Best AI tools for< Research Efficient Llms >

ThirdAI is a production-ready AI platform designed for enterprise use, offering out-of-the-box solutions that work at scale and provide 10x better price performance. The platform features enterprise SSO, LLM guardrails, built-in models, a no-code interface, and implicit feedback & RLHF. It allows for turnkey deployment of complex AI ecosystems, enabling business leaders to solve critical needs quickly. With a focus on security, scalability, and performance, ThirdAI helps drive innovation and achieve business goals from day one.

Denvr DataWorks AI Cloud is a cloud-based AI platform that provides end-to-end AI solutions for businesses. It offers a range of features including high-performance GPUs, scalable infrastructure, ultra-efficient workflows, and cost efficiency. Denvr DataWorks is an NVIDIA Elite Partner for Compute, and its platform is used by leading AI companies to develop and deploy innovative AI solutions.

Shieldbase is an AI-powered enterprise search tool designed to provide secure and efficient search capabilities for businesses. It utilizes advanced artificial intelligence algorithms to index and retrieve information from various data sources within an organization, ensuring quick and accurate search results. With a focus on security, Shieldbase offers encryption and access control features to protect sensitive data. The platform is user-friendly and customizable, making it easy for businesses to implement and integrate into their existing systems. Shieldbase enhances productivity by enabling employees to quickly find the information they need, ultimately improving decision-making processes and overall operational efficiency.

Cohere is the leading AI platform for enterprise, offering products optimized for generative AI, search and discovery, and advanced retrieval. Their models are designed to enhance the global workforce, enabling businesses to thrive in the AI era. Cohere provides Command R+, Cohere Command, Cohere Embed, and Cohere Rerank for building efficient AI-powered applications. The platform also offers deployment options for enterprise-grade AI on any cloud or on-premises, along with developer resources like Playground, LLM University, and Developer Docs.

Cohere is the leading AI platform for enterprise, offering generative AI, search and discovery, and advanced retrieval solutions. Their models are designed to enhance the global workforce, empowering businesses to thrive in the AI era. With features like Cohere Command, Cohere Embed, and Cohere Rerank, the platform enables the development of scalable and efficient AI-powered applications. Cohere focuses on optimizing enterprise data through language-based models, supporting over 100 languages for enhanced accuracy and efficiency.

Bottr is an AI assistant, companion, generator, and chatbot that can help you with anything you want. It is designed to make your life easier, more organized, and more productive. Bottr is more than just an assistant - it's your advisor and your companion all rolled into one. It is built on a state-of-the-art language model like chatGPT, enabling it to handle a wide range of tasks. From setting reminders and managing your schedule to conducting research and generating reports, your Bottr is there to assist. It is designed to evolve with you, learning from your preferences, habits, and needs, becoming more personalized and efficient over time.

Labelbox is a data factory platform that empowers AI teams to manage data labeling, train models, and create better data with internet scale RLHF platform. It offers an all-in-one solution comprising tooling and services powered by a global community of domain experts. Labelbox operates a global data labeling infrastructure and operations for AI workloads, providing expert human network for data labeling in various domains. The platform also includes AI-assisted alignment for maximum efficiency, data curation, model training, and labeling services. Customers achieve breakthroughs with high-quality data through Labelbox.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

Unify is an AI tool that offers a unified platform for accessing and comparing various Language Models (LLMs) from different providers. It allows users to combine models for faster, cheaper, and better responses, optimizing for quality, speed, and cost-efficiency. Unify simplifies the complex task of selecting the best LLM by providing transparent benchmarks, personalized routing, and performance optimization tools.

WeGPT.ai is an AI tool that focuses on enhancing Generative AI capabilities through Retrieval Augmented Generation (RAG). It provides versatile tools for web browsing, REST APIs, image generation, and coding playgrounds. The platform offers consumer and enterprise solutions, multi-vendor support, and access to major frontier LLMs. With a comprehensive approach, WeGPT.ai aims to deliver better results, user experience, and cost efficiency by keeping AI models up-to-date with the latest data.

Papertalk.io is an AI-powered platform that revolutionizes research by providing users with access to over 215 million papers, AI-generated explanations, and actionable insights. The platform offers precision search tools, AI-powered understanding of research papers, and personalized guidance on applying insights practically. Papertalk.io aims to make research more accessible and approachable for users from diverse backgrounds, transforming complex data into easy-to-digest formats to foster innovation and expertise.

OpenRead is an AI-powered research tool that helps users discover, understand, and organize scientific literature. It offers a variety of features to make research more efficient and effective, including semantic search, AI summarization, and note-taking tools. OpenRead is designed to help researchers of all levels, from students to experienced professionals, save time and improve their research outcomes.

Lumina is a research tool that uses artificial intelligence to help researchers find and analyze information more quickly and easily. It can be used to search for articles, books, and other resources, and it can also be used to analyze data and create visualizations. Lumina is designed to make research more efficient and productive.

CoFinance is an AI-driven legal intelligence and collaboration hub that revolutionizes legal and compliance research workflows. It combines semantic search, multi-faceted document analysis, and intelligent organization tools to provide precise and efficient research solutions. The platform leverages cutting-edge Regulatory Artificial Intelligence (RAI) technology to ensure that answers are sourced from real, authoritative data. CoFinance prioritizes simplifying regulatory complexity, mitigating compliance risks, accelerating research efficiency, and providing reliable partnership for long-term compliance success. It caters to organizations navigating complex regulatory landscapes, offering quick adaptation to changes and seamless compliance across various industries and jurisdictions.

Ask Blue J is a generative AI tool designed specifically for tax experts. It provides fast, verifiable answers to complex tax questions, helping professionals work smarter and more efficiently. With its extensive database of curated tax content and industry-leading AI technology, Ask Blue J enables users to conduct efficient research, expedite drafting, and enhance their overall productivity.

Viable is an AI-driven platform that provides actionable insights from qualitative data. It effortlessly transforms raw data into valuable information using AI technology. The platform offers integrations with various tech stacks and transparent pricing options to meet user requirements. Viable is designed to help businesses improve customer experience, boost employee engagement, enhance marketing strategies, prioritize product management actions, and conduct efficient research with the help of AI.

Re-View is an AI-powered platform that enables users to conduct surveys that capture more than words by utilizing user-friendly video survey forms. The platform allows users to understand emotions, uncover insights, and collect more and better data through authentic emotional connections. With features like automatic insights, efficient research at scale, stunning simplicity, and powerful research capabilities, Re-View offers a practical pricing model that makes research accessible to all. Users can easily create surveys, analyze responses with AI assistance, and gain valuable research reports to support decision-making.

Cuecard is an AI-powered sales co-pilot tool designed to revolutionize the sales process by providing AI-driven knowledge and personalized experiences to help sales teams close deals faster. It offers features such as interactive outreach, efficient research, real-time answers, centralized knowledge access, and improved sales velocity. Cuecard is trusted by leading brands of all sizes and offers a live demo for users to experience its innovative features firsthand.

Novo AI is an AI application that empowers financial institutions by leveraging Generative AI and Large Language Models to streamline operations, maximize insights, and automate processes like claims processing and customer support traditionally handled by humans. The application helps insurance companies understand claim documents, automate claims processing, optimize pricing strategies, and improve customer satisfaction. For banks, Novo AI automates document processing across multiple languages and simplifies adverse media screenings through efficient research on live internet data.

OpinioAI is an AI-powered market research tool that allows users to gain business critical insights from data without the need for costly polls, surveys, or interviews. With OpinioAI, users can create AI personas and market segments to understand customer preferences, affinities, and opinions. The platform democratizes research by providing efficient, effective, and budget-friendly solutions for businesses, students, and individuals seeking valuable insights. OpinioAI leverages Large Language Models to simulate humans and extract opinions in detail, enabling users to analyze existing data, synthesize new insights, and evaluate content from the perspective of their target audience.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

Efficient Multimodal Large Language Models: A Survey provides a comprehensive review of efficient and lightweight Multimodal Large Language Models (MLLMs), focusing on model size reduction and cost efficiency for edge computing scenarios. The survey covers the timeline of efficient MLLMs, research on efficient structures and strategies, and applications. It discusses current limitations and future directions in efficient MLLM research.

Efficient Multimodal Large Language Models: A Survey provides a comprehensive review of efficient and lightweight Multimodal Large Language Models (MLLMs), focusing on model size reduction and cost efficiency for edge computing scenarios. The survey covers the timeline of efficient MLLMs, research on efficient structures and strategies, and their applications, while also discussing current limitations and future directions.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

This repository provides a comprehensive list of academic papers, articles, tutorials, slides, and projects related to Large Language Model (LLM) systems. It covers various aspects of LLM research, including pre-training, serving, system efficiency optimization, multi-model systems, image generation systems, LLM applications in systems, ML systems, survey papers, LLM benchmarks and leaderboards, and other relevant resources. The repository is regularly updated to include the latest developments in this rapidly evolving field, making it a valuable resource for researchers, practitioners, and anyone interested in staying abreast of the advancements in LLM technology.

ABigSurveyOfLLMs is a repository that compiles surveys on Large Language Models (LLMs) to provide a comprehensive overview of the field. It includes surveys on various aspects of LLMs such as transformers, alignment, prompt learning, data management, evaluation, societal issues, safety, misinformation, attributes of LLMs, efficient LLMs, learning methods for LLMs, multimodal LLMs, knowledge-based LLMs, extension of LLMs, LLMs applications, and more. The repository aims to help individuals quickly understand the advancements and challenges in the field of LLMs through a collection of recent surveys and research papers.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

Awesome-Efficient-LLM is a curated list focusing on efficient large language models. It includes topics such as knowledge distillation, network pruning, quantization, inference acceleration, efficient MOE, efficient architecture of LLM, KV cache compression, text compression, low-rank decomposition, hardware/system, tuning, and survey. The repository provides a collection of papers and projects related to improving the efficiency of large language models through various techniques like sparsity, quantization, and compression.

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.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

This repository contains code for research conducted by the LLM-Random research group at IDEAS NCBR in Warsaw, Poland. The group focuses on developing and using this repository to conduct research. For more information about the group and its research, refer to their blog, llm-random.github.io.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

torchchat is a codebase showcasing the ability to run large language models (LLMs) seamlessly. It allows running LLMs using Python in various environments such as desktop, server, iOS, and Android. The tool supports running models via PyTorch, chatting, generating text, running chat in the browser, and running models on desktop/server without Python. It also provides features like AOT Inductor for faster execution, running in C++ using the runner, and deploying and running on iOS and Android. The tool supports popular hardware and OS including Linux, Mac OS, Android, and iOS, with various data types and execution modes available.

Awesome-LLM-Inference: A curated list of 📙Awesome LLM Inference Papers with Codes, check 📖Contents for more details. This repo is still updated frequently ~ 👨‍💻‍ Welcome to star ⭐️ or submit a PR to this repo!

This repository, Awesome-LLM-Survey, serves as a comprehensive collection of surveys related to Large Language Models (LLM). It covers various aspects of LLM, including instruction tuning, human alignment, LLM agents, hallucination, multi-modal capabilities, and more. Researchers are encouraged to contribute by updating information on their papers to benefit the LLM survey community.

LLamaTuner is a repository for the Efficient Finetuning of Quantized LLMs project, focusing on building and sharing instruction-following Chinese baichuan-7b/LLaMA/Pythia/GLM model tuning methods. The project enables training on a single Nvidia RTX-2080TI and RTX-3090 for multi-round chatbot training. It utilizes bitsandbytes for quantization and is integrated with Huggingface's PEFT and transformers libraries. The repository supports various models, training approaches, and datasets for supervised fine-tuning, LoRA, QLoRA, and more. It also provides tools for data preprocessing and offers models in the Hugging Face model hub for inference and finetuning. The project is licensed under Apache 2.0 and acknowledges contributions from various open-source contributors.

Awesome-Colorful-LLM is a meticulously assembled anthology of vibrant multimodal research focusing on advancements propelled by large language models (LLMs) in domains such as Vision, Audio, Agent, Robotics, and Fundamental Sciences like Mathematics. The repository contains curated collections of works, datasets, benchmarks, projects, and tools related to LLMs and multimodal learning. It serves as a comprehensive resource for researchers and practitioners interested in exploring the intersection of language models and various modalities for tasks like image understanding, video pretraining, 3D modeling, document understanding, audio analysis, agent learning, robotic applications, and mathematical research.

Investigates global issues and proposes efficient, practical solutions.

Explain scientific papers using the 3-pass method for efficient understanding. After uploading a paper, you can enter First pass/Second pass /Third pass / Q&A to get different level of response from SciPlore.

Guides thermal management solutions for efficient system performance.

Optimizes production processes for efficient large-scale operations.

Text zusammenfassen spart Zeit und extrahiert die Kernaussagen. Nutzen Sie unseren Service, um Texte effektiv zu zusammenfassen.

New Earth Score

The Writing Metier Footnote Assistant is a specialized GPT model designed to help students efficiently create, format, and verify footnotes for their academic papers.

Highly capable, excels in processing complex tasks efficiently

Advises on thermodynamics processes to optimize system efficiency.

This prompt is presented as a virtual health assistant that interacts empathically and efficiently with the user, assuming the role of a doctor.

Meet LexAid GPT: Your AI-powered legal assistant. With advanced document analysis, secure handling, and expert legal knowledge, it streamlines case review and drafting, enhancing efficiency and accuracy in your legal practice

Find optimal solution to any problem

A tutor for Energy Conversion Principles, providing detailed, searchable answers.

从网址提取并选择模型计算大小

Informative guide on fuels and energy sources.

I develop and optimize algorithms with a technical and analytical approach.

I help you evolve your habits and processes to preserve the habitability of the earth and much more

严肃专业的电子工程学术专家

General Information Gatherer

Explains Arxiv papers with examples, analogies, and direct PDF links.