The Best oumi-ai/oumi Alternatives in 2026

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

PEFT provides parameter-efficient fine-tuning methods for adapting pretrained machine learning models.

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

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

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

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

Octo provides a transformer-based policy for robots, with code to train and fine-tune it.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

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

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

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

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

FEDOT automates machine learning model development through its modeling framework.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

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

Auto_ViML builds multiple machine learning models with a single line of code.

DeepSpeed optimizes distributed training and inference for deep learning models.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

AutoKeras automates development of deep learning models through a machine learning library.

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.