databerry VS xTuring

Sources: https://github.com/gmpetrov/databerry

You can install it from databerry.ai

I just created Databerry.ai, an open source no-code platform which helps connecting a conversational agents to your own data and deploy it anywhere.

Try for free on https://databerry.ai

Explore the project on GitHub here.

If you're curious, I encourage you to: - Dive deeper with the LLaMA 2 tutorial here. - Explore the project on GitHub here. - Connect with our community on Discord here.

If you want best performance, you need to do both RAG and fine-tuning very well. There are plenty of resources on doing fine-tuning thought. I'm one of the contributors to https://github.com/stochasticai/xturing project focused on fine-tuning LLMs. You can find help in the discord channel listed on the GitHub.

What tools do you use and achieved great results ? … For me i have tried xturing and SFTTrainer and they got me a semi okay results.

Code: https://github.com/stochasticai/xTuring/blob/main/examples/llama/llama_lora_int8.py Colab: https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing

We are a group of researchers out of Harvard working on open-source library called xTuring, focused on fine-tuning LLMs: https://github.com/stochasticai/xturing.

Fine tuning I was thinking about using this (low GPU memory footprint): https://github.com/stochasticai/xturing/blob/main/examples/int4_finetuning/README.md

I can't speak for QLORA, as I haven't had a chance to get an implementation working, but I've had success with StochasticAI's Xturing. It's by far the most streamlined method of finetuning I've come across, and they offer int8 and int4 fintuning (but only for llama-7B).