simple-llm-finetuner

Join the appropriate reddit groups machinelearning, locallamma, deeplearning, and follow folks in the ML space 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.

Shameless plug: I built a toy tool to really quickly get into llm finetuning by just pasting your training samples into a textbox: https://github.com/lxe/simple-llm-finetuner

This already exists for LLaMA, I'm sure it'll be implemented for StableLM as well. https://github.com/lxe/simple-llm-finetuner

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.

Fine tuning a model like llama is super easy. You can fine tune models up to 33B parameters on a single consumer GPU at around 3 epochs per minute (less than 6 hours for 1000 epochs). You can even fine tune on a free Google Colab GPU. Start here: https://github.com/lxe/simple-llama-finetuner

I've been playing around with https://github.com/zphang/minimal-llama/ and https://github.com/tloen/alpaca-lora/blob/main/finetune.py, and wanted to create a simple UI where you can just paste text, tweak the parameters, and finetune the model quickly using a modern GPU. To prepare the data, simply separate your text with two blank lines. There's an inference tab, so you can test how the tuned model behaves. This is my first foray into the world of LLM finetuning, Python, Torch, Transformers, LoRA, PEFT, and Gradio. Enjoy!