Open Source Machine Learning Projects
Discover 191 open source Machine Learning repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Machine Learning projects here are most often combined with Python, Deep Learning and LLM. Last updated October 4, 2026.
191 repositories · updated October 4, 2026
FlashVideo: Generate and Upscale High-Resolution Videos
FlashVideo is a two-stage text-to-video project that generates 270p clips and enhances them to 1080p. It is aimed at researchers and developers exploring efficient high-resolution video generation, with inference code and model weights available.
text-generation-inference: Serve Large Language Models
A toolkit for serving large language models through text-generation APIs. It provides GPU-oriented inference features such as continuous batching, token streaming, tensor parallelism, and quantization, but is now in maintenance mode.
HunyuanWorld-1.0: Generate Interactive 3D Worlds from Text or Images
HunyuanWorld-1.0 turns text prompts or images into immersive, explorable 3D scenes. It combines panoramic image generation with semantic layering and 3D reconstruction, targeting creators and researchers who need editable world assets.
lance: Store and Query Multimodal Lakehouse Data
Lance is a Rust-based lakehouse format and SDK for AI and machine-learning data. It combines columnar storage with random access, vector and full-text search, and dataset versioning for teams working with multimodal data.
data-prep-kit: Prepare Data for LLM Applications
Data-Prep-Kit is a toolkit for cleaning, transforming, and enriching unstructured data used in LLM training and RAG pipelines. It offers reusable transforms that run with Python or Ray, from local experiments to larger-scale processing.
gradio: Build and Share Python Web Apps for Machine Learning
Gradio turns Python functions and machine-learning models into interactive web apps without requiring frontend development. Use it to prototype interfaces, share demos, or build more customized apps with components and event-driven layouts.
Step-Video-T2V: Generate Videos from Text Prompts
Step-Video-T2V is a 30-billion-parameter text-to-video model that generates clips up to 204 frames from English or Chinese prompts. It offers downloadable weights and inference code, but practical use requires substantial GPU memory and multi-GPU setup.
LitServe: Build Custom AI Inference Servers in Python
LitServe is a Python framework for building custom AI inference APIs, from single models to agents and multi-model pipelines. Use it when you need control over request logic, batching, streaming, and deployment rather than a fixed serving abstraction.
Wan2.2: Generate Videos from Text, Images, and Audio
Wan2.2 is a family of open video-generation models for text, image, and audio-driven workflows, plus character animation and replacement. It suits researchers and creators who can run large models on GPU hardware and want local inference options.
whisper.cpp: Transcribe Speech with Whisper in C/C++
whisper.cpp runs OpenAI Whisper speech recognition through a lightweight C/C++ implementation. It suits developers building offline transcription into applications across desktop, mobile, browser, and server environments.
OLMoE.swift: Run OLMoE Locally on Apple Devices
OLMoE.swift is an Apple app for chatting with Ai2’s OLMoE language model on-device. It suits people who want private, offline-capable model use on iOS or macOS, provided their device can run the app and model.
flyte: Orchestrate ML Pipelines, Models, and Agents
Flyte is a workflow orchestration platform for running machine learning pipelines, models, and agents at scale. Its Python-first SDK supports async tasks, distributed execution, and serving, with a Kubernetes-native open-source backend for Flyte 2 still in development.