Best AI tools for< Train Efficient Decoders >

Apache MXNet is a flexible and efficient deep learning library designed for research, prototyping, and production. It features a hybrid front-end that seamlessly transitions between imperative and symbolic modes, enabling both flexibility and speed. MXNet also supports distributed training and performance optimization through Parameter Server and Horovod. With bindings for multiple languages, including Python, Scala, Julia, Clojure, Java, C++, R, and Perl, MXNet offers wide accessibility. Additionally, it boasts a thriving ecosystem of tools and libraries that extend its capabilities in computer vision, NLP, time series, and more.

Backend.AI is an enterprise-scale cluster backend for AI frameworks that offers scalability, GPU virtualization, HPC optimization, and DGX-Ready software products. It provides a fast and efficient way to build, train, and serve AI models of any type and size, with flexible infrastructure options. Backend.AI aims to optimize backend resources, reduce costs, and simplify deployment for AI developers and researchers. The platform integrates seamlessly with existing tools and offers fractional GPU usage and pay-as-you-play model to maximize resource utilization.

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.

Aide is an AI platform designed to enhance customer support operations. It offers a range of features to help businesses gain insights into customer needs, automate support processes, improve agent efficiency, and train AI chatbots. Aide's key capabilities include customer insights, workflow automation, agent assist, and AI chatbots. With Aide, businesses can analyze customer conversations, identify pain points, and automate repetitive tasks to streamline support operations and improve customer satisfaction.

Compliance Quarter is a leading provider of compliance solutions for the energy industry. We offer a range of services to help businesses manage their compliance obligations, including expert advice, document review, and technology solutions. Our team of experienced professionals has deep expertise in the energy industry and is committed to providing our clients with the highest level of service. We are proud to be the trusted partner of some of the world's largest energy companies.

Docsumo is an advanced Document AI platform designed for scalability and efficiency. It offers a wide range of capabilities such as pre-processing documents, extracting data, reviewing and analyzing documents. The platform provides features like document classification, touchless processing, ready-to-use AI models, auto-split functionality, and smart table extraction. Docsumo is a leader in intelligent document processing and is trusted by various industries for its accurate data extraction capabilities. The platform enables enterprises to digitize their document processing workflows, reduce manual efforts, and maximize data accuracy through its AI-powered solutions.

Triplo AI is an AI-powered assistant designed to enhance productivity by providing real-time assistance, content generation, translation services, and automation functionalities. It offers a suite of versatile features tailored to meet diverse needs, such as SmartPrompts, Custom SmartPrompts, and scraping capabilities. Users can customize prompts, train the AI, and integrate it with various tools and software across multiple operating systems. Triplo AI prioritizes user privacy and data security, ensuring a safe and reliable experience for users worldwide.

RAG ChatBot is a service that allows users to easily train and share chatbots. It can transform PDFs, URLs, and text into smart chatbots that can be embedded anywhere with an iframe. RAG ChatBot is designed to make knowledge sharing easier and more efficient. It offers a variety of features to help users create and manage their chatbots, including easy knowledge training, continuous improvement, seamless integration with OpenAI Custom GPTs, secure API key integration, continuous optimization, and online privacy control.

Cody is an intelligent AI assistant designed to boost team productivity by providing instant answers, support, troubleshooting, and idea generation. It can be trained on your business knowledge base to cater to your specific needs, making it a valuable asset for various departments such as marketing, HR, IT support, business consultancy, creative tasks, sales, training, hiring, customer support, and translation. Cody offers features like prompt manager, focus mode, conversation logs, scratchpad, and source checking, ensuring efficient and tailored assistance. With multilingual capabilities and customizable access controls, Cody prioritizes data security and user experience.

Chatness AI is an AI-powered chatbot assistant application that serves as an Intercom alternative, helping businesses enhance customer service and reduce costs. It allows users to automate tasks, sales, and marketing, create custom chat tools, and integrate with various platforms like Discord, Facebook Messenger, Google Calendar, and WhatsApp. With features such as support for anonymous and authenticated users, lead collection, custom automations, and comprehensive branding customizations, Chatness AI offers a seamless and efficient solution for businesses looking to streamline their customer support processes.

Kong.ai is an AI-powered platform offering Conversational Chatbots and AI Agents to automate and streamline various business operations such as customer support, sales, HR, and marketing workflows. The platform leverages state-of-the-art language models and machine learning to provide natural and intelligent conversations. Kong.ai provides specialized AI Agents for tasks like lead generation, social media management, recruitment, and more, helping businesses enhance efficiency and productivity.

RunPod is a cloud platform specifically designed for AI development and deployment. It offers a range of features to streamline the process of developing, training, and scaling AI models, including a library of pre-built templates, efficient training pipelines, and scalable deployment options. RunPod also provides access to a wide selection of GPUs, allowing users to choose the optimal hardware for their specific AI workloads.

FluidStack is a leading GPU cloud platform designed for AI and LLM (Large Language Model) training. It offers unlimited scale for AI training and inference, allowing users to access thousands of fully-interconnected GPUs on demand. Trusted by top AI startups, FluidStack aggregates GPU capacity from data centers worldwide, providing access to over 50,000 GPUs for accelerating training and inference. With 1000+ data centers across 50+ countries, FluidStack ensures reliable and efficient GPU cloud services at competitive prices.

Eggheads is an AI-powered microlearning platform that offers conversational micro content through an e-learning chatbot. It enables businesses to create, share, analyze, and integrate short, personalized learning modules for employees. With a focus on reaching employees where they are, Eggheads aims to make learning fun, easy, and engaging by delivering bite-sized knowledge in a conversational format. The platform helps improve employee performance, retention, and awareness by providing a more efficient and effective way of training.

TractoAI is an advanced AI platform that offers deep learning solutions for various industries. It provides Batch Inference with no rate limits, DeepSeek offline inference, and helps in training open source AI models. TractoAI simplifies training infrastructure setup, accelerates workflows with GPUs, and automates deployment and scaling for tasks like ML training and big data processing. The platform supports fine-tuning models, sandboxed code execution, and building custom AI models with distributed training launcher. It is developer-friendly, scalable, and efficient, offering a solution library and expert guidance for AI projects.

UnravelX is an AI-powered platform that transforms documents into 3D interactive virtual scenarios for training purposes. It automates training processes by using generative AI to create immersive learning experiences. The platform caters to various industries such as F&B, Retail, Sales, and Hospitality, offering a cost-effective and efficient solution for upskilling employees. With over 60 years of combined AI and training expertise, UnravelX leads the innovation in scenario-based learning, providing a seamless onboarding experience for organizations.

Datacog is an AI application that offers a comprehensive solution for efficient data warehouse management, application integration, and machine learning. It enables organizations to leverage the complete capabilities of their data assets through intuitive data organization and model training features. With zero configuration, instant deployment, scalability, and real-time monitoring, Datacog simplifies model training and streamlines decision-making. Join the ranks of industry leaders who have harnessed the power of organized data and automation with Datacog.

EmbedAI is a platform that enables users to create custom AI chatbots powered by ChatGPT. Users can train the chatbot on their own data and embed it on their website. The platform allows for customization of the chatbot's appearance and supports multiple languages. EmbedAI aims to provide efficient management of information and automated responses to user queries.

InsightFace is an open-source deep face analysis library that provides a rich variety of state-of-the-art algorithms for face recognition, detection, and alignment. It is designed to be efficient for both training and deployment, making it suitable for research institutions and industrial organizations. InsightFace has achieved top rankings in various challenges and competitions, including the ECCV 2022 WCPA Challenge, NIST-FRVT 1:1 VISA, and WIDER Face Detection Challenge 2019.

UBIAI is a powerful text annotation tool that helps businesses accelerate their data labeling process. With UBIAI, businesses can annotate any type of document, including PDFs, images, and text. UBIAI also offers a variety of features to make the annotation process easier and more efficient, such as auto-labeling, multi-lingual annotation, and team collaboration. With UBIAI, businesses can save time and money on their data labeling projects.

Consistency Large Language Models (CLLMs) is a family of efficient parallel decoders that reduce inference latency by efficiently decoding multiple tokens in parallel. The models are trained to perform efficient Jacobi decoding, mapping any randomly initialized token sequence to the same result as auto-regressive decoding in as few steps as possible. CLLMs have shown significant improvements in generation speed on various tasks, achieving up to 3.4 times faster generation. The tool provides a seamless integration with other techniques for efficient Large Language Model (LLM) inference, without the need for draft models or architectural modifications.

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

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)

EmbodiedScan is a holistic multi-modal 3D perception suite designed for embodied AI. It introduces a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. The dataset includes over 5k scans with 1M ego-centric RGB-D views, 1M language prompts, 160k 3D-oriented boxes spanning 760 categories, and dense semantic occupancy with 80 common categories. The suite includes a baseline framework named Embodied Perceptron, capable of processing multi-modal inputs for 3D perception tasks and language-grounded tasks.

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.

A curated list of high-quality papers on resource-efficient Large Language Models (LLMs) with a focus on various aspects such as architecture design, pre-training, fine-tuning, inference, system design, and evaluation metrics. The repository covers topics like efficient transformer architectures, non-transformer architectures, memory efficiency, data efficiency, model compression, dynamic acceleration, deployment optimization, support infrastructure, and other related systems. It also provides detailed information on computation metrics, memory metrics, energy metrics, financial cost metrics, network communication metrics, and other metrics relevant to resource-efficient LLMs. The repository includes benchmarks for evaluating the efficiency of NLP models and references for further reading.

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, Awesome Efficient AIGC, collects efficient approaches for AI-generated content (AIGC) to cope with its huge demand for computing resources. It includes efficient Large Language Models (LLMs), Diffusion Models (DMs), and more. The repository is continuously improving and welcomes contributions of works like papers and repositories that are missed by the collection.

The DALM (Domain Adapted Language Modeling) toolkit is designed to unify general LLMs with vector stores to ground AI systems in efficient, factual domains. It provides developers with tools to build on top of Arcee's open source Domain Pretrained LLMs, enabling organizations to deeply tailor AI according to their unique intellectual property and worldview. The toolkit contains code for fine-tuning a fully differential Retrieval Augmented Generation (RAG-end2end) architecture, incorporating in-batch negative concept alongside RAG's marginalization for efficiency. It includes training scripts for both retriever and generator models, evaluation scripts, data processing codes, and synthetic data generation code.

EOLE is an open language modeling toolkit based on PyTorch. It aims to provide a research-friendly approach with a comprehensive yet compact and modular codebase for experimenting with various types of language models. The toolkit includes features such as versatile training and inference, dynamic data transforms, comprehensive large language model support, advanced quantization, efficient finetuning, flexible inference, and tensor parallelism. EOLE is a work in progress with ongoing enhancements in configuration management, command line entry points, reproducible recipes, core API simplification, and plans for further simplification, refactoring, inference server development, additional recipes, documentation enhancement, test coverage improvement, logging enhancements, and broader model support.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

EVE is an official PyTorch implementation of Unveiling Encoder-Free Vision-Language Models. The project aims to explore the removal of vision encoders from Vision-Language Models (VLMs) and transfer LLMs to encoder-free VLMs efficiently. It also focuses on bridging the performance gap between encoder-free and encoder-based VLMs. EVE offers a superior capability with arbitrary image aspect ratio, data efficiency by utilizing publicly available data for pre-training, and training efficiency with a transparent and practical strategy for developing a pure decoder-only architecture across modalities.

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

MockingBird is a toolbox designed for Mandarin speech synthesis using PyTorch. It supports multiple datasets such as aidatatang_200zh, magicdata, aishell3, and data_aishell. The toolbox can run on Windows, Linux, and M1 MacOS, providing easy and effective speech synthesis with pretrained encoder/vocoder models. It is webserver ready for remote calling. Users can train their own models or use existing ones for the encoder, synthesizer, and vocoder. The toolbox offers a demo video and detailed setup instructions for installation and model training.

ViiTor-Voice is an LLM based TTS Engine that offers a lightweight design with 0.5B parameters for efficient deployment on various platforms. It provides real-time streaming output with low latency experience, a rich voice library with over 300 voice options, flexible speech rate adjustment, and zero-shot voice cloning capabilities. The tool supports both Chinese and English languages and is suitable for applications requiring quick response and natural speech fluency.

PyTorch Forecasting is a PyTorch-based package designed for state-of-the-art timeseries forecasting using deep learning architectures. It offers a high-level API and leverages PyTorch Lightning for efficient training on GPU or CPU with automatic logging. The package aims to simplify timeseries forecasting tasks by providing a flexible API for professionals and user-friendly defaults for beginners. It includes features such as a timeseries dataset class for handling data transformations, missing values, and subsampling, various neural network architectures optimized for real-world deployment, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. Built on pytorch-lightning, it supports training on CPUs, single GPUs, and multiple GPUs out-of-the-box.

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