Best AI tools for< Save Data Sequences >

Callbook.ai is a phone AI system designed specifically for seamless integration with Zoho CRM. It offers human-like AI calls, pre-configured AI assistants, easy integration without the need for extra tools or developers, and call data formatted for Zoho CRM fields. The application aims to address common issues faced by companies when implementing an AI phone system with Zoho CRM, providing a solution that eliminates the need for fine-tuning models and investing in developers.

Brave is a privacy-focused web browser that prioritizes user experience by blocking ads, trackers, and unwanted content. It offers features such as Shields, VPN, Leo AI Wallet, Rewards, Playlist, News Talk, and advanced privacy settings. Brave aims to provide a faster and more secure browsing experience while protecting user data and privacy. The browser is built on the open-source Chromium Web core and offers a private search engine called Brave Search. With a focus on user-first design, Brave is designed to save time, data, and battery life for its users.

Routine is an all-in-one work platform designed to help professionals and teams get things done faster. It offers a range of features including a planner-based calendar, time blocking, agenda view, custom types, and AI search. Routine integrates with a variety of services and provides mechanisms to quickly save data from anywhere. It is available for macOS, Windows, iOS, and the web.

ScrapeComfort is an AI-driven web scraping tool that offers an effortless and intuitive data mining solution. It leverages AI technology to extract data from websites without the need for complex coding or technical expertise. Users can easily input URLs, download data, set up extractors, and save extracted data for immediate use. The tool is designed to cater to various needs such as data analytics, market investigation, and lead acquisition, making it a versatile solution for businesses and individuals looking to streamline their data collection process.

AI Social Bio is an AI application that helps users create their social media bio using artificial intelligence technology. Users can add keywords, choose influencers for inspiration, and generate personalized bios. The app was created by two Indie Makers and offers a quick and easy way to enhance social media profiles. AI Social Bio does not save user data and provides unlimited usage within a fair use limit. It can also be used as a lead generator for businesses.

Snaplet is a data management tool for developers that provides AI-generated dummy data for local development, end-to-end testing, and debugging. It uses a real programming language (TypeScript) to define and edit data, ensuring type safety and auto-completion. Snaplet understands database structures and relationships, automatically transforming personally identifiable information and seeding data accordingly. It integrates seamlessly into development workflows, providing data where it's needed most: on local machines, for CI/CD testing, and preview environments.

GoX Data Automation Software is a cloud-based tool designed to save time with data analytics and automation. It allows users to connect to different APIs/sources, create reports with beautiful charts and graphs, automate report generation, and consolidate data from various sources into reports or dashboards. The software, known as Two Minute Reports (TMR), works seamlessly with Google Sheets and Looker Studio to help users efficiently manage their reporting tasks.

Extracto.bot is an AI web scraping tool that automates the process of extracting data from websites. It is a no-configuration, intelligent web scraper that allows users to collect data from any site using Google Sheets and AI technology. The tool is designed to be simple, instant, and intelligent, enabling users to save time and effort in collecting and organizing data for various purposes.

HouseCanary is a leading AI-powered data and analytics platform for residential real estate. With a full suite of industry-leading products and tools, HouseCanary provides real estate investors, mortgage lenders, investment banks, whole loan buyers, and prop techs with the most comprehensive and accurate residential real estate data and analytics in the industry. HouseCanary's AI algorithms analyze a vast array of real estate data to generate meaningful insights to help teams be more efficient, ultimately saving time and money.

Eztrackr is an AI-powered application designed to help users organize their job hunt efficiently. It offers features such as job tracking, AI answer generation, AI cover letter generation, skill matching, job statistics tracking, timeline view, kanban board visualization, and contact/document management. Users can track job applications effortlessly, generate personalized answers and cover letters, match their skills with job descriptions, and streamline their job search process. Eztrackr aims to simplify the job application process and provide valuable insights to job seekers.

Avanty is an AI-powered Chrome extension designed as an assistant for data analysts using Metabase. It helps data analysts save time on generating, editing, and understanding SQL queries. Avanty offers features like auto-generating SQL queries from natural language, automagically editing SQL queries, auto-formatting queries for readability, smart error debugging, and breaking down complicated SQL queries into understandable chunks with explanations.

TurboDoc is an AI-powered tool designed to extract information from invoices and transform unstructured data into easy-to-read structured data. It offers a user-friendly interface for efficient work with accounts payable, budget planning, and control. The tool ensures high accuracy through advanced AI models and provides secure data storage with AES256 encryption. Users can automate invoice processing, link Gmail for seamless integration, and optimize workflow with various applications.

OpenTrain AI is a data labeling marketplace that leverages artificial intelligence to streamline the process of labeling data for machine learning models. It provides a platform where users can crowdsource data labeling tasks to a global community of annotators, ensuring high-quality labeled datasets for training AI algorithms. With advanced AI algorithms and human-in-the-loop validation, OpenTrain AI offers efficient and accurate data labeling services for various industries such as autonomous vehicles, healthcare, and natural language processing.

LISUTO AI is a data enhancement and e-commerce optimization platform that helps businesses improve their online presence and increase sales. The platform uses artificial intelligence (AI) to automatically tag and categorize product data, which makes it easier for customers to find the products they're looking for. LISUTO AI also provides insights into customer behavior, which can help businesses improve their marketing and sales strategies.

Easy Save AI is a comprehensive directory of Digital Marketing AI tools available online and curated by a digital marketing expert, Muritala Yusuf. Easy Save AI's primary objective is to ensure that AI is accessible to everyone. You can conveniently utilize our website to discover new AI tools and services or locate specific ones based on your requirements by Using our easy-to-use filter on the home page. AI technology is constantly progressing, and experts are continuously developing sophisticated models for various applications. Our directory includes an array of AI tools such as AI copywriters, text and image generators, AI transcription, SEO automation tools, and more. There is something suitable for every individual! Our website is committed to offering user-friendly AI tools and resources that can contribute to the success of you and your business in the digital era. We meticulously evaluate and curate each tool to ensure they possess valuable features and are accessible to both novices and experts. With the Easy Save AI platform, you can locate the AI tools you require and save valuable time and money. We sometimes have discounts on AI Tools and we always specify on the product page for you to use.

Simple Metrics' Gemini AI for Google Sheets is a powerful tool that allows users to harness the power of AI directly within Google Sheets. With a suite of 10 AI formulas and the ability to perform full sheet analysis, Gemini AI empowers users to automate tasks, gain valuable insights, and streamline their workflows. Its user-friendly interface and customizable settings make it accessible to users of all levels, while its free tier ensures that everyone can experience the benefits of AI in Google Sheets.

Autojob is a job search automation platform that helps users find jobs faster and more efficiently. It offers a range of features, including automatic job application, job filtering, and CV optimization. Autojob is designed to save users time and effort by automating the job search process.

GPT-Zip is an AI tool designed to optimize web content for RAG (Retrieval-Augmented Generation) processes in GPT-4 prompts. It compresses web content into pure information, reducing input tokens by up to 87%. The tool streamlines data processing for faster and more accurate responses, enhancing the speed and performance of GPT-4. GPT-Zip employs techniques like HTML stripping, CSS and JavaScript removal, and language compression to optimize web content for efficient processing by GPT-4.

Azara.AI is an AI tool that automates workflows, saving hours and enabling fast scaling. It allows users to handle tasks with the help of AI-generated workflows and actions. The tool offers no-code automations with secure, encrypted, and compliant features. Users can easily build automations from private company data, enhancing productivity and efficiency. Azara.AI simplifies the process of building, deploying, and managing AI agents without requiring technical expertise.

AskCodi is an AI-powered coding assistant designed to enhance developer productivity and efficiency. It offers a range of features, including AI-powered chat, workbooks, and integrations, to streamline coding tasks and improve code quality. AskCodi is trusted by developers worldwide for its ability to automate repetitive processes, provide real-time code suggestions, and enhance overall coding performance.

AIGT is a repository containing scripts for deep learning in guided medical interventions, focusing on ultrasound imaging. It provides a complete workflow from formatting and annotations to real-time model deployment. Users can set up an Anaconda environment, run Slicer notebooks, acquire tracked ultrasound data, and process exported data for training. The repository includes tools for segmentation, image export, and annotation creation.

AQLM is the official PyTorch implementation for Extreme Compression of Large Language Models via Additive Quantization. It includes prequantized AQLM models without PV-Tuning and PV-Tuned models for LLaMA, Mistral, and Mixtral families. The repository provides inference examples, model details, and quantization setups. Users can run prequantized models using Google Colab examples, work with different model families, and install the necessary inference library. The repository also offers detailed instructions for quantization, fine-tuning, and model evaluation. AQLM quantization involves calibrating models for compression, and users can improve model accuracy through finetuning. Additionally, the repository includes information on preparing models for inference and contributing guidelines.

uni-api is a project that unifies the management of large language model APIs, allowing you to call multiple backend services through a single unified API interface, converting them all to OpenAI format, and supporting load balancing. It supports various backend services such as OpenAI, Anthropic, Gemini, Vertex, Azure, xai, Cohere, Groq, Cloudflare, OpenRouter, and more. The project offers features like no front-end, pure configuration file setup, unified management of multiple backend services, support for multiple standard OpenAI format interfaces, rate limiting, automatic retry, channel cooling, fine-grained model timeout settings, and fine-grained permission control.

ShortcutsBench is a project focused on collecting and analyzing workflows created in the Shortcuts app, providing a dataset of shortcut metadata, source files, and API information. It aims to study the integration of large language models with Apple devices, particularly focusing on the role of shortcuts in enhancing user experience. The project offers insights for Shortcuts users, enthusiasts, and researchers to explore, customize workflows, and study automated workflows, low-code programming, and API-based agents.

This repository contains an implementation of Ring Attention, a technique for processing large sequences in transformers. Ring Attention splits the data across the sequence dimension and applies ring reduce to the processing of the tiles of the attention matrix, similar to flash attention. It also includes support for Striped Attention, a follow-up paper that permutes the sequence for better workload balancing for autoregressive transformers, and grouped query attention, which saves on communication costs during the ring reduce. The repository includes a CUDA version of the flash attention kernel, which is used for the forward and backward passes of the ring attention. It also includes logic for splitting the sequence evenly among ranks, either within the attention function or in the external ring transformer wrapper, and basic test cases with two processes to check for equivalent output and gradients.

Instructor for PHP is a library designed for structured data extraction in PHP, powered by Large Language Models (LLMs). It simplifies the process of extracting structured, validated data from unstructured text or chat sequences. Instructor enhances workflow by providing a response model, validation capabilities, and max retries for requests. It supports classes as response models and provides features like partial results, string input, extracting scalar and enum values, and specifying data models using PHP type hints or DocBlock comments. The library allows customization of validation and provides detailed event notifications during request processing. Instructor is compatible with PHP 8.2+ and leverages PHP reflection, Symfony components, and SaloonPHP for communication with LLM API providers.

Grounded 3D-LLM is a unified generative framework that utilizes referent tokens to reference 3D scenes, enabling the handling of sequences that interleave 3D and textual data. It transforms 3D vision tasks into language formats through task-specific prompts, curating grounded language datasets and employing Contrastive Language-Scene Pre-training (CLASP) to bridge the gap between 3D vision and language models. The model covers tasks like 3D visual question answering, dense captioning, object detection, and language grounding.

Text2Text is a comprehensive language modeling toolkit that offers a wide range of functionalities for text processing and generation. It provides tools for tokenization, embedding, TF-IDF calculations, BM25 scoring, indexing, translation, data augmentation, distance measurement, training/finetuning models, language identification, and serving models via a web server. The toolkit is designed to be user-friendly and efficient, offering a variety of features for natural language processing tasks.

InfLLM is a training-free memory-based method that unveils the intrinsic ability of LLMs to process streaming long sequences. It stores distant contexts into additional memory units and employs an efficient mechanism to lookup token-relevant units for attention computation. Thereby, InfLLM allows LLMs to efficiently process long sequences while maintaining the ability to capture long-distance dependencies. Without any training, InfLLM enables LLMs pre-trained on sequences of a few thousand tokens to achieve superior performance than competitive baselines continually training these LLMs on long sequences. Even when the sequence length is scaled to 1, 024K, InfLLM still effectively captures long-distance dependencies.

SeaLLMs are a family of language models optimized for Southeast Asian (SEA) languages. They were pre-trained from Llama-2, on a tailored publicly-available dataset, which comprises texts in Vietnamese 🇻🇳, Indonesian 🇮🇩, Thai 🇹🇭, Malay 🇲🇾, Khmer🇰🇭, Lao🇱🇦, Tagalog🇵🇭 and Burmese🇲🇲. The SeaLLM-chat underwent supervised finetuning (SFT) and specialized self-preferencing DPO using a mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which **adapt to the local cultural norms, customs, styles and laws in these areas**. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform **ChatGPT-3.5** in non-Latin languages, such as Thai, Khmer, Lao, and Burmese.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Model2Vec is a technique to turn any sentence transformer into a really small static model, reducing model size by 15x and making the models up to 500x faster, with a small drop in performance. It outperforms other static embedding models like GLoVe and BPEmb, is lightweight with only `numpy` as a major dependency, offers fast inference, dataset-free distillation, and is integrated into Sentence Transformers, txtai, and Chonkie. Model2Vec creates powerful models by passing a vocabulary through a sentence transformer model, reducing dimensionality using PCA, and weighting embeddings using zipf weighting. Users can distill their own models or use pre-trained models from the HuggingFace hub. Evaluation can be done using the provided evaluation package. Model2Vec is licensed under MIT.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It supports training, inference, evaluation, quantization, and deployment of over 400 large models and 100+ multi-modal large models. The framework includes various training technologies and accelerates inference, evaluation, and deployment modules. It offers a Gradio-based Web-UI interface and best practices for easy application of large models. ms-swift supports a wide range of model types, dataset types, hardware support, lightweight training methods, distributed training techniques, quantization training, RLHF training, multi-modal training, interface training, plugin and extension support, inference acceleration engines, model evaluation, and model quantization.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

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