Popular GitHub Repositories for Portable Executable Internals

A portable accelerated data query and LLM-inference engine, written in Rust, for data-grounded AI apps and agents.

A powerful tool that translates ComfyUI workflows into executable Python code.

An extensible, easy-to-use, and portable diffusion web UI ???

Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

Fast, Flexible and Portable Structured Generation

A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends

[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

ENFUGUE is an open-source web app for making studio-grade images and video using generative AI.

The nnsight package enables interpreting and manipulating the internals of deep learned models.

Lightweight and portable LLM sandbox runtime (code interpreter) Python library.

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

? A portable, header-only, artificial neural network library written in C99

WebUI X from MMRL but portable!

Paper collection on building and evaluating language model agents via executable language grounding

PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

Gain insights into MS-RPC implementations that may be vulnerable using an automated approach and make it easy to visualize the data. By following this approach, a security researcher will hopefully identify interesting RPC services in such a time that would take a manual approach significantly more.

VSCode extension of Quack Companion ? Turn your team insights into a portable plug-and-play context for code generation. Alternative to GitHub Copilot powered by OSS LLMs (Mistral, Gemma, etc.), served with Ollama.

Embedded no-code VM executing human-like language to manipulate data and process cryptographic operations.

We turn natural language descriptions of behaviors into machine-executable code

Gain insights into COM/DCOM implementations that may be vulnerable using an automated approach and make it easy to visualize the data. By following this approach, a security researcher will hopefully identify interesting (D)COM classes/implementations in such a time that would take a manual approach significantly more.