Tools to Fine-Tune and Train AI Models Locally

MLX LM is a Python package for developers and researchers who want to generate text and adapt language models on Apple Silicon Macs. It uses MLX for local inference and training, with access through Python or a terminal.

SkyRL is a Python library for researchers and developers who want to train LLMs with reinforcement learning on their own hardware. Its Tinker API support lets you run existing Tinker training scripts against a server you control without changing the scripts. It's open source under Apache 2.0.

Stable Baselines3 is a Python library for training reinforcement learning agents on your own hardware using PyTorch. It builds on Stable Baselines and gives researchers and developers tested algorithm implementations for comparing approaches or building their own projects. It's free and open source under the MIT license.

Stable Audio Tools is a Python toolkit for developers and audio researchers who want to generate audio or train models on their own recordings. It supports work with pretrained models as well as training from scratch.

Faceswap replaces faces in photos and videos using deep learning. It's for VFX artists, hobbyists and researchers who want to train face-swapping models on their own material and run them locally.

CleanRL is a Python deep reinforcement learning library for researchers and developers who want to study or modify an algorithm without tracing it through a large modular codebase. Each algorithm variant lives in one standalone file. Experiments can run locally, while Docker and AWS integration support cloud execution through AWS Batch.

AutoGluon is a Python AutoML library for developers and data scientists who want predictive models without selecting every algorithm themselves. It chooses combinations of classic machine learning and foundation models for the task.

GPT-SoVITS is a local voice cloning and text-to-speech tool for people creating speech in a custom voice. Its web interface covers speech generation, voice training and preparation of audio datasets.

Alexandria Audiobook turns books into audio with separate narrator and character voices. It's for people who want to choose the cast and correct individual passages before exporting an audiobook.

Arize Phoenix helps developers investigate failures in AI agents and LLM applications and compare proposed fixes. It connects recorded application activity with evaluations and repeatable experiments.

MiniMind is a toolkit for learners and developers who want to understand language models by training small ones on their own hardware. Its core training algorithms use native PyTorch code that you can inspect and change.

Ultralytics YOLO is a Python computer vision library for developers building applications that interpret images and video. Models can run on hardware you control for tasks such as visual inspection, inventory monitoring and robotics perception.

Agent Lightning is a Python framework for developers and researchers training AI agents through reinforcement learning. It keeps the agent's existing tools, context and task environment involved in training, so feedback comes from the workflow the agent actually uses.

DeepPoseKit is an open-source Python toolkit for researchers measuring animal movement and behavior in images or video. You define the body points you want to track and train a model on your own annotations. It runs through TensorFlow and Keras on your own hardware, with Windows support documented, and uses the Apache 2.0 license.

Lightning Pose is an open-source package for researchers who estimate animal poses from video for behavior analysis. It supports 2D and 3D pose estimation with single-camera and multiple-camera datasets, including footage where body parts become hidden from view.

Megatron-LM is a Python framework for researchers training large language models on NVIDIA GPU infrastructure. It provides reference training scripts for experimenting with models and studying distributed training.

PaddleX is a low-code toolkit for developers building AI applications with PaddlePaddle models. It brings pretrained pipelines and model training into one tool for applications that process documents, images or time-series data.

MLForecast is an open-source Python library for data scientists and developers who need to forecast many time series with machine learning. It combines time series feature engineering with scikit-learn-compatible models, so teams can use regressors such as LightGBM and LinearRegression rather than adopt a dedicated forecasting model. Its license is Apache 2.0.

CAMEL is a Python framework for developers and researchers building AI agent teams with models running locally through Ollama or through remote providers. Its Ollama integration connects agents to a local model service; choosing OpenAI instead sends model requests to OpenAI and requires an API key. The framework is open source under Apache 2.0.

DL4MicEverywhere lets microscopy researchers train and use deep learning models on their own computers through graphical Jupyter notebooks. It builds on ZeroCostDL4Mic, bringing its bioimage analysis workflows to local machines and remote computing infrastructure. You don't need coding skills to use the notebook interfaces.

DVC is a data version control tool for individual data scientists and small machine learning projects. It links datasets and model artifacts to code history so you can recover the inputs behind an experiment and reproduce its results.

LMDeploy is an open-source toolkit for developers who want to run language and vision-language models on their own GPU hardware. It supports offline batch processing and model servers for applications.

DeepEval is an LLM evaluation framework for developers testing AI agents, RAG pipelines and chatbots. It helps compare prompts and models and catch quality regressions within Python tests or CI/CD.

Avalanche is a Python library for researchers and practitioners studying continual learning, where models learn across a sequence of data or tasks. Built on PyTorch, it brings benchmark datasets, training strategies and evaluation into one framework, reducing the custom code needed to compare experiments. It's open source under the MIT license and can train models on your own CPU or CUDA GPU.

Intel Neural Compressor is a Python library for developers who want to compress AI models for deployment on their own hardware or servers. It supports LLMs and other deep learning models, with extensive testing on Intel platforms.

F5-TTS is a local text-to-speech system for developers and creators generating speech on their own hardware. Its Gradio app provides a browser interface for speech generation, with tools for training and fine-tuning models.

XGBoost is a machine learning library for training and running gradient-boosted tree models on your own hardware. Data scientists use it for classification, regression and ranking, with parallel computation for larger workloads. The code is licensed under Apache 2.0.

CSBDeep is a Python toolbox for researchers who want to restore fluorescence microscopy images using content-aware restoration (CARE). It covers training restoration models and applying them to images, with deep learning built on Keras and TensorFlow.

PaddleClas is a PaddlePaddle toolkit for developers and researchers building image classification and recognition applications on their own hardware. It covers fixed-category classifiers and image matching for large or frequently changing catalogs.

PaliGemma is a family of vision-language models for developers and researchers building applications that interpret images alongside text prompts. Pretrained models need fine-tuning to produce useful results; mix models can handle common tasks without further tuning.

Lumina-Image 2.0 generates images from text on your own hardware. It's for developers, researchers and people who want a model they can use in visual workflows or adapt with their own image collections.

DeepEthogram assigns predefined behavior labels to individual video frames. It's designed for neuroscience researchers measuring animal activity, and can also serve other projects that need action detection at frame level.

MosaicML Composer is a Python library for training neural networks on your own GPUs or multi-node clusters. Built on PyTorch, it's aimed at researchers and engineers who want distributed training with room to customize their experiments. It's open source under Apache 2.0, and prebuilt Docker images are available.

TensorZero is a self-hosted platform for developers who need to compare and improve models in LLM applications. The project is archived and no longer maintained.