AI open source rankings.
JSTprove - Fast, verifiable AI
Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
solution of exercises of the book "probabilistic robotics"
This project uses a variety of advanced voiceprint recognition models such as EcapaTdnn, ResNetSE, ERes2Net, CAM++, etc. It is not excluded that more models will be supported in the future. At the same time, this project also supports MelSpectrogram, Spectrogram data preprocessing methods
A lightweight adapter bridges SAM with medical imaging [MedIA]
chatGPT、prompt、LLM
ChatGPT for Mac, living in your menubar.
A simple toy demo of a local voice assistant with whisper and large language model.
State-of-the-art, simple, fast unbounded / large-scale NeRFs.
The Zoo Design Studio app.
PyTorch implementation of MoCo v3 https//arxiv.org/abs/2104.02057
A batched offline inference oriented version of segment-anything
This is now the official location of the Merlin project.
The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.
The TypeScript library for building AI applications.
Arm NN ML Software.
Hypernetworks that adapt LLMs for specific benchmark tasks using only textual task description as the input
A curated list of the most impressive AI papers
A clean and readable Pytorch implementation of CycleGAN
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
Neo4j GraphRAG for Python
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
Offline inference engine for art, real-time voice conversations, LLM powered chatbots and automated workflows
An open-source AI agent that is compatible with multiple LLM models
Highly Performant, Modular, Memory Safe and Production-ready Inference, Ingestion and Indexing built in Rust 🦀
ComfyUI related stuff and things
韭菜盘 (JCP AI) - AI 驱动的智能A股分析系统，基于 Wails + Go + React，支持多 Agent 协作分析
This repository collects papers for "A Survey on Knowledge Distillation of Large Language Models". We break down KD into Knowledge Elicitation and Distillation Algorithms, and explore the Skill & Vertical Distillation of LLMs.
Image Completion with Deep Learning in TensorFlow