open. It also has a bunch of garbage in it; see https://github.com/gururise/AlpacaDataCleaned

The alpaca cleaned dataset has integrated the Microsoft GPT-4 dataset and cleaned many of the issues.

13b Alpaca Cleaned (trained on the cleaned dataset) is very impressive and works well as an instruct model w/o any censorship.

There's already a community maintained Alpaca with cleaned data. https://github.com/gururise/AlpacaDataCleaned And a huge amount of work has already been done.

this might be what you're looking for: https://github.com/gururise/AlpacaDataCleaned