All repos — GH Trending Explorer

A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs. Supports Pi0.5, GR00T-N1.7, FastWAM, Cosmos3, DreamZero, DeepSeek-V4, GLM-5.3, Kimi-K3 and more.

loop-wam / LoopWAM

LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

RadixRootMind / Awesome-Quant-VLA-WAM

Curated quantization research for VLA & WAM models — 180+ papers, a five-category taxonomy, a roadmap, and a companion PTQ + LIBERO toolkit (quant-vla).

OpenWAM-Official / OpenWAM

Official repository for "OpenWAM: An Open, Modular Exploration Towards Systematic World–Action Model Pretraining".

swamimalode07 / rare-ui

A collection of rare, ready-to-use UI components and animations you can actually drop into your next project. Free and open source.

wasm-micro-runtime / wasm-micro-runtime

WebAssembly Micro Runtime (WAMR)

mewamew / my_ai_town

我的AI小镇

kawamataryo / sky-follower-bridge

Instantly find and follow the same users from your X(Twitter) follower on Bluesky.

worldbench / awesome-vla-for-ad

🌐 Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future

neoteai / N0-TWAM

N₀-TWAM: A Tactile-Native World Action Model for Contact-Rich Manipulation

teee000 / LiLa-WAM

LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation

LeapLabTHU / Simple-WAM

Official implementation of "What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling"

crossbario / autobahn-python

WebSocket and WAMP in Python for Twisted and asyncio

crossbario / crossbar

Crossbar.io - WAMP application router

AzureAD / microsoft-authentication-library-for-dotnet

Microsoft Authentication Library (MSAL) for .NET

crossbario / autobahn-js

WAMP in JavaScript for Browsers and NodeJS

DravenALG / awesome-vla-wam

A Curated List of Vision-Language-Action (VLA) and World Action Models (WAM) Research and Beyond

swamp-club / swamp

Stinemates, Watson, Adam, Mahir, Paul

hwameistor / hwameistor

Hwameistor is an HA local storage system for cloud-native stateful workloads.

deanwampler / programming-scala-book-code-examples

The code examples used in Programming Scala, 2nd and 3rd Editions (O'Reilly)

Siddhant-Goswami / 100x-LLM

Code snippets and examples from the 100x Applied AI cohort lectures.

Trans-N-ai / swama

High-performance MLX-based LLM inference engine for macOS with native Swift implementation

pawamoy / aria2p

Command-line tool and library to interact with an aria2c daemon process with JSON-RPC.

robbyant-research / Zero-WAM

In‑Context World‑Action Modeling from Human Videos for Open‑Ended Task Generalization

OpenMOSS / EasyWAM

A unified framework for training, fine-tuning, and evaluating World Action Models

OpenMOSS / OpenETA

An embodied task agent that connects perception, action, verification, and learning in a continuous physical-world loop

H-EmbodVis / SimWAM

SimWAM: A Simple World Action Model for End-to-End Autonomous Driving.

dirkjanm / askWAM

Ask the Web Account Manager (WAM) for Entra ID tokens

UniWAM / UniWAM

Official code of UniWAM: Unified World-Action Model

yunncheng / GameWAM

GameWAM: A World Action Model for Video Games

TeleHuman / WNM-3D

Official Implementation of "WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN"

fudan-generative-vision / WAM-Diff2

Hierarchical AR-to-Diffusion Distillation for Efficient Autonomous Driving VLA

hustvl / FasterWAM

Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models

SchwammbilOo2 / Adeptus-Battle-Forge

Adeptus Army Forge 2026: Ultimate Warhammer 40K Army Builder and Battle Analytics Guide

SpriteWithoutIce / JEPA_WAM

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elliottdehn / wam

A text language + compiler for LLM-authored low-poly WoW-style characters: mesh, rig, and animations from ~150 lines. Ships as a Claude Code plugin.

ma-wam / MA-WAM

MA-WAM implementation — code release in preparation

Clausy9 / EVO-WAM

EVO-WAM: Evolving World Action Models through Video-Action Verification. For implementation and experimental details, contact shiyangzhou@stu.hit.edu.cn.

hustvl / DreamWAM

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