Tools to Fine-Tune and Train AI Models Locally

TorchAO is a PyTorch library for developers who want to train and run models on their own hardware with less memory. It supports language models and image or video generation, and works with torch.compile and FSDP2 across most Hugging Face PyTorch models.

Cactus is an on-device AI engine for developers adding automation to mobile apps, wearables and other small devices. Its Needle model turns requests into tool calls locally, including when the device has no internet connection.

Fed-BioMed lets hospitals and researchers train AI models across institutions while patient data stays on each institution's own infrastructure. It's a self-hosted platform for biomedical research that needs access to data across multiple sites but faces restrictions on sharing clinical records.

nanochat is a toolkit for researchers and developers who want to train an LLM and inspect the pipeline themselves. Its small Python codebase covers the stages between preparing a tokenizer and chatting with the resulting model.

PyTorch Forecasting is a Python library for developers and researchers who want to predict time series with neural networks on their own hardware. It combines ready-made forecasting architectures with data preparation, training, and evaluation tools. It's open source under the MIT license.

nnU-Net is an open-source framework for researchers who need to segment biomedical images using labeled training data. It examines each dataset and chooses suitable U-Net configurations, reducing the amount of pipeline design needed for a new imaging task.

X-AnyLabeling is a desktop annotation workspace for researchers and teams preparing text, image, video, point cloud, and multimodal training datasets. Models generate initial labels, and reviewers correct them in the same workspace before exporting the data. It runs on Windows, Linux, and macOS under the GPL-3.0 open-source license.

DistillKit is a Python toolkit for developers and researchers training smaller language models to learn from larger ones. It addresses the memory and storage demands of distillation, particularly when you want to reuse one teacher's outputs across several student models.

ncnn is a C++ inference framework for developers adding on-device AI to mobile, desktop and embedded applications. It focuses on a small memory footprint and doesn't require third-party runtime dependencies.

Phosphene is a generative media studio for people creating videos on their own Mac. It combines video, still images, music and character training in one interface, with generation running on-device.

Hivemind is a Python library for researchers and teams that want to train a shared neural network across computers owned by universities, companies or volunteers. It uses PyTorch and connects participants over the internet, so contributors can pool their machines for a training job without a central master node.

VAR is an image generation research toolkit for researchers and developers studying autoregressive models as an alternative to diffusion. It generates images through next-scale prediction, building finer detail across successive image scales.

SMAC3 is a Python tool for researchers and developers tuning machine learning models or other algorithms on their own hardware. It combines Bayesian optimization with a racing mechanism that decides which configurations deserve further evaluation. It runs locally on Linux, Windows and macOS, and supports parallel evaluation on clusters through Dask.

PaddleSpeech is a speech and audio toolkit for developers and researchers building applications on their own machines or servers. Built on PaddlePaddle, it supports pretrained models alongside training and evaluation workflows.

Wav2Lip changes a face's mouth movements in an existing video to match supplied speech. Its research version is for noncommercial projects, including experiments with lip-sync generation and model training.

IP-Adapter adds reference-image prompting to Stable Diffusion for artists and developers who want to guide generation with an image rather than describe every detail in words. It works with an existing text-to-image model and can combine image references with text prompts.

Flair is a free Python library for developers and researchers who need to extract information from text or train their own natural language processing models. It combines pretrained text analysis models with tools for building word and document embeddings, all within a framework built directly on PyTorch.

Dopamine is a reinforcement learning framework for researchers who want to modify agents and test algorithm ideas. Its compact codebase focuses on a limited set of established algorithms and supports experiments on your own hardware.

RD Agent is a self-hosted AI research agent for data scientists, researchers and quantitative developers. It runs experiments on a Linux machine or server, while chat and embedding requests use providers through LiteLLM, including OpenAI and Azure OpenAI. Those remote configurations require API credentials and internet access; hosting the agent yourself doesn't move their inference onto your hardware.

XTuner is an LLM training engine for researchers working with very large mixture-of-experts (MoE) models. Its focus is reducing memory pressure and improving distributed training efficiency when model experts receive uneven workloads.

ACE-Step Studio is a desktop AI music generator for musicians and anyone who wants to create songs on their own hardware. It turns a short idea, or a description with lyrics, into a full vocal track or instrumental.

OLMo Core is a Python library for researchers and developers building and training OLMo language models. Its PyTorch components and official training scripts suit teams working with model training code on their own hardware or servers.

Flower is a federated AI framework for developers and researchers who want to train models across separate organizations or devices. Participants retain control of their data rather than gathering everyone's data in one place.

Soup is a local LLM fine-tuning tool for developers and teams adapting models to their own datasets. It brings training, evaluation and export into one workflow that runs on your own hardware without requiring a cloud service.

Argilla is a data annotation and feedback tool for AI engineers and domain experts building training and evaluation datasets. It gives reviewers a shared interface for checking examples and contributing judgments that inform model development.

Axolotl is a free, open-source LLM fine-tuning framework for developers and researchers adapting models to their own data. It supports training on local hardware or cloud infrastructure you control.

TagGUI is a desktop app for creators preparing image datasets for generative AI models. It brings automatic captioning and manual tag editing into one workspace so you can review and correct generated descriptions.

PaddleSeg is an image segmentation toolkit for developers building computer vision applications on their own hardware. It covers annotation, training and deployment for tasks such as medical image analysis, industrial inspection and portrait effects.

CatBoost is an open-source machine learning library for developers and data scientists building prediction models with numerical and categorical data. Its built-in handling of categories reduces the work needed to turn non-numeric inputs into usable features. It runs computations on your own CPU or GPU and uses the Apache 2.0 license.

OneTrainer trains diffusion models on your own machine for people adapting image or video models to their own datasets. Its graphical interface brings dataset preparation, training and output previews into one application.

SAMJ-IJ brings local AI segmentation into ImageJ and Fiji for researchers annotating microscopy images. It turns point or rectangle prompts into object regions, so users can work with Segment Anything models without writing code or leaving their image analysis environment.

OpenRLHF is a self-hosted Python framework for researchers and teams training language models with human feedback or custom rewards. It supports fine-tuning and reinforcement learning on your own GPU hardware, including training agents that interact with an environment.

Labkit is a local image segmentation plugin for Fiji, aimed at researchers working with microscopy datasets that can be too large for conventional desktop workflows. It supports manual labeling and automated pixel classification, with a design that lets consumer laptops work with images spanning multiple terabytes.

IndicTrans2 is a family of translation models for developers building multilingual applications and researchers working on Indian languages. It covers all 22 scheduled languages of India, including languages with limited training data.