Tools to Fine-Tune and Train AI Models Locally

BiRefNet is an AI image segmentation model for developers and image workflow builders who need to separate subjects from backgrounds. It produces foreground masks for cutouts and has dedicated models for matting, where edges can include partial transparency.

Segment Anything for Microscopy adapts Meta AI's Segment Anything to biomedical images, with models fine-tuned on microscopy data. It's for researchers who need to label cells or structures such as mitochondria in electron microscopy volumes. The software runs locally on Windows, macOS and Linux and is open source under the MIT license.

Ludwig is a framework for developers and researchers training custom AI models without writing their own training loops. It handles preprocessing, training and evaluation from a model definition, covering LLMs as well as tabular and multimodal tasks.

SpecForge is an open-source framework for teams training draft models to speed up LLM inference in SGLang. It suits developers who want to train on their own hardware and use the resulting drafts directly with SGLang serving.

Curator is a Python library for developers building LLM training datasets or extracting structured records from existing data. It supports pipelines that generate data in stages and process many model requests.

Hyperopt is a Python library for developers and researchers who need to tune model hyperparameters across complex search spaces. It can handle continuous values, discrete choices and conditional parameters, such as settings that apply only when a particular model or approach is selected. It runs within your Python project, with support for both serial and parallel optimization.

Accelerate is a Python library for developers and researchers who write their own PyTorch training loops. It handles hardware and distributed execution so they can keep control of how their models train.

NVIDIA FLARE is a Python SDK for researchers and data scientists who want to train models across participating sites while keeping training data local. Platform developers can also use it to build systems for collaboration between organizations. It supports on-premise and cloud deployments and is open source under the Apache 2.0 license.

FastChat is a self-hosted LLM platform for developers and researchers who want to serve chat models on their own hardware and compare their responses. It powers Chatbot Arena and includes a locally hosted side-by-side comparison interface. The Python code is open source under Apache 2.0; model licenses apply separately, including Llama's license for Vicuna.

Distilabel is a Python framework for engineers building synthetic datasets and model-generated feedback with local LLM backends such as Ollama, llama.cpp and Hugging Face Transformers. It also connects to cloud services, including OpenAI and Anthropic; requests through those integrations go to the remote provider. Where inference runs depends on the backend you choose.

GPT-NeoX is EleutherAI's framework for researchers and teams training language models with billions of parameters on their own GPU infrastructure. It focuses on training from scratch, with support for fine-tuning existing checkpoints.

Colossal-AI is a Python framework for developers and researchers training or serving large AI models on their own GPUs. It provides tools to spread workloads across GPUs and work within available memory.

LabelMe is a desktop image annotation app for people preparing computer vision datasets. It combines manual drawing with model assistance for labeling objects and outlining image regions.

AgileRL is an open-source Python framework for teams training AI agents on their own tasks, including LLM agents and agents for robotics, finance or logistics. Its evolutionary hyperparameter tuning adjusts training settings during a run, reducing the need for separate tuning trials. The framework uses the Apache 2.0 license.

Ragas is a Python library for developers evaluating LLM applications and retrieval-augmented generation systems. Its non-LLM metrics compute text comparisons in your Python environment without a model, including string similarity, BLEU and ROUGE. Its model-based quickstart uses OpenAI's GPT-4o and an API key, sending evaluation inputs to OpenAI's cloud service. Local text scoring and cloud model judgments are separate evaluation options.

Step1X Edit edits existing images from written requests. It's for developers and researchers who want to run image editing on their own hardware, with control over the model and specialized training.

LightGBM is a decision-tree gradient boosting framework for classification, regression and ranking. It trains models on your own machine or across multiple machines, with GPU support for compatible configurations. The code is open source under the MIT license.

Real-ESRGAN is a local AI upscaler for people enlarging images or restoring degraded images and video. It builds on ESRGAN, with dedicated models for general images, anime illustrations and animated video.

NVIDIA PhysicsNeMo is an open-source Python framework for engineers and researchers building AI models of physical systems. It combines physics constraints with simulation and observed data to train surrogate models that can predict behavior in near real time. The framework uses PyTorch and carries the Apache 2.0 license.

tiny-cuda-nn is a neural network framework for developers and researchers building small models, NeRF renderers and neural graphics applications. It handles training and inference on local NVIDIA GPUs.

PEFT is a library for developers adapting pretrained AI models on their own hardware. It trains a small portion of a model's parameters to reduce the compute and storage costs of full fine-tuning.

QuPath is a free, open source desktop application for researchers analyzing whole slide pathology and microscopy images on their own computers. It combines image viewing and annotation with cell segmentation and interactive machine learning, so researchers can examine tissue and classify what they find within the same application. It runs on Windows, macOS and Linux.

FederatedScope is an open-source federated learning platform for researchers and teams building training systems across separate data holders. It can simulate clients and servers on one device or run them as separate distributed participants. Each participant loads its own data, while models can pass between participants through messages.

FinGPT is a financial LLM framework for researchers and developers building tools that interpret market news and company information. It includes trained models, financial datasets, and tools for adapting models to specific finance tasks.

SDMetrics is an open source Python library for people who need to assess synthetic tabular data before using it in software testing or machine learning development. It compares generated datasets with the real data they're meant to resemble. It works with data from any model, so your choice of generator doesn't determine how you evaluate the results.

Gemma is Google DeepMind's family of open-weight AI models for developers building applications on their own hardware. It includes compact models for mobile and IoT devices and larger models for reasoning on personal computers and servers.

BGE Embeddings is a family of models for developers who need to find relevant passages for search or retrieval-augmented generation (RAG). It includes embedding models for finding candidate text and rerankers for assessing how closely those results match a query.

DeepReg is an open-source Python toolkit for researchers and educators studying medical image registration with deep learning. It brings together algorithms for aligning medical images, with an emphasis on clinical applications and methods that use limited supervision.

GPTQModel is a Python toolkit for developers compressing LLMs and running them on their own hardware or servers. It combines model preparation, quality evaluation and inference so you can assess compression choices within one workflow.

Kohya's GUI is a browser interface for training image generation models, aimed at artists and model makers who want to teach a model a style or subject. It builds on Kohya's Stable Diffusion training scripts and also provides a command-line interface.

PaddleDetection is a computer vision toolkit for developers building image and video analysis systems. It covers training, adapting and deploying models for uses such as inspection and traffic analysis.

DeepCell is an open-source Python library for researchers analyzing individual cells in biological images. It runs locally and uses TensorFlow to identify whole cells or nuclei and track cells through time-lapse movies. Its Apache 2.0 license allows users to adapt the library for their own research.

Substra is open-source federated learning software for teams that need to train and validate machine learning models across distributed datasets. It supports production use across a network, as well as simulations on a single machine for testing and debugging federated learning code.

Molmo is Ai2's family of vision-language models for working with images and text on your own hardware. It's aimed at developers and researchers who want to train models or evaluate them on visual tasks.