Best AI tools for< Test Inference >

AIMLAPI.com is an AI tool that provides access to over 200 AI models through a single AI API. It offers a wide range of AI features for tasks such as chat, code, image generation, music generation, video, voice embedding, language, genomic models, and 3D generation. The platform ensures fast inference, top-tier serverless infrastructure, high data security, 99% uptime, and 24/7 support. Users can integrate AI features easily into their products and test API models in a sandbox environment before deployment.

Cirrascale Cloud Services is an AI tool that offers cloud solutions for Artificial Intelligence applications. The platform provides a range of cloud services and products tailored for AI innovation, including NVIDIA GPU Cloud, AMD Instinct Series Cloud, Qualcomm Cloud, Graphcore, Cerebras, and SambaNova. Cirrascale's AI Innovation Cloud enables users to test and deploy on leading AI accelerators in one cloud, democratizing AI by delivering high-performance AI compute and scalable deep learning solutions. The platform also offers professional and managed services, tailored multi-GPU server options, and high-throughput storage and networking solutions to accelerate development, training, and inference workloads.

Glato AI is an innovative AI tool designed to help users create short video ads that sell. It offers a fast and simple way to generate engaging video content for marketing purposes. With features like real creator clones, expressive videos, auto B-roll, and trend analysis, Glato AI empowers users to boost their ROI and drive traffic effectively. The tool is loved by founders and brands for its ability to streamline the video creation process and enhance user-generated content production.

Tube Pro is an all-in-one toolkit designed for YouTubers to enhance their video content creation process. The application offers features such as generating new viral scripts, improving existing scripts, testing titles and thumbnails before uploading, and powerful thumbnail testing tools. With Tube Pro, users can easily create viral scripts, improve current ones, and optimize their thumbnails to catch attention on YouTube. The tool also provides AI-powered suggestions for relevant title ideas, simplifying the content creation process for YouTubers.

Keplar is an AI-powered platform that provides unlimited customer access by delivering interactive models of target customers using influencer audiences and 1st-party data. It helps marketing teams iterate on creative assets, discover new markets, compare audiences, and save resources for bigger impact. Keplar offers a new way to unlock creative and audience insights faster, enabling teams to maintain velocity at scale.

Unscript AI is an AI-powered video creation platform that allows users to generate professional videos with personalized content at scale. The platform offers features such as AI-driven video testing, seamless multilingual videos, impactful ad creation, AI avatars, and virtual influencers. Unscript AI helps users create hyper-realistic videos in multiple languages without the need for cameras or production crews. The platform is trusted by over 100 brands and offers a cost-effective solution for video production with premium quality.

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.

Spur is an AI QA tool that allows users to test websites using natural language, eliminating the need for complex test scripts. It offers reliable automated tests that adapt to UI changes, real-time playback for debugging, and powerful validations. Spur's AI-powered tests reduce manual testing time, improve software testing processes, and ensure the reliability of tests even with site changes. The tool is user-friendly, requires no coding skills, and supports API testing.

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.

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.

Carbonate is an AI-driven automated end-to-end testing tool that allows users to create auto-healing browser tests without any coding. By leveraging its unique AI engine, Carbonate generates test scripts from recorded tests, enabling users to run tests using a cloud test runner or within their own CI. With Carbonate, users can create tests in seconds by simply using their application, as the tool automatically detects interactions and records them as part of the test. Carbonate's intelligent AI recorder ensures that tests heal themselves and adapt to changes in the application, providing fast results without the hassle.

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.

AI Toolkit for Visual Studio Code simplifies generative AI app development by bringing together cutting-edge AI development tools and models from Azure AI Studio Catalog and other catalogs like Hugging Face. Users can browse the AI models catalog, download them locally, fine-tune, test, and deploy them to the cloud. The toolkit offers actions such as finding supported models, testing model inference, fine-tuning models locally or remotely, and deploying fine-tuned models to the cloud. It also provides optimized AI models for Windows and a Q&A section for common issues and resolutions.

ExLlamaV2 is an inference library designed for running local LLMs on modern consumer GPUs. The library supports paged attention via Flash Attention 2.5.7+, offers a new dynamic generator with features like dynamic batching, smart prompt caching, and K/V cache deduplication. It also provides an API for local or remote inference using TabbyAPI, with extended features like HF model downloading and support for HF Jinja2 chat templates. ExLlamaV2 aims to optimize performance and speed across different GPU models, with potential future optimizations and variations in speeds. The tool can be integrated with TabbyAPI for OpenAI-style web API compatibility and supports a standalone web UI called ExUI for single-user interaction with chat and notebook modes. ExLlamaV2 also offers support for text-generation-webui and lollms-webui through specific loaders and bindings.

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

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.

CodeTF is a Python transformer-based library for code large language models (Code LLMs) and code intelligence. It provides an interface for training and inferencing on tasks like code summarization, translation, and generation. The library offers utilities for code manipulation across various languages, including easy extraction of code attributes. Using tree-sitter as its core AST parser, CodeTF enables parsing of function names, comments, and variable names. It supports fast model serving, fine-tuning of LLMs, various code intelligence tasks, preprocessed datasets, model evaluation, pretrained and fine-tuned models, and utilities to manipulate source code. CodeTF aims to facilitate the integration of state-of-the-art Code LLMs into real-world applications, ensuring a user-friendly environment for code intelligence tasks.

KsanaLLM is a high-performance engine for LLM inference and serving. It utilizes optimized CUDA kernels for high performance, efficient memory management, and detailed optimization for dynamic batching. The tool offers flexibility with seamless integration with popular Hugging Face models, support for multiple weight formats, and high-throughput serving with various decoding algorithms. It enables multi-GPU tensor parallelism, streaming outputs, and an OpenAI-compatible API server. KsanaLLM supports NVIDIA GPUs and Huawei Ascend NPU, and seamlessly integrates with verified Hugging Face models like LLaMA, Baichuan, and Qwen. Users can create a docker container, clone the source code, compile for Nvidia or Huawei Ascend NPU, run the tool, and distribute it as a wheel package. Optional features include a model weight map JSON file for models with different weight names.

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework known for its lightweight design, scalability, and high-speed performance. It offers features like tri-process asynchronous collaboration, Nopad for efficient attention operations, dynamic batch scheduling, FlashAttention integration, tensor parallelism, Token Attention for zero memory waste, and Int8KV Cache. The tool supports various models like BLOOM, LLaMA, StarCoder, Qwen-7b, ChatGLM2-6b, Baichuan-7b, Baichuan2-7b, Baichuan2-13b, InternLM-7b, Yi-34b, Qwen-VL, Llava-7b, Mixtral, Stablelm, and MiniCPM. Users can deploy and query models using the provided server launch commands and interact with multimodal models like QWen-VL and Llava using specific queries and images.

llm-swarm is a tool designed to manage scalable open LLM inference endpoints in Slurm clusters. It allows users to generate synthetic datasets for pretraining or fine-tuning using local LLMs or Inference Endpoints on the Hugging Face Hub. The tool integrates with huggingface/text-generation-inference and vLLM to generate text at scale. It manages inference endpoint lifetime by automatically spinning up instances via `sbatch`, checking if they are created or connected, performing the generation job, and auto-terminating the inference endpoints to prevent idling. Additionally, it provides load balancing between multiple endpoints using a simple nginx docker for scalability. Users can create slurm files based on default configurations and inspect logs for further analysis. For users without a Slurm cluster, hosted inference endpoints are available for testing with usage limits based on registration status.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

llm-export is a tool for exporting llm models to onnx and mnn formats. It has features such as passing onnxruntime correctness tests, optimizing the original code to support dynamic shapes, reducing constant parts, optimizing onnx models using OnnxSlim for performance improvement, and exporting lora weights to onnx and mnn formats. Users can clone the project locally, clone the desired LLM project locally, and use LLMExporter to export the model. The tool supports various export options like exporting the entire model as one onnx model, exporting model segments as multiple models, exporting model vocabulary to a text file, exporting specific model layers like Embedding and lm_head, testing the model with queries, validating onnx model consistency with onnxruntime, converting onnx models to mnn models, and more. Users can specify export paths, skip optimization steps, and merge lora weights before exporting.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

This repository, GraphWiz, focuses on developing an instruction-following Language Model (LLM) for solving graph problems. It includes GraphWiz LLMs with strong graph problem-solving abilities, GraphInstruct dataset with over 72.5k training samples across nine graph problem tasks, and models like GPT-4 and Mistral-7B for comparison. The project aims to map textual descriptions of graphs and structures to solve various graph problems explicitly in natural language.

ChatRex is a Multimodal Large Language Model (MLLM) designed to seamlessly integrate fine-grained object perception and robust language understanding. By adopting a decoupled architecture with a retrieval-based approach for object detection and leveraging high-resolution visual inputs, ChatRex addresses key challenges in perception tasks. It is powered by the Rexverse-2M dataset with diverse image-region-text annotations. ChatRex can be applied to various scenarios requiring fine-grained perception, such as object detection, grounded conversation, grounded image captioning, and region understanding.

ZhiLight is a highly optimized large language model (LLM) inference engine developed by Zhihu and ModelBest Inc. It accelerates the inference of models like Llama and its variants, especially on PCIe-based GPUs. ZhiLight offers significant performance advantages compared to mainstream open-source inference engines. It supports various features such as custom defined tensor and unified global memory management, optimized fused kernels, support for dynamic batch, flash attention prefill, prefix cache, and different quantization techniques like INT8, SmoothQuant, FP8, AWQ, and GPTQ. ZhiLight is compatible with OpenAI interface and provides high performance on mainstream NVIDIA GPUs with different model sizes and precisions.

ABQ-LLM is a novel arbitrary bit quantization scheme that achieves excellent performance under various quantization settings while enabling efficient arbitrary bit computation at the inference level. The algorithm supports precise weight-only quantization and weight-activation quantization. It provides pre-trained model weights and a set of out-of-the-box quantization operators for arbitrary bit model inference in modern architectures.

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.

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