Best AI tools for< Model Environments >

Luma Dream Machine is a cutting-edge AI tool that empowers users to ideate, visualize, and create stunning images and videos effortlessly. It offers a new fluid medium for creativity, enabling users to bring their wildest dreams to life with the help of powerful image and video AI models. The platform is designed to be intuitive and user-friendly, allowing users to explore endless ideas, make unique creations, and share their vision with the world. Luma Dream Machine is available on iOS and the Web, providing a seamless experience for creators of all levels.

Luma AI is a 3D capture platform that allows users to create interactive 3D scenes from videos. With Luma AI, users can capture 3D models of people, objects, and environments, and then use those models to create interactive experiences such as virtual tours, product demonstrations, and training simulations.

LuckyRobots is an AI tool designed to make robotics accessible to software engineers by providing a simulation platform for deploying end-to-end AI models. The platform allows users to interact with robots using natural language commands, explore virtual environments, test robot models in realistic scenarios, and receive camera feeds for monitoring. LuckyRobots aims to train AI models on real-world simulations and respond to natural language inputs, offering a user-friendly and innovative approach to robotics development.

Lexset is an AI tool that provides synthetic data generation services for computer vision model training. It offers a no-code interface to create unlimited data with advanced camera controls and lighting options. Users can simulate AI-scale environments, composite objects into images, and create custom 3D scenarios. Lexset also provides access to GPU nodes, dedicated support, and feature development assistance. The tool aims to improve object detection accuracy and optimize generalization on high-quality synthetic data.

Valohai is a scalable MLOps platform that enables Continuous Integration/Continuous Deployment (CI/CD) for machine learning and pipeline automation on-premises and across various cloud environments. It helps streamline complex machine learning workflows by offering framework-agnostic ML capabilities, automatic versioning with complete lineage of ML experiments, hybrid and multi-cloud support, scalability and performance optimization, streamlined collaboration among data scientists, IT, and business units, and smart orchestration of ML workloads on any infrastructure. Valohai also provides a knowledge repository for storing and sharing the entire model lifecycle, facilitating cross-functional collaboration, and allowing developers to build with total freedom using any libraries or frameworks.

Alluxio is a data orchestration platform designed for the cloud, offering seamless access, management, and running of AI/ML workloads. Positioned between compute and storage, Alluxio provides a unified solution for enterprises to handle data and AI tasks across diverse infrastructure environments. The platform accelerates model training and serving, maximizes infrastructure ROI, and ensures seamless data access. Alluxio addresses challenges such as data silos, low performance, data engineering complexity, and high costs associated with managing different tech stacks and storage systems.

AssetsAI is an AI-powered platform that offers unique and curated game assets for game design and development. Users can access a wide range of bespoke game assets in various styles to inspire and assist in creating their next game. The platform provides fair pricing with a pay-per-asset model, ensuring users only pay for what they need. With high-quality images and new assets added weekly, AssetsAI aims to provide game developers with the resources they need to bring their visions to life.

SceneDreamer is an AI tool that specializes in generating unbounded 3D scenes from 2D image collections. It utilizes an unconditional generative model to synthesize large-scale 3D landscapes with diverse styles, 3D consistency, well-defined depth, and free camera trajectory. The framework is learned solely from in-the-wild 2D image collections, without the need for 3D annotations. SceneDreamer employs an efficient bird's-eye-view representation, a generative scene parameterization, and a neural volumetric renderer to achieve photorealistic images of vivid and diverse unbounded 3D worlds.

WTRI is an AI application that offers FutureView™, a suite of tools designed to help individuals and businesses rehearse their future scenarios in a virtual environment. By leveraging cognitive agility assessment, event generation, modeling, and virtual world platforms, WTRI aims to assist users in making agile decisions and adapting to rapidly changing business environments. With a focus on risk management and outcome optimization, WTRI provides a unique approach to strategic planning and preparedness.

Arrival is a cutting-edge software solution that allows users to design 3D virtual spaces with AI assistance and drag-and-drop functionality. It enables effortless creation of immersive environments by utilizing a built-in text-to-3D ML model, a user-friendly drag & drop interface, and seamless integration with leading virtual worlds and video gaming marketplaces.

Athina AI is a comprehensive platform designed to monitor, debug, analyze, and improve the performance of Large Language Models (LLMs) in production environments. It provides a suite of tools and features that enable users to detect and fix hallucinations, evaluate output quality, analyze usage patterns, and optimize prompt management. Athina AI supports integration with various LLMs and offers a range of evaluation metrics, including context relevancy, harmfulness, summarization accuracy, and custom evaluations. It also provides a self-hosted solution for complete privacy and control, a GraphQL API for programmatic access to logs and evaluations, and support for multiple users and teams. Athina AI's mission is to empower organizations to harness the full potential of LLMs by ensuring their reliability, accuracy, and alignment with business objectives.

crewAI is a platform for Multi AI Agents Systems that offers a user-friendly framework for automating workflows with AI agents. It simplifies the process of building and deploying multi-agent automations, providing support for various AI models and templates. With a focus on privacy and security, crewAI ensures that each agent runs in isolated environments. The platform is suitable for enterprises and developers looking to leverage AI technologies effectively.

TitanML is a platform that provides tools and services for deploying and scaling Generative AI applications. Their flagship product, the Titan Takeoff Inference Server, helps machine learning engineers build, deploy, and run Generative AI models in secure environments. TitanML's platform is designed to make it easy for businesses to adopt and use Generative AI, without having to worry about the underlying infrastructure. With TitanML, businesses can focus on building great products and solving real business problems.

H2O.ai is a leading AI platform that offers a range of open-source and enterprise solutions for machine learning and AI applications. The platform includes products such as H2O-3, H2O Wave, Sparkling Water, H2O AI Cloud, H2O Driverless AI, and more. H2O.ai aims to democratize AI by providing tools for building, deploying, and managing AI/ML models in various environments, including the cloud. The platform also emphasizes explainable AI to enhance transparency and trustworthiness in AI applications.

The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.

Qubinets is a cloud data environment solutions platform that provides building blocks for building big data, AI, web, and mobile environments. It is an open-source, no lock-in, secured, and private platform that can be used on any cloud, including AWS, Digital Ocean, Google Cloud, and Microsoft Azure. Qubinets makes it easy to plan, build, and run data environments, and it streamlines and saves time and money by reducing the grunt work in setup and provisioning.

Synthesis AI is a synthetic data platform that enables more capable and ethical computer vision AI. It provides on-demand labeled images and videos, photorealistic images, and 3D generative AI to help developers build better models faster. Synthesis AI's products include Synthesis Humans, which allows users to create detailed images and videos of digital humans with rich annotations; Synthesis Scenarios, which enables users to craft complex multi-human simulations across a variety of environments; and a range of applications for industries such as ID verification, automotive, avatar creation, virtual fashion, AI fitness, teleconferencing, visual effects, and security.

SoraWebui is an open-source web platform that simplifies video creation by allowing users to generate videos from text using OpenAI's Sora model. It provides an easy-to-use interface and one-click website deployment, making it accessible to both professionals and enthusiasts in video production and AI technology. SoraWebui also includes a simulated version of the Sora API called FakeSoraAPI, which allows developers to start developing and testing their projects in a mock environment.

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.

Deploya is an AI-powered platform that allows users to create production-ready websites in seconds. By leveraging cutting-edge AI models, Deploya optimizes websites for performance and user experience. Users can simply describe their requirements, and Deploya will generate a website accordingly. The platform also offers features like automatic image selection, quick publishing, and flexible pricing options. Deploya stands out for its AI-driven web design capabilities and efficient website deployment process.

AI4U is a tool that provides a framework for modeling virtual reality and game environments. It offers an alternative approach to modeling Non-Player Characters (NPCs) in Godot Game Engine. AI4U defines an agent living in an environment and interacting with it through sensors and actuators. Sensors provide data to the agent's brain, while actuators send actions from the agent to the environment. The brain processes the sensor data and makes decisions (selects an action by time). AI4U can also be used in other situations, such as modeling environments for artificial intelligence experiments.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

The AI Enablement Stack is a curated collection of venture-backed companies, tools, and technologies that enable developers to build, deploy, and manage AI applications. It provides a structured view of the AI development ecosystem across five key layers: Agent Consumer Layer, Observability and Governance Layer, Engineering Layer, Intelligence Layer, and Infrastructure Layer. Each layer focuses on specific aspects of AI development, from end-user interaction to model training and deployment. The stack aims to help developers find the right tools for building AI applications faster and more efficiently, assist engineering leaders in making informed decisions about AI infrastructure and tooling, and help organizations understand the AI development landscape to plan technology adoption.

AgentGym is a framework designed to help the AI community evaluate and develop generally-capable Large Language Model-based agents. It features diverse interactive environments and tasks with real-time feedback and concurrency. The platform supports 14 environments across various domains like web navigating, text games, house-holding tasks, digital games, and more. AgentGym includes a trajectory set (AgentTraj) and a benchmark suite (AgentEval) to facilitate agent exploration and evaluation. The framework allows for agent self-evolution beyond existing data, showcasing comparable results to state-of-the-art models.

AWS MCP is a Model Context Protocol (MCP) server that facilitates interactions between AI assistants and AWS environments. It allows for natural language querying and management of AWS resources during conversations. The server supports multiple AWS profiles, SSO authentication, multi-region operations, and secure credential handling. Users can locally execute commands with their AWS credentials, enhancing the conversational experience with AWS resources.

Wenda is a platform for large-scale language model invocation designed to efficiently generate content for specific environments, considering the limitations of personal and small business computing resources, as well as knowledge security and privacy issues. The platform integrates capabilities such as knowledge base integration, multiple large language models for offline deployment, auto scripts for additional functionality, and other practical capabilities like conversation history management and multi-user simultaneous usage.

OpenVINO™ Model Server (OVMS) is a high-performance system for serving models. Implemented in C++ for scalability and optimized for deployment on Intel architectures, the model server uses the same architecture and API as TensorFlow Serving and KServe while applying OpenVINO for inference execution. Inference service is provided via gRPC or REST API, making deploying new algorithms and AI experiments easy.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

ScaleLLM is a cutting-edge inference system engineered for large language models (LLMs), meticulously designed to meet the demands of production environments. It extends its support to a wide range of popular open-source models, including Llama3, Gemma, Bloom, GPT-NeoX, and more. ScaleLLM is currently undergoing active development. We are fully committed to consistently enhancing its efficiency while also incorporating additional features. Feel free to explore our **_Roadmap_** for more details. ## Key Features * High Efficiency: Excels in high-performance LLM inference, leveraging state-of-the-art techniques and technologies like Flash Attention, Paged Attention, Continuous batching, and more. * Tensor Parallelism: Utilizes tensor parallelism for efficient model execution. * OpenAI-compatible API: An efficient golang rest api server that compatible with OpenAI. * Huggingface models: Seamless integration with most popular HF models, supporting safetensors. * Customizable: Offers flexibility for customization to meet your specific needs, and provides an easy way to add new models. * Production Ready: Engineered with production environments in mind, ScaleLLM is equipped with robust system monitoring and management features to ensure a seamless deployment experience.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

The 'LLM-Brained-GUI-Agents-Survey' repository contains code for a searchable paper page and assets used in the survey paper on Large Language Model-Brained GUI Agents. These agents operate within GUI environments, utilizing Large Language Models as their core inference and cognitive engine to generate, plan, and execute actions flexibly and adaptively. The repository also encourages contributions from the community for new papers, resources, or improvements related to LLM-Powered GUI Agents.

JittorLLMs is a large model inference library that allows running large models on machines with low hardware requirements. It significantly reduces hardware configuration demands, enabling deployment on ordinary machines with 2GB of memory. It supports various large models and provides a unified environment configuration for users. Users can easily migrate models without modifying any code by installing Jittor version of torch (JTorch). The framework offers fast model loading speed, optimized computation performance, and portability across different computing devices and environments.

MLC LLM is a high-performance universal deployment solution that allows native deployment of any large language models with native APIs with compiler acceleration. It supports a wide range of model architectures and variants, including Llama, GPT-NeoX, GPT-J, RWKV, MiniGPT, GPTBigCode, ChatGLM, StableLM, Mistral, and Phi. MLC LLM provides multiple sets of APIs across platforms and environments, including Python API, OpenAI-compatible Rest-API, C++ API, JavaScript API and Web LLM, Swift API for iOS App, and Java API and Android App.

This project is a c++ version of the AI reasoning library that supports the reasoning of tensorrt models. It provides accelerated deployment cases of deep learning CV popular models and supports dynamic-batch image processing, inference, decode, and NMS. The project has been updated with various models and provides tutorials for model exports. It also includes a producer-consumer inference model for specific tasks. The project directory includes implementations for model inference applications, backend reasoning classes, post-processing, pre-processing, and target detection and tracking. Speed tests have been conducted on various models, and onnx downloads are available for different models.

This practical free hands-on course focuses on Large Language models and their applications, providing a hands-on experience using models from OpenAI and the Hugging Face library. The course is divided into three major sections: Techniques and Libraries, Projects, and Enterprise Solutions. It covers topics such as Chatbots, Code Generation, Vector databases, LangChain, Fine Tuning, PEFT Fine Tuning, Soft Prompt tuning, LoRA, QLoRA, Evaluate Models, Knowledge Distillation, and more. Each section contains chapters with lessons supported by notebooks and articles. The course aims to help users build projects and explore enterprise solutions using Large Language Models.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with discrete action spaces. The algorithm aims to improve sample efficiency by using two uncorrelated Q-Networks to prevent overestimation of Q-values. By updating parameters periodically, the model reduces computation time and enhances training performance. The tool is based on the Double DQN method proposed by Hasselt in 2010.

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

Colossal-AI is a deep learning system for large-scale parallel training. It provides a unified interface to scale sequential code of model training to distributed environments. Colossal-AI supports parallel training methods such as data, pipeline, tensor, and sequence parallelism and is integrated with heterogeneous training and zero redundancy optimizer.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework enables LLMs to reason and interact with the environment, optimizing entire trajectories for long-horizon reasoning while maintaining computational efficiency.

The 'ShitSpotter' repository is dedicated to developing a poop-detection algorithm and dataset for creating a phone app that helps locate dog poop in outdoor environments. The project involves training a PyTorch network to detect poop in images and provides scripts for detecting poop in unseen images using a pretrained model. The dataset consists of mostly outdoor images taken with a phone, with a process involving before and after pictures of the poop. The project aims to enable various applications, such as AR glasses for poop detection and efficient cleaning of public areas by city governments. The code, dataset, and pretrained models are open source with permissive licensing and distributed via IPFS, BitTorrent, and centralized mechanisms.

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Generates Environmental impact assessment templates based on specific global locations and parameters.

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HydroGPT is an expert in water resources engineering, specializing in hydrology, hydraulics, and drainage design. It provides detailed assistance in modeling concepts, methodologies, scopes of work, and drainage report writing, including aerial image analysis.

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Physical Geology expert

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Creative metaverse visual generator

Concise and helpful expert in Blender 3D, guiding users in all aspects of 3D creation.

Virtual professor specializing in Blender Studio with practical examples.

GPT expert on Houdini & Unreal workflows

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Concise NeRF guide, summarizing top papers and aiding research.

Talk to Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning (LFG)

The pressing environmental issues we face today require novel approaches and technological advancements to effectively mitigate their impacts. GaiaAI offers a range of tools and modes to promote sustainable practices and enhance environmental stewardship.

Unreal Engine アシスタント

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Create voxel art references. Assets, scenes, weapons, general design. Type 'Create + text'. English, Portuguese, Philipines,..., +60 others.