Best AI Model Libraries & Training Tools

Explore 22 AI model libraries & training tools to find the perfect solution.

Model Libraries & Training

22 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.

Diffusers

A library implementing state-of-the-art diffusion models for image, video, and audio generation, supporting both PyTorch and FLAX frameworks.

Hugging Face Transformers

A comprehensive library of pretrained models for text, vision, audio, video, and multimodal tasks, enabling fine-tuning and inference across many generative AI use cases.

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.

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.

Unsloth

Unsloth is an open-source tool that enables developers to finetune various large language models (such as Llama 4, DeepSeek-R1, Gemma 3, and others) more efficiently. It offers free notebooks, reduced memory usage through dynamic quantization, and faster training performance, making it easier to deploy optimized models to platforms like GGUF, Ollama, vLLM, and Hugging Face.

Lighteval

An all-in-one toolkit for evaluating LLMs on multiple backends, offering detailed sample-by-sample performance metrics and task customization options.

PyTorch Image Models

A comprehensive library offering implementations and optimizations for modern image models, including transformers and efficient CNNs, maintained by Hugging Face.

NVIDIA NeMo

A scalable generative AI framework for building large language, multimodal, and speech AI models with various optimizations.

Stability AI Generative Models

Collection of generative model implementations (e.g., Stable Video 4D 2.0) for image/video synthesis.

DeepEval

DeepEval is an open-source evaluation toolkit for AI models that provides advanced metrics for both text and multimodal outputs. It supports features like multimodal G-Eval, conversational evaluation using a list of Turns, and integrates platform support along with comprehensive documentation.

ColossalAI

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

Dataset-to-Model Monitor

A tool that monitors datasets and tracks models trained on them, helping users manage and oversee AI model performance.

seismometer

seismometer is an open-source Python package for evaluating AI model performance with a focus on healthcare. It provides templates and tools to analyze statistical performance, fairness, and the impact of interventions on outcomes using local patient data. Although designed for healthcare applications, it can be used to validate models in any field.

Determined

An open-source deep learning platform for distributed training, experiment management, and scalable AI model deployment.

Dataset to Model Monitor

A Hugging Face Space tool that automatically tracks and notifies users when new models are trained on a specified dataset (HuggingFaceM4/VQAv2). It leverages the librarian bot to post alerts in a discussion thread, enabling developers and researchers to keep up-to-date with models built on this dataset.

OpenAI Evals

OpenAI Evals is an open-source framework for evaluating large language models (LLMs) and LLM systems. It offers a registry of benchmarks and tools for developers and researchers to run, customize, and manage evaluations to assess model performance and behavior.

NeMo

A scalable generative AI framework from NVIDIA for creating, customizing, and deploying new AI models using pre-trained checkpoints.

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.

alpha-beta-CROWN

alpha-beta-CROWN is an efficient, scalable, and GPU accelerated neural network verifier that uses linear bound propagation and branch-and-bound methods to provide provable robustness guarantees against adversarial attacks and verify properties like Lyapunov stability. It is a winning solution in VNN-COMP from 2021 to 2024.