Open-Source Training and RLHF Frameworks

Reef is self-hosted infrastructure for developers who want AI agents to improve through feedback on actual interactions. It connects inference and learning with versioned deployment, so an agent can update its model weights or its prompts, rules, and skills while continuing to serve requests. It's open source under Apache 2.0.

MLX is a machine learning array framework for researchers and developers building models on their own hardware. Its distinctive feature on Apple silicon is shared CPU and GPU memory: both processors can work on the same arrays without copying data between them. It's open source under the MIT license.

LLaMA-Factory is an open-source framework for developers and researchers who want to adapt language and multimodal models on their own hardware. It brings training and inference into one toolkit, with support for LLaMA, Qwen3, Qwen3-VL, DeepSeek, Gemma, Mistral and LLaVA. Its license is Apache 2.0.

Transformers is a Python library for developers and researchers who want to run pretrained AI models or train their own on hardware they control. It covers language, images, audio, video and multimodal work through a shared way of defining models. The library runs in a local Python environment; pretrained checkpoints are available from the separate Hugging Face Hub.

DataDreamer connects LLM prompting, synthetic data generation, and model training in one Python library. It's for researchers and developers who want to build datasets and use them to fine-tune or align models in reproducible workflows. The library is open source under the MIT license.

so-vits-svc is an offline AI framework for changing the voice in an existing singing recording while preserving its pitch and intonation. It's aimed at developers and researchers who want to train their own singing voices, including fictional character voices. The project is archived and no longer maintained.

nanoGPT is a Python toolkit for developers and researchers who want to train GPT models on their own hardware or fine-tune existing GPT-2 checkpoints. Its author has deprecated the project and points readers to nanochat. The MIT-licensed code remains available for study and modification.

Ludwig is an open-source Python framework for developers and researchers who want to train custom AI models on their own hardware. A YAML file describes the model and training pipeline, while Ludwig handles preprocessing, training and evaluation. It uses the Apache 2.0 license. Install the Python package with the optional LLM dependencies for fine-tuning; current source requires Python 3.12 or later.

AutoTrain trains custom machine learning models from your own data through a no-code interface. It's for people who need to fine-tune an LLM or build a classifier without writing a training pipeline. The local AutoTrain Advanced project is no longer maintained, so it won't receive bug fixes or new features.

OpenCLIP is a Python and PyTorch library for developers and researchers who want to match images with text on their own hardware. It implements OpenAI's CLIP approach: images and descriptions become numerical representations that the model can compare. This supports image search and zero-shot classification, where text labels define the categories without a separate classifier trained for each task.

PyTorch Lightning is a Python framework for researchers and developers who want to pretrain or fine-tune models on their own hardware or in the cloud. It handles repetitive training code while leaving model logic under your control. The framework is open source under Apache 2.0.

Nanotron is a Python library for researchers and developers who want to pretrain language models on their own datasets and GPU infrastructure. Built on PyTorch, it supports NVIDIA CUDA GPUs and training across multiple servers with Slurm. It's open source under Apache 2.0.

Colossal-AI is a Python framework for developers and researchers training or serving large AI models on their own GPU hardware. It addresses the memory and computing demands of models that are difficult to fit on a single GPU, with tools for distributing work across a cluster. It's open source under Apache 2.0.

diffusion-pipe is a local diffusion model training tool for people fine-tuning image and video models on their own GPU hardware. Its main distinction is that it can divide a model across several GPUs when it won't fit on one, while also distributing training work across GPUs. The Python project is open source under GPL-3.0 and uses DeepSpeed.

MusicGen is Meta AI's music generation model within AudioCraft, a PyTorch library for developers and audio researchers who want to generate music in their own computing environment. It creates music from text descriptions and can use a melody to guide the result. AudioCraft includes both inference and training code, so it's suited to people building audio tools or studying music generation.

SimpleTuner is an open-source toolkit for fine-tuning image, video and audio generation models on your own hardware or GPU servers. It's for creators and researchers adapting models to their datasets, and teams sharing training infrastructure. A web dashboard manages training jobs.

XTuner is an open-source LLM training engine for researchers and teams training large mixture-of-experts (MoE) models on their own hardware. It supports GPU and Ascend NPU training, with an emphasis on memory use and distributed training efficiency at scales reaching a trillion parameters.

Oumi builds specialized AI models for teams that want control over their training data, model weights, and deployment. Its Apache 2.0 open-source stack runs on laptops, clusters, and your own servers, while its hosted service automates model development from a plain-English task description. You own the resulting weights, data, and training recipes.

Optimum is a collection of Python packages for developers who want to train or run Hugging Face models more efficiently on specific hardware. It extends Transformers, Diffusers, TIMM and Sentence Transformers, with integrations for local machines, mobile and edge devices, and cloud accelerators. It's open source under Apache 2.0.

Detectron2 is an open-source Python library for developers and researchers building computer vision applications. It provides algorithms for locating objects in images and segmenting image regions, with support for training models and building research projects on top of the library. Facebook AI Research developed it as the successor to Detectron and maskrcnn-benchmark.

ModelScope combines a hosted model and dataset hub with a Python library you can run locally. It's for developers and researchers who want to use AI models in their own applications, fine-tune them on their own data, or compare their performance. The library is open source under Apache 2.0.

nanochat is an MIT-licensed toolkit for training your own LLM and chatting with it on hardware you control. It's aimed at researchers and developers who want to study or modify the full training pipeline, with a small Python codebase built on PyTorch.

MMDetection is a Python toolkit for researchers and developers building object detection and image segmentation systems. Part of OpenMMLab, it combines ready-made model architectures with interchangeable components for custom models. It's open source under Apache 2.0.

torchtitan is an open-source training platform for researchers and developers building generative AI models on their own GPU machines or server clusters. It uses PyTorch's distributed training tools and keeps the model code relatively simple when spreading work across GPUs. The Python codebase has extension points and replaceable components for experiments with model architectures and training infrastructure.

TorchAO is a PyTorch library for developers who want to train or run models on their own hardware with less memory and faster computation. It reduces the precision of model weights and activations, with options for language models and image or video generation. Its PyTorch integration works with torch.compile and FSDP2 across most Hugging Face PyTorch models.

DeepSpeed is an open-source library for developers and researchers training or running large AI models on their own hardware or compute clusters. It works with PyTorch and focuses on memory use, training speed, and distributing work across GPUs. It's licensed under Apache 2.0.

ONNX Runtime is an open source inference and training engine for developers building AI into apps and services. It runs ONNX models across desktop systems, mobile devices, web browsers and servers. It's a fit when you need the same model format to work in several places, including on a user's device.

TRL is a Python library for developers and researchers who want to adapt foundation models on their own hardware. It builds on Hugging Face Transformers and covers supervised fine-tuning, reinforcement learning and training from preference feedback. It's open source under Apache 2.0.

ms-swift is a Python framework for developers and researchers who want to train and deploy language or multimodal models on their own hardware. It brings fine-tuning, evaluation and model serving into one project, with support for Qwen3, DeepSeek-R1, Llama4 and Mistral, plus multimodal models such as Qwen3-VL and InternVL3.5. It's open source under Apache 2.0.

AI Toolkit (ostris) is an MIT-licensed training suite for people who want to fine-tune image and video models on their own hardware or a self-hosted server. It targets consumer NVIDIA GPUs and runs on Linux and Windows, including ARM64 Linux systems such as DGX Spark. An experimental installer also supports Apple Silicon Macs. GPU memory needs depend on the model and training task.

Molmo is Ai2's family of vision-language models, with code for running and training models on your own hardware. It's for developers and researchers who need to work with images and text, adapt a model, or evaluate it against visual tasks. The Python codebase is open source under Apache 2.0 and builds on OLMo, adding image encoding and generative evaluation.

verl is a Python library for teams training large language models on their own GPU infrastructure. It's the open-source implementation of HybridFlow, aimed at researchers and engineers who need reinforcement learning after initial model training. It uses the Apache 2.0 license.

LitGPT is a Python toolkit for developers and researchers who want to train, adapt and serve language models on their own hardware or servers. Its model implementations are written directly, with little abstraction between you and the code, so you can inspect model behavior and modify it for research or custom applications. It's open source under Apache 2.0.

OpenRLHF is a self-hosted Python framework for researchers and teams training language models with human feedback or custom rewards. It runs on your own NVIDIA GPU hardware, with Docker support and distributed training across servers. It's open source under Apache 2.0.

torchtune is a Python library for developers and researchers who want to adapt LLMs on their own GPU hardware using PyTorch. Its editable training recipes suit work that needs control over the training code and model implementations. The project is no longer actively maintained.

Stable Audio Tools is an MIT-licensed Python toolkit for developers and audio researchers who want to generate audio on their own hardware or train models on their own recordings. It combines model inference with training and fine-tuning, so you can work with pretrained models or build a model around a specific audio dataset.