Agenta is an open-source LLM developer platform for prompt engineering, evaluation, human feedback, and deployment of complex LLM applications. It provides tools for prompt engineering and management, evaluation, human annotation, and deployment, all without imposing any restrictions on your choice of framework, library, or model. Agenta allows developers and product teams to collaborate in building production-grade LLM-powered applications in less time.

Bitcart is a platform designed for merchants, users, and developers, providing easy setup and usage. It includes various linked repositories for core daemons, admin panel, ready store, Docker packaging, Python library for coins connection, BitCCL scripting language, documentation, and official site. The platform aims to simplify the process for merchants and developers to interact and transact with cryptocurrencies, offering a comprehensive ecosystem for managing transactions and payments.

Codemod platform is a tool that helps developers create, distribute, and run codemods in codebases of any size. The AI-powered, community-led codemods enable automation of framework upgrades, large refactoring, and boilerplate programming with speed and developer experience. It aims to make dream migrations a reality for developers by providing a platform for seamless codemod operations.

Smriti AI is an intelligent learning assistant that helps users organize, understand, and retain study materials. It transforms passive content into active learning tools by capturing resources, converting them into summaries and quizzes, providing spaced revision with reminders, tracking progress, and offering a multimodal interface. Suitable for students, self-learners, professionals, educators, and coaching institutes.

LLMOne is an open-source, lightweight enterprise-level platform for deploying and serving large language models. It aims to address pain points in traditional large model private deployment such as long cycles, complex configurations, performance challenges, and high operational costs. LLMOne simplifies the deployment process with highly automated workflows and optimized runtime environments, ensuring enterprise-level performance and stability. It caters to developers, manufacturers, and users of large language models, providing features like rapid deployment, professional inference performance, broad compatibility with AI hardware, flexible model and application management, visual operational monitoring, and an open application ecosystem.

GoMaxAI Pro is an AI-powered application for personal, team, and enterprise private operations. It supports various models like ChatGPT, Claude, Gemini, Kimi, Wenxin Yiyuan, Xunfei Xinghuo, Tsinghua Zhipu, Suno-v3.5, and Luma-video. The Pro version offers a new UI interface, member points system, management backend, homepage features, support for various content formats, AI video capabilities, SAAS multi-opening function, bug fixes, and more. It is built using web frontend with Vue3, mobile frontend with Uniapp, management frontend with Vue3, backend with Nodejs, and uses MySQL5.7(+) + Redis for data support. It can be deployed on Linux, Windows, or MacOS, with data storage options including local storage, Aliyun OSS, Tencent Cloud COS, and Chevereto image bed.

Robusta is a tool designed to enhance Prometheus notifications for Kubernetes environments. It offers features such as smart grouping to reduce notification spam, AI investigation for alert analysis, alert enrichment with additional data like pod logs, self-healing capabilities for defining auto-remediation rules, advanced routing options, problem detection without PromQL, change-tracking for Kubernetes resources, auto-resolve functionality, and integration with various external systems like Slack, Teams, and Jira. Users can utilize Robusta with or without Prometheus, and it can be installed alongside existing Prometheus setups or as part of an all-in-one Kubernetes observability stack.

AI-on-the-edge-device is a project that enables users to digitize analog water, gas, power, and other meters using an ESP32 board with a supported camera. It integrates Tensorflow Lite for AI processing, offers a small and affordable device with integrated camera and illumination, provides a web interface for administration and control, supports Homeassistant, Influx DB, MQTT, and REST API. The device captures meter images, extracts Regions of Interest (ROIs), runs them through AI for digitization, and allows users to send data to MQTT, InfluxDb, or access it via REST API. The project also includes 3D-printable housing options and tools for logfile management.

AnyLabeling is a tool for effortless data labeling with AI support from YOLO and Segment Anything. It combines features from LabelImg and Labelme with an improved UI and auto-labeling capabilities. Users can annotate images with polygons, rectangles, circles, lines, and points, as well as perform auto-labeling using YOLOv5 and Segment Anything. The tool also supports text detection, recognition, and Key Information Extraction (KIE) labeling, with multiple language options available such as English, Vietnamese, and Chinese.

Neuromatch Academy NeuroAI Course Syllabus is a repository that contains the schedule and licensing information for the NeuroAI course. The course is designed to provide participants with a comprehensive understanding of artificial intelligence in neuroscience. It covers various topics related to AI applications in neuroscience, including machine learning, data analysis, and computational modeling. The content is primarily accessed from the ebook provided in the repository, and the course is scheduled for July 15-26, 2024. The repository is shared under a Creative Commons Attribution 4.0 International License and software elements are additionally licensed under the BSD (3-Clause) License. Contributors to the project are acknowledged and welcomed to contribute further.

Timeline Studio is a next-generation professional video editor with AI integration that automates content creation for social media. It combines the power of desktop applications with the convenience of web interfaces. With 257 AI tools, GPU acceleration, plugin system, multi-language interface, and local processing, Timeline Studio offers complete video production automation. Users can create videos for various social media platforms like TikTok, YouTube, Vimeo, Telegram, and Instagram with optimized versions. The tool saves time, understands trends, provides professional quality, and allows for easy feature extension through plugins. Timeline Studio is open source, transparent, and offers significant time savings and quality improvements for video editing tasks.

Jarvis is a powerful virtual AI assistant designed to simplify daily tasks through voice command integration. It features automation, device management, and personalized interactions, transforming technology engagement. Built using Python and AI models, it serves personal and administrative needs efficiently, making processes seamless and productive.

Omnia is a deployment tool designed to turn servers with RPM-based Linux images into functioning Slurm/Kubernetes clusters. It provides an Ansible playbook-based deployment for Slurm and Kubernetes on servers running an RPM-based Linux OS. The tool simplifies the process of setting up and managing clusters, making it easier for users to deploy and maintain their infrastructure.

Clash Royale Build-A-Bot is a project that allows users to build their own bot to play Clash Royale. It provides an advanced state generator that accurately returns detailed information using cutting-edge technologies. The project includes tutorials for setting up the environment, building a basic bot, and understanding state generation. It also offers updates such as replacing YOLOv5 with YOLOv8 unit model and enhancing performance features like placement and elixir management. The future roadmap includes plans to label more images of diverse cards, add a tracking layer for unit predictions, publish tutorials on Q-learning and imitation learning, release the YOLOv5 training notebook, implement chest opening and card upgrading features, and create a leaderboard for the best bots developed with this repository.

AIResume is an open-source resume creation platform that helps users easily create professional resumes, integrating AI technology to assist users in polishing their resumes. The project allows for template development using Vue 3, Vite, TypeScript, and Ant Design Vue. Users can edit resumes, export them as PDFs, switch between multiple resume templates, and collaborate on template development. AI features include resume refinement, deep optimization based on individual projects or experiences, and simulated interviews for user practice. Additional functionalities include theme color switching, high customization options, dark/light mode switching, real-time preview, drag-and-drop resume scaling, data export/import, data clearing, sample data prefilling, template market showcasing, and more.

Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

Agenta is an open-source LLM developer platform for prompt engineering, evaluation, human feedback, and deployment of complex LLM applications. It provides tools for prompt engineering and management, evaluation, human annotation, and deployment, all without imposing any restrictions on your choice of framework, library, or model. Agenta allows developers and product teams to collaborate in building production-grade LLM-powered applications in less time.

Self Learn LLMs is a repository containing resources for self-learning about Large Language Models. It includes theoretical and practical hands-on resources to facilitate learning. The repository aims to provide a clear roadmap with milestones for proper understanding of LLMs. The owner plans to refactor the repository to remove irrelevant content, organize model zoo better, and enhance the learning experience by adding contributors and hosting notes, tutorials, and open discussions.

LitGPT is a command-line tool designed to easily finetune, pretrain, evaluate, and deploy 20+ LLMs **on your own data**. It features highly-optimized training recipes for the world's most powerful open-source large-language-models (LLMs).

Torchtune is a PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. It provides native-PyTorch implementations of popular LLMs using composable and modular building blocks, easy-to-use and hackable training recipes for popular fine-tuning techniques, YAML configs for easily configuring training, evaluation, quantization, or inference recipes, and built-in support for many popular dataset formats and prompt templates to help you quickly get started with training.

TruLens provides a set of tools for developing and monitoring neural nets, including large language models. This includes both tools for evaluation of LLMs and LLM-based applications with _TruLens-Eval_ and deep learning explainability with _TruLens-Explain_. _TruLens-Eval_ and _TruLens-Explain_ are housed in separate packages and can be used independently.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

This repository provides a comprehensive guide to training, evaluating, and building applications with Large Language Models (LLMs). It covers various aspects of LLMs, including pretraining, fine-tuning, reinforcement learning from human feedback (RLHF), and more. The repository also includes practical examples and code snippets to help users get started with LLMs quickly and easily.

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.