VLMEvalKit

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

MM-RLHF is a comprehensive project for aligning Multimodal Large Language Models (MLLMs) with human preferences. It includes a high-quality MLLM alignment dataset, a Critique-Based MLLM reward model, a novel alignment algorithm MM-DPO, and benchmarks for reward models and multimodal safety. The dataset covers image understanding, video understanding, and safety-related tasks with model-generated responses and human-annotated scores. The reward model generates critiques of candidate texts before assigning scores for enhanced interpretability. MM-DPO is an alignment algorithm that achieves performance gains with simple adjustments to the DPO framework. The project enables consistent performance improvements across 10 dimensions and 27 benchmarks for open-source MLLMs.

MARBLE (Multi-Agent Coordination Backbone with LLM Engine) is a modular framework for developing, testing, and evaluating multi-agent systems leveraging Large Language Models. It provides a structured environment for agents to interact in simulated environments, utilizing cognitive abilities and communication mechanisms for collaborative or competitive tasks. The framework features modular design, multi-agent support, LLM integration, shared memory, flexible environments, metrics and evaluation, industrial coding standards, and Docker support.

Easy Diffusion 3.0 is a user-friendly tool for installing and using Stable Diffusion on your computer. It offers hassle-free installation, clutter-free UI, task queue, intelligent model detection, live preview, image modifiers, multiple prompts file, saving generated images, UI themes, searchable models dropdown, and supports various image generation tasks like 'Text to Image', 'Image to Image', and 'InPainting'. The tool also provides advanced features such as custom models, merge models, custom VAE models, multi-GPU support, auto-updater, developer console, and more. It is designed for both new users and advanced users looking for powerful AI image generation capabilities.

ebook2audiobook is a CPU/GPU converter tool that converts eBooks to audiobooks with chapters and metadata using tools like Calibre, ffmpeg, XTTSv2, and Fairseq. It supports voice cloning and a wide range of languages. The tool is designed to run on 4GB RAM and provides a new v2.0 Web GUI interface for user-friendly interaction. Users can convert eBooks to text format, split eBooks into chapters, and utilize high-quality text-to-speech functionalities. Supported languages include Arabic, Chinese, English, French, German, Hindi, and many more. The tool can be used for legal, non-DRM eBooks only and should be used responsibly in compliance with applicable laws.

Visionatrix is a project aimed at providing easy use of ComfyUI workflows. It offers simplified setup and update processes, a minimalistic UI for daily workflow use, stable workflows with versioning and update support, scalability for multiple instances and task workers, multiple user support with integration of different user backends, LLM power for integration with Ollama/Gemini, and seamless integration as a service with backend endpoints and webhook support. The project is approaching version 1.0 release and welcomes new ideas for further implementation.

RealtimeSTT is an easy-to-use, low-latency speech-to-text library for realtime applications. It listens to the microphone and transcribes voice into text, making it ideal for voice assistants and applications requiring fast and precise speech-to-text conversion. The library utilizes Voice Activity Detection, Realtime Transcription, and Wake Word Activation features. It supports GPU-accelerated transcription using PyTorch with CUDA support. RealtimeSTT offers various customization options for different parameters to enhance user experience and performance. The library is designed to provide a seamless experience for developers integrating speech-to-text functionality into their applications.

VeritasGraph is an enterprise-grade graph RAG framework designed for secure, on-premise AI applications. It leverages a knowledge graph to perform complex, multi-hop reasoning, providing transparent, auditable reasoning paths with full source attribution. The framework excels at answering complex questions that traditional vector search engines struggle with, ensuring trust and reliability in enterprise AI. VeritasGraph offers full control over data and AI models, verifiable attribution for every claim, advanced graph reasoning capabilities, and open-source deployment with sovereignty and customization.

Kheish is an open-source, multi-role agent designed for complex tasks that require structured, step-by-step collaboration with Large Language Models (LLMs). It acts as an intelligent agent that can request modules on demand, integrate user feedback, switch between specialized roles, and deliver refined results. By harnessing multiple 'sub-agents' within one framework, Kheish tackles tasks like security audits, file searches, RAG-based exploration, and more.

SmythOS is an operating system designed for building, deploying, and managing intelligent AI agents at scale. It provides a unified SDK and resource abstraction layer for various AI services, making it easy to scale and flexible. With an agent-first design, developer-friendly SDK, modular architecture, and enterprise security features, SmythOS offers a robust foundation for AI workloads. The system is built with a philosophy inspired by traditional operating system kernels, ensuring autonomy, control, and security for AI agents. SmythOS aims to make shipping production-ready AI agents accessible and open for everyone in the coming Internet of Agents era.

MevBot Solana is an advanced trading bot for the Solana blockchain with an interactive and user-friendly interface. It offers features like scam token scanning, automatic network connection, and focus on trading $TRUMP and $MELANIA tokens. Users can set stop-loss and take-profit thresholds, filter tokens based on market cap, and configure purchase amounts. The bot requires a starting balance of at least 3 SOL for optimal performance. It can be managed through a main menu in VS Code and requires prerequisites like Git, Node.js, and VS Code for installation and usage.

The Simple Chat Application is a web-based platform that facilitates secure interactions with generative AI models, leveraging Azure OpenAI. It features Retrieval-Augmented Generation (RAG) for grounding conversations in user data. Users can upload personal or group documents processed using Azure AI Document Intelligence and Azure OpenAI Embeddings. The application offers optional features like Content Safety, Image Generation, Video and Audio processing, Document Classification, User Feedback, Conversation Archiving, Metadata Extraction, and Enhanced Citations. It uses Azure Cosmos DB for storage, Azure Active Directory for authentication, and runs on Azure App Service. Suitable for enterprise use, it supports knowledge discovery, content generation, and collaborative AI tasks in a secure, Azure-native framework.

Groqqle 2.1 is a revolutionary, free AI web search and API that instantly returns ORIGINAL content derived from source articles, websites, videos, and even foreign language sources, for ANY target market of ANY reading comprehension level! It combines the power of large language models with advanced web and news search capabilities, offering a user-friendly web interface, a robust API, and now a powerful Groqqle_web_tool for seamless integration into your projects. Developers can instantly incorporate Groqqle into their applications, providing a powerful tool for content generation, research, and analysis across various domains and languages.

Eliza is a versatile AI agent operating system designed to support various models and connectors, enabling users to create chatbots, autonomous agents, handle business processes, create video game NPCs, and engage in trading. It offers multi-agent and room support, document ingestion and interaction, retrievable memory and document store, and extensibility to create custom actions and clients. Eliza is easy to use and provides a comprehensive solution for AI agent development.

WatermarkRemover-AI is an advanced application that utilizes AI models for precise watermark detection and seamless removal. It leverages Florence-2 for watermark identification and LaMA for inpainting. The tool offers both a command-line interface (CLI) and a PyQt6-based graphical user interface (GUI), making it accessible to users of all levels. It supports dual modes for processing images, advanced watermark detection, seamless inpainting, customizable output settings, real-time progress tracking, dark mode support, and efficient GPU acceleration using CUDA.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

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.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.

This repo hosts tutorial documentation for running generative AI models on NVIDIA Jetson devices. The documentation is auto-generated and hosted on GitHub Pages using their CI/CD feature to automatically generate/update the HTML documentation site upon new commits.

Emgu CV is a cross-platform .Net wrapper for the OpenCV image-processing library. It allows OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio, Unity, and "dotnet" command, and it can run on Windows, Mac OS, Linux, iOS, and Android.

MMStar is an elite vision-indispensable multi-modal benchmark comprising 1,500 challenge samples meticulously selected by humans. It addresses two key issues in current LLM evaluation: the unnecessary use of visual content in many samples and the existence of unintentional data leakage in LLM and LVLM training. MMStar evaluates 6 core capabilities across 18 detailed axes, ensuring a balanced distribution of samples across all dimensions.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.