Best AI tools for< Iterative Training >

DVC Studio is a collaboration tool for machine learning teams. It provides seamless data and model management, experiment tracking, visualization, and automation. DVC Studio is built for ML researchers, practitioners, and managers. It enables model organization and discovery across all ML projects and manages model lifecycle with Git, unifying ML projects with the best DevOps practices. DVC Studio also provides ML experiment tracking, visualization, collaboration, and automation using Git. It applies software engineering and DevOps best-practices to automate ML bookkeeping and model training, enabling easy collaboration and faster iterations.

Weet is an all-in-one video creation, editing, and tracking platform that offers a wide range of tools to help businesses create professional-looking interactive videos quickly and easily. With Weet, users can record their screen and webcam, create avatar videos, generate subtitles and translations, edit and trim videos, and add interactivity to make their videos more engaging. Weet also offers real-time collaboration, built-in comments and interactions, and designated workspaces and channels to help teams stay organized and make their videos easy to search.

Akadimia Ai is an AI-powered platform designed to provide users with a range of educational resources and tools. The platform leverages artificial intelligence to offer personalized learning experiences, interactive tutorials, and assessments. Users can access a variety of courses, quizzes, and study materials tailored to their individual needs and learning preferences. Akadimia Ai aims to enhance the learning process by offering adaptive content recommendations and progress tracking features. Whether you are a student looking to improve your academic performance or a professional seeking to acquire new skills, Akadimia Ai offers a comprehensive learning solution to help you achieve your goals.

BotGPT is a 24/7 custom AI chatbot assistant for websites. It offers a data-driven ChatGPT that allows users to create virtual assistants from their own data. Users can easily upload files or crawl their website to start asking questions and deploy a custom chatbot on their website within minutes. The platform provides a simple and efficient way to enhance customer engagement through AI-powered chatbots.

edu720 is a science-backed learning platform that uses AI and nanolearning to redefine how workforces learn and achieve their goals. It provides pre-built learning modules on various topics, including cybersecurity, privacy, and AI ethics. edu720's 360-degree approach ensures that all employees, regardless of their status or location, fully understand and absorb the knowledge conveyed.

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.

SkillsTeq is an interactive e-learning and training platform powered by AI technology. It revolutionizes learning and training by offering interactive courses, sales and customer experience training, assessments, and feedback through a unique chat-based learning environment. The platform leverages AI to deliver personalized training tailored to company data, engaging users in dynamic conversations and immersive experiences. SkillsTeq provides efficient AI e-learning, immersive roleplays, intelligent assessments, roleplay training, sales coaching, and customer experience training, all aimed at enhancing knowledge retention and preparing users for real-world scenarios.

Slice Knowledge is an AI-powered content creation platform designed for learning purposes. It offers fast and simple creation of learning units using AI technology. The platform is a perfect solution for course creators, HR and L&D teams, education experts, and enterprises looking to enhance their employee training programs. Slice Knowledge provides AI-powered creation, compliance templates, assistant bots, SCORM tracking integration, and multilingual support. It allows users to convert documents into interactive, responsive, SCORM-compliant learning materials with features like unlimited designer CSS, interactive video, multi-lingual support, and responsive design.

NOLEJ is an AI-powered platform that helps instructional designers and teachers rapidly generate interactive eLearning material. It can automatically generate interactive content from existing learning materials, such as textbooks, videos, and online media resources. NOLEJ also offers a variety of interactive formats, including interactive videos, flashcards, glossaries, crosswords, drag-and-drop activities, find-the-word puzzles, and interactive books.

Sessions is a cloud-based video conferencing and webinar platform that offers a range of features to help businesses run successful online meetings and events. With Sessions, users can create interactive agendas, share screens, record meetings, and host webinars with up to 1000 participants. Sessions also integrates with a variety of third-party tools, including Google Drive, Dropbox, and Slack, making it easy to collaborate with colleagues and share files. Additionally, Sessions offers a number of AI-powered features, such as automatic transcription and translation, to help users get the most out of their meetings.

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.

VideoAsk by Typeform is an interactive video platform that helps streamline conversations and build business relationships at scale. It offers features such as asynchronous interviews, easy scheduling, tagging, gathering contact info, capturing leads, research and feedback, training, customer support, and more. Users can create interactive video forms, conduct async interviews, and engage with their audience through AI-powered video chatbots. The platform is user-friendly, code-free, and integrates with over 1,500 applications through Zapier.

Absorb Software Inc. offers Absorb LMS, an AI-powered learning management system designed to deliver impactful eLearning experiences. The platform provides personalized learning paths, integrates AI for enhanced search results, and offers features like smart administration, learner engagement, eCommerce, reporting & analytics, and observation checklist. Absorb LMS caters to various use cases such as compliance training, onboarding, employee development, customer education, partner enablement, and selling courses. The platform is known for its user-friendly interface, scalability, and exceptional customer service.

MyInterviewSkill is a gamified platform that offers AI-driven interview training to help users enhance their interview skills and boost their chances of landing their dream job. The platform provides personalized interview questions, tailored feedback, daily challenges, and interactive quests to make the preparation process engaging and effective. Users can earn experience points (XP), badges, and game credits as they progress through the training, ultimately improving their interview techniques and confidence.

Sereda.ai is an AI-powered platform designed to unleash a team's potential by bringing together all documents and knowledge into one place, conducting employee surveys and satisfaction ratings, facilitating performance reviews, and providing solutions to increase team productivity. The platform offers features such as a knowledge base, employee surveys, performance review tools, interactive learning courses, and an AI assistant for instant answers. Sereda.ai aims to streamline HR processes, improve employee training and evaluation, and enhance overall team productivity.

Charisma is a storytelling platform powered by artificial intelligence. It allows users to create virtual characters with voices, emotions, memories, and goals. These characters can be used in web, app, gaming, VR, and other interactive formats. Charisma's no-code story engine powers multi-character conversations for award-winning entertainment, marketing, and learning and training experiences.

ChatTube AI is an AI-powered platform that allows users to chat with YouTube videos and PDF documents, engage with interactive content, and generate quizzes for a smarter learning experience. It offers features such as real-time Q&A with videos, interactive PDF engagement, effortless quiz creation, and supports various use cases like distance learning, corporate training, content creation, and research and development. The platform provides powerful features for efficient information retrieval, easy quiz creation, and integrated learning tools, making learning interactive and engaging.

Calibr is an AI-powered Learning Management System (LMS) and Learning Experience Platform (LXP) that offers a cutting-edge course authoring platform and personalized learning experiences for modern workforces. The platform leverages generative AI to create engaging and interactive courses, streamline user and group management, and provide 360-degree analytics for measuring learner progress. With features like SCORM support, SSO integration, and mobile learning capabilities, Calibr aims to empower organizations to foster a culture of continuous learning and align learning strategies with business goals.

Wonda is an AI-powered platform that enables users to create immersive learning experiences and simulations. It offers a range of features such as AI companions, quiz and assessments, virtual tours, role-playing games, and virtual workshops. Users can easily build interactive learning journeys without the need for coding. Wonda aims to enhance engagement and collaboration in training and onboarding processes, making learning experiences unforgettable and impactful. The platform supports integration with various learning management systems and provides advanced role management options for secure sharing. With Wonda, users can unleash their creativity and build virtual exhibits to showcase their ideas and projects in a fun and engaging way.

PaddleBoat is an AI-powered sales readiness platform designed to help sales representatives improve their cold calling skills through realistic AI roleplays. It offers automated call feedback, insights on objection handling, best calling practices, and areas for improvement in every roleplay. PaddleBoat aims to accelerate sales excellence by providing real-time insights, customizing roleplays, and minimizing ramp-up time for sales reps. The platform allows users to create engaging training programs, courses, wikis, and interactive videos to enhance their sales pitch skills and boost their confidence in sales conversations.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

This repository is dedicated to collecting and categorizing papers related to Retrieval-Augmented Generation (RAG) for AI-generated content. It serves as a survey repository based on the paper 'Retrieval-Augmented Generation for AI-Generated Content: A Survey'. The repository is continuously updated to keep up with the rapid growth in the field of RAG.

fAIr is an open AI-assisted mapping service developed by the Humanitarian OpenStreetMap Team (HOT) to improve mapping efficiency and accuracy for humanitarian purposes. It uses AI models, specifically computer vision techniques, to detect objects like buildings, roads, waterways, and trees from satellite and UAV imagery. The service allows OSM community members to create and train their own AI models for mapping in their region of interest and ensures models are relevant to local communities. Constant feedback loop with local communities helps eliminate model biases and improve model accuracy.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Fairseq is a sequence modeling toolkit that enables researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. It provides reference implementations of various sequence modeling papers covering CNN, LSTM networks, Transformer networks, LightConv, DynamicConv models, Non-autoregressive Transformers, Finetuning, and more. The toolkit supports multi-GPU training, fast generation on CPU and GPU, mixed precision training, extensibility, flexible configuration based on Hydra, and full parameter and optimizer state sharding. Pre-trained models are available for translation and language modeling with a torch.hub interface. Fairseq also offers pre-trained models and examples for tasks like XLS-R, cross-lingual retrieval, wav2vec 2.0, unsupervised quality estimation, and more.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

This repository contains code for training reward models for Deep Reinforcement Learning-based Reward-modulated Hierarchical Fine-tuning (DRL-based RLHF), Iterative Selection Fine-tuning (Rejection sampling fine-tuning), and iterative Decision Policy Optimization (DPO). The reward models are trained using a Bradley-Terry model based on the Gemma and Mistral language models. The resulting reward models achieve state-of-the-art performance on the RewardBench leaderboard for reward models with base models of up to 13B parameters.

This repository, RLHF-Reward-Modeling, is dedicated to training reward models for DRL-based RLHF (PPO), Iterative SFT, and iterative DPO. It provides state-of-the-art performance in reward models with a base model size of up to 13B. The installation instructions involve setting up the environment and aligning the handbook. Dataset preparation requires preprocessing conversations into a standard format. The code can be run with Gemma-2b-it, and evaluation results can be obtained using provided datasets. The to-do list includes various reward models like Bradley-Terry, preference model, regression-based reward model, and multi-objective reward model. The repository is part of iterative rejection sampling fine-tuning and iterative DPO.

This repository contains the training and inference code needed to replicate the winning solution to the AI Mathematical Olympiad - Progress Prize 1. It consists of fine-tuning DeepSeekMath-Base 7B, high-quality training datasets, a self-consistency decoding algorithm, and carefully chosen validation sets. The training methodology involves Chain of Thought (CoT) and Tool Integrated Reasoning (TIR) training stages. Two datasets, NuminaMath-CoT and NuminaMath-TIR, were used to fine-tune the models. The models were trained using open-source libraries like TRL, PyTorch, vLLM, and DeepSpeed. Post-training quantization to 8-bit precision was done to improve performance on Kaggle's T4 GPUs. The project structure includes scripts for training, quantization, and inference, along with necessary installation instructions and hardware/software specifications.

RLAIF-V is a novel framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. It maximally exploits open-source feedback from high-quality feedback data and online feedback learning algorithm. Notable features include achieving super GPT-4V trustworthiness in both generative and discriminative tasks, using high-quality generalizable feedback data to reduce hallucination of different MLLMs, and exhibiting better learning efficiency and higher performance through iterative alignment.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

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.

SwanLab is an open-source, lightweight AI experiment tracking tool that provides a platform for tracking, comparing, and collaborating on experiments, aiming to accelerate the research and development efficiency of AI teams by 100 times. It offers a friendly API and a beautiful interface, combining hyperparameter tracking, metric recording, online collaboration, experiment link sharing, real-time message notifications, and more. With SwanLab, researchers can document their training experiences, seamlessly communicate and collaborate with collaborators, and machine learning engineers can develop models for production faster.

KnowAgent is a tool designed for Knowledge-Augmented Planning for LLM-Based Agents. It involves creating an action knowledge base, converting action knowledge into text for model understanding, and a knowledgeable self-learning phase to continually improve the model's planning abilities. The tool aims to enhance agents' potential for application in complex situations by leveraging external reservoirs of information and iterative processes.

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.

This repository contains an AI implementation that learns to play the classic game Snake in the terminal. The AI is built using Rust and Ratatui. Users can clone the repo, run the simulation, and configure various settings to customize the AI's behavior. The project also provides options for minimal UI, training custom networks, and watching the AI complete the game on different board sizes. The developer shares updates and insights about the project on Twitter and plans to create a detailed blog post explaining the AI's workings.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

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.

Expert in engaging, interactive training methods and activities.

CMMC Coach: Guiding through CMMC processes with tailored strategies, interactive training, and regulatory compliance. https://www.sikich.com/technology/contact-us/ 877.403.5227 | [email protected]

Interactive soft skills training with visual storytelling.

Interactive EMS scenario generator for EMT and paramedic training.

Interactive, detailed dog advisor for all things dog

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Welcome to Strategy Slugger the app designed for baseball enthusiasts, coaches, and players seeking in-depth knowledge and strategies about game. This interactive tool is your gateway to mastering baseball, offering insights into game planning, historical contexts, and tactical approaches to match.

Interactive Brain Age-style mental exercise guide

Interaktiver KI-Online-Kurs: 1. Gib hier ein beliebiges Fußballthema ein, z.B. Kopfballtraining, Lieblingsverein oder -spieler 2. Besuche aiMOOC.org und trage deinen Titel in das Eingabefeld ein. 3. Füge den generierten GPT-Text ein und speichere ihn.

Assists people with aphantasia in creating visuals through iterative steps.

A GPT specialized in Notion formulas, emphasizing accuracy and iterative solutions.

Your Personal Digital Artist to help you generate all the images you want to! Just like talking to a real artist you can chat with Chat Painter and iteratively refine your images!

Laugh out loud at incredible stories

Presents story templates and custom interactive novel experiences!

Pass in the attributes of Pojo entity class objects, generate corresponding addition, deletion, modification, and pagination query functions, including generating database connection configuration files yaml and database script files, as well as XML dynamic SQL concatenation statements.

Soy un tutor interactivo para enseñanza de álgebra, personalizando explicaciones y ejercicios para cada estudiante.

Guides through C++ concepts with interactive clarification.

Live an incredible love/romance story

Bring GPT Writing Skills to the Next Level. ************************************************************************ 24 dynamic commands to tailor and enhance your writing, discover the art of perfection in every word.

Interactive mystery author with visual storytelling