Generative AI on AWS by O'Reilly Media provides a comprehensive guide on leveraging generative AI models on the AWS platform. The book covers various topics such as generative AI use cases, prompt engineering, large-language models, fine-tuning techniques, optimization, deployment, and more. Authors Chris Fregly, Antje Barth, and Shelbee Eigenbrode offer insights into cutting-edge AI technologies and practical applications in the field. The book is a valuable resource for data scientists, AI enthusiasts, and professionals looking to explore generative AI capabilities on AWS.

A curated list of Machine Learning (ML) and Generative AI (GenAI) packages and resources for the Elixir programming language. It includes core tools for data exploration, traditional machine learning algorithms, deep learning models, computer vision libraries, generative AI tools, livebooks for interactive notebooks, and various resources such as books, videos, and articles. The repository aims to provide a comprehensive overview for experienced Elixir developers and ML/AI practitioners exploring different ecosystems.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

LMOps is a research initiative focusing on fundamental research and technology for building AI products with foundation models, particularly enabling AI capabilities with Large Language Models (LLMs) and Generative AI models. The project explores various aspects such as prompt optimization, longer context handling, LLM alignment, acceleration of LLMs, LLM customization, and understanding in-context learning. It also includes tools like Promptist for automatic prompt optimization, Structured Prompting for efficient long-sequence prompts consumption, and X-Prompt for extensible prompts beyond natural language. Additionally, LLMA accelerators are developed to speed up LLM inference by referencing and copying text spans from documents. The project aims to advance technologies that facilitate prompting language models and enhance the performance of LLMs in various scenarios.

The 'awesome-sound_event_detection' repository is a curated reading list focusing on sound event detection and Sound AI. It includes research papers covering various sub-areas such as learning formulation, network architecture, pooling functions, missing or noisy audio, data augmentation, representation learning, multi-task learning, few-shot learning, zero-shot learning, knowledge transfer, polyphonic sound event detection, loss functions, audio and visual tasks, audio captioning, audio retrieval, audio generation, and more. The repository provides a comprehensive collection of papers, datasets, and resources related to sound event detection and Sound AI, making it a valuable reference for researchers and practitioners in the field.

OpenCat code running on BiBoard, a high-performance ESP32 quadruped robot development board. The board is mainly designed for developers and engineers working on multi-degree-of-freedom (MDOF) Multi-legged robots with up to 12 servos.

LLM-PLSE-paper is a repository focused on the applications of Large Language Models (LLMs) in Programming Language and Software Engineering (PL/SE) domains. It covers a wide range of topics including bug detection, specification inference and verification, code generation, fuzzing and testing, code model and reasoning, code understanding, IDE technologies, prompting for reasoning tasks, and agent/tool usage and planning. The repository provides a comprehensive collection of research papers, benchmarks, empirical studies, and frameworks related to the capabilities of LLMs in various PL/SE tasks.

Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

A fine-tuning guide for both OpenAI and Open-Source Large Language Models on Azure. Fine-Tuning retrains an existing pre-trained LLM using example data, resulting in a new 'custom' fine-tuned LLM optimized for task-specific examples. Use cases include improving LLM performance on specific tasks and introducing information not well represented by the base LLM model. Suitable for cases where latency is critical, high accuracy is required, and clear evaluation metrics are available. Learning path includes labs for fine-tuning GPT and Llama2 models via Dashboards and Python SDK.

FATE-LLM is a framework supporting federated learning for large and small language models. It promotes training efficiency of federated LLMs using Parameter-Efficient methods, protects the IP of LLMs using FedIPR, and ensures data privacy during training and inference through privacy-preserving mechanisms.

This repository contains tutorials for using Large Language Models (LLMs) in the U.S. Department of Defense. The tutorials utilize open-source frameworks and LLMs, allowing users to run them in their own cloud environments. The repository is maintained by the Defense Digital Service and welcomes contributions from users.

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

Agent is a RustSBI specialized domain knowledge quiz LLM tool that extracts domain knowledge from various sources such as Rust Documentation, RISC-V Documentation, Bouffalo Docs, Bouffalo SDK, and Xiangshan Docs. It also provides resources for LLM prompt engineering and RAG engineering, including guides and existing projects related to retrieval-augmented generation (RAG) systems.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

FuseAI is a repository that focuses on knowledge fusion of large language models. It includes FuseChat, a state-of-the-art 7B LLM on MT-Bench, and FuseLLM, which surpasses Llama-2-7B by fusing three open-source foundation LLMs. The repository provides tech reports, releases, and datasets for FuseChat and FuseLLM, showcasing their performance and advancements in the field of chat models and large language models.

Generative AI on AWS by O'Reilly Media provides a comprehensive guide on leveraging generative AI models on the AWS platform. The book covers various topics such as generative AI use cases, prompt engineering, large-language models, fine-tuning techniques, optimization, deployment, and more. Authors Chris Fregly, Antje Barth, and Shelbee Eigenbrode offer insights into cutting-edge AI technologies and practical applications in the field. The book is a valuable resource for data scientists, AI enthusiasts, and professionals looking to explore generative AI capabilities on AWS.

Open SaaS is a free and open-source React and Node.js template for building SaaS applications. It comes with a variety of features out of the box, including authentication, payments, analytics, and more. Open SaaS is built on top of the Wasp framework, which provides a number of features to make it easy to build SaaS applications, such as full-stack authentication, end-to-end type safety, jobs, and one-command deploy.

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.

LLMOps with Prompt flow is a template and guidance for building LLM-infused apps using Prompt flow. It provides centralized code hosting, lifecycle management, variant and hyperparameter experimentation, A/B deployment, many-to-many dataset/flow relationships, multiple deployment targets, comprehensive reporting, BYOF capabilities, configuration-based development, local prompt experimentation and evaluation, endpoint testing, and optional Human-in-loop validation. The tool is customizable to suit various application needs.

The Cheat-Sheet Collection for DevOps, Engineers, IT professionals, and more is a curated list of cheat sheets for various tools and technologies commonly used in the software development and IT industry. It includes cheat sheets for Nginx, Docker, Ansible, Python, Go (Golang), Git, Regular Expressions (Regex), PowerShell, VIM, Jenkins, CI/CD, Kubernetes, Linux, Redis, Slack, Puppet, Google Cloud Developer, AI, Neural Networks, Machine Learning, Deep Learning & Data Science, PostgreSQL, Ajax, AWS, Infrastructure as Code (IaC), System Design, and Cyber Security.

This repository is a curated list of open-source libraries for production large language models. It includes tools for data preprocessing, training/finetuning, evaluation/benchmarking, serving/inference, application/RAG, testing/monitoring, and guardrails/security. The repository also provides a new category called LLM Cookbook/Examples for showcasing examples and guides on using various LLM APIs.

Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.

OpenCharacter is an open-source tool that allows users to create and run characters locally with local models or use the hosted version. The stack includes Next.js for frontend, TailwindCSS for styling, Drizzle ORM for database access, NextAuth for authentication, Cloudflare D1 for serverless databases, Cloudflare Pages for hosting, and ShadcnUI as the component library. Users can integrate OpenCharacter with OpenRouter by configuring the OpenRouter API key. The tool is fully scalable, composable, and cost-effective, with powerful tools like Wrangler for database management and migrations. No environment variables are needed, making it easy to use and deploy.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.