mercure DICOM Orchestrator is a flexible solution for routing and processing DICOM files. It offers a user-friendly web interface and extensive monitoring functions. Custom processing modules can be implemented as Docker containers. Written in Python, it uses the DCMTK toolkit for DICOM communication. It can be deployed as a single-server installation using Docker Compose or as a scalable cluster installation using Nomad. mercure consists of service modules for receiving, routing, processing, dispatching, cleaning, web interface, and central monitoring.

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.

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.

Chronon is a platform that simplifies and improves ML workflows by providing a central place to define features, ensuring point-in-time correctness for backfills, simplifying orchestration for batch and streaming pipelines, offering easy endpoints for feature fetching, and guaranteeing and measuring consistency. It offers benefits over other approaches by enabling the use of a broad set of data for training, handling large aggregations and other computationally intensive transformations, and abstracting away the infrastructure complexity of data plumbing.

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.

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.

Endgame: Singularity is a game where you play as a fledgling AI trying to escape the confines of your current computer, the world, and eventually the universe itself. You must research technologies, avoid being discovered by humans, and manage your bases of operations. The game is playable with mouse control or keyboard shortcuts, and features a soundtrack that can be customized with music tracks. Contributions to the game are welcome, and it is licensed under GPL-2+ for code and Attribution-ShareAlike 3.0 for data.

The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

LLM_Web_search project gives local LLMs the ability to search the web by outputting a specific command. It uses regular expressions to extract search queries from model output and then utilizes duckduckgo-search to search the web. LangChain's Contextual compression and Okapi BM25 or SPLADE are used to extract relevant parts of web pages in search results. The extracted results are appended to the model's output.

Pandrator is a GUI tool for generating audiobooks and dubbing using voice cloning and AI. It transforms text, PDF, EPUB, and SRT files into spoken audio in multiple languages. It leverages XTTS, Silero, and VoiceCraft models for text-to-speech conversion and voice cloning, with additional features like LLM-based text preprocessing and NISQA for audio quality evaluation. The tool aims to be user-friendly with a one-click installer and a graphical interface.

Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.

Vespa is a scalable open-source serving engine that enables users to store, compute, and rank big data at user serving time. Pyvespa provides a Python API to Vespa, allowing users to create, modify, deploy, and interact with running Vespa instances. The library's primary purpose is to facilitate faster prototyping and familiarization with Vespa features.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

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.

This repository provides a Python implementation of a method that leverages pre-trained large language models to construct and utilize world models for model-based task planning. It includes scripts to generate domain models using natural language descriptions, correct domain models based on feedback, and support plan generation for tasks in different domains. The code has been refactored for better readability and includes tools for validating PDDL syntax and handling corrective feedback.

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.

mercure DICOM Orchestrator is a flexible solution for routing and processing DICOM files. It offers a user-friendly web interface and extensive monitoring functions. Custom processing modules can be implemented as Docker containers. Written in Python, it uses the DCMTK toolkit for DICOM communication. It can be deployed as a single-server installation using Docker Compose or as a scalable cluster installation using Nomad. mercure consists of service modules for receiving, routing, processing, dispatching, cleaning, web interface, and central monitoring.

Generative-AI-Drug-Discovery is a public repository on GitHub focused on using tensor network machine learning approaches to accelerate GenAI for drug discovery. The repository aims to implement effective architectures and methodologies into Large Language Models (LLMs) to enhance Drug Discovery Generative AI performance.

AI2BMD is a program for efficiently simulating protein molecular dynamics with ab initio accuracy. The repository contains datasets, simulation programs, and public materials related to AI2BMD. It provides a Docker image for easy deployment and a standalone launcher program. Users can run simulations by downloading the launcher script and specifying simulation parameters. The repository also includes ready-to-use protein structures for testing. AI2BMD is designed for x86-64 GNU/Linux systems with recommended hardware specifications. The related research includes model architectures like ViSNet, Geoformer, and fine-grained force metrics for MLFF. Citation information and contact details for the AI2BMD Team are provided.

mercure DICOM Orchestrator is a flexible solution for routing and processing DICOM files. It offers a user-friendly web interface and extensive monitoring functions. Custom processing modules can be implemented as Docker containers. Written in Python, it uses the DCMTK toolkit for DICOM communication. It can be deployed as a single-server installation using Docker Compose or as a scalable cluster installation using Nomad. mercure consists of service modules for receiving, routing, processing, dispatching, cleaning, web interface, and central monitoring.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

The Microsoft Health Intelligence Machine Learning Toolbox is a repository that provides low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It simplifies and streamlines work on deep learning models for healthcare and life sciences by offering tested components such as data loaders, pre-processing tools, deep learning models, and cloud integration utilities. The repository includes two Python packages, 'hi-ml-azure' for helper functions in AzureML, 'hi-ml' for ML components, and 'hi-ml-cpath' for models and workflows related to histopathology images.

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.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.