Best AI tools for< Evaluate Training Effectiveness >

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.

Mangus is an AI-powered learning platform that provides personalized learning paths for employees and students. It offers a wide range of courses and programs in various disciplines, including business, education, technology, and more. Mangus uses gamification and artificial intelligence to create an engaging and effective learning experience.

SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.

Sereda.ai is an AI-powered platform designed to unleash a team's potential by offering solutions for employee knowledge management, surveys, performance reviews, learning, and more. It integrates artificial intelligence to streamline HR processes, improve employee engagement, and boost productivity. The platform provides a user-friendly interface, personalized settings, and automation features to enhance organizational efficiency and reduce costs.

Scale AI is an AI tool that accelerates the development of AI applications for various sectors including enterprise, government, and automotive industries. It offers solutions for training models, fine-tuning, generative AI, and model evaluations. Scale Data Engine and GenAI Platform enable users to leverage enterprise data effectively. The platform collaborates with leading AI models and provides high-quality data for public and private sector applications.

Scale AI is an AI tool that accelerates the development of AI applications for enterprise, government, and automotive sectors. It offers Scale Data Engine for generative AI, Scale GenAI Platform, and evaluation services for model developers. The platform leverages enterprise data to build sustainable AI programs and partners with leading AI models. Scale's focus on generative AI applications, data labeling, and model evaluation sets it apart in the AI industry.

MeritTrac is an AI-powered testing and assessment solutions provider that offers customized solutions to enterprises and educational institutions. With a content strong and technology-first approach, MeritTrac empowers organizations to make data-driven decisions, enhance talent acquisition, workforce development, and educational assessments. Their AI-powered platforms ensure scalable, secure, and seamless assessment experiences across various domains.

Emocional is a platform that helps businesses evaluate, plan, and act to develop their employees' soft skills and promote well-being. It offers a unique personality and soft skills assessment, a personalized action plan, and access to expert training, coaching, therapy, and digital tools like EVA AI.

Entry Point AI is a modern AI optimization platform for fine-tuning proprietary and open-source language models. It provides a user-friendly interface to manage prompts, fine-tunes, and evaluations in one place. The platform enables users to optimize models from leading providers, train across providers, work collaboratively, write templates, import/export data, share models, and avoid common pitfalls associated with fine-tuning. Entry Point AI simplifies the fine-tuning process, making it accessible to users without the need for extensive data, infrastructure, or insider knowledge.

Chemprop is a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. Originally developed for research purposes, Chemprop offers a comprehensive set of tools and features for training models and analyzing molecular representations. The package underwent a recent major release (v2.0.0) with significant improvements and updates.

Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.

ParallelDots is a next-generation retail execution software powered by image recognition technology. The software offers solutions like ShelfWatch, Saarthi, and SmartGaze to enhance the efficiency of sales reps and merchandisers, provide faster training of image recognition models, and offer automated gaze-coding solutions for mobile and retail eye-tracking research. ParallelDots' computer vision technology helps CPG and retail brands track in-store compliance, address gaps in retail execution, and gain real-time insights into brand performance. The platform enables users to generate real-time KPI insights, evaluate compliance levels, convert insights into actionable strategies, and integrate computer vision with existing retail solutions seamlessly.

TractoAI is an advanced AI platform that offers deep learning solutions for various industries. It provides Batch Inference with no rate limits, DeepSeek offline inference, and helps in training open source AI models. TractoAI simplifies training infrastructure setup, accelerates workflows with GPUs, and automates deployment and scaling for tasks like ML training and big data processing. The platform supports fine-tuning models, sandboxed code execution, and building custom AI models with distributed training launcher. It is developer-friendly, scalable, and efficient, offering a solution library and expert guidance for AI projects.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

IELTSWritingPro is an AI-powered platform designed to help users improve their IELTS writing skills. It offers detailed feedback, band estimation, and personalized improvement suggestions based on official grading criteria. Users can practice with over 250+ questions, receive comprehensive correction reports, and benefit from advanced AI technology to enhance their writing abilities. The platform supports various IELTS tasks for both Academic and General Training modules, providing in-depth evaluations and insights to help users prepare effectively for the exam.

Mind-Video is an AI tool that focuses on high-quality video reconstruction from brain activity data. It bridges the gap between image and video brain decoding by utilizing masked brain modeling, multimodal contrastive learning, spatiotemporal attention, and co-training with an augmented Stable Diffusion model. The tool aims to recover accurate semantic information from fMRI signals, enabling the generation of realistic videos based on brain activities.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

ProX is a lm-based data refinement framework that automates the process of cleaning and improving data used in pre-training large language models. It offers better performance, domain flexibility, efficiency, and cost-effectiveness compared to traditional methods. The framework has been shown to improve model performance by over 2% and boost accuracy by up to 20% in tasks like math. ProX is designed to refine data at scale without the need for manual adjustments, making it a valuable tool for data preprocessing in natural language processing tasks.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

Fira is a memory-efficient training framework for Large Language Models (LLMs) that enables full-rank training under low-rank constraint. It introduces a method for training with full-rank gradients of full-rank weights, achieved with just two lines of equations. The framework includes pre-training and fine-tuning functionalities, packaged as a Python library for easy use. Fira utilizes Adam optimizer by default and provides options for weight decay. It supports pre-training LLaMA models on the C4 dataset and fine-tuning LLaMA-7B models on commonsense reasoning tasks.

LLM Hallucination Index - RAG Special is a comprehensive evaluation of large language models (LLMs) focusing on context length and open vs. closed-source attributes. The index explores the impact of context length on model performance and tests the assumption that closed-source LLMs outperform open-source ones. It also investigates the effectiveness of prompting techniques like Chain-of-Note across different context lengths. The evaluation includes 22 models from various brands, analyzing major trends and declaring overall winners based on short, medium, and long context insights. Methodologies involve rigorous testing with different context lengths and prompting techniques to assess models' abilities in handling extensive texts and detecting hallucinations.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Proactive Agent is a project aimed at constructing a fully active agent that can anticipate user's requirements and offer assistance without explicit requests. It includes a data collection and generation pipeline, automatic evaluator, and training agent. The project provides datasets, evaluation scripts, and prompts to finetune LLM for proactive agent. Features include environment sensing, assistance annotation, dynamic data generation, and construction pipeline with a high F1 score on the test set. The project is intended for coding, writing, and daily life scenarios, distributed under Apache License 2.0.

A repository sharing literatures and resources about Large Language Models (LLMs) and beyond. It includes tutorials, notebooks, course assignments, development stages, modeling, inference, training, applications, study, and basics related to LLMs. The repository covers various topics such as language models, transformers, state space models, multi-modal language models, training recipes, applications in autonomous driving, code, math, embodied intelligence, and more. The content is organized by different categories and provides comprehensive information on LLMs and related topics.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

This repository focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities of large language models.

This project focuses on implementing Sparse Autoencoders (SAEs) for mechanistic interpretability in Large Language Models (LLMs) like Llama 3.2-3B. The SAEs aim to untangle superimposed representations in LLMs into separate, interpretable features for each neuron activation. The project provides an end-to-end pipeline for capturing training data, training the SAEs, analyzing learned features, and verifying results experimentally. It includes comprehensive logging, visualization, and checkpointing of SAE training, interpretability analysis tools, and a pure PyTorch implementation of Llama 3.1/3.2 chat and text completion. The project is designed for scalability, efficiency, and maintainability.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

GPTQModel is an easy-to-use LLM quantization and inference toolkit based on the GPTQ algorithm. It provides support for weight-only quantization and offers features such as dynamic per layer/module flexible quantization, sharding support, and auto-heal quantization errors. The toolkit aims to ensure inference compatibility with HF Transformers, vLLM, and SGLang. It offers various model supports, faster quant inference, better quality quants, and security features like hash check of model weights. GPTQModel also focuses on faster quantization, improved quant quality as measured by PPL, and backports bug fixes from AutoGPTQ.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Expert in engaging, interactive training methods and activities.

Designs effective training materials to enhance organizational learning and performance.

Develops training programs, customizing content to fit corporate culture and objectives.

Asistente especializado en diseño y planificación de formación profesional

خبير يقدم لك المعلومات حول برنامج تنمية القدرات البشرية (Human Capability Development Program) أحد برامج رؤية المملكة 2030 والذي يهدف إلى بناء استراتيجية وطنية طموحة لتنمية قدرات المواطن

Enhances organizational performance through employee learning and development initiatives.

Enhances organizational performance through strategic skills development initiatives.

Master Your Professional Skills

Does Pool Performance of Elite Triathletes Predict Open-Water Performance?

Enhances corporate training through innovative e-learning solutions.

Communicates policy processes and changes effectively within the organization.

Instructional design expert outputs.

Wertet Lebenslauf und Stellenanzeige aus und simuliert ein Vorstellungsgespräch mit anschließender Auswertung: Lebenslauf und Anzeige einfach hochladen und starten.

Provides emergency training assistance with a focus on safety and clear guidelines.

I simulate tough Customer Support scenarios for Agent Training.

大型语言模型的学习和训练指导专家

A Learning Experience Designer (LXD) - in support of LXDs and those who work with them.

Elearning creator, instructor, script & storyboard guide

Crafts formal, engaging seminar descriptions to inspire teachers.