Tools to Fine-Tune and Train AI Models Locally

LiteRT is Google's framework for developers adding machine learning and generative AI to apps that run on users' devices. It succeeds TensorFlow Lite and supports local inference without a server round trip.

Prodigy is an annotation tool for developers and research teams building training and evaluation datasets for custom AI models. It runs on your own machines and provides a web interface where annotators can label data without programming knowledge.

MOSS is a conversational language model from Fudan University for developers and researchers who want to run and adapt a local LLM. It includes model weights, dialogue datasets and fine-tuning code.

Tianshou is a Python reinforcement learning library built on PyTorch and Gymnasium. It's for researchers developing learning algorithms and developers training agents in custom environments. Its two API levels let you choose between a high-level interface for applying existing algorithms and modular interfaces for changing how an algorithm learns.

APPFL is an MIT-licensed Python framework for researchers and data scientists who want to train machine learning models across distributed clients while keeping training data decentralized. It supports user-defined models and lets teams test privacy-preserving federated learning methods in simulations or real distributed experiments.

TorchMD-NET is an open-source Python library for researchers who train neural network potentials for molecular dynamics. It exposes its models as PyTorch modules and connects them to GPU-accelerated simulation software, including ACEMD, OpenMM and TorchMD. It's available under the MIT license.

Label Studio is a platform for teams preparing training datasets or reviewing AI outputs. It brings human annotation and evaluation into a browser interface that can run locally or on your own server.

pyannote.audio is a Python toolkit for developers and researchers who need to separate recordings by speaker. Speaker diarization identifies who spoke when, so applications can associate parts of an audio recording with different speakers.

H2O LLM Studio helps teams adapt language models to their own datasets through a browser interface, without writing training code. It's aimed at organizations building models for specific tasks.

DataFlow is a Python system for researchers and teams preparing data for LLM training or RAG. It turns noisy PDFs, plain text, and question-answer datasets into data suited to a specific domain or training task.

Sentence Transformers is a Python library for developers building search and document retrieval on their own hardware. It helps match content by meaning and rank results against a query.

Transformer Engine is a library for developers building and running Transformer models on NVIDIA GPUs. It reduces compute and memory demands in training and inference through low-precision arithmetic and optimized model components.

Fish Diffusion is a Python framework for developers and researchers training diffusion models for text-to-speech, singing voice synthesis and singing voice conversion. It works with your own datasets and provides code you can adapt for different voice generation tasks.

Needle is an AI model for developers building device assistants that turn requests into actions. It prioritizes automation over general chat, with smaller model variants for devices that have limited resources.

Diffusers is a Python library for developers and researchers building image, video and audio generation into applications or running models on their own hardware. It supports pretrained generation pipelines as well as custom diffusion systems.

ms-swift is a Python framework for developers and researchers training and deploying language and multimodal models on their own hardware. It covers model adaptation through evaluation and serving, with support for Qwen3, DeepSeek-R1, Llama4, Mistral, Qwen3-VL and InternVL3.5.

Flash Linear Attention is a Python library for researchers and developers building language models on their own hardware. It supplies reusable components for exploring linear attention, sparse attention and state space architectures.

GLiNER is a Python framework for extracting named entities from text using small models that run locally on CPUs and consumer hardware. It's for developers building document processing, personal data detection or search systems who need to choose their own entity types without training a model for each task.

DiffSynth-Studio is a diffusion model engine for developers and researchers generating media or training models on their own hardware. It combines inference and training, with memory-saving features for consumer GPUs.

ClearML is a self-hostable MLOps platform for researchers and engineering teams managing model development and production. It connects experiment records with datasets, training jobs and model deployment so teams can trace results back to the work that produced them.

NeMo Curator is an open source Python toolkit for ML engineers and data teams preparing AI training datasets. It provides repeatable curation pipelines that can run on a laptop or across a multi-node Ray cluster.

mistral.rs is an inference engine for developers who want to run AI models on their own computer or server. It supports browser chat and inference embedded directly in Python or Rust applications.

npcpy is a Python library for developers and researchers building AI agents and applications with language models. It supports local LLM backends as well as cloud providers, so projects can choose where model requests run.

Draw Things creates and edits images on iPhone, iPad and Mac. It's for artists and other creators who want to work offline and keep generation on their own hardware.

LitGPT is a Python toolkit for developers and researchers who want to train and deploy language models on their own hardware. Its directly editable model implementations suit custom applications and research experiments.

NeMo RL is NVIDIA's Python library for researchers and engineering teams adapting language and multimodal models on their own hardware. It supports experiments on a GPU workstation and distributed training across multiple nodes.

DiffusionBee is an AI image app for Mac users who want to create and edit artwork locally through a visual interface. Artists and designers can work with text prompts, existing images and drawings without writing code.

MLX Tune is a Python library for adapting AI models on an Apple Silicon Mac. Formerly unsloth-mlx, it is an independent project and is not affiliated with Unsloth. Its Unsloth-compatible API lets you prototype training locally with minimal changes to existing scripts, then move to NVIDIA hardware with Unsloth. Training runs through Apple's MLX framework and doesn't require a cloud GPU.

MaxText is an LLM training library for researchers and engineers building models from scratch or adapting existing ones. It provides a reference implementation teams can modify for their own model designs, with training that scales from a single host to large clusters.

CVAT is a data annotation platform for teams preparing datasets for computer vision and audio models. It combines labeling tools with shared assignments and review workflows.

MusicGen is Meta AI's model for generating music from written descriptions. It's part of AudioCraft, a PyTorch library for developers and audio researchers building music generation tools in their own computing environment.

FiftyOne is a local tool for computer vision teams that need to inspect their training data and understand where their models fail. It runs on macOS, Windows and Linux, with Docker support, and is open source under Apache 2.0. Its Python tools and visual App connect dataset curation with model evaluation.

NeuralForecast is an open-source Python library for data scientists and developers who want to train and compare neural time series forecasting models in their own Python environment. It brings recurrent networks and forecasting-specific architectures under one interface, so comparing models doesn't require working with separate research implementations. The license is Apache 2.0.

torchtitan is a training platform for researchers and developers experimenting with generative AI models on their own GPU servers or clusters. Its replaceable components and extension points support work on model architectures and training infrastructure.