Best AI tools for< Reduce Pdf Size >

HiPDF is a free online PDF solution that offers a wide range of tools for editing, converting, compressing, and organizing PDFs. It also includes AI-powered tools such as Chat with PDF and AI Detector. With HiPDF, you can easily edit PDFs in your browser, convert PDFs to and from other formats, compress PDFs to reduce their size, and merge, split, and extract images from PDFs. You can also protect your PDFs with passwords and redact sensitive information. HiPDF is a convenient and easy-to-use tool that can help you with all your PDF needs.

Beebzi.AI is an all-in-one AI content creation platform that offers a wide array of tools for generating various types of content such as articles, blogs, emails, images, voiceovers, and more. The platform utilizes advanced AI technology and behavioral science to empower businesses and individuals in their marketing and sales endeavors. With features like AI Article Wizard, AI Room Designer, AI Landing Page Generator, and AI Code Generation, Beebzi.AI revolutionizes content creation by providing customizable templates, multiple language support, and real-time data insights. The platform also offers various subscription plans tailored for individual entrepreneurs, teams, and businesses, with flexible pricing models based on word count allocations. Beebzi.AI aims to streamline content creation processes, enhance productivity, and drive organic traffic through SEO-optimized content.

LedgerBox is an AI tool that specializes in converting bank statements into digital formats. It simplifies the process of managing financial data by automatically extracting and organizing information from bank statements. With LedgerBox, users can easily convert paper-based bank statements into digital files, enabling quick and efficient financial analysis and reporting. The tool is designed to save time and reduce errors associated with manual data entry, making it a valuable asset for individuals and businesses looking to streamline their financial processes.

PDFMerse is an AI-powered data extraction tool that revolutionizes how users handle document data. It allows users to effortlessly extract information from PDFs with precision, saving time and enhancing workflow. With cutting-edge AI technology, PDFMerse automates data extraction, ensures data accuracy, and offers versatile output formats like CSV, JSON, and Excel. The tool is designed to dramatically reduce processing time and operational costs, enabling users to focus on higher-value tasks.

THE POLICY CHATBOT is an AI-powered tool that transforms Standard Operating Procedures into a dynamic chatbot, providing instant answers and guidance to users within a company. It allows authorized employees to access and interact with company policies in real-time, enhancing efficiency and accuracy in policy-related queries. The chatbot leverages AI technology to extract information from uploaded PDF SOPs, offering a seamless user experience and freeing up employees to focus on more critical tasks.

SmartExam is an AI-powered platform designed to assist students in exam preparation by generating test exams based on uploaded lectures. The tool aims to help students succeed in their exams by providing tailored interactive exams and study materials. SmartExam is trusted by top students worldwide and offers a user-friendly experience. Users can upload lecture materials in PDF format, generate test exams in seconds, and download them for further training. The platform reduces exam preparation time by 50% and has received positive feedback for its efficiency and effectiveness.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

DailyBot is an AI-powered toolkit for teams that want automation, better reporting, and customization. It offers a range of features to enhance team visibility, reduce meetings, and improve collaboration. With DailyBot, teams can run asynchronous standups, retros, and other meetings, send kudos and recognition, create surveys and collect data, and access a variety of add-ons like watercoolers and random coffees. DailyBot also integrates with popular tools like Zapier, Jira, and Trello, making it easy to connect with the tools teams already use. Trusted by leading companies and backed by Y Combinator, DailyBot is a valuable tool for teams looking to improve their collaboration and productivity.

Nametag is an identity verification solution designed specifically for IT helpdesks. It helps businesses prevent social engineering attacks, account takeovers, and data breaches by verifying the identity of users at critical moments, such as password resets, MFA resets, and high-risk transactions. Nametag's unique approach to identity verification combines mobile cryptography, device telemetry, and proprietary AI models to provide unmatched security and better user experiences.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages AI technology to anticipate threats, manage vulnerabilities, and protect resources across the enterprise ecosystem. SentinelOne provides real-time threat hunting, managed services, and actionable insights through its unified data lake, empowering security teams to respond effectively to cyber threats. With a focus on automation, efficiency, and value maximization, SentinelOne is a trusted cybersecurity solution for leading enterprises worldwide.

Codimite is an AI-assisted offshore development company that provides a range of services to help businesses accelerate their software development, reduce costs, and drive innovation. Codimite's team of experienced engineers and project managers use AI-powered tools and technologies to deliver exceptional results for their clients. The company's services include AI-assisted software development, cloud modernization, and data and artificial intelligence solutions.

SentinelOne is an advanced enterprise cybersecurity AI platform that offers a comprehensive suite of AI-powered security solutions for endpoint, cloud, and identity protection. The platform leverages artificial intelligence to anticipate threats, manage vulnerabilities, and protect resources across the entire enterprise ecosystem. With features such as Singularity XDR, Purple AI, and AI-SIEM, SentinelOne empowers security teams to detect and respond to cyber threats in real-time. The platform is trusted by leading enterprises worldwide and has received industry recognition for its innovative approach to cybersecurity.

SnapMeasureAI is an AI-powered application that provides 99% accurate body measurements without the need to visit a tailor. It uses advanced AI technology to analyze body shapes and measurements from photos or videos, offering unparalleled precision and reliability. The application aims to reduce returns, save costs, and increase shopping confidence by ensuring a perfect fit for users. SnapMeasureAI is designed to accommodate any body type, skin tone, pose, or background, making it a versatile and user-friendly tool for personalized body measurements.

Video Highlight is an AI-powered tool that helps you summarize and take notes from videos. It uses the latest AI technology to generate timestamped summaries and transcripts, highlight key moments, and engage in interactive chats. With Video Highlight, you can save hours of research time and focus on exploring, analyzing, and absorbing content.

Hypergro is an AI-powered platform that specializes in UGC video ads for smart customer acquisition. Leveraging the 4th Generation of AI-powered growth marketing on Meta and Youtube, Hypergro helps businesses discover their audience, drive sales, and increase revenue through real-time AI insights. The platform offers end-to-end solutions for creating impactful short video ads that combine creator authenticity with AI-driven research for compelling storytelling. With a focus on precision targeting, competitor analysis, and in-depth research, Hypergro ensures maximum ROI for brands looking to elevate their growth strategies.

hirex.ai is an AI tool designed to make job interviews open, fair, and accessible for all. The platform offers the GenAI assistant to help users get interviewed instantly and jump the line to secure their dream job. With no credit card required, users can register for free early access and benefit from faster screening processes by interviewing candidates using the GenAI assistant. The website aims to transform HR and talent acquisition by providing innovative solutions for job seekers and employers.

Parasoft is an intelligent automated testing and quality platform that offers a range of tools covering every stage of the software development lifecycle. It provides solutions for compliance standards, automated software testing, and various industries' needs. Parasoft helps users accelerate software delivery, ensure quality, and comply with safety and security standards.

LiveChatAI is an AI chatbot application that works with your data to provide interactive and personalized customer support solutions. It blends AI and human support to deliver dynamic and accurate responses, improving customer satisfaction and reducing support volume. With features like AI Actions, custom question & answers, and content import, LiveChatAI offers a seamless integration for businesses across various platforms and languages. The application is designed to be user-friendly, requiring no AI expertise, and offers instant localization in 95 languages.

GrapixAI is a leading provider of low-cost cloud GPU rental services and AI server solutions. The company's focus on flexibility, scalability, and cutting-edge technology enables a variety of AI applications in both local and cloud environments. GrapixAI offers the lowest prices for on-demand GPUs such as RTX4090, RTX 3090, RTX A6000, RTX A5000, and A40. The platform provides Docker-based container ecosystem for quick software setup, powerful GPU search console, customizable pricing options, various security levels, GUI and CLI interfaces, real-time bidding system, and personalized customer support.

Sprockets is an AI-powered hiring software designed to help businesses overcome today's unique hiring challenges. It automates manual tasks, reduces employee turnover, and helps businesses hire the best workers every time. Sprockets offers a range of features, including a virtual recruiter, sourcing, screening, applicant tracking, reporting, time to hire, background checks, and tax credits. It also integrates with a variety of other HR systems, making it easy to use alongside your existing tools. With Sprockets, businesses can improve their hiring process, save time and money, and find the best talent for their open positions.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

RLAIF-V is a novel framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. It maximally exploits open-source feedback from high-quality feedback data and online feedback learning algorithm. Notable features include achieving super GPT-4V trustworthiness in both generative and discriminative tasks, using high-quality generalizable feedback data to reduce hallucination of different MLLMs, and exhibiting better learning efficiency and higher performance through iterative alignment.

Baal is an active learning library that supports both industrial applications and research use cases. It provides a framework for Bayesian active learning methods such as Monte-Carlo Dropout, MCDropConnect, Deep ensembles, and Semi-supervised learning. Baal helps in labeling the most uncertain items in the dataset pool to improve model performance and reduce annotation effort. The library is actively maintained by a dedicated team and has been used in various research papers for production and experimentation.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

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.

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

Indexify is an open-source engine for building fast data pipelines for unstructured data (video, audio, images, and documents) using reusable extractors for embedding, transformation, and feature extraction. LLM Applications can query transformed content friendly to LLMs by semantic search and SQL queries. Indexify keeps vector databases and structured databases (PostgreSQL) updated by automatically invoking the pipelines as new data is ingested into the system from external data sources. **Why use Indexify** * Makes Unstructured Data **Queryable** with **SQL** and **Semantic Search** * **Real-Time** Extraction Engine to keep indexes **automatically** updated as new data is ingested. * Create **Extraction Graph** to describe **data transformation** and extraction of **embedding** and **structured extraction**. * **Incremental Extraction** and **Selective Deletion** when content is deleted or updated. * **Extractor SDK** allows adding new extraction capabilities, and many readily available extractors for **PDF**, **Image**, and **Video** indexing and extraction. * Works with **any LLM Framework** including **Langchain**, **DSPy**, etc. * Runs on your laptop during **prototyping** and also scales to **1000s of machines** on the cloud. * Works with many **Blob Stores**, **Vector Stores**, and **Structured Databases** * We have even **Open Sourced Automation** to deploy to Kubernetes in production.

AnnA is a Python script designed to create filtered decks in optimal review order for Anki flashcards. It uses Machine Learning / AI to ensure semantically linked cards are reviewed far apart. The script helps users manage their daily reviews by creating special filtered decks that prioritize reviewing cards that are most different from the rest. It also allows users to reduce the number of daily reviews while increasing retention and automatically identifies semantic neighbors for each note.

AWQ (Activation-aware Weight Quantization) is a tool designed for efficient and accurate low-bit weight quantization (INT3/4) for Large Language Models (LLMs). It supports instruction-tuned models and multi-modal LMs, providing features such as AWQ search for accurate quantization, pre-computed AWQ model zoo for various LLMs, memory-efficient 4-bit linear in PyTorch, and efficient CUDA kernel implementation for fast inference. The tool enables users to run large models on resource-constrained edge platforms, delivering more efficient responses with LLM/VLM chatbots through 4-bit inference.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

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.

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.

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.

This repository provides an LLM-driven audio codec model, LLM-Codec, for building multi-modal LLMs (text and audio modalities). The model enables frozen LLMs to achieve multiple audio tasks in a few-shot style without parameter updates. It compresses the audio modality into a well-trained LLMs token space, treating audio representation as a 'foreign language' that LLMs can learn with minimal examples. The proposed approach supports tasks like speech emotion classification, audio classification, text-to-speech generation, speech enhancement, etc., demonstrating feasibility and effectiveness in simple scenarios. The LLM-Codec model is open-sourced to facilitate research on few-shot audio task learning and multi-modal LLMs.

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.

Atom is an accurate low-bit weight-activation quantization algorithm that combines mixed-precision, fine-grained group quantization, dynamic activation quantization, KV-cache quantization, and efficient CUDA kernels co-design. It introduces a low-bit quantization method, Atom, to maximize Large Language Models (LLMs) serving throughput with negligible accuracy loss. The codebase includes evaluation of perplexity and zero-shot accuracy, kernel benchmarking, and end-to-end evaluation. Atom significantly boosts serving throughput by using low-bit operators and reduces memory consumption via low-bit quantization.

Uses 'simple_compress_pdf' function to significantly reduce PDF size.

I compress images to reduce file size without losing quality.

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