Best AI tools for< Test Scaling >

Testim is an AI-powered UI and functional testing platform that helps accelerate test authoring, reduce test maintenance, and release higher-quality apps faster. It offers a range of features such as fast authoring speed, test stability, root cause analysis, and TestOps, making it an efficient and effective solution for product development teams.

Katalon is a modern, comprehensive quality management platform that helps teams of any size deliver the highest quality digital experiences. It offers a range of features including test authoring, test management, test execution, reporting & analytics, and AI-powered testing. Katalon is suitable for testers of all backgrounds, providing a single platform for testing web, mobile, API, desktop, and packaged apps. With AI capabilities, Katalon simplifies test automation, streamlines testing operations, and scales testing programs for enterprise teams.

Byterat is a cloud-based platform that provides battery data management, visualization, and analytics. It offers an end-to-end data pipeline that automatically synchronizes, processes, and visualizes materials, manufacturing, and test data from all labs. Byterat also provides 24/7 access to experiments from anywhere in the world and integrates seamlessly with current workflows. It is customizable to specific cell chemistries and allows users to build custom visualizations, dashboards, and analyses. Byterat's AI-powered battery research has been published in leading journals, and its team has pioneered a new class of models that extract tell-tale signals of battery health from electrical signals to forecast future performance.

Tonic.ai is a platform that allows users to build AI models on their unstructured data. It offers various products for software development and LLM development, including tools for de-identifying and subsetting structured data, scaling down data, handling semi-structured data, and managing ephemeral data environments. Tonic.ai focuses on standardizing, enriching, and protecting unstructured data, as well as validating RAG systems. The platform also provides integrations with relational databases, data lakes, NoSQL databases, flat files, and SaaS applications, ensuring secure data transformation for software and AI developers.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

TestArmy is an AI-driven software testing platform that offers an army of testing agents to help users achieve software quality by balancing cost, speed, and quality. The platform leverages AI agents to generate Gherkin tests based on user specifications, automate test execution, and provide detailed logs and suggestions for test maintenance. TestArmy is designed for rapid scaling and adaptability to changes in the codebase, making it a valuable tool for both technical and non-technical users.

AI Generated Test Cases is an innovative tool that leverages artificial intelligence to automatically generate test cases for software applications. By utilizing advanced algorithms and machine learning techniques, this tool can efficiently create a comprehensive set of test scenarios to ensure the quality and reliability of software products. With AI Generated Test Cases, software development teams can save time and effort in the testing phase, leading to faster release cycles and improved overall productivity.

AI Test Kitchen is a website that provides a variety of AI-powered tools for creative professionals. These tools can be used to generate images, music, and text, as well as to explore different creative concepts. The website is designed to be a place where users can experiment with AI and learn how to use it to enhance their creative process.

Face Symmetry Test is an AI-powered tool that analyzes the symmetry of facial features by detecting key landmarks such as eyes, nose, mouth, and chin. Users can upload a photo to receive a personalized symmetry score, providing insights into the balance and proportion of their facial features. The tool uses advanced AI algorithms to ensure accurate results and offers guidelines for improving the accuracy of the analysis. Face Symmetry Test is free to use and prioritizes user privacy and security by securely processing uploaded photos without storing or sharing data with third parties.

The AI-powered Cambridge English Test platform offers exercises for English levels B1, B2, C1, and C2. Users can select exercise types such as Reading and Use of English, including activities like Open Cloze, Multiple Choice, Word Formation, and more. The AI, developed by Shining Apps in partnership with Use of English PRO, provides a unique learning experience by generating exercises from a database of over 5000 official exams. It uses advanced Natural Language Processing (NLP) to understand context, tweak exercises, and offer detailed feedback for effective learning.

FaceSymAI is an online tool that utilizes advanced AI algorithms to analyze and determine the symmetry of your face. By uploading a photo, the AI examines your facial features, including the eyes, nose, mouth, and overall structure, to provide an accurate assessment of your facial symmetry. The analysis is based on mathematical and statistical methods, ensuring reliable and precise results. FaceSymAI is designed to be user-friendly and accessible, offering a free service to everyone. The uploaded photos are treated with utmost confidentiality and are not stored or used for any other purpose, ensuring your privacy is respected.

Leapwork is an AI-powered test automation platform that enables users to build, manage, maintain, and analyze complex data-driven testing across various applications, including AI apps. It offers a democratized testing approach with an intuitive visual interface, composable architecture, and generative AI capabilities. Leapwork supports testing of diverse application types, web, mobile, desktop applications, and APIs. It allows for scalable testing with reusable test flows that adapt to changes in the application under test. Leapwork can be deployed on the cloud or on-premises, providing full control to the users.

Vocera is an AI voice agent testing tool that allows users to test and monitor voice AI agents efficiently. It enables users to launch voice agents in minutes, ensuring a seamless conversational experience. With features like testing against AI-generated datasets, simulating scenarios, and monitoring AI performance, Vocera helps in evaluating and improving voice agent interactions. The tool provides real-time insights, detailed logs, and trend analysis for optimal performance, along with instant notifications for errors and failures. Vocera is designed to work for everyone, offering an intuitive dashboard and data-driven decision-making for continuous improvement.

Thumblytics is a tool that helps YouTubers test their YouTube thumbnails and titles before they publish them. It uses a combination of machine learning and human feedback to help users choose the best thumbnail and title combination for their videos. Thumblytics is designed to be easy to use, even for beginners. Users simply upload their thumbnail and title variants to Thumblytics, and the tool will preview them in a YouTube template and show them to hundreds of real people to collect click data. Thumblytics then crunches the data to help users pick the highest click-through rate (CTR) thumbnail and title.

ILoveMyQA is an AI-powered QA testing service that provides comprehensive, well-documented bug reports. The service is affordable, easy to get started with, and requires no time-zapping chats. ILoveMyQA's team of Rockstar QAs is dedicated to helping businesses find and fix bugs before their customers do, so they can enjoy the results and benefits of having a QA team without the cost, management, and headaches.

Webomates is an AI-powered test automation platform that helps users release software faster by providing comprehensive AI-enhanced testing services. It offers solutions for DevOps, code coverage, media & telecom, small and medium businesses, cross-browser testing, and intelligent test automation. The platform leverages AI and machine learning to predict defects, reduce false positives, and accelerate software releases. Webomates also features intelligent automation, smart reporting, and scalable payment options. It seamlessly integrates with popular development tools and processes, providing analytics and support for manual and AI automation testing.

Carbonate is an AI-driven automated end-to-end testing tool that allows users to create auto-healing browser tests without any coding. It understands the behavior of applications and adapts tests accordingly, mimicking real user interactions. The tool features an intelligent recorder that translates user actions into runnable tests, interactive test playback for real-time debugging, and supports dynamic rendering and shadow DOM. Carbonate aims to simplify the testing process and improve efficiency by leveraging AI technology.

Webo.AI is a test automation platform powered by AI that offers a smarter and faster way to conduct testing. It provides generative AI for tailored test cases, AI-powered automation, predictive analysis, and patented AiHealing for test maintenance. Webo.AI aims to reduce test time, production defects, and QA costs while increasing release velocity and software quality. The platform is designed to cater to startups and offers comprehensive test coverage with human-readable AI-generated test cases.

Checkmyidea-IA is an AI-powered tool that helps entrepreneurs and businesses evaluate their business ideas before launching them. It uses a variety of factors, such as customer interest, uniqueness, initial product development, and launch strategy, to provide users with a comprehensive review of their idea's potential for success. Checkmyidea-IA can help users save time, increase their chances of success, reduce risk, and improve their decision-making.

The website is a platform where users can submit fake hacker news for testing purposes. Users can log in to submit their titles and test their submissions. The platform allows users to see how readers may respond to their posts. The website was built by Justin and Michael.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

The repository 'Awesome LLM Reasoning Openai-o1 Survey' provides a collection of survey papers and related works on OpenAI o1, focusing on topics such as LLM reasoning, self-play reinforcement learning, complex logic reasoning, and scaling law. It includes papers from various institutions and researchers, showcasing advancements in reasoning bootstrapping, reasoning scaling law, self-play learning, step-wise and process-based optimization, and applications beyond math. The repository serves as a valuable resource for researchers interested in exploring the intersection of language models and reasoning techniques.

Awesome LLM Strawberry is a collection of research papers and blogs related to OpenAI Strawberry(o1) and Reasoning. The repository is continuously updated to track the frontier of LLM Reasoning.

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

This repository contains a curated list of papers focusing on the self-correction of large language models (LLMs) during inference. It covers various frameworks for self-correction, including intrinsic self-correction, self-correction with external tools, self-correction with information retrieval, and self-correction with training designed specifically for self-correction. The list includes survey papers, negative results, and frameworks utilizing reinforcement learning and OpenAI o1-like approaches. Contributions are welcome through pull requests following a specific format.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

llama_deploy is an async-first framework for deploying, scaling, and productionizing agentic multi-service systems based on workflows from llama_index. It allows building workflows in llama_index and deploying them seamlessly with minimal changes to code. The system includes services endlessly processing tasks, a control plane managing state and services, an orchestrator deciding task handling, and fault tolerance mechanisms. It is designed for high-concurrency scenarios, enabling real-time and high-throughput applications.

ENOVA is an open-source service for Large Language Model (LLM) deployment, monitoring, injection, and auto-scaling. It addresses challenges in deploying stable serverless LLM services on GPU clusters with auto-scaling by deconstructing the LLM service execution process and providing configuration recommendations and performance detection. Users can build and deploy LLM with few command lines, recommend optimal computing resources, experience LLM performance, observe operating status, achieve load balancing, and more. ENOVA ensures stable operation, cost-effectiveness, efficiency, and strong scalability of LLM services.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

llm-finetuning is a repository that provides a serverless twist to the popular axolotl fine-tuning library using Modal's serverless infrastructure. It allows users to quickly fine-tune any LLM model with state-of-the-art optimizations like Deepspeed ZeRO, LoRA adapters, Flash attention, and Gradient checkpointing. The repository simplifies the fine-tuning process by not exposing all CLI arguments, instead allowing users to specify options in a config file. It supports efficient training and scaling across multiple GPUs, making it suitable for production-ready fine-tuning jobs.

Smile (Statistical Machine Intelligence and Learning Engine) is a comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. It covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc. Smile implements major machine learning algorithms and provides interactive shells for Java, Scala, and Kotlin. It supports model serialization, data visualization using SmilePlot and declarative approach, and offers a gallery showcasing various algorithms and visualizations.

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.

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. |  |

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

Test Shaman: Guiding software testing with Grug wisdom and humor, balancing fun with practical advice.

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

I craft tests first, then code, validating new features.

An AI conducting 30-question IQ tests, assessing and providing detailed feedback.

I will provide guidance on testing, verification, and validation for QA roles.

Formal and structured interactive IQ test platform.

Assists teachers in marking tests and generating score reports.

How smart are you? Solve it with your friends, find out who is smarter!

Software code tester and debugger

Formal, concise Angular 2 test guide.

Personality test by birthday.

Helps user learn essential vocabulary for GRE test with multiple choice questions

I'm your lab test consultant for blood tests and microbial cultures. How can I help you today?

Creates Application Security Test cases in YAML

Helping you study for cybersecurity certifications and get the job you want!

Expert in advanced data analysis and interpretation

Crafting full, accurate Apex test classes, with 100% user service.

Train to pass the Reverse Turing Test!

Your go-to MBTI assessment tool, providing personalized insights.

Friendly assistant for React app testing, detailed and accessible.