REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

Ollama Autocoder is a simple to use autocompletion engine that integrates with Ollama AI. It provides options for streaming functionality and requires specific settings for optimal performance. Users can easily generate text completions by pressing a key or using a command pallete. The tool is designed to work with Ollama API and a specified model, offering real-time generation of text suggestions.

The 'airwin2rack' repository is a collection of Airwindows audio plugins presented in various formats, including as a static library, a module for VCV Rack, and as CLAP/VST3/AU/LV2/Standalone plugins for DAWs. Users can access these plugins through different methods and interfaces, such as a uniform registry and access pattern, making it easy to integrate Airwindows plugins into their audio projects. The repository also provides instructions for updating the Airwindows sub-library and information on licensing, ensuring that users can utilize the plugins in both open and closed source environments.

GPT-Subtrans is an open-source subtitle translator that utilizes large language models (LLMs) as translation services. It supports translation between any language pairs that the language model supports. Note that GPT-Subtrans requires an active internet connection, as subtitles are sent to the provider's servers for translation, and their privacy policy applies.

LLM training in simple, pure C/CUDA. There is no need for 245MB of PyTorch or 107MB of cPython. For example, training GPT-2 (CPU, fp32) is ~1,000 lines of clean code in a single file. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. I chose GPT-2 as the first working example because it is the grand-daddy of LLMs, the first time the modern stack was put together.

This project aims to create a real-life version of GLaDOS, an aware, interactive, and embodied AI entity. It involves training a voice generator, developing a 'Personality Core,' implementing a memory system, providing vision capabilities, creating 3D-printable parts, and designing an animatronics system. The software architecture focuses on low-latency voice interactions, utilizing a circular buffer for data recording, text streaming for quick transcription, and a text-to-speech system. The project also emphasizes minimal dependencies for running on constrained hardware. The hardware system includes servo- and stepper-motors, 3D-printable parts for GLaDOS's body, animations for expression, and a vision system for tracking and interaction. Installation instructions cover setting up the TTS engine, required Python packages, compiling llama.cpp, installing an inference backend, and voice recognition setup. GLaDOS can be run using 'python glados.py' and tested using 'demo.ipynb'.

Open-LLM-VTuber is a project in early stages of development that allows users to interact with Large Language Models (LLM) using voice commands and receive responses through a Live2D talking face. The project aims to provide a minimum viable prototype for offline use on macOS, Linux, and Windows, with features like long-term memory using MemGPT, customizable LLM backends, speech recognition, and text-to-speech providers. Users can configure the project to chat with LLMs, choose different backend services, and utilize Live2D models for visual representation. The project supports perpetual chat, offline operation, and GPU acceleration on macOS, addressing limitations of existing solutions on macOS.

RouteLLM is a framework for serving and evaluating LLM routers. It allows users to launch an OpenAI-compatible API that routes requests to the best model based on cost thresholds. Trained routers are provided to reduce costs while maintaining performance. Users can easily extend the framework, compare router performance, and calibrate cost thresholds. RouteLLM supports multiple routing strategies and benchmarks, offering a lightweight server and evaluation framework. It enables users to evaluate routers on benchmarks, calibrate thresholds, and modify model pairs. Contributions for adding new routers and benchmarks are welcome.

AirSane is a SANE frontend and scanner server that supports Apple's AirScan protocol. It automatically detects scanners and publishes them through mDNS. Acquired images can be transferred in JPEG, PNG, and PDF/raster format. The tool is intended to be used with AirScan/eSCL clients such as Apple's Image Capture, sane-airscan on Linux, and the eSCL client built into Windows 10 and 11. It provides a simple web interface and encodes images on-the-fly to keep memory/storage demands low, making it suitable for devices like Raspberry Pi. Authentication and secure communication are supported in conjunction with a proxy server like nginx. AirSane has been reverse-engineered from Apple's AirScanScanner client communication protocol and offers a range of installation and configuration options for different operating systems.

RAG Me Up is a generic framework that enables users to perform Retrieve and Generate (RAG) on their own dataset easily. It consists of a small server and UIs for communication. Best run on GPU with 16GB vRAM. Users can combine RAG with fine-tuning using LLaMa2Lang repository. The tool allows configuration for LLM, data, LLM parameters, prompt, and document splitting. Funding is sought to democratize AI and advance its applications.

TotalSegmentator is a 3D Slicer extension designed for fully automatic whole body CT segmentation using the 'TotalSegmentator' AI model. The computation time is less than one minute, making it efficient for research purposes. Users can set up GPU acceleration for faster segmentation. The tool provides a user-friendly interface for loading CT images, creating segmentations, and displaying results in 3D. Troubleshooting steps are available for common issues such as failed computation, GPU errors, and inaccurate segmentations. Contributions to the extension are welcome, following 3D Slicer contribution guidelines.

JavaScript tokenizer for LLaMA 3 designed for client-side use in the browser and Node, with TypeScript support. It accurately calculates token count, has 0 dependencies, optimized running time, and somewhat optimized bundle size. Compatible with most LLaMA 3 models. Can encode and decode text, but training is not supported. Pollutes global namespace with `llama3Tokenizer` in the browser. Mostly compatible with LLaMA 3 models released by Facebook in April 2024. Can be adapted for incompatible models by passing custom vocab and merge data. Handles special tokens and fine tunes. Developed by belladore.ai with contributions from xenova, blaze2004, imoneoi, and ConProgramming.

EZKL is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). It enables the following workflow: 1. Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow. 2. Export the final graph of operations as an .onnx file and some sample inputs to a .json file. 3. Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as: > "I ran this publicly available neural network on some private data and it produced this output" > "I ran my private neural network on some public data and it produced this output" > "I correctly ran this publicly available neural network on some public data and it produced this output" In the backend we use the collaboratively-developed Halo2 as a proof system. The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device.

Claude.vim is a Vim plugin that integrates Claude, an AI pair programmer, into your Vim workflow. It allows you to chat with Claude about what to build or how to debug problems, and Claude offers opinions, proposes modifications, or even writes code. The plugin provides a chat/instruction-centric interface optimized for human collaboration, with killer features like access to chat history and vimdiff interface. It can refactor code, modify or extend selected pieces of code, execute complex tasks by reading documentation, cloning git repositories, and more. Note that it is early alpha software and expected to rapidly evolve.

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.