An Open Source Machine Learning Framework for Everyone
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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Cross-platform, customizable ML solutions for live and streaming media.
Caffe: a fast open framework for deep learning.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018.
ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
☁️ Build multimodal AI applications with cloud-native stack
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
Burn is a next generation tensor library and Deep Learning Framework that doesn't compromise on flexibility, efficiency and portability.
A hyperparameter optimization framework
A very simple framework for state-of-the-art Natural Language Processing (NLP)
Open Machine Learning Compiler Framework
Low-code framework for building custom LLMs, neural networks, and other AI models
A unified framework for machine learning with time series
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.
ML.NET is an open source and cross-platform machine learning framework for .NET.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Largest list of models for Core ML (for iOS 11+)
A list of useful Java frameworks, libraries, software and hello worlds examples
header only, dependency-free deep learning framework in C++14