ML Training & Optimization

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

Hugging Face Accelerate launches and trains PyTorch models across devices and distributed configurations.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

This Common Lisp repository collects algorithms for online linear classification.

OpenMetricLearning provides metric learning pipelines, models, and a model zoo.

PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

P-tuning v2 is a deep prompt tuning strategy designed as an alternative to full model fine-tuning.

This repository implements DreamBooth subject fine-tuning for Stable Diffusion using textual inversion.

This repository implements random forest models in Common Lisp for classification tasks.

neural-fortran provides a parallel framework for deep learning in Fortran.

This metric-learn repository contains Python implementations of metric learning algorithms.

doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.

Fine-tune LLaMA on consumer hardware with alpaca-lora and its LoRA training approach.

LightlyTrain supports pretraining, fine-tuning, and distillation for computer vision models.

metric-learn provides Python implementations of metric learning algorithms.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

mgl provides a machine learning library in Common Lisp, including neural networks and Gaussian processes.

Scikit-Opt provides genetic algorithms, particle swarm optimization, simulated annealing, ant colony optimization, and other search algorithms.

Edward provides a library for probabilistic modeling, inference, and model criticism.

AutoGluon provides tools for automated machine learning and model development.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.