Best AI tools for< Hybrid Parallelism >

LogicMonitor is a cloud-based infrastructure monitoring platform that provides real-time insights and automation for comprehensive, seamless monitoring with agentless architecture. It offers a unified platform for monitoring infrastructure, applications, and business services, with advanced features for hybrid observability. LogicMonitor's AI-driven capabilities simplify complex IT ecosystems, accelerate incident response, and empower organizations to thrive in the digital landscape.

LogicMonitor is a cloud-based infrastructure monitoring platform that provides real-time insights and automation for comprehensive, seamless monitoring with agentless architecture. It offers a wide range of features including infrastructure monitoring, network monitoring, server monitoring, remote monitoring, virtual machine monitoring, SD-WAN monitoring, database monitoring, storage monitoring, configuration monitoring, cloud monitoring, container monitoring, AWS Monitoring, GCP Monitoring, Azure Monitoring, digital experience SaaS monitoring, website monitoring, APM, AIOPS, Dexda Integrations, security dashboards, and platform demo logs. LogicMonitor's AI-driven hybrid observability helps organizations simplify complex IT ecosystems, accelerate incident response, and thrive in the digital landscape.

MemFree is a hybrid AI search tool that allows users to search for information instantly and receive accurate answers from the internet, bookmarks, notes, and documents. With MemFree, users can easily index their bookmarks and web pages with just one click. The tool leverages GPT-4o mini for enhanced search capabilities, making it a powerful and efficient AI application for information retrieval.

Reworked is a leading online community for professionals in the fields of employee experience, digital workplace, and talent management. It provides news, research, and events on the latest trends and best practices in these areas. Reworked also offers a variety of resources for members, including a podcast, awards program, and research library.

Reworked is a leading online community for professionals in the fields of employee experience, digital workplace, and talent management. It provides news, research, and events on the latest trends and best practices in these areas. Reworked also offers a variety of resources for members, including a podcast, awards program, and research library.

ZEBCOIN is an AI-powered platform that focuses on decentralized identity verification, intellectual property rights, AI-driven decentralized finance (DeFi) and micro-lending, environmental sustainability, and transparency in supply chains. It aims to leverage AI and blockchain technology to offer innovative financial solutions, promote clean energy initiatives, ensure transparency in global supply chains, and provide decentralized identity verification services. The platform also plans to launch its own blockchain (ZEB Chain) with features like an explorer, decentralized exchanges (DEX) transparency, NFT marketplace, and lending and DeFi services. ZEBCOIN aims to revolutionize the blockchain space by integrating AI technology into various aspects of its ecosystem.

Derwen is an open-source integration platform for production machine learning in enterprise, specializing in natural language processing, graph technologies, and decision support. It offers expertise in developing knowledge graph applications and domain-specific authoring. Derwen collaborates closely with Hugging Face and provides strong data privacy guarantees, low carbon footprint, and no cloud vendor involvement. The platform aims to empower AI engineers and domain experts with quality, time-to-value, and ownership since 2017.

BalancedWork is an AI-powered platform that helps teams make data-driven decisions about when to work in-person. By combining AI and Social Science, the application addresses challenges such as frustrating virtual meetings, decreasing employee satisfaction due to mandates, weakening team bonds, and underutilized office spaces. BalancedWork offers solutions like ingesting data from enterprise APIs, evaluating work patterns, generating team schedules, and providing ongoing recommendations to adapt to changing needs. The platform aims to boost productivity, engagement, and collaboration in organizations by optimizing work interactions and relationships.

Read AI is an AI-powered application that enhances productivity by generating summaries, transcripts, and highlights for meetings, emails, and messages. It offers features like real-time meeting summaries, smart scheduler, speaker coach insights, and multi-language support. Read AI helps users save time, improve communication, and stay organized across various platforms. With a focus on security and actionable accountability, it aims to streamline workflows and maximize productivity for knowledge workers.

Read AI is an AI-powered application that enhances productivity by generating summaries, transcripts, and highlights for meetings, emails, and messages. It offers features like playback, coaching, smart scheduling, and integrations with various platforms. With multi-language support and secure handling of data, Read AI aims to streamline communication and collaboration for users across different languages and industries.

RingCentral Events is a comprehensive platform for organizing hybrid and virtual events. It allows users to host engaging events with AI-powered features, personalized branding, and immersive experiences. The platform offers tools for event management, engagement tracking, and content creation, making it a valuable solution for businesses of all sizes.

Snyk is a developer security platform powered by DeepCode AI, offering solutions for application security, software supply chain security, and secure AI-generated code. It provides comprehensive vulnerability data, license compliance management, and self-service security education. Snyk integrates AI models trained on security-specific data to secure applications and manage tech debt effectively. The platform ensures developer-first security with one-click security fixes and AI-powered recommendations, enhancing productivity while maintaining security standards.

Leapmax is a workforce analytics software designed to enhance operational efficiency by improving employee productivity, ensuring data security, facilitating communication and collaboration, and managing compliance. The application offers features such as productivity management, data security, remote team collaboration, reporting management, and network health monitoring. Leapmax provides advantages like AI-based user detection, real-time activity tracking, remote co-browsing, collaboration suite, and actionable analytics. However, some disadvantages include the need for employee monitoring, potential privacy concerns, and dependency on internet connectivity. The application is commonly used by contact centers, outsourcers, enterprises, and back offices. Users can perform tasks like productivity monitoring, app usage tracking, communication and collaboration, compliance management, and remote workforce monitoring.

LifeShack is an AI-powered job search tool that revolutionizes the job application process. It automates job searching, evaluation, and application submission, saving users time and increasing their chances of landing top-notch opportunities. With features like automatic job matching, AI-optimized cover letters, and tailored resumes, LifeShack streamlines the job search experience and provides peace of mind to job seekers.

Google Cloud is a suite of cloud computing services that runs on the same infrastructure as Google. Its services include computing, storage, networking, databases, machine learning, and more. Google Cloud is designed to make it easy for businesses to develop and deploy applications in the cloud. It offers a variety of tools and services to help businesses with everything from building and deploying applications to managing their infrastructure. Google Cloud is also committed to sustainability, and it has a number of programs in place to reduce its environmental impact.

IBM Watsonx is an enterprise studio for AI builders. It provides a platform to train, validate, tune, and deploy AI models quickly and efficiently. With Watsonx, users can access a library of pre-trained AI models, build their own models, and deploy them to the cloud or on-premises. Watsonx also offers a range of tools and services to help users manage and monitor their AI models.

IBM is a leading technology company that offers a wide range of AI and machine learning solutions to help businesses innovate and grow. From AI models to cloud services, IBM provides cutting-edge technology to address various business challenges. The company also focuses on AI ethics and offers training programs to enhance skills in cybersecurity and data analytics. With a strong emphasis on research and development, IBM continues to push the boundaries of technology to solve real-world problems and drive digital transformation across industries.

Palo Alto Networks is a cybersecurity company offering advanced security solutions powered by Precision AI to protect modern enterprises from cyber threats. The company provides network security, cloud security, and AI-driven security operations to defend against AI-generated threats in real time. Palo Alto Networks aims to simplify security and achieve better security outcomes through platformization, intelligence-driven expertise, and proactive monitoring of sophisticated threats.

MixMode is the world's most advanced AI for threat detection, offering a dynamic threat detection platform that utilizes patented Third Wave AI technology. It provides real-time detection of known and novel attacks with high precision, self-supervised learning capabilities, and context-awareness to defend against modern threats. MixMode empowers modern enterprises with unprecedented speed and scale in threat detection, delivering unrivaled capabilities without the need for predefined rules or human input. The platform is trusted by top security teams and offers rapid deployment, customization to individual network dynamics, and state-of-the-art AI-driven threat detection.

Scrumbuiss is the ultimate AI-powered management software designed to streamline project, task, and team management. It offers lightning-fast operations, kanban board tracking, and notifications to enhance productivity. With dedicated 24/7 customer support, secure and reliable features, unbeatable prices, and detailed reports and analytics, Scrumbuiss revolutionizes workflows with first-class tools. Users can create and manage projects effortlessly, achieve more with less effort, and supercharge their projects with subscription-based access to premium features. Testimonials from users highlight the system's intuitive interface, customization options, and impact on project efficiency and success.

Long-Context-Attention (YunChang) is a unified sequence parallel approach that combines the strengths of DeepSpeed-Ulysses-Attention and Ring-Attention to provide a versatile and high-performance solution for long context LLM model training and inference. It addresses the limitations of both methods by offering no limitation on the number of heads, compatibility with advanced parallel strategies, and enhanced performance benchmarks. The tool is verified in Megatron-LM and offers best practices for 4D parallelism, making it suitable for various attention mechanisms and parallel computing advancements.

Chitu is a high-performance inference framework for large language models, focusing on efficiency, flexibility, and availability. It supports various mainstream large language models, including DeepSeek, LLaMA series, Mixtral, and more. Chitu integrates latest optimizations for large language models, provides efficient operators with online FP8 to BF16 conversion, and is deployed for real-world production. The framework is versatile, supporting various hardware environments beyond NVIDIA GPUs. Chitu aims to enhance output speed per unit computing power, especially in decoding processes dependent on memory bandwidth.

KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

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.

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.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

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!

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

veRL is a flexible and efficient reinforcement learning training framework designed for large language models (LLMs). It allows easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructures, and flexible device mapping. The framework achieves state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine. It supports popular HuggingFace models and is suitable for users working with PyTorch FSDP, Megatron-LM, and vLLM backends.

LitGPT is a command-line tool designed to easily finetune, pretrain, evaluate, and deploy 20+ LLMs **on your own data**. It features highly-optimized training recipes for the world's most powerful open-source large-language-models (LLMs).

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained 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 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

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.

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

Advises organizations on optimizing hybrid work models, blending remote and in-office strategies.

Crafting hybrids of any two creatures into ultra-high-resolution wonders.

Advisor on Hybrid Work Environment Strategies

An advanced remote leadership coach with dynamic updates and expanded scenarios.

Animal fusions not for the faint of heart. Create with us!

HybridCreator GPT crafts unique animal hybrids with emphasis weighting for traits. Input animals and desired trait balance for imaginative, detailed creations.

An interactive image-guessing game with animal hybrids.

Allows users to upload pictures of their pets and witness fascinating visualizations of potential crossbreeds with other species or different breeds.

I create fun, semi-realistic dog-human hybrids with humor.