Stars
MineRL Competition for Sample Efficient Reinforcement Learning - Python Package
The lean application framework for Python. Build sophisticated user interfaces with a simple Python API. Run your apps in the terminal and a web browser.
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
LLM Council works together to answer your hardest questions
Wolfenstein 3D raycasting engine made in JS and P5.js
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Proces…
Lab Materials for MIT 6.S191: Introduction to Deep Learning
Code and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"
A web-based collaborative LaTeX editor
maze datasets for investigating OOD behavior of ML systems
A framework for few-shot evaluation of language models.
Recursive-Open-Meta-Agent v0.1 (Beta). A meta-agent framework to build high-performance multi-agent systems.
Tools to Design or Visualize Architecture of Neural Network
A single hub to find Claude Skills, Agents, Commands, Hooks, Plugins, and Marketplace collections to extend Claude Code
This dataset code generates mathematical question and answer pairs, from a range of question types at roughly school-level difficulty.
Pretraining and inference code for a large-scale depth-recurrent language model
This is a named entity recognition (NER) dataset for OSINT towards the national defense domain.
Optimised Neural Network functions for Espressif chipsets
Chrome Extension Boilerplate with React + Vite + Typescript
Jarvis is a voice-activated, conversational AI assistant powered by a local LLM (Qwen via Ollama). It listens for a wake word, processes spoken commands using a local language model with LangChain,…
A curated list for Efficient Large Language Models
Fine-tuning LLMs to resist hallucination in Retrieval-Augmented Generation by training on mixed factual and fictitious contexts.
A graph-based approach to exploring Wikipedia clickstream data; built with Neo4j, neovis.js, and React