LLM Training

NeMo Curator

NeMo Curator is a Python library specifically designed for scalable and efficient dataset preparation. It greatly accelerates data curation by leveraging GPUs with Dask and RAPIDS, resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline expansion and accelerating model conv... See more

Mistral-finetune

mistral-finetune is a light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models. It is based on LoRA, a training paradigm where most weights are frozen and only 1-2% additional weights in the form of low-rank matrix perturbations are trained.

For maximum efficiency it is recommended to use a A... See more

LLM-PowerHouse: A Curated Guide for Large Language Models with Custom Training and Inferencing

Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers, researche... See more

Unitxt is a python library for getting data fired up and set for utilization. In one line of code, it preps a dataset or mixtures-of-datasets into an input-output format for training and evaluation. We aspire to be simple, adaptable and transparent.

Unitxt builds on separation. Separation allows adding a dataset, without knowing anything about the m... See more

2-5x faster 50% less memory local LLM finetuning

• Manual autograd engine - hand derived backprop steps.


• 2x to 5x faster than QLoRA. 50% less memory usage.


• All kernels written in OpenAI's Triton language.


• 0% loss in accuracy - no approximation methods - all exact.


• No change of hardware necessary. Supports NVIDIA GPUs since 2018+. Minimum CUDA Compute Cap

... See more