Best AI tools for< Split On Word >

LightPDF is an AI-powered, free online PDF editor, converter, and reader. It offers a wide range of PDF tools, including the ability to convert PDFs to and from other formats, edit PDFs, add watermarks, split and merge PDFs, rotate PDFs, annotate PDFs, optimize PDFs, compress PDFs, perform OCR on PDFs, and protect PDFs. LightPDF also offers a variety of AI-powered features, such as an AI chatbot that can answer questions about documents and an AI-powered OCR engine that can convert scanned PDFs and images to text.

SendEngage is a B2B cold email platform that uses AI to read, categorize and route interested replies directly to your sales team, at any scale. It offers features such as unlimited inbox rotation, message variation split testing, automated follow-ups, open/reply/sentiment tracking, and AI reply management. With SendEngage, businesses can create high converting, automated email campaigns, manage replies and engage with interested prospects, and hand off interested leads directly to sales reps' main inboxes and map to CRM records.

VocalRemover.org is a website that offers a simple and efficient tool to remove vocals from music tracks. Users can upload their audio files and the tool will process them to create a version with the vocals removed. The site aims to provide a hassle-free experience for users looking to create karaoke tracks or instrumental versions of songs. With a focus on performance and security, VocalRemover.org ensures a smooth process for its users.

LALAL.AI is a next-generation vocal remover and music source separation service that offers fast, easy, and precise stem extraction. It allows users to remove vocals, instrumental tracks, drums, bass, piano, electric guitar, acoustic guitar, and synthesizer tracks without compromising quality. The service leverages advanced AI technology to provide high-quality stem splitting based on cutting-edge algorithms. Users can also enjoy features like voice cleaning, voice changing, echo and reverb removal, and lead/back vocal splitting. LALAL.AI caters to both individual and business users, offering various pricing packages and enterprise solutions for seamless integration and cross-platform support.

Gaudio Studio is an AI music separation tool designed for creators to unleash their creativity with ease. It allows users to extract background music, separate instruments, and remove vocals from any music content. Powered by GSEP (Gaudio source SEParation), a high-quality and easy-to-use AI stem separation model, Gaudio Studio offers a seamless experience for audio separation. Users can upload their songs in various formats, access the tool from desktop or mobile devices, and enjoy Studio Plans for advanced processing. Additionally, Gaudio Studio can be integrated with cloud APIs and On-device SDKs for business applications, offering a versatile solution for music professionals and enthusiasts.

Music Demixer is a cutting-edge AI tool that allows users to separate songs, split stems, create instrumental breakdowns, remove vocals, extract instruments, and generate karaoke tracks. It is powered by the Demucs AI model, a winner of the Sony Music and Sound Demixing Challenges. The tool operates in the browser without cloud services, ensuring 100% privacy. Users can choose various parameters like components and quality to customize their demixing experience. Music Demixer is best experienced on a laptop or desktop and offers a free tier with limited demixes per week.

AdCopy is an AI-powered advertising platform that helps businesses create high-quality ads and optimize their ad campaigns. The platform uses AI to generate ad copy, create ad creatives, and provide insights into ad performance. AdCopy is designed to help businesses save time and money on their advertising campaigns, while also improving their results.

SplitMyExpenses is an AI-powered application designed to simplify shared expenses with friends. It allows users to create groups, split bills, track debts, and settle up seamlessly. The app offers modern design, AI receipt itemization, friend data integration from payment apps, spending visualization, and secure payment handling. With over 150 supported currencies and no limits on expenses, SplitMyExpenses revolutionizes the age-old problem of bill splitting, providing users with time, money, and sanity-saving solutions.

StemRoller is an AI-powered application that allows users to create stems, instrumental, or acapella versions of any song. Users can simply type the name of a song into the search bar, and StemRoller will find the song online and split it into vocals, drums, bass, and other stems. Additionally, an instrumental track is created with all non-vocal stems mixed down into one track. StemRoller is free and open-source, utilizing Facebook's advanced AI and machine learning research project Demucs. Users can also donate to support the app and receive assistance on Discord for any issues or questions.

**万兴科技** is an AI-powered tool that helps users create and edit PDF documents. It offers a wide range of features, including the ability to convert PDFs to other formats, edit text and images, and add annotations. **万兴科技** is a valuable tool for anyone who needs to work with PDFs on a regular basis.

Kamara is an AI-powered coder that functions as a VS Code extension. It adapts to your codebase, effortlessly implementing features across multiple files. Kamara works best with short files and specific implementation ideas. It uses a credit-based system for payment, where users pay for the code read and written. The team actively working on Kamara includes Gonza Nardini and Diego Vazquez. Users can provide feedback and join the Discord server for support.

Tablesmith is a free, privacy-first, and intuitive spreadsheet automation tool that allows users to build reusable data flows, effortlessly sort, filter, group, format, or split data across files/sheets based on cell values. It is designed to be easy to learn and use, with a focus on privacy and cross-platform compatibility. Tablesmith also offers an AI autofill feature that suggests and fills in information based on the user's prompt.

Videolulu is an AI-powered tool that enables users to generate faceless videos on autopilot. It allows users to turn their ideas into viral shorts in minutes by creating engaging content in popular formats for platforms like TikTok, Instagram, and YouTube. With a simple 4-step process, users can choose a video type, select a voice from a variety of AI voices, add background music, and select a video format using AI images, stock videos, or split screens. Videolulu offers different pricing plans to suit varying needs, from a free plan with limited features to premium plans with more credits and options.

SecondSoul is an AI platform that enables users to create their AI clone for engaging 24/7 conversations on Telegram. It allows users to customize their AI clone with unique traits, voice, and train it to mimic their style. The platform offers a straightforward pricing model with a revenue split, where creators earn 80% of the messages fee from users of their clone. SecondSoul aims to enhance user experience, provide companionship, and monetize community interactions through AI technology.

SigmaOS is a revolutionary AI-powered browser application that offers a fresh and organized browsing experience. It features innovative tools such as Workspaces for tab organization, Vertical tabs for task management, Split Screen for multitasking, Lazy Search for quick searches, and Ask Anything for contextual information retrieval. With advantages like ad-free browsing, focus mode, easy migration, and community support, SigmaOS aims to simplify and enhance users' internet interactions. However, it has limitations such as being available only on macOS and lacking support for Windows. SigmaOS leverages AI technology, including the A1Kit browser engine, to provide intelligent assistance and streamline daily tasks.

Rapid Muscle is a science-powered hypertrophy workout generator that offers a cutting-edge platform to accelerate muscle growth. It provides tools like the Hypertrophy Split Generator and Workout Tracker to optimize exercise selection, sequencing, and volume for physique development. The upcoming AI Personal Trainer chatbot enhances user experience by providing expert advice on training-related queries. Rapid Muscle aims to revolutionize hypertrophy programming by offering evidence-based solutions and eliminating contradictory influencer advice.

OpenSpace is a reality capture and construction site capture application that utilizes AI-powered analytics for builders. It offers a reliable way to build faster with less risk by providing a complete, as-built record of the building from preconstruction to handover and operation. OpenSpace helps users stay on top of progress, verify work-in-place, improve coordination, and reduce risk through features like BIM Compare, Split View, Field Notes, and integrations with project management software. The application has been trusted by industry leaders globally and has captured billions of square feet across thousands of projects in various countries.

Luma Dream Machine is an AI video generator tool that creates high-quality, realistic videos from text and images. It is a scalable and efficient transformer model trained directly on videos, capable of generating physically accurate and eventful shots. The tool aims to build a universal imagination engine, enabling users to bring their creative visions to life effortlessly.

Luma AI is a 3D capture platform that allows users to create interactive 3D scenes from videos. With Luma AI, users can capture 3D models of people, objects, and environments, and then use those models to create interactive experiences such as virtual tours, product demonstrations, and training simulations.

GitBrain is an AI-powered Git client designed for Mac users. It simplifies the Git workflow by offering features like AI commit messages, code splitting, self-code review, auto-detection of projects, and keyboard-friendly design. With GitBrain, developers can focus on coding while the AI handles Git operations efficiently. The application enhances productivity by intelligently splitting code changes into multiple AI-generated commits, providing summaries for code changes, and offering a seamless Git management experience. GitBrain is optimized for Mac performance with a native UI and supports light & dark mode themes.

Node.js bindings for OpenAI's Whisper model that automatically converts audio to WAV format with a 16000 Hz frequency to support the whisper model. It outputs transcripts to various formats, is optimized for CPU including Apple Silicon ARM, provides timestamp precision to single word, allows splitting on word rather than token, translation from source language to English, and conversion of audio format to WAV for whisper model support.

WordLlama is a fast, lightweight NLP toolkit optimized for CPU hardware. It recycles components from large language models to create efficient word representations. It offers features like Matryoshka Representations, low resource requirements, binarization, and numpy-only inference. The tool is suitable for tasks like semantic matching, fuzzy deduplication, ranking, and clustering, making it a good option for NLP-lite tasks and exploratory analysis.

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.

WDoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It supports querying tens of thousands of documents simultaneously, offers tailored summaries to efficiently manage large amounts of information, and includes features like supporting multiple file types, various LLMs, local and private LLMs, advanced RAG capabilities, advanced summaries, trust verification, markdown formatted answers, sophisticated embeddings, extensive documentation, scriptability, type checking, lazy imports, caching, fast processing, shell autocompletion, notification callbacks, and more. WDoc is ideal for researchers, students, and professionals dealing with extensive information sources.

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It aims to handle large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. wdoc uses LangChain to process and analyze documents, supporting tens of thousands of documents simultaneously. The system includes features like high recall and specificity, support for various Language Model Models (LLMs), advanced RAG capabilities, advanced document summaries, and support for multiple tasks. It offers markdown-formatted answers and summaries, customizable embeddings, extensive documentation, scriptability, and runtime type checking. wdoc is suitable for power users seeking document querying capabilities and AI-powered document summaries.

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.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

The Keras-llm-robot Web UI project is an open-source tool designed for offline deployment and testing of various open-source models from the Hugging Face website. It allows users to combine multiple models through configuration to achieve functionalities like multimodal, RAG, Agent, and more. The project consists of three main interfaces: chat interface for language models, configuration interface for loading models, and tools & agent interface for auxiliary models. Users can interact with the language model through text, voice, and image inputs, and the tool supports features like model loading, quantization, fine-tuning, role-playing, code interpretation, speech recognition, image recognition, network search engine, and function calling.

SimplerLLM is an open-source Python library that simplifies interactions with Large Language Models (LLMs) for researchers and beginners. It provides a unified interface for different LLM providers, tools for enhancing language model capabilities, and easy development of AI-powered tools and apps. The library offers features like unified LLM interface, generic text loader, RapidAPI connector, SERP integration, prompt template builder, and more. Users can easily set up environment variables, create LLM instances, use tools like SERP, generic text loader, calling RapidAPI APIs, and prompt template builder. Additionally, the library includes chunking functions to split texts into manageable chunks based on different criteria. Future updates will bring more tools, interactions with local LLMs, prompt optimization, response evaluation, GPT Trainer, document chunker, advanced document loader, integration with more providers, Simple RAG with SimplerVectors, integration with vector databases, agent builder, and LLM server.

Notes on AI state of the art, with a focus on generative and large language models. These are the "raw materials" for the https://lspace.swyx.io/ newsletter. This repo used to be called https://github.com/sw-yx/prompt-eng, but was renamed because Prompt Engineering is Overhyped. This is now an AI Engineering notes repo.

Awesome Khmer Language is a comprehensive collection of resources for the Khmer language, including tools, datasets, research papers, projects/models, blogs/slides, and miscellaneous items. It covers a wide range of topics related to Khmer language processing, such as character normalization, word segmentation, part-of-speech tagging, optical character recognition, text-to-speech, and more. The repository aims to support the development of natural language processing applications for the Khmer language by providing a diverse set of resources and tools for researchers and developers.

RayLLM (formerly known as Aviary) is an LLM serving solution that makes it easy to deploy and manage a variety of open source LLMs, built on Ray Serve. It provides an extensive suite of pre-configured open source LLMs, with defaults that work out of the box. RayLLM supports Transformer models hosted on Hugging Face Hub or present on local disk. It simplifies the deployment of multiple LLMs, the addition of new LLMs, and offers unique autoscaling support, including scale-to-zero. RayLLM fully supports multi-GPU & multi-node model deployments and offers high performance features like continuous batching, quantization and streaming. It provides a REST API that is similar to OpenAI's to make it easy to migrate and cross test them. RayLLM supports multiple LLM backends out of the box, including vLLM and TensorRT-LLM.

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.

Mobius is an AI infra platform including realtime computing and training. It is built on Ray, a distributed computing framework, and provides a number of features that make it well-suited for online machine learning tasks. These features include: * **Cross Language**: Mobius can run in multiple languages (only Python and Java are supported currently) with high efficiency. You can implement your operator in different languages and run them in one job. * **Single Node Failover**: Mobius has a special failover mechanism that only needs to rollback the failed node itself, in most cases, to recover the job. This is a huge benefit if your job is sensitive about failure recovery time. * **AutoScaling**: Mobius can generate a new graph with different configurations in runtime without stopping the job. * **Fusion Training**: Mobius can combine TensorFlow/Pytorch and streaming, then building an e2e online machine learning pipeline. Mobius is still under development, but it has already been used to power a number of real-world applications, including: * A real-time recommendation system for a major e-commerce company * A fraud detection system for a large financial institution * A personalized news feed for a major news organization If you are interested in using Mobius for your own online machine learning projects, you can find more information in the documentation.

InternLM-XComposer2 is a groundbreaking vision-language large model (VLLM) based on InternLM2-7B excelling in free-form text-image composition and comprehension. It boasts several amazing capabilities and applications: * **Free-form Interleaved Text-Image Composition** : InternLM-XComposer2 can effortlessly generate coherent and contextual articles with interleaved images following diverse inputs like outlines, detailed text requirements and reference images, enabling highly customizable content creation. * **Accurate Vision-language Problem-solving** : InternLM-XComposer2 accurately handles diverse and challenging vision-language Q&A tasks based on free-form instructions, excelling in recognition, perception, detailed captioning, visual reasoning, and more. * **Awesome performance** : InternLM-XComposer2 based on InternLM2-7B not only significantly outperforms existing open-source multimodal models in 13 benchmarks but also **matches or even surpasses GPT-4V and Gemini Pro in 6 benchmarks** We release InternLM-XComposer2 series in three versions: * **InternLM-XComposer2-4KHD-7B** 🤗: The high-resolution multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _High-resolution understanding_ , _VL benchmarks_ and _AI assistant_. * **InternLM-XComposer2-VL-7B** 🤗 : The multi-task trained VLLM model with InternLM-7B as the initialization of the LLM for _VL benchmarks_ and _AI assistant_. **It ranks as the most powerful vision-language model based on 7B-parameter level LLMs, leading across 13 benchmarks.** * **InternLM-XComposer2-VL-1.8B** 🤗 : A lightweight version of InternLM-XComposer2-VL based on InternLM-1.8B. * **InternLM-XComposer2-7B** 🤗: The further instruction tuned VLLM for _Interleaved Text-Image Composition_ with free-form inputs. Please refer to Technical Report and 4KHD Technical Reportfor more details.

LLM Finetuning toolkit is a config-based CLI tool for launching a series of LLM fine-tuning experiments on your data and gathering their results. It allows users to control all elements of a typical experimentation pipeline - prompts, open-source LLMs, optimization strategy, and LLM testing - through a single YAML configuration file. The toolkit supports basic, intermediate, and advanced usage scenarios, enabling users to run custom experiments, conduct ablation studies, and automate fine-tuning workflows. It provides features for data ingestion, model definition, training, inference, quality assurance, and artifact outputs, making it a comprehensive tool for fine-tuning large language models.

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)

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. |  |

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

Simply Enter your Niche and we'll create your Split Screen Ads for you.

Step 1: Upload RFP Step 2: Prompt: I need a comprehensive summary of the RFP. Split the summary in multiple blocks / section. After giving me one section wait for my command to move to the next section. Step 3: Prompt: Move to the next section, please :)

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A witty tipping calculator with literary quips.

资深英语教师，提供翻译、意群拆分、语法分析和句型鉴定。

Expert in PDF tasks and tool advice.

Provides running splits for Strava Routes, accounting for distance and elevation changes