The Best peft Alternatives in 2026

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

oumi supports fine-tuning, evaluation, and deployment of open-weight language models.

FEDOT automates machine learning model development through its modeling framework.

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

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

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

LeRobot provides tools and learning resources for developing AI systems for robotics.

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.

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

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

Learnergy is a Python library for energy-based machine learning models.

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

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.

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

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

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

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

Archai supports neural architecture search with fast, reproducible, and modular research tools.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

Guild AI automates and optimizes machine learning experiments while tracking each run.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.