Best AI Training Tools

Explore 16 AI training tools to find the perfect solution.

Training

16 tools

Hugging Face Accelerate

A simple way to launch, train, and use PyTorch models on almost any device with support for distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP/DeepSpeed.

lucataco/ai-toolkit

A Cog implementation of ostris/ai-toolkit designed for training LoRA models (specifically for FLUX.1-dev) using a custom image dataset. Note that it is marked as deprecated in favor of ostris/flux-dev-lora-trainer.

Unsloth AI

Unsloth AI is an enterprise platform that accelerates fine-tuning of large language models and vision models by leveraging innovative quantization techniques. It enables faster performance (up to 2.2x faster) and uses significantly less VRAM, making model deployment and training more efficient. The organization also offers open-source tools and models, and is integrated with Hugging Face, with additional details available on its website.

AutoTrain

Hugging Face AutoTrain is an automated machine learning (AutoML) tool that allows users to train, evaluate, and deploy state-of-the-art ML models without writing code. It supports a range of tasks including text classification, image classification, token classification, summarization, question answering, translation, tabular data tasks, and LLM finetuning, with seamless integration into the Hugging Face ecosystem.

AI Toolkit (ostris)

All-in-one training suite for diffusion models (e.g., FLUX.1) with a UI and Modal/Docker workflows; supports LoRA/LoKr and conv training.

ostris/ai-toolkit

A GitHub repository offering a collection of AI scripts primarily for Stable Diffusion and related AI model training. It includes a web UI for managing and monitoring jobs as well as tools for training models like FLUX.1-dev.

DeepScaleR

DeepScaleR is an open-source project that democratizes reinforcement learning (RL) for large language models (LLMs). The repository provides training scripts, model checkpoints, detailed hyperparameter configurations, datasets, and evaluation logs to reproduce and scale RL techniques on LLMs, aimed at reproducibility and research in advanced AI training.

TRL

TRL is a comprehensive open-source library that enables post-training of transformer language models using reinforcement learning techniques such as Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO). It integrates with Hugging Face’s Transformers ecosystem and supports efficient scaling with tools like Accelerate and PEFT.

FluxGym

A dead simple web UI for training FLUX LoRA models with low VRAM support, built on Gradio UI (forked from AI-Toolkit) and powered by Kohya Scripts. It simplifies the fine-tuning of LoRA models on systems with limited VRAM (12GB/16GB/20GB).

SkyThought

SkyThought is an open-source toolkit that provides data curation, training (including reinforcement learning enhancements), and evaluation pipelines for cost-effective large language model training (Sky-T1 series). It offers scripts for building, training, and evaluating models such as Sky-T1-32B-Preview, making it a valuable resource for AI developers.

PyTorch Image Models (timm)

Large collection of PyTorch image encoders/backbones with training, eval, inference and pretrained weights.

ostris/flux-dev-lora-trainer

A Replicate-hosted tool for fine-tuning the FLUX.1-dev model using the ai-toolkit with a LoRA approach. Users can initiate training jobs on Nvidia H100 GPUs to obtain custom-trained weights via an automated, cloud-based workflow.

ColossalAI

An open-source platform that reduces the cost of training and inference for large AI models, enhancing efficiency and scalability.

Determined AI

Open‑source ML platform for distributed training, hyperparameter search, experiment tracking, and resource management.

OpenAI Universe

A tool that transforms existing programs into OpenAI Gym environments using Docker, enabling real-time AI interaction with software.

PyTorch Lightning

A deep learning framework for PyTorch that simplifies model training by automating backpropagation, mixed precision, multi-GPU & TPU distributed training, and deployment, all without requiring extensive code modifications.