Best AI tools for< Test Llms >

OpenPlayground is a cloud-based platform that provides access to a variety of AI tools and resources. It allows users to train and deploy machine learning models, access pre-trained models, and collaborate on AI projects. OpenPlayground is designed to make AI more accessible and easier to use for everyone, from beginners to experienced data scientists.

Roost.ai is an AI-driven testing tool that offers automated test case generation using Large Language Models (LLMs). It helps in building reliable software by providing 100% test coverage, every single time. Roost.ai acts as a testing co-pilot, powered by generative AI, and is trusted by global financial institutions. The tool automates test case generation, freeing up developer time to focus on coding and innovation. It enhances test accuracy and coverage by uncovering overlooked edge cases and detects static vulnerabilities in artifacts like source code and logs.

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.

Freeplay is a tool that helps product teams experiment, test, monitor, and optimize AI features for customers. It provides a single pane of glass for the entire team, lightweight developer SDKs for Python, Node, and Java, and deployment options to meet compliance needs. Freeplay also offers best practices for the entire AI development lifecycle.

Cabina.AI is a free AI platform that allows users to generate content, text, and images online through a single chat interface. It offers a range of AI models such as ChatGpt, DALLE, Claude, Gemini, Flux, Mistral, and more for tasks like content creation, research, and real-time task solving. Users can access different LLMs, compare results, and find the best solutions faster. Cabina.AI also provides personalized actions, organization of chats, and the ability to track various data points. With flexible pricing plans and a friendly community, Cabina.AI aims to be a universal tool for research and content creation.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Langtail is a platform that helps developers build, test, and deploy AI-powered applications. It provides a suite of tools to help developers debug prompts, run tests, and monitor the performance of their AI models. Langtail also offers a community forum where developers can share tips and tricks, and get help from other users.

Repromptify is an AI tool that simplifies the process of creating AI prompts. It allows users to generate end-to-end optimized prompts for various AI models such as GPT-4, LLMs, DALLE•2, and Midjourney ChatGPT. With Repromptify, users can easily test and generate images and responses tailored to their needs without worrying about ambiguity or details. The tool offers a free trial for users to explore its features upon signing up.

Confident AI is an open-source evaluation infrastructure for Large Language Models (LLMs). It provides a centralized platform to judge LLM applications, ensuring substantial benefits and addressing any weaknesses in LLM implementation. With Confident AI, companies can define ground truths to ensure their LLM is behaving as expected, evaluate performance against expected outputs to pinpoint areas for iterations, and utilize advanced diff tracking to guide towards the optimal LLM stack. The platform offers comprehensive analytics to identify areas of focus and features such as A/B testing, evaluation, output classification, reporting dashboard, dataset generation, and detailed monitoring to help productionize LLMs with confidence.

LLMChess is a web-based chess game that utilizes large language models (LLMs) to power the gameplay. Players can select the LLM model they wish to play against, and the game will commence once the "Start" button is clicked. The game logs are displayed in a black-bordered pane on the right-hand side of the screen. LLMChess is compatible with the Google Chrome browser. For more information on the game's functionality and participation guidelines, please refer to the provided link.

Imandra is a company that provides automated logical reasoning for Large Language Models (LLMs). Imandra's technology allows LLMs to build mental models and reason about them, unlocking the potential of generative AI for industries where correctness and compliance matter. Imandra's platform is used by leading financial firms, the US Air Force, and DARPA.

BotStacks is a conversational AI solution that offers premium AI sidekicks to automate, engage, and succeed. It provides a platform for designing, building, and deploying AI chatbots with advanced technology accessible to everyone. With features like canvas designer, knowledge base, and chat SDKs, BotStacks empowers users to create personalized and scalable AI assistants. The application focuses on easy design flow, seamless integration, customization, scalability, and accessibility for non-technical users, making it a gateway to the future of conversational AI.

Riku is a no-code platform that allows users to build and deploy powerful generative AI for their business. With access to over 40 industry-leading LLMs, users can easily test different prompts to find just the right one for their needs. Riku's platform also allows users to connect siloed data sources and systems together to feed into powerful AI applications. This makes it easy for businesses to automate repetitive tasks, test ideas rapidly, and get answers in real-time.

Cameron Jones is an AI tool developed by a Cognitive Science PhD student focusing on persuasion, deception, and social intelligence in humans and Large Language Models (LLMs). The tool analyzes LLM performance on tasks like the False Belief task and the Turing test. It also compares humans and LLMs on theory of mind evaluation. Cameron Jones provides select publications, recent media, and projects related to understanding, grounding, and reference in LLMs.

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.

Empirical is a tool that allows you to test different LLMs, prompts, and other model configurations across all the scenarios that matter for your application. With Empirical, you can run your test datasets locally against off-the-shelf models, test your own custom models and RAG applications, view, compare, and analyze outputs on a web UI, score your outputs with scoring functions, and run tests on CI/CD.

LLMinator is a Gradio-based tool with an integrated chatbot designed to locally run and test Language Model Models (LLMs) directly from HuggingFace. It provides an easy-to-use interface made with Gradio, LangChain, and Torch, offering features such as context-aware streaming chatbot, inbuilt code syntax highlighting, loading any LLM repo from HuggingFace, support for both CPU and CUDA modes, enabling LLM inference with llama.cpp, and model conversion capabilities.

Bench is a tool for evaluating LLMs for production use cases. It provides a standardized workflow for LLM evaluation with a common interface across tasks and use cases. Bench can be used to test whether open source LLMs can do as well as the top closed-source LLM API providers on specific data, and to translate the rankings on LLM leaderboards and benchmarks into scores that are relevant for actual use cases.

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.

Moonshot is a simple and modular tool developed by the AI Verify Foundation to evaluate Language Model Models (LLMs) and LLM applications. It brings Benchmarking and Red-Teaming together to assist AI developers, compliance teams, and AI system owners in assessing LLM performance. Moonshot can be accessed through various interfaces including User-friendly Web UI, Interactive Command Line Interface, and seamless integration into MLOps workflows via Library APIs or Web APIs. It offers features like benchmarking LLMs from popular model providers, running relevant tests, creating custom cookbooks and recipes, and automating Red Teaming to identify vulnerabilities in AI systems.

LLM Confabulation Leaderboard evaluates large language models based on confabulations and non-response rates to challenging questions. It includes carefully curated questions with no answers in provided texts, aiming to differentiate between various models. The benchmark combines confabulation and non-response rates for comprehensive ranking, offering insights into model performance and tendencies. Additional notes highlight the meticulous human verification process, challenges faced by LLMs in generating valid responses, and the use of temperature settings. Updates and other benchmarks are also mentioned, providing a holistic view of the evaluation landscape.

LLM-RGB is a repository containing a collection of detailed test cases designed to evaluate the reasoning and generation capabilities of Language Learning Models (LLMs) in complex scenarios. The benchmark assesses LLMs' performance in understanding context, complying with instructions, and handling challenges like long context lengths, multi-step reasoning, and specific response formats. Each test case evaluates an LLM's output based on context length difficulty, reasoning depth difficulty, and instruction compliance difficulty, with a final score calculated for each test case. The repository provides a score table, evaluation details, and quick start guide for running evaluations using promptfoo testing tools.

This repository contains the code for LongRAG, a framework that enhances retrieval-augmented generation with long-context LLMs. LongRAG introduces a 'long retriever' and a 'long reader' to improve performance by using a 4K-token retrieval unit, offering insights into combining RAG with long-context LLMs. The repo provides instructions for installation, quick start, corpus preparation, long retriever, and long reader.

Vivaria is a web application tool designed for running evaluations and conducting agent elicitation research. Users can interact with Vivaria using a web UI and a command-line interface. It allows users to start task environments based on METR Task Standard definitions, run AI agents, perform agent elicitation research, view API requests and responses, add tags and comments to runs, store results in a PostgreSQL database, sync data to Airtable, test prompts against LLMs, and authenticate using Auth0.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

TigerGraph CoPilot is an AI assistant that combines graph databases and generative AI to enhance productivity across various business functions. It includes three core component services: InquiryAI for natural language assistance, SupportAI for knowledge Q&A, and QueryAI for GSQL code generation. Users can interact with CoPilot through a chat interface on TigerGraph Cloud and APIs. CoPilot requires LLM services for beta but will support TigerGraph's LLM in future releases. It aims to improve contextual relevance and accuracy of answers to natural-language questions by building knowledge graphs and using RAG. CoPilot is extensible and can be configured with different LLM providers, graph schemas, and LangChain tools.

This repository contains code for grounding large language models' knowledge in BabyAI-Text using the GLAM method. It includes the BabyAI-Text environment, code for experiments, and training agents. The repository is structured with folders for the environment, experiments, agents, configurations, SLURM scripts, and training scripts. Installation steps involve creating a conda environment, installing PyTorch, required packages, BabyAI-Text, and Lamorel. The launch process involves using Lamorel with configs and training scripts. Users can train a language model and evaluate performance on test episodes using provided scripts and config entries.

ChainForge is a visual programming environment for battle-testing prompts to LLMs. It is geared towards early-stage, quick-and-dirty exploration of prompts, chat responses, and response quality that goes beyond ad-hoc chatting with individual LLMs. With ChainForge, you can: * Query multiple LLMs at once to test prompt ideas and variations quickly and effectively. * Compare response quality across prompt permutations, across models, and across model settings to choose the best prompt and model for your use case. * Setup evaluation metrics (scoring function) and immediately visualize results across prompts, prompt parameters, models, and model settings. * Hold multiple conversations at once across template parameters and chat models. Template not just prompts, but follow-up chat messages, and inspect and evaluate outputs at each turn of a chat conversation. ChainForge comes with a number of example evaluation flows to give you a sense of what's possible, including 188 example flows generated from benchmarks in OpenAI evals. This is an open beta of Chainforge. We support model providers OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and Dalai-hosted models Alpaca and Llama. You can change the exact model and individual model settings. Visualization nodes support numeric and boolean evaluation metrics. ChainForge is built on ReactFlow and Flask.

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

LLMFarm is an iOS and MacOS app designed to work with large language models (LLM). It allows users to load different LLMs with specific parameters, test the performance of various LLMs on iOS and macOS, and identify the most suitable model for their projects. The tool is based on ggml and llama.cpp by Georgi Gerganov and incorporates sources from rwkv.cpp by saharNooby, Mia by byroneverson, and LlamaChat by alexrozanski. LLMFarm features support for MacOS (13+) and iOS (16+), various inferences and sampling methods, Metal compatibility (not supported on Intel Mac), model setting templates, LoRA adapters support, LoRA finetune support, LoRA export as model support, and more. It also offers a range of inferences including LLaMA, GPTNeoX, Replit, GPT2, Starcoder, RWKV, Falcon, MPT, Bloom, and others. Additionally, it supports multimodal models like LLaVA, Obsidian, and MobileVLM. Users can customize inference options through JSON files and access supported models for download.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features such as a Virtual API System with caching and API simulators, a new set of solvable queries determined by LLMs, and a Stable Evaluation System using GPT-4. The Virtual API Server can be set up either by building from source or using a prebuilt Docker image. Users can test the server using provided scripts and evaluate models with Solvable Pass Rate and Solvable Win Rate metrics. The tool also includes model experiments results comparing different models' performance.

llm-colosseum is a tool designed to evaluate Language Model Models (LLMs) in real-time by making them fight each other in Street Fighter III. The tool assesses LLMs based on speed, strategic thinking, adaptability, out-of-the-box thinking, and resilience. It provides a benchmark for LLMs to understand their environment and take context-based actions. Users can analyze the performance of different LLMs through ELO rankings and win rate matrices. The tool allows users to run experiments, test different LLM models, and customize prompts for LLM interactions. It offers installation instructions, test mode options, logging configurations, and the ability to run the tool with local models. Users can also contribute their own LLM models for evaluation and ranking.

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

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.

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

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Friendly assistant for React app testing, detailed and accessible.