open-instruct
Investigate the tuning of language models with leading-edge approaches on publicly accessible datasets. This project provides a unified codebase for training and assessing, featuring modern enhancements like LoRA, QLoRA, and efficient parameter updates. Find further insights and advancements through related research publications. The repository contains datasets, evaluation scripts for key benchmarks, and offers models such as Tülu tailored to diverse datasets, facilitating improved language model outcomes. Engage in fine-tuning for instruction adherence, employing advanced practices and reliable evaluation techniques.
