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Open-Source Evaluation & Testing for AI & LLM systems.
The testing framework dedicated to ML models, from tabular to LLMs.
Control risks of performance, bias and security issues in AI systems.
High performance array computing.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
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Fetch an entire site and save it as a text file (to be used with AI models).
MLOps Attack Toolkit
MLOKit is a toolkit that can be used to attack MLOps platforms by taking advantage of the available REST API. This tool allows the user to specify an attack module, along with specifying valid credentials (API key or stolen access token) for the respective MLOps platform. The attack modules supported include reconnaissance, data extraction and model extraction. MLOKit was built in a modular approach, so that new modules can be added in the future by the information security community.
Take control of the media you consume every day with Fast Music Remover!.
A C++ based, lightweight music and noise remover for YouTube and other internet media, using DeepFilterNet for audio enhancement.
We consume, willingly or not, large amounts of media everyday, and that includes content that is emposed on us. Fast Music Remover gives you the choice to opt-out of them without missing out on the core content.
We're building a feature rich media processor that is efficient, modular and cross platform. It's being built for you! That means clean APIs for programmers, containerized on GHCR for remote users, with a Web UI providing seamless access to anyone interested!
NVIDIA Cosmos
is a platform of state-of-the-art generative world foundation models, advanced tokenizers, guardrails, and an accelerated data processing and curation pipeline for autonomous vehicles (AVs) and robotics developers.
Cosmos is a world model development platform that consists of world foundation models, tokenizers and video processing pipeline to accelerate the development of Physical AI at Robotics & AV labs. Cosmos is purpose built for physical AI. The Cosmos repository will enable end users to run the Cosmos models, run inference scripts and generate videos.
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A course on neural networks that starts all the way at the basics. The course is a series of YouTube videos where we code and train neural networks together. The Jupyter notebooks we build in the videos are then captured here inside the lectures directory. Every lecture also has a set of exercises included in the video description. (This may grow into something more respectable).
MMAudio generates synchronized audio given video and/or text inputs.
Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis.
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Euclid scientists need your help. Euclid captures images of tens of millions of galaxies like those we’re showing here. To classify that impossibly-large pile of galaxies, we’re using your classifications to train AI algorithms (remember the Zoobot AI assistant?). But the AI algorithms need to be ready for the scientists by the end of August - in only one month! We need as many volunteer classifications for teaching the AI algorithms as we can get; our goal is 100,000 classifications. We’re calling this The Euclid Challenge. Spread the word and dive in!
OpenVINO is an open-source toolkit for optimizing and deploying AI inference.
OpenVINO is an open-source toolkit for optimizing and deploying deep learning models from cloud to edge. It accelerates deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel
hardware and environments, on-premises and on-device, in the browser or in the cloud.
Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark.
EmoBox, a groundbreaking multilingual multi-corpus speech emotion recognition (SER) toolkit designed to streamline research in this field. EmoBox is accompanied by a meticulously curated benchmark tailored for both intra-corpus and cross-corpus evaluation settings.
QRNet is a state-of-the-art neural network that transforms the Quad-Bayer pattern into sharp and clean RGB images
A smarter web fuzzing tool that combines local LLM models and ffuf to optimize directory and file discovery.
This tool enhances traditional web fuzzing by using local AI language models (via Ollama) to generate intelligent guesses for potential paths and filenames.
This repository contains the code and resources used in the article "Stop Wasting Time, Automate Your Presentation with Python" published on Medium.
Awesome LLM Apps
Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models.
A curated collection of awesome LLM apps built with RAG and AI agents. This repository features LLM apps that use models from OpenAI, Anthropic, Google, and even open-source models like LLaMA that you can run locally on your computer.
TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data.
TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
The Powerful Multi-modal LLM Family for OCR-free Document Understanding.
Modularized Multimodal Large Language Model for Document Understanding.
ChainReactor is a research project that leverages AI planning to discover exploitation chains for privilege escalation on Unix systems. The project models the problem as a sequence of actions to achieve privilege escalation from initial access to a target system.
A natural language interface for computers.
Open Interpreter lets LLMs run code (Python, Javascript, Shell, and more) locally. You can chat with Open Interpreter through a ChatGPT-like interface in your terminal by running $ interpreter after installing.
The Typescript AI framework.
Mastra is an opinionated Typescript framework that helps you build AI applications and features quickly. It gives you the set of primitives you need: workflows, agents, RAG, integrations, syncs and evals. You can run Mastra on your local machine, or deploy to a serverless cloud.
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