Open-Source Training and RLHF Frameworks

Transformers is a Python library for developers and researchers running pretrained AI models or training models on their own hardware. It covers text, images, audio, video and multimodal tasks through shared model definitions.

MLX is a machine learning array framework for researchers and developers building and testing models on their own hardware. It supports model training and inference, with APIs for numerical work and neural networks.

River is a Python library for models that learn from incoming data one observation at a time. It's for developers and data scientists working with event streams, changing data patterns, or applications that need to update models without revisiting past training data. It runs in your own Python environment on Linux, macOS and Windows, and is open source under the BSD-3-Clause license.

DeepXDE is an open-source Python library for researchers and engineers who use neural networks to solve scientific problems. It combines physics-informed neural networks (PINNs) with operator learning, so you can work with equations and physical constraints alongside training data. Its license is LGPL-2.1.

AutoKeras is a Python AutoML library built on Keras for automated deep learning. It searches for neural-network configurations and trains models from your data, reducing the amount of manual architecture design needed for an experiment.

Kubeflow is a self-hosted AI platform for teams developing and running machine learning workloads on Kubernetes. Its modular projects cover interactive development, training and the work of moving models into production.

tsai is a Python library for researchers and developers building machine learning models for time series and sequences. Built on PyTorch and fastai, it brings model training, prediction and data preparation into one package. It's open source under Apache 2.0.

TPOT is a Python AutoML library for data scientists and researchers who want to automate machine learning pipeline selection on their own hardware. It uses genetic programming to search candidate pipelines, reducing the manual work of comparing models and tuning their parameters. It's free and open source under LGPL-3.0.

Acme is a Python reinforcement learning library for researchers who need reference agents and reusable components for their own experiments. Its agents provide baselines for comparing algorithm performance, while the underlying building blocks let researchers develop new approaches without starting from scratch.

Coqui TTS is a text-to-speech toolkit for developers and speech researchers who want to generate voices locally or train their own models. The idiap fork builds on coqui-ai/TTS.

GluonTS is a Python library for data scientists and researchers who need forecasts that express uncertainty. Its models predict a probability distribution over future values, so you can examine prediction intervals alongside the forecast. You train and use models within your own Python environment.

OpenCLIP is a Python library for developers and researchers building image-text matching and zero-shot classification applications. It supports pretrained CLIP models and training models on your own hardware.

SetFit is a framework for developers and NLP practitioners who need to train text classifiers with a small amount of labeled data. It adapts pretrained Sentence Transformers to a classification task without requiring handcrafted prompts or words that stand in for class labels. Sentiment analysis is one supported use case.

AutoTS is an open-source Python package for people who need forecasts across many time series without choosing a model and preprocessing method for each one. It runs in your own Python environment and uses automated search to compare forecasting approaches for your data. The license is MIT.

Optimum is a collection of Python packages for developers adapting Hugging Face models to their deployment hardware. It extends Transformers, Diffusers, TIMM and Sentence Transformers with tools for faster inference and training.

Gensim is a free, open-source Python library for developers and researchers working with large text collections on their own machines or servers. It trains models that capture topics and semantic relationships, then uses those representations to find related documents. It runs on Linux, Windows and macOS under the GNU Lesser General Public License v2.1 (LGPL-2.1).

4M is a framework for researchers and developers who want to train and run models that combine images, text and other visual information. A single model can perform vision tasks and generate images from mixed or partial inputs.

sktime is a Python library for developers and data scientists who want to compare time series models without rebuilding their workflow for each algorithm. It puts classical statistical methods, machine learning, deep learning and foundation models behind a consistent interface. You can run it on your own macOS, Linux or Windows machine.

Nanotron is a Python library for researchers and developers training language models on their own datasets and GPU infrastructure. It supports training on one machine or across multiple servers.

Detectron2 is a Python library for developers and researchers building object detection and image segmentation applications. It provides model training tools and reusable components for computer vision research and production software.

AutoTrain supports model training on your own hardware through AutoTrain Advanced, a no-code application for people fine-tuning LLMs or building models from their data. Advanced can run locally or on cloud infrastructure. The project is no longer maintained, and it won't receive bug fixes or new features.

VoxelMorph is a Python library for researchers and developers who need to align medical images using learned deformations. It provides registration networks and training components, including unsupervised methods, with PyTorch and TensorFlow backends. The code is open source under the Apache 2.0 license.

DeePMD-kit is open-source software for molecular and materials researchers who want to train and run AI interatomic potentials on their own hardware. It learns atomic interactions from quantum-mechanical reference data, then uses those models in molecular dynamics simulations of molecules, solids and metals. It runs on CPUs and GPUs, with support for CUDA and ROCm source builds, and carries the LGPL-3.0-or-later license.

RAGatouille is a Python library for developers building retrieval-augmented generation (RAG) applications with ColBERT. It builds and searches document indexes on your own hardware or server, with compressed indexes stored on disk that your application can query directly.

HanLP is a multilingual natural language processing toolkit for developers and researchers who need structured text analysis. Its native Python API runs models on your own hardware. The separate REST API sends text to a hosted server.

DataDreamer is a Python library for researchers and developers who need to create training datasets and adapt LLMs to specific tasks. It connects data preparation and model training in reproducible workflows.

TRL is a Python library for researchers and developers adapting foundation models with their own datasets. It builds on Hugging Face Transformers and supports training on a single GPU or across multiple machines.

Oumi helps ML researchers and teams build specialized AI models for their own tasks. It brings data preparation, training, evaluation, and deployment into one platform, with reusable recipes and components you can customize.

Darts is an open source Python library for data scientists and developers who need to forecast time series or detect unusual observations in their own Python environment. Its shared model interface lets you compare statistical methods such as ARIMA with neural networks such as N-BEATS without rewriting the surrounding forecasting workflow. It uses the Apache 2.0 license.

MONAI is a framework for researchers and developers building AI models for medical images. Its Core library provides medical-specific tools for training and evaluating models within existing PyTorch workflows.

ESPnet is a speech AI toolkit for researchers and developers who want to run pretrained models on their own hardware or train speech systems. Its recipes cover data preparation through evaluation, with published results for comparison.

WeNet is a speech recognition toolkit for developers adding transcription to applications or services. It covers model training and production deployment, so teams can adapt a recognizer to their audio or start with a pretrained model.

DeepSpeed is an open-source PyTorch library for developers and researchers training or running large AI models on their own hardware or compute clusters. It helps address memory limits and distribute workloads across devices.

ModelScope is an AI model hub with a Python library for developers and researchers who want to run models locally or adapt them to their own data. It covers language, vision, speech, multimodal tasks and scientific computing.