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.

Lovelaice is an AI-powered assistant for your terminal and editor. It can run bash commands, search the Internet, answer general and technical questions, complete text files, chat casually, execute code in various languages, and more. Lovelaice is configurable with API keys and LLM models, and can be used for a wide range of tasks requiring bash commands or coding assistance. It is designed to be versatile, interactive, and helpful for daily tasks and projects.

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.

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.

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.

Mapperatorinator is a multi-model framework that uses spectrogram inputs to generate fully featured osu! beatmaps for all gamemodes and assist modding beatmaps. The project aims to automatically generate rankable quality osu! beatmaps from any song with a high degree of customizability. The tool is built upon osuT5 and osu-diffusion, utilizing GPU compute and instances on vast.ai for development. Users can responsibly use AI in their beatmaps with this tool, ensuring disclosure of AI usage. Installation instructions include cloning the repository, creating a virtual environment, and installing dependencies. The tool offers a Web GUI for user-friendly experience and a Command-Line Inference option for advanced configurations. Additionally, an Interactive CLI script is available for terminal-based workflow with guided setup. The tool provides generation tips and features MaiMod, an AI-driven modding tool for osu! beatmaps. Mapperatorinator tokenizes beatmaps, utilizes a model architecture based on HF Transformers Whisper model, and offers multitask training format for conditional generation. The tool ensures seamless long generation, refines coordinates with diffusion, and performs post-processing for improved beatmap quality. Super timing generator enhances timing accuracy, and LoRA fine-tuning allows adaptation to specific styles or gamemodes. The project acknowledges credits and related works in the osu! community.

ModelBench is a tool for running safety benchmarks against AI models and generating detailed reports. It is part of the MLCommons project and is designed as a proof of concept to aggregate measures, relate them to specific harms, create benchmarks, and produce reports. The tool requires LlamaGuard for evaluating responses and a TogetherAI account for running benchmarks. Users can install ModelBench from GitHub or PyPI, run tests using Poetry, and create benchmarks by providing necessary API keys. The tool generates static HTML pages displaying benchmark scores and allows users to dump raw scores and manage cache for faster runs. ModelBench is aimed at enabling users to test their own models and create tests and benchmarks.

Lumigator is an open-source platform developed by Mozilla.ai to help users select the most suitable language model for their specific needs. It supports the evaluation of summarization tasks using sequence-to-sequence models such as BART and BERT, as well as causal models like GPT and Mistral. The platform aims to make model selection transparent, efficient, and empowering by providing a framework for comparing LLMs using task-specific metrics to evaluate how well a model fits a project's needs. Lumigator is in the early stages of development and plans to expand support to additional machine learning tasks and use cases in the future.

Boxcars is a Ruby gem that enables users to create new systems with AI composability, incorporating concepts such as LLMs, Search, SQL, Rails Active Record, Vector Search, and more. It allows users to work with Boxcars, Trains, Prompts, Engines, and VectorStores to solve problems and generate text results. The gem is designed to be user-friendly for beginners and can be extended with custom concepts. Boxcars is actively seeking ways to enhance security measures to prevent malicious actions. Users can use Boxcars for tasks like running calculations, performing searches, generating Ruby code for math operations, and interacting with APIs like OpenAI, Anthropic, and Google SERP.

Brokk is a code assistant designed to understand code semantically, allowing LLMs to work effectively on large codebases. It offers features like agentic search, summarizing related classes, parsing stack traces, adding source for usages, and autonomously fixing errors. Users can interact with Brokk through different panels and commands, enabling them to manipulate context, ask questions, search codebase, run shell commands, and more. Brokk helps with tasks like debugging regressions, exploring codebase, AI-powered refactoring, and working with dependencies. It is particularly useful for making complex, multi-file edits with o1pro.

EdgeChains is an open-source chain-of-thought engineering framework tailored for Large Language Models (LLMs)- like OpenAI GPT, LLama2, Falcon, etc. - With a focus on enterprise-grade deployability and scalability. EdgeChains is specifically designed to **orchestrate** such applications. At EdgeChains, we take a unique approach to Generative AI - we think Generative AI is a deployment and configuration management challenge rather than a UI and library design pattern challenge. We build on top of a tech that has solved this problem in a different domain - Kubernetes Config Management - and bring that to Generative AI. Edgechains is built on top of jsonnet, originally built by Google based on their experience managing a vast amount of configuration code in the Borg infrastructure.

HackBot is an AI-powered cybersecurity chatbot designed to provide accurate answers to cybersecurity-related queries, conduct code analysis, and scan analysis. It utilizes the Meta-LLama2 AI model through the 'LlamaCpp' library to respond coherently. The chatbot offers features like local AI/Runpod deployment support, cybersecurity chat assistance, interactive interface, clear output presentation, static code analysis, and vulnerability analysis. Users can interact with HackBot through a command-line interface and utilize it for various cybersecurity tasks.

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.

WilmerAI is a middleware system designed to process prompts before sending them to Large Language Models (LLMs). It categorizes prompts, routes them to appropriate workflows, and generates manageable prompts for local models. It acts as an intermediary between the user interface and LLM APIs, supporting multiple backend LLMs simultaneously. WilmerAI provides API endpoints compatible with OpenAI API, supports prompt templates, and offers flexible connections to various LLM APIs. The project is under heavy development and may contain bugs or incomplete code.

Ultravox is a fast multimodal Language Model (LLM) that can understand both text and human speech in real-time without the need for a separate Audio Speech Recognition (ASR) stage. By extending Meta's Llama 3 model with a multimodal projector, Ultravox converts audio directly into a high-dimensional space used by Llama 3, enabling quick responses and potential understanding of paralinguistic cues like timing and emotion in human speech. The current version (v0.3) has impressive speed metrics and aims for further enhancements. Ultravox currently converts audio to streaming text and plans to emit speech tokens for direct audio conversion. The tool is open for collaboration to enhance this functionality.

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.

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.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.