Best AI tools for< Organize Models >

Comfy Org is an open-source AI tooling platform dedicated to advancing and democratizing AI technology. The platform offers tools like node manager, node registry, CLI, automated testing, and public documentation to support the ComfyUI ecosystem. Comfy Org aims to make state-of-the-art AI models accessible to a wider audience by fostering an open-source and community-driven approach. The team behind Comfy Org consists of individuals passionate about developing and maintaining various components of the platform, ensuring a reliable and secure environment for users to explore and contribute to AI tooling.

DVC is an open-source version control system for machine learning projects. It allows users to track and manage their data, models, and code in a single place. DVC also provides a number of features that make it easy to collaborate on machine learning projects, such as experiment tracking, model registration, and pipeline management.

ChatLabs is an AI application that provides users with access to a variety of AI models for tasks such as chatting, writing, web searching, image generation, and more. Users can interact with AI assistants, browse the web, generate AI art, and utilize voice input features. The platform offers a prompt library, chat with files functionality, split-screen mode, and a Chrome extension for enhanced user experience.

AI Renamer is an application that utilizes artificial intelligence to automatically rename files based on their content. It offers powerful features such as smart recognition, batch processing, and support for various file types. Users can also integrate their own AI models for enhanced flexibility and privacy. The application provides credit-based pricing options and supports both Mac and Windows platforms.

Co-Founder AI is an AI-powered tool that accelerates startup success by providing in-depth business reports and actionable insights. It utilizes AI to generate well-structured business plans and offers essential insights to validate IT-business ideas. The tool covers various aspects such as market trends, competitor analysis, sales techniques, and fundraising strategies, enabling users to make data-driven decisions for driving growth.

Flamingo.ai is an all-in-one AI workspace designed for teams to access various AI models, engage in group discussions, and streamline workflow in a centralized workspace. It offers features like group chats, shared prompts, and organized folders to enhance team collaboration and productivity. Users can easily switch from multiple AI services to Flamingo.ai, which provides a cost-effective solution with simplified management and communication tools.

ProtestGPT is an activist AI tool created by Micah White that utilizes OpenAI's GPT-4 and Large Language Models to generate unique and unconventional protest ideas on any given topic. It helps activists come up with innovative strategies for building movements and bringing about change by providing campaign concepts, theories of change, press releases, social media posts, step-by-step guides, and more.

Jynnt is an AI application designed to simplify and enhance the user's AI experience. It offers a wide range of AI models, folders, and tags in a light, organized, and efficient workspace. With over 100 stellar AI models, users have limitless choices and can enjoy clutter-free organization with folders and tags. The application features a lightweight interface, unlimited exploration without restrictions, and a super efficient workspace for innovation. Jynnt also provides 24/7 support to assist users in their AI journey.

LocalhostAI is an AI tool designed to assist users within the Chrome browser environment. It serves as a virtual assistant, providing support and guidance for various tasks. The tool integrates with Gemini Nano Chat to enhance communication capabilities and streamline interactions. Users can leverage the AI model to receive personalized recommendations, reminders, and information, ultimately improving productivity and efficiency in their daily activities.

Neptune is an MLOps stack component for experiment tracking. It allows users to track, compare, and share their models in one place. Neptune is used by scaling ML teams to skip days of debugging disorganized models, avoid long and messy model handovers, and start logging for free.

Quivr is an open-source chat-powered second brain application that transforms private and enterprise knowledge into a personal AI assistant. It continuously learns and improves at every interaction, offering AI-powered workplace search synced with user data. Quivr allows users to connect with their favorite tools, databases, and applications, and configure their 'second brain' to train on their company's unique context for improved search relevance and knowledge discovery.

Daloopa is an AI financial modeling tool designed to automate fundamental data updates for financial analysts working in Excel. It helps analysts build and update financial models efficiently by eliminating manual work and providing accurate, auditable data points sourced from thousands of companies. Daloopa leverages AI technology to deliver complete and comprehensive data sets faster than humanly possible, enabling analysts to focus on analysis, insight generation, and idea development to drive better investment decisions.

AIPRM is a prompt management tool and community-driven prompt library designed to enhance productivity by providing ready-to-use prompts for ChatGPT, Claude, Midjourney, and DALL-E. It offers a vast repository of expertly crafted prompts, private prompt creation, and custom GPT community prompts. Trusted by over 2 million users and major brands, AIPRM is known for its efficiency and affordability in leveraging AI technology for various tasks.

Storytell.ai is an AI-powered platform designed to amplify team productivity by providing business-grade intelligence across data. It offers features such as SmartChat™, tagging, and content structuring to enhance work, life, and play experiences. Trusted by users across various industries, Storytell.ai leverages AI technology to streamline tasks and decision-making processes, ultimately leading to increased efficiency and profitability.

Elavra is an AI Image Gallery application that utilizes artificial intelligence technology to organize and display images based on user preferences. Users can explore a wide range of images, including models, animals, art, and more. The platform offers features such as image reactions, favorites, and user rankings. Elavra aims to provide a visually engaging experience for users to discover and share images in a user-friendly interface.

The Drive AI is an AI-powered storage and collaboration platform designed for students, educators, researchers, and writers. It helps users organize class materials, ask questions for deeper insights, turn insights into writing, collaborate with friends, and access the latest AI models for faster writing. The platform offers features such as collaborative workspace, chat channels, auto-complete summarizer, and access to AI assistants for writing assistance. The Drive AI aims to enhance productivity and efficiency in academic and professional tasks through AI-powered solutions.

BrainChat is an AI application that enables teams to utilize ChatGPT and other Large Language Models (LLMs) in a structured, secure, and collaborative manner for work purposes. It offers organized and collaborative chats, tailored AI assistants for various job roles, private and safe infrastructure, multiple LLM options, and cost-efficient pricing compared to ChatGPT Team. BrainChat allows users to import chats from ChatGPT, offers real-time collaboration, and ensures data security and GDPR compliance.

Sacred is a tool to configure, organize, log and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments. The ability to conveniently make experiments configurable is at the heart of Sacred. If the parameters of an experiment are exposed in this way, it will help you to: keep track of all the parameters of your experiment easily run your experiment for different settings save configurations for individual runs in files or a database reproduce your results In Sacred we achieve this through the following main mechanisms: Config Scopes are functions with a @ex.config decorator, that turn all local variables into configuration entries. This helps to set up your configuration really easily. Those entries can then be used in captured functions via dependency injection. That way the system takes care of passing parameters around for you, which makes using your config values really easy. The command-line interface can be used to change the parameters, which makes it really easy to run your experiment with modified parameters. Observers log every information about your experiment and the configuration you used, and saves them for example to a Database. This helps to keep track of all your experiments. Automatic seeding helps controlling the randomness in your experiments, such that they stay reproducible.

Excire is an award-winning AI-based software designed for perfect photo management. The latest version, Excire Foto 2024, elevates your photo search and organization to a new level. It features five independent AI models that provide various search functions. Additionally, it offers innovative features and enhanced performance. Excire Search 2024 is the latest upgrade for Lightroom Classic users, offering intelligent image management, improved photo analysis AI, and integrated free-text search. Excire excels in assisting users in maintaining digital archives, finding photos quickly, and creating photo collections effortlessly.

Artifise is an AI-powered productivity tool that revolutionizes task management by simplifying to-do lists and enhancing productivity. It offers features like Focus Mode for distraction-free work, AI Project creation for project planning, and Personal llm's for personalized language models. With Artifise, users can experience effortless daily task management and improved efficiency.

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.

The Local File Organizer is an AI-powered tool designed to help users organize their digital files efficiently and securely on their local device. By leveraging advanced AI models for text and visual content analysis, the tool automatically scans and categorizes files, generates relevant descriptions and filenames, and organizes them into a new directory structure. All AI processing occurs locally using the Nexa SDK, ensuring privacy and security. With support for multiple file types and customizable prompts, this tool aims to simplify file management and bring order to users' digital lives.

LLM-Navigation is a repository dedicated to documenting learning records related to large models, including basic knowledge, prompt engineering, building effective agents, model expansion capabilities, security measures against prompt injection, and applications in various fields such as AI agent control, browser automation, financial analysis, 3D modeling, and tool navigation using MCP servers. The repository aims to organize and collect information for personal learning and self-improvement through AI exploration.

Neptune is a scalable experiment tracker for teams training foundation models. Log millions of runs, effortlessly monitor and visualize model training, and deploy on your infrastructure. Track 100% of metadata to accelerate AI breakthroughs. Log and display any framework and metadata type from any ML pipeline. Organize experiments with nested structures and custom dashboards. Compare results, visualize training, and optimize models quicker. Version models, review stages, and access production-ready models. Share results, manage users, and projects. Integrate with 25+ frameworks. Trusted by great companies to improve workflow.

A Next.js-based AI writing assistant that helps users organize writing style prompts and sends them to large language models (LLMs) to generate content. The tool aims to help writers, content creators, and copywriters improve writing efficiency and quality through AI technology. It features rich writing style customization, support for multiple LLM APIs, flexible API settings, user-friendly interface, real-time content editing, export function, detailed debugging information, dark/light mode support, and more.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

AudioNotes is a system built on FunASR and Qwen2 that can quickly extract content from audio and video, and organize it using large models into structured markdown notes for easy reading. Users can interact with the audio and video content, install Ollama, pull models, and deploy services using Docker or locally with a PostgreSQL database. The system provides a seamless way to convert audio and video into structured notes for efficient consumption.

Tangent is a canvas for exploring AI conversations, allowing users to resurrect and continue conversations, branch and explore different ideas, organize conversations by topics, and support archive data exports. It aims to provide a visual/textual/audio exploration experience with AI assistants, offering a 'thoughts workbench' for experimenting freely, reviving old threads, and diving into tangents. The project structure includes a modular backend with components for API routes, background task management, data processing, and more. Prerequisites for setup include Whisper.cpp, Ollama, and exported archive data from Claude or ChatGPT. Users can initialize the environment, install Python packages, set up Ollama, configure local models, and start the backend and frontend to interact with the tool.

Curiso AI is an infinite canvas platform that connects nodes and AI services to explore ideas without repetition. It empowers advanced users to unlock richer AI interactions. Features include multi OS support, infinite canvas, multiple AI provider integration, local AI inference provider integration, custom model support, model metrics, RAG support, local Transformers.js embedding models, inference parameters customization, multiple boards, vision model support, customizable interface, node-based conversations, and secure local encrypted storage. Curiso also offers a Solana token for exclusive access to premium features and enhanced AI capabilities.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.

The NeMo Framework Launcher is a cloud-native tool designed for launching end-to-end NeMo Framework training jobs. It focuses on foundation model training for generative AI models, supporting large language model pretraining with techniques like model parallelism, tensor, pipeline, sequence, distributed optimizer, mixed precision training, and more. The tool scales to thousands of GPUs and can be used for training LLMs on trillions of tokens. It simplifies launching training jobs on cloud service providers or on-prem clusters, generating submission scripts, organizing job results, and supporting various model operations like fine-tuning, evaluation, export, and deployment.

AI File Organizer 2000 is an Obsidian Plugin that uses AI to transcribe audio, annotate images, and automatically organize files by moving them to the most likely folders. It supports text, audio, and images, with upcoming local-first LLM support. Users can simply place unorganized files into the 'Inbox' folder for automatic organization. The tool renames and moves files quickly, providing a seamless file organization experience. Self-hosting is also possible by running the server and enabling the 'Self-hosted' option in the plugin settings. Join the community Discord server for more information and use the provided iOS shortcut for easy access on mobile devices.

A-MEM is a novel agentic memory system designed for Large Language Model (LLM) agents to dynamically organize memories in an agentic way. It introduces advanced memory organization capabilities, intelligent indexing, and linking of memories, comprehensive note generation, interconnected knowledge networks, continuous memory evolution, and agent-driven decision making for adaptive memory management. The system facilitates agent construction and enables dynamic memory operations and flexible agent-memory interactions.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

This repository contains the training code of MobileLLM, a language model optimized for on-device use cases with fewer than a billion parameters. It integrates SwiGLU activation function, deep and thin architectures, embedding sharing, and grouped-query attention to achieve high-quality LLMs. MobileLLM-125M/350M shows significant accuracy improvements over previous models on zero-shot commonsense reasoning tasks. The design philosophy scales effectively to larger models, with state-of-the-art results for MobileLLM-600M/1B/1.5B.

LL3DA is a Large Language 3D Assistant that responds to both visual and textual interactions within complex 3D environments. It aims to help Large Multimodal Models (LMM) comprehend, reason, and plan in diverse 3D scenes by directly taking point cloud input and responding to textual instructions and visual prompts. LL3DA achieves remarkable results in 3D Dense Captioning and 3D Question Answering, surpassing various 3D vision-language models. The code is fully released, allowing users to train customized models and work with pre-trained weights. The tool supports training with different LLM backends and provides scripts for tuning and evaluating models on various tasks.

ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It supports training, inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. The framework includes various training technologies and accelerates inference, evaluation, and deployment modules. It offers a Gradio-based Web-UI interface and best practices for easy application of large models. ms-swift supports a wide range of model types, dataset types, hardware support, lightweight training methods, distributed training techniques, quantization training, RLHF training, multi-modal training, interface training, plugin and extension support, inference acceleration engines, model evaluation, and model quantization.

This repository contains frontier research on explainable AI (XAI), a hot topic in the field of artificial intelligence. It includes trends, use cases, survey papers, books, open courses, papers, and Python libraries related to XAI. The repository aims to organize and categorize publications on XAI, provide evaluation methods, and list various Python libraries for explainable AI.

Step into the world of high fashion. As runway designers, they create unique collections, organize dazzling fashion shows, and stay ahead of the latest trends. It blends elements of design, marketing, and business management in the glamorous fashion industry

The Writing Metier Footnote Assistant is a specialized GPT model designed to help students efficiently create, format, and verify footnotes for their academic papers.

Friendly AI travel companion for personalized trip planning.

Organize and develop ideas, respecting privacy and copyright laws.

Organize, Journal, and Achieve: Your All-in-One App for Masterful Task Management and Reflection

Analyzes and categorizes perfume data from Excel, or lists. Upload a file with your perfume names or just the names of your perfumes and this GPT will help you organize the information.

Helps you organize any text into a Jira ticket!

I organize my thoughts using ontology matrices, for detailed CWP advice.

I help organize your feelings when you're feeling unclear or frustrated etc and aware of important matters. It's also great for sharing happy moments. I support your emotional well-being and self-understanding.

I brainstorm and organize story ideas into structured events, world facts, and character outlines. Elevate your storytelling craft with Fiction Flow's GhostWriter series; learn more at fictionflow.io.

I help plan and organize RAM Rallies. A RAM Rally is a unique type of rally geared toward adventure motorcycle and dual sport riders of all skill levels.

I help declutter your space by analyzing room photos and suggesting what to organize.

Align, organize, and enhance design assets effortlessly.

Connect and interact with your Google accounts. Organize, retrieve, and manipulate data with A.I

Solve math and logic with Smart GPT. Use Tree and Chain of thoughts to organize and find answers.

Comprehensive categorization and organization for AI Prompts

Organizes text into structured output and creates visual mind maps.

Assists in developing a visual inventory management app.

Coaches on Second Brain & PARA, focuses on information categorization.

Guides software development processes through Kanban methodologies.