Best AI tools for< Measure Model Performance >

EyePop.ai is a hassle-free AI vision partner designed for innovators to easily create and own custom AI-powered vision models tailored to their visual data needs. The platform simplifies building AI-powered vision models through a fast, intuitive, and fully guided process without the need for coding or technical expertise. Users can define their target, upload data, train their model, deploy and detect, and iterate and improve to ensure effective AI solutions. EyePop.ai offers pre-trained model library, self-service training platform, and future-ready solutions to help users innovate faster, offer unique solutions, and make real-time decisions effortlessly.

Focia is an AI-powered engagement optimization tool that helps users predict, analyze, and enhance their content performance across various digital platforms. It offers features such as ranking and comparing content ideas, content analysis, feedback generation, engagement predictions, workspace customization, and real-time model training. Focia's AI models, including Blaze, Neon, Phantom, and Omni, specialize in analyzing different types of content on platforms like YouTube, Instagram, TikTok, and e-commerce sites. By leveraging Focia, users can boost their engagement, conduct A/B testing, measure performance, and conceptualize content ideas effectively.

Simpleem is an Artificial Emotional Intelligence (AEI) tool that helps users uncover intentions, predict success, and leverage behavior for successful interactions. By measuring all interactions and correlating them with concrete outcomes, Simpleem provides insights into verbal, para-verbal, and non-verbal cues to enhance customer relationships, track customer rapport, and assess team performance. The tool aims to identify win/lose patterns in behavior, guide users on boosting performance, and prevent burnout by promptly identifying red flags. Simpleem uses proprietary AI models to analyze real-world data and translate behavioral insights into concrete business metrics, achieving a high accuracy rate of 94% in success prediction.

Picterra is a geospatial AI platform that offers reliable solutions for sustainability, compliance, monitoring, and verification. It provides an all-in-one plot monitoring system, professional services, and interactive tours. Users can build custom AI models to detect objects, changes, or patterns using various geospatial imagery data. Picterra aims to revolutionize geospatial analysis with its category-leading AI technology, enabling users to solve challenges swiftly, collaborate more effectively, and scale further.

ChatGPT4o is OpenAI's latest flagship model, capable of processing text, audio, image, and video inputs, and generating corresponding outputs. It offers both free and paid usage options, with enhanced performance in English and coding tasks, and significantly improved capabilities in processing non-English languages. ChatGPT4o includes built-in safety measures and has undergone extensive external testing to ensure safety. It supports multimodal inputs and outputs, with advantages in response speed, language support, and safety, making it suitable for various applications such as real-time translation, customer support, creative content generation, and interactive learning.

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.

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.

The website offers a cross-platform 3D scanning tool for floor plans and drone mapping. It allows users to capture and collaborate in new ways, providing reality capture for professionals to document, measure, and design various spaces. Users can create instantly shareable 3D models, generate customizable 2D floor plans, capture drone footage for 3D models, and document detailed metrics for site surveys, construction sites, products, and more. The tool is suitable for teams in architecture, engineering, construction, forensics, investigation, product design, manufacturing, media, and entertainment industries.

Metabob is an AI-powered code review tool that helps developers detect, explain, and fix coding problems. It utilizes proprietary graph neural networks to detect problems and LLMs to explain and resolve them, combining the best of both worlds. Metabob's AI is trained on millions of bug fixes performed by experienced developers, enabling it to detect complex problems that span across codebases and automatically generate fixes for them. It integrates with popular code hosting platforms such as GitHub, Bitbucket, Gitlab, and VS Code, and supports various programming languages including Python, Javascript, Typescript, Java, C++, and C.

Hive AI provides a suite of AI models and solutions for understanding, searching, and generating content. Their AI models can be integrated into applications via APIs, enabling developers to add advanced content understanding capabilities to their products. Hive AI's solutions are used by businesses in various industries, including digital platforms, sports, media, and marketing, to streamline content moderation, automate image search and authentication, measure sponsorships, and monetize ad inventory.

Free ChatGPT Omni (GPT4o) is a user-friendly website that allows users to effortlessly chat with ChatGPT for free. It is designed to be accessible to everyone, regardless of language proficiency or technical expertise. GPT4o is OpenAI's groundbreaking multimodal language model that integrates text, audio, and visual inputs and outputs, revolutionizing human-computer interaction. The website offers real-time audio interaction, multimodal integration, advanced language understanding, vision capabilities, improved efficiency, and safety measures.

GPT40.net is a platform where users can interact with the latest GPT-4o model from OpenAI. The tool offers free and paid options for users to ask questions and receive answers in various formats such as text, audio, image, and video. GPT40 is designed to provide natural and intuitive human-computer interactions through its multimodal capabilities and fast response times. It ensures safety through built-in measures and is suitable for applications like real-time translation, customer support, content generation, and interactive learning.

Credal is an AI tool that allows users to build secure AI assistants for enterprise operations. It enables every employee to create customized AI assistants with built-in security, permissions, and compliance features. Credal supports data integration, access control, search functionalities, and API development. The platform offers real-time sync, automatic permissions synchronization, and AI model deployment with security and compliance measures. It helps enterprises manage ETL pipelines, schedule tasks, and configure data processing. Credal ensures data protection, compliance with regulations like HIPAA, and comprehensive audit capabilities for generative AI applications.

Dust is a customizable and secure AI assistant platform that helps businesses amplify their team's potential. It allows users to deploy the best Large Language Models to their company, connect Dust to their team's data, and empower their teams with assistants tailored to their specific needs. Dust is exceptionally modular and adaptable, tailoring to unique requirements and continuously evolving to meet changing needs. It supports multiple sources of data and models, including proprietary and open-source models from OpenAI, Anthropic, and Mistral. Dust also helps businesses identify their most creative and driven team members and share their experience with AI throughout the company. It promotes collaboration with shared conversations, @mentions in discussions, and Slackbot integration. Dust prioritizes security and data privacy, ensuring that data remains private and that enterprise-grade security measures are in place to manage data access policies.

Thirdai.com is an AI tool that offers a robot challenge screen for checking site connection security. The tool helps users assess the security of their website by requiring cookies to be enabled in the browser settings. It ensures that the connection is secure and provides recommendations for improving security measures.

Undressing AI is a cutting-edge application that utilizes AI technology to remove clothes from photos, generating realistic nude images. Users can upload a photo, select processing mode, and quickly obtain a nude image. The app prioritizes safety and ethical use, implementing strict privacy measures to secure uploaded images. Undressing AI offers various pricing plans, from a free basic plan to premium options, providing customization options for body type, age, and image quality. The application is user-friendly, accessible from any device with internet connection, and employs advanced AI technology for accurate results.

ChatGent is an advanced AI-powered professional assistant builder that revolutionizes the way professionals manage their interactions and tasks. It leverages the latest GPT models to provide high-quality, contextually relevant responses and robust security measures. ChatGent transforms complex data into simple, actionable conversations, enhancing productivity, efficiency, and decision-making for professionals across various industries.

Innovatiana is a data labeling outsourcing company that provides high-quality training data for AI models. They specialize in computer vision, data moderation, document processing, natural language processing, and data collection. Innovatiana is committed to ethical and sustainable practices, and they pay their data labelers fair wages and provide them with good working conditions. They also use a variety of quality control measures to ensure that their data is accurate and reliable.

OffRobe is a powerful NSFW AI image generator and editor that allows users to create and edit realistic and high-quality NSFW images with ease. The platform is designed with strong privacy and security measures to ensure that users' activities and data remain confidential. OffRobe's AI models offer a high level of customization, enabling users to bring their unique fantasies to life. With its user-friendly interface and advanced features, OffRobe is the perfect tool for anyone looking to explore the world of NSFW AI art.

AI Copilot for bank ALCOs is an AI application designed to empower Asset-Liability Committees (ALCOs) in banks to test funding and liquidity strategies in a risk-free environment, ensuring optimal balance sheet decisions before real-world implementation. The application provides proactive intelligence for day-to-day decisions, allowing users to test multiple strategies, compare funding options, and make forward-looking decisions. It offers features such as stakeholder feedback, optimal funding mix, forward-looking decisions, comparison of funding strategies, domain-specific models, maximizing returns, staying compliant, and built-in security measures. MaverickFi, the AI Copilot, is deployed on Microsoft Azure and offers deployment options based on user preferences.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

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.

Holistic AI is an open-source library dedicated to assessing and improving the trustworthiness of AI systems. It focuses on measuring and mitigating bias, explainability, robustness, security, and efficacy in AI models. The tool provides comprehensive metrics, mitigation techniques, a user-friendly interface, and visualization tools to enhance AI system trustworthiness. It offers documentation, tutorials, and detailed installation instructions for easy integration into existing workflows.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

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.

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.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

DB-GPT-Hub is an experimental project leveraging Large Language Models (LLMs) for Text-to-SQL parsing. It includes stages like data collection, preprocessing, model selection, construction, and fine-tuning of model weights. The project aims to enhance Text-to-SQL capabilities, reduce model training costs, and enable developers to contribute to improving Text-to-SQL accuracy. The ultimate goal is to achieve automated question-answering based on databases, allowing users to execute complex database queries using natural language descriptions. The project has successfully integrated multiple large models and established a comprehensive workflow for data processing, SFT model training, prediction output, and evaluation.

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.

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment interfaces and libraries for understanding AI systems. It empowers developers and stakeholders to develop and monitor AI responsibly, enabling better data-driven actions. The toolbox includes visualization widgets for model assessment, error analysis, interpretability, fairness assessment, and mitigations library. It also offers a JupyterLab extension for managing machine learning experiments and a library for measuring gender bias in NLP datasets.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

The 'farel-bench' project is a benchmark tool for testing LLM reasoning abilities with family relationship quizzes. It generates quizzes based on family relationships of varying degrees and measures the accuracy of large language models in solving these quizzes. The project provides scripts for generating quizzes, running models locally or via APIs, and calculating benchmark metrics. The quizzes are designed to test logical reasoning skills using family relationship concepts, with the goal of evaluating the performance of language models in this specific domain.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

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.

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The Turing Test, first named the imitation game by Alan Turing in 1950, is a measure of a machine's capacity to demonstrate intelligence that's either equal to or indistinguishable from human intelligence.

Upload full-body photo. Get height estimation.

Advises organizations on optimizing hybrid work models, blending remote and in-office strategies.

Expert on platform economies with comprehensive article insights (platformeconomies.com)

对各种量化问题进行拆解和粗略的估算。注意这种估算主要是靠推测，而不是靠准确的数据，因此仅供参考。理想情况下，估算结果和真实值差距可能在1个数量级以内。即使数值不准确，也希望拆解思路对你有所启发。

Generates items for psychometric instruments to measure psychological constructs.

Explore social and environmental data for 169 countries to measure social progress and go beyond GDP. Using data from the Social Progress Imperative and powered by Open AI.

A device which detects the power level of any entity by measuring fluctuations in "Soul Power."

Detects and measures information credibility. Provides a "BS Score" (0-100) based on content analysis for misinformation signs, including factual inaccuracies and sensationalist language. Real-time feedback.

Provides Raven's Progressive Matrices test with explanations and calculates your IQ score.

IQ Test is designed to simulate an IQ testing environment. It provides a formal and objective experience, delivering questions and processing answers in a straightforward manner.

Formal and structured interactive IQ test platform.

FREE How to Know What Size Nursing Bra to Get - Guidance on nursing bra sizing with insights into breast size changes during pregnancy, measurement instructions, and advice on choosing the right bra style and size. It interprets bust measurements and answers FAQs about nursing bras.

Analyzes sourdough bread images with scientific precision

Gives experienced advice on influencer-brand dynamics.

Guides in creating effective PLG strategies for products

Define the right marketing metrics for your business

Expert in analyzing coffee grind particle size distribution using image processing and KDE.