Tools to Fine-Tune and Train AI Models Locally

sktime is a Python library for developers and data scientists who want to compare time series models without rebuilding their workflow for each algorithm. It puts classical statistical methods, machine learning, deep learning and foundation models behind a consistent interface. You can run it on your own macOS, Linux or Windows machine.

StarCoder2 is a family of code generation models for developers who want code completion on their own hardware. These models predict continuations of code; they aren't instruction models, so conversational requests aren't their intended use.

Nanotron is a Python library for researchers and developers training language models on their own datasets and GPU infrastructure. It supports training on one machine or across multiple servers.

KTransformers is a research framework for developers running or fine-tuning large language models on their own hardware. It focuses on mixture-of-experts (MoE) models, using CPU resources alongside GPUs to work within limited GPU memory.

MTPLX is a native Mac app, command-line tool and local LLM server for people running chat or coding models on Apple Silicon. It uses a model's own multi-token prediction heads to accelerate generation without a separate draft model.

OpenVINO is an AI inference toolkit for developers deploying models in their own applications. It converts and optimizes models for local hardware or servers, covering generative AI and conventional machine-learning workloads.

Detectron2 is a Python library for developers and researchers building object detection and image segmentation applications. It provides model training tools and reusable components for computer vision research and production software.

StarDist detects individual cells and nuclei in crowded 2D microscopy images and 3D volumes. It's for researchers and bioimage analysts who need separate outlines for closely packed objects. Its shape-based approach suits objects that can be represented by star-convex polygons in 2D or polyhedra in 3D.

SwanLab is an open-source AI experiment tracking platform for researchers and model training teams. It brings training results into one place so you can spot problems and compare runs before choosing your next experiment.

AutoTrain supports model training on your own hardware through AutoTrain Advanced, a no-code application for people fine-tuning LLMs or building models from their data. Advanced can run locally or on cloud infrastructure. The project is no longer maintained, and it won't receive bug fixes or new features.

MNN is a C++ framework for developers running AI models on phones, PCs and embedded devices. It supports inference and training on the device, with a small application footprint and hardware acceleration.

VoxelMorph is a Python library for researchers and developers who need to align medical images using learned deformations. It provides registration networks and training components, including unsupervised methods, with PyTorch and TensorFlow backends. The code is open source under the Apache 2.0 license.

DeePMD-kit is open-source software for molecular and materials researchers who want to train and run AI interatomic potentials on their own hardware. It learns atomic interactions from quantum-mechanical reference data, then uses those models in molecular dynamics simulations of molecules, solids and metals. It runs on CPUs and GPUs, with support for CUDA and ROCm source builds, and carries the LGPL-3.0-or-later license.

RAGatouille is a Python library for developers building retrieval-augmented generation (RAG) applications with ColBERT. It builds and searches document indexes on your own hardware or server, with compressed indexes stored on disk that your application can query directly.

HanLP is a multilingual natural language processing toolkit for developers and researchers who need structured text analysis. Its native Python API runs models on your own hardware. The separate REST API sends text to a hosted server.

DataDreamer is a Python library for researchers and developers who need to create training datasets and adapt LLMs to specific tasks. It connects data preparation and model training in reproducible workflows.

Indic TTS is a text-to-speech toolkit from AI4Bharat for developers and researchers building speech applications in Indian languages. It provides trained models and training code for generating speech on your own hardware.

LLM Compressor prepares models for deployment with vLLM by reducing their storage and memory requirements through quantization. It's for developers working with local checkpoints or models from Hugging Face repositories.

Liger Kernel is a Python library for developers training or fine-tuning LLMs on their own hardware. Fused kernels reduce memory use during model operations and post-training.

TRL is a Python library for researchers and developers adapting foundation models with their own datasets. It builds on Hugging Face Transformers and supports training on a single GPU or across multiple machines.

DSPy is a Python framework for developers building language model applications with local backends such as Ollama on a laptop or SGLang on a GPU server. It connects application code to those model servers, which handle inference on your hardware. It also supports cloud providers, so where model requests go depends on the backend you choose.

Oumi helps ML researchers and teams build specialized AI models for their own tasks. It brings data preparation, training, evaluation, and deployment into one platform, with reusable recipes and components you can customize.

HQQ is a Python library for developers compressing language and vision models to fit the memory available on their own hardware. Its Half-Quadratic Quantization method doesn't require a calibration dataset.

Adala is a Python framework for AI engineers and data scientists who want to run data-labeling and evaluation workflows in their own process. It works with local pandas DataFrames and ground-truth examples, but the documented OpenAI and OpenRouter configurations send task inputs to cloud models. Those connections need internet access and an API key.

Darts is an open source Python library for data scientists and developers who need to forecast time series or detect unusual observations in their own Python environment. Its shared model interface lets you compare statistical methods such as ARIMA with neural networks such as N-BEATS without rewriting the surrounding forecasting workflow. It uses the Apache 2.0 license.

OpenResearch is an MIT-licensed, open-source workspace for people using AI coding agents to conduct research. It gives each research direction a separate agent session and isolated Git worktree, so experiments can proceed in parallel without sharing the same working files. Desktop builds support macOS and Linux; the Windows beta requires Git for Windows. A local browser dashboard is also available.

MONAI is a framework for researchers and developers building AI models for medical images. Its Core library provides medical-specific tools for training and evaluating models within existing PyTorch workflows.

Voice-Clone-Studio brings voice cloning and speech production into one browser interface for people making podcasts, audiobooks and dialogue recordings. It combines several engines with tools for preparing samples and training custom voices.

ESPnet is a speech AI toolkit for researchers and developers who want to run pretrained models on their own hardware or train speech systems. Its recipes cover data preparation through evaluation, with published results for comparison.

Orpheus TTS generates speech for developers building voice applications or creating custom voices. Its Llama-based models support local inference and let you control how spoken text sounds.

WeNet is a speech recognition toolkit for developers adding transcription to applications or services. It covers model training and production deployment, so teams can adapt a recognizer to their audio or start with a pretrained model.

Cellpose is local AI software for biologists and microscopy researchers who need to identify individual cells and nuclei in images. It runs on Windows, Linux and macOS, with a graphical interface for automatic segmentation, manual annotation and correcting model results.

DeepSpeed is an open-source PyTorch library for developers and researchers training or running large AI models on their own hardware or compute clusters. It helps address memory limits and distribute workloads across devices.

ModelScope is an AI model hub with a Python library for developers and researchers who want to run models locally or adapt them to their own data. It covers language, vision, speech, multimodal tasks and scientific computing.