ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models. Users can add documents to the tree, answer questions based on indexed documents, save and load the tree, and extend RAPTOR with custom summarization, question-answering, and embedding models. The tool is designed to be flexible and customizable for various NLP tasks.

llm-compressor is an easy-to-use library for optimizing models for deployment with vllm. It provides a comprehensive set of quantization algorithms, seamless integration with Hugging Face models and repositories, and supports mixed precision, activation quantization, and sparsity. Supported algorithms include PTQ, GPTQ, SmoothQuant, and SparseGPT. Installation can be done via git clone and local pip install. Compression can be easily applied by selecting an algorithm and calling the oneshot API. The library also offers end-to-end examples for model compression. Contributions to the code, examples, integrations, and documentation are appreciated.

BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

aiexe is a cutting-edge command-line interface (CLI) and graphical user interface (GUI) tool that integrates powerful AI capabilities directly into your terminal or desktop. It is designed for developers, tech enthusiasts, and anyone interested in AI-powered automation. aiexe provides an easy-to-use yet robust platform for executing complex tasks with just a few commands. Users can harness the power of various AI models from OpenAI, Anthropic, Ollama, Gemini, and GROQ to boost productivity and enhance decision-making processes.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

The AI Scientist is a comprehensive system for fully automatic scientific discovery, enabling Foundation Models to perform research independently. It aims to tackle the grand challenge of developing agents capable of conducting scientific research and discovering new knowledge. The tool generates papers on various topics using Large Language Models (LLMs) and provides a platform for exploring new research ideas. Users can create their own templates for specific areas of study and run experiments to generate papers. However, caution is advised as the codebase executes LLM-written code, which may pose risks such as the use of potentially dangerous packages and web access.

Vitron is a unified pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing static images and dynamic videos. It addresses challenges in existing vision LLMs such as superficial instance-level understanding, lack of unified support for images and videos, and insufficient coverage across various vision tasks. The tool requires Python >= 3.8, Pytorch == 2.1.0, and CUDA Version >= 11.8 for installation. Users can deploy Gradio demo locally and fine-tune their models for specific tasks.

Shinkai apps unlock the full capabilities/automation of first-class LLM (AI) support in the web browser. It enables creating multiple agents, each connected to either local or 3rd-party LLMs (ex. OpenAI GPT), which have permissioned (meaning secure) access to act in every webpage you visit. There is a companion repo called Shinkai Node, that allows you to set up the node anywhere as the central unit of the Shinkai Network, handling tasks such as agent management, job processing, and secure communications.

llmgraph is a tool that enables users to create knowledge graphs in GraphML, GEXF, and HTML formats by extracting world knowledge from large language models (LLMs) like ChatGPT. It supports various entity types and relationships, offers cache support for efficient graph growth, and provides insights into LLM costs. Users can customize the model used and interact with different LLM providers. The tool allows users to generate interactive graphs based on a specified entity type and Wikipedia link, making it a valuable resource for knowledge graph creation and exploration.

LLM-Term is a Rust-based CLI tool that generates and executes terminal commands using OpenAI's language models or local Ollama models. It offers configurable model and token limits, works on both PowerShell and Unix-like shells, and provides a seamless user experience for generating commands based on prompts. Users can easily set up the tool, customize configurations, and leverage different models for command generation.

Fragments is an open-source tool that leverages Anthropic's Claude Artifacts, Vercel v0, and GPT Engineer. It is powered by E2B Sandbox SDK and Code Interpreter SDK, allowing secure execution of AI-generated code. The tool is based on Next.js 14, shadcn/ui, TailwindCSS, and Vercel AI SDK. Users can stream in the UI, install packages from npm and pip, and add custom stacks and LLM providers. Fragments enables users to build web apps with Python interpreter, Next.js, Vue.js, Streamlit, and Gradio, utilizing providers like OpenAI, Anthropic, Google AI, and more.

HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables Large Language Models (LLMs) to continuously integrate knowledge across external documents. It provides RAG systems with capabilities that usually require a costly and high-latency iterative LLM pipeline for only a fraction of the computational cost. The tool facilitates setting up retrieval corpus, indexing, and retrieval processes for LLMs, offering flexibility in choosing different online LLM APIs or offline LLM deployments through LangChain integration. Users can run retrieval on pre-defined queries or integrate directly with the HippoRAG API. The tool also supports reproducibility of experiments and provides data, baselines, and hyperparameter tuning scripts for research purposes.

SDFX is the ultimate no-code platform for building and sharing AI apps with beautiful UI. It enables the creation of user-friendly interfaces for complex workflows by combining Comfy workflow with a UI. The tool is designed to merge the benefits of form-based UI and graph-node based UI, allowing users to create intricate graphs with a high-level UI overlay. SDFX is fully compatible with ComfyUI, abstracting the need for installing ComfyUI. It offers features like animated graph navigation, node bookmarks, UI debugger, custom nodes manager, app and template export, image and mask editor, and more. The tool compiles as a native app or web app, making it easy to maintain and add new features.

OSWorld is a benchmarking tool designed to evaluate multimodal agents for open-ended tasks in real computer environments. It provides a platform for running experiments, setting up virtual machines, and interacting with the environment using Python scripts. Users can install the tool on their desktop or server, manage dependencies with Conda, and run benchmark tasks. The tool supports actions like executing commands, checking for specific results, and evaluating agent performance. OSWorld aims to facilitate research in AI by providing a standardized environment for testing and comparing different agent baselines.

Awesome-LLMs-for-Video-Understanding is a repository dedicated to exploring Video Understanding with Large Language Models. It provides a comprehensive survey of the field, covering models, pretraining, instruction tuning, and hybrid methods. The repository also includes information on tasks, datasets, and benchmarks related to video understanding. Contributors are encouraged to add new papers, projects, and materials to enhance the repository.

Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in Video Analysis. It assesses the capabilities of MLLMs in processing video data, covering a wide range of visual domains, temporal durations, and data modalities. The dataset comprises 900 videos with 256 hours and 2,700 human-annotated question-answer pairs. It distinguishes itself through features like duration variety, diversity in video types, breadth in data modalities, and quality in annotations.

ControlLLM is a framework that empowers large language models to leverage multi-modal tools for solving complex real-world tasks. It addresses challenges like ambiguous user prompts, inaccurate tool selection, and inefficient tool scheduling by utilizing a task decomposer, a Thoughts-on-Graph paradigm, and an execution engine with a rich toolbox. The framework excels in tasks involving image, audio, and video processing, showcasing superior accuracy, efficiency, and versatility compared to existing methods.

This repository is a rich resource offering examples of synthetic image generation, manipulation, and reasoning using Azure Machine Learning, Computer Vision, OpenAI, and open-source frameworks like Stable Diffusion. It provides practical insights into image processing applications, including content generation, video analysis, avatar creation, and image manipulation with various tools and APIs.

Outspeed is a PyTorch-inspired SDK for building real-time AI applications on voice and video input. It offers low-latency processing of streaming audio and video, an intuitive API familiar to PyTorch users, flexible integration of custom AI models, and tools for data preprocessing and model deployment. Ideal for developing voice assistants, video analytics, and other real-time AI applications processing audio-visual data.

This repository contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using Generative AI on Google Cloud, powered by Vertex AI. For more Vertex AI samples, please visit the Vertex AI samples Github repository.

Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

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.