Tools to Fine-Tune and Train AI Models Locally

Candle is a Rust machine learning framework for developers embedding local AI in applications or deploying models on their own servers. It supports inference and training, including small CPU deployments for serverless applications.

Parler-TTS is a text-to-speech library for developers adding speech to applications and researchers adapting speech models. It generates audio with a voice style you describe in text.

OpenPipe ART is a Python framework for developers who want to train multi-step AI agents on the tasks their applications actually perform. It uses reinforcement learning through GRPO (Group Relative Policy Optimization) to improve model behavior based on experience and task rewards. It's open source under Apache 2.0.

BitNet is Microsoft's local LLM inference framework for developers and researchers working with compatible 1-bit models. Its bitnet.cpp runtime focuses on faster inference and lower energy use on your own hardware.

FLAML is a Python AutoML library for data scientists and developers who want to reduce manual model selection and tuning. Its search accounts for computational cost when choosing which configurations to evaluate.

spaCy is a Python library for developers and data scientists who need to extract structured information from text in their own applications. It runs on macOS, Windows and Linux, with model packages that become part of your application. It's free and open source under the MIT license.

ilastik is a desktop tool for researchers who need to identify, classify or count cells and other objects in experimental images. It runs on Linux, macOS and Windows. Its main draw is interactive training: you mark examples in an image with brush strokes and see predictions as you work, without needing machine learning expertise.

BasicSR is an image and video restoration toolkit for researchers and developers building software to recover detail or reduce visual defects. It provides restoration models and pipelines for training, testing and inference.

Deepchecks is a Python toolkit for data scientists and ML teams checking datasets and models before deployment and watching for problems in production. Its testing, CI and monitoring components reuse the same checks across those stages.

PyTorch Lightning is a Python framework for researchers and developers pretraining or fine-tuning AI models. It handles recurring training infrastructure while leaving the model logic in your PyTorch code.

NeMo Automodel is a Python library for researchers and developers who want to train or adapt AI models on their own GPU workstations or clusters. Its PyTorch-based training works with Hugging Face models without converting checkpoints.

NVIDIA NeMo Data Designer is a Python framework for developers and data teams creating synthetic datasets with controlled variation and relationships between fields. It can start with an empty dataset or build on your own seed data. The framework is open source under Apache 2.0.

FastVLM is a family of local vision-language models for developers and researchers building applications that answer questions about images. It focuses on reducing the delay before an answer starts, especially with high-resolution images.

LazyLLM is a Python framework for developers building applications with multiple AI agents. It brings application workflows, model training and evaluation together so teams can refine a prototype using task-specific data.

RVC WebUI is a local AI voice conversion tool for people who want to change the voice in recordings or use a live voice changer. It also lets you train a voice model from your own recordings.

NVIDIA Cosmos provides world models for developers building robots, autonomous vehicles and industrial vision systems. It supports reasoning about physical scenes, generating training data and learning actions before real-world deployment.

Open Instruct is AllenAI's toolkit for researchers and developers adapting pretrained language models with public datasets. It includes the training recipes behind TÜLU and downloadable checkpoints for comparing training stages.

Sana is a local AI image and video generation framework for creators and developers who want to generate media on their own hardware. It includes training and inference tools for working with its diffusion models.

DataTrove is a Python library for teams preparing large text datasets, including LLM training corpora. It combines reusable processing blocks with custom cleanup logic, so teams can apply their own rules across large workloads.

Nerfstudio is a 3D reconstruction framework for researchers and developers turning photos or phone video into scenes they can view from new angles. It supports model training, testing and inspection within one Python framework.

icefall is a speech model training toolkit for developers and researchers building recognition systems on their own hardware. Its Python recipes use k2 and lhotse to train, fine-tune, and evaluate models across speech datasets.

VideoHighlighter is a desktop app for finding useful moments in long recordings without uploading footage for analysis. It helps creators cut highlights, reviewers inspect camera recordings, and interview editors locate material worth keeping.

Amphion is an audio generation toolkit for researchers and engineers developing speech and singing systems. It brings model implementations, dataset preparation and evaluation into one research codebase.

LLaMA-Factory is a local model training platform for developers and researchers who want to adapt pretrained models to their own tasks. Its web UI lets you fine-tune models without writing code.

Laminar helps developers inspect AI agent runs, find recurring failures and test fixes. It connects production traces with debugging and evaluation tools.

BrushNet fills selected areas of an image using a text prompt while retaining the surrounding content. It's for developers and researchers who want local image editing with pretrained diffusion models.

Segment Anything 2 (SAM 2) is Meta's model for creating object masks in images and videos. It's for developers and researchers building visual applications or annotating footage, with interactive selection and correction.

JoyCaption generates image descriptions for people preparing datasets to train or fine-tune diffusion models. It can caption images on your own GPU, reducing the work of writing training descriptions by hand.

Kiln is a desktop workbench for teams building and testing AI applications. Engineers, QA staff and subject matter experts can work with the same tasks and datasets, including contributing ratings and examples without writing code.

Ferret is a local vision-language model for researchers studying how AI understands specific parts of an image. It supports questions about selected regions and can identify image regions in its answers. Its code and data are restricted to research use.

torchtune is a Python library for developers and researchers who want to adapt LLMs on their own hardware with editable PyTorch training code. The project is no longer actively maintained.

LLaVA is a vision-language model project for researchers and developers who want to ask questions about images on their own hardware.

FluxGym is a browser interface for training FLUX LoRAs on your own images. It's for people who want to customize image models locally, with support for GPUs with 12GB, 16GB or 20GB of VRAM.

TorchGeo is an open source Python library for machine learning researchers and remote sensing specialists working with satellite and aerial imagery. It brings geographic datasets and pretrained models into PyTorch training workflows. The MIT license allows you to use and adapt it for your own projects.