Best AI tools for< Matrix Architect >

Matrix AI Consulting Services is an expert AI consultancy firm based in New Zealand, offering bespoke AI consulting services to empower businesses and government entities to embrace responsible AI. With over 24 years of experience in transformative technology, the consultancy provides services ranging from AI business strategy development to seamless integration, change management, training workshops, and governance frameworks. Matrix AI Consulting Services aims to help organizations unlock the full potential of AI, enhance productivity, streamline operations, and gain a competitive edge through the strategic implementation of AI technologies.

d-Matrix is an AI tool that offers ultra-low latency batched inference for generative AI technology. It introduces Corsair™, the world's most efficient AI inference platform for datacenters, providing high performance, efficiency, and scalability for large-scale inference tasks. The tool aims to transform the economics of AI inference by delivering fast, sustainable, and scalable AI solutions without compromising on speed or usability.

Hebbia is an AI tool designed to help users collaborate with AI agents more confidently over all the documents that matter. It offers Matrix agents that can handle questions about millions of documents at a time, executing workflows with hundreds of steps. Hebbia is known for its Trustworthy AI approach, showing its work at each step to build user trust. The tool is used by top enterprises, financial institutions, governments, and law firms worldwide, saving users time and making them more efficient in their work.

Untools is an AI-powered personal management toolset designed to help users make better, faster, and more confident decisions. It offers a unique blend of features that prioritize urgency and importance, such as the Eisenhower Matrix and AI Assistant for data-backed decision-making. Users can track past decisions, gain insights, and improve their decision-making process. Untools caters to professionals like entrepreneurs, researchers, and neurodivergent individuals, helping them reduce impulsive choices, prevent distractions, and improve focus. The app provides affordable pricing options and is supported by a team of experienced professionals in product design and software engineering.

Wintract is an AI-powered government contracting platform that simplifies the public sales process by providing smart discovery, compliance matrix, AI analysis, market intel, and smart workflows. It helps businesses find and analyze contract opportunities, make confident bid decisions, and save time and costs. The platform offers a personalized experience by creating a virtual capture team that evaluates company strengths, matches opportunities, and continuously learns from user feedback.

The AI Innovation Platform is a comprehensive suite of AI-powered tools designed to empower businesses in navigating their digital evolution journey. From generating detailed user personas to exploring future scenarios and transforming traditional business models using AI capabilities, the platform offers strategic insights and implementation guidance for AI transformation. With features such as AI Reinvention Blueprint, AI Strategy Matrix, and AI Transformation Simulator, users can assess their AI positioning, simulate different transformation strategies, and make informed decisions about AI implementation. The platform aims to revolutionize operations, create new value, and help businesses stay ahead in the rapidly evolving digital landscape.

Connex AI is an advanced AI platform offering a wide range of AI solutions for businesses across various industries. The platform provides cutting-edge features such as AI Agent, AI Guru, AI Voice, AI Analytics, Real-Time Coaching, Automated Speech Recognition, Sentiment Analysis, Keyphrase Analysis, Entity Recognition, LLM Topic-Based Modelling, SMS Live Chat, WhatsApp Voice, Email Dialler, PCI DSS, Social Media Flow, Calendar Schedular, Staff Management, Gamify Shop, PDF Builder, Pricing Matrix, Themes, Article Builder, Marketplace Integrations, and more. Connex AI aims to enhance customer engagement, workforce productivity, sales, and customer satisfaction through its innovative AI-driven solutions.

Bidlytics is a privacy-focused capture and proposal solution for Government Contracts (GovCon). It automates the process of identifying opportunities, analyzing solicitations, creating compliance matrices, and generating quality proposal and compliance documents. With enhanced privacy and security features, Bidlytics offers seamless bid discovery, automatic solicitation shredding, compliance matrix on autopilot, fast and accurate proposal generation, and continuous learning and optimization through AI-driven tools.

Claude Artifacts Store is an AI-powered platform that offers a wide range of innovative tools and games. It provides users with interactive simulations, gaming experiences, and customization options for cartoon characters. The platform also features strategic planning tools like BCG Matrix visualizations and job search artifacts. With captivating website animations and a word cloud generator, Claude Artifacts Store aims to enhance user engagement and provide a unique online experience.

NVIDIA Generative AI Examples are state-of-the-art examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs. These examples showcase the capabilities of NVIDIA's Generative AI platform, which includes tools, frameworks, and models for building and deploying generative AI applications.

Mistral.rs is a fast LLM inference platform written in Rust. We support inference on a variety of devices, quantization, and easy-to-use application with an Open-AI API compatible HTTP server and Python bindings.

MatMul-Free LM is a language model architecture that eliminates the need for Matrix Multiplication (MatMul) operations. This repository provides an implementation of MatMul-Free LM that is compatible with the 🤗 Transformers library. It evaluates how the scaling law fits to different parameter models and compares the efficiency of the architecture in leveraging additional compute to improve performance. The repo includes pre-trained models, model implementations compatible with 🤗 Transformers library, and generation examples for text using the 🤗 text generation APIs.

xLSTM is a new Recurrent Neural Network architecture based on ideas of the original LSTM. Through Exponential Gating with appropriate normalization and stabilization techniques and a new Matrix Memory it overcomes the limitations of the original LSTM and shows promising performance on Language Modeling when compared to Transformers or State Space Models. The package is based on PyTorch and was tested for versions >=1.8. For the CUDA version of xLSTM, you need Compute Capability >= 8.0. The xLSTM tool provides two main components: xLSTMBlockStack for non-language applications or integrating in other architectures, and xLSTMLMModel for language modeling or other token-based applications.

This repository contains the source code for a GEMM & deep learning hardware accelerator system used to validate proposed systolic array hardware architectures implementing efficient matrix multiplication algorithms to increase performance-per-area limits of GEMM & AI accelerators. Achieved results include up to 3× faster CNN inference, >2× higher mults/multiplier/clock cycle, and low area with high clock frequency. The system is specialized for inference of non-sparse DNN models with fixed-point/quantized inputs, fully accelerating all DNN layers in hardware, and highly optimizing GEMM acceleration.

The xLSTM-jax repository contains code for training and evaluating the xLSTM model on language modeling using JAX. xLSTM is a Recurrent Neural Network architecture that improves upon the original LSTM through Exponential Gating, normalization, stabilization techniques, and a Matrix Memory. It is optimized for large-scale distributed systems with performant triton kernels for faster training and inference.

T-MAC is a kernel library that directly supports mixed-precision matrix multiplication without the need for dequantization by utilizing lookup tables. It aims to boost low-bit LLM inference on CPUs by offering support for various low-bit models. T-MAC achieves significant speedup compared to SOTA CPU low-bit framework (llama.cpp) and can even perform well on lower-end devices like Raspberry Pi 5. The tool demonstrates superior performance over existing low-bit GEMM kernels on CPU, reduces power consumption, and provides energy savings. It achieves comparable performance to CUDA GPU on certain tasks while delivering considerable power and energy savings. T-MAC's method involves using lookup tables to support mpGEMM and employs key techniques like precomputing partial sums, shift and accumulate operations, and utilizing tbl/pshuf instructions for fast table lookup.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Katib can perform training jobs using any Kubernetes Custom Resources with out of the box support for Kubeflow Training Operator, Argo Workflows, Tekton Pipelines and many more.

This repository provides a setup for basic gesture recognition using the TI AWR1642 mmWave sensor. Users can collect data from the sensor and choose from various neural network architectures for gesture recognition. The supported gestures include Swipe Up, Swipe Down, Swipe Right, Swipe Left, Spin Clockwise, Spin Counterclockwise, Letter Z, Letter S, and Letter X. The repository includes data and models for training and inference, along with instructions for installation, serial permissions setup, flashing firmware, running the system, collecting data, training models, selecting different models, and accessing help documentation. The project is developed using Python and TensorFlow 2.15.

KernelBench is a benchmark tool designed to evaluate Large Language Models' (LLMs) ability to generate GPU kernels. It focuses on transpiling operators from PyTorch to CUDA kernels at different levels of granularity. The tool categorizes problems into four levels, ranging from single-kernel operators to full model architectures, and assesses solutions based on compilation, correctness, and speed. The repository provides a structured directory layout, setup instructions, usage examples for running single or multiple problems, and upcoming roadmap features like additional GPU platform support and integration with other frameworks.

The 'awesome-sound_event_detection' repository is a curated reading list focusing on sound event detection and Sound AI. It includes research papers covering various sub-areas such as learning formulation, network architecture, pooling functions, missing or noisy audio, data augmentation, representation learning, multi-task learning, few-shot learning, zero-shot learning, knowledge transfer, polyphonic sound event detection, loss functions, audio and visual tasks, audio captioning, audio retrieval, audio generation, and more. The repository provides a comprehensive collection of papers, datasets, and resources related to sound event detection and Sound AI, making it a valuable reference for researchers and practitioners in the field.

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.

femtoGPT is a pure Rust implementation of a minimal Generative Pretrained Transformer. It can be used for both inference and training of GPT-style language models using CPUs and GPUs. The tool is implemented from scratch, including tensor processing logic and training/inference code of a minimal GPT architecture. It is a great start for those fascinated by LLMs and wanting to understand how these models work at deep levels. The tool uses random generation libraries, data-serialization libraries, and a parallel computing library. It is relatively fast on CPU and correctness of gradients is checked using the gradient-check method.

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.

This repository is a collection of tools and resources for deep learning infrastructure, covering programming languages, algorithms, acceleration techniques, and engineering aspects. It provides information on various online tools for chip architecture, CPU and GPU benchmarks, and code analysis. Additionally, it includes content on AI compilers, deep learning models, high-performance computing, Docker and Kubernetes tutorials, Protobuf and gRPC guides, and programming languages such as C++, Python, and Shell. The repository aims to bridge the gap between algorithm understanding and engineering implementation in the fields of AI and deep learning.

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

ABQ-LLM is a novel arbitrary bit quantization scheme that achieves excellent performance under various quantization settings while enabling efficient arbitrary bit computation at the inference level. The algorithm supports precise weight-only quantization and weight-activation quantization. It provides pre-trained model weights and a set of out-of-the-box quantization operators for arbitrary bit model inference in modern architectures.

AIKA (Artificial Intelligence for Knowledge Acquisition) is a new type of artificial neural network designed to mimic the behavior of a biological brain more closely and bridge the gap to classical AI. The network conceptually separates activations from neurons, creating two separate graphs to represent acquired knowledge and inferred information. It uses different types of neurons and synapses to propagate activation values, binding signals, causal relations, and training gradients. The network structure allows for flexible topology and supports the gradual population of neurons and synapses during training.

Provides expert guidance on Matrix-themed project development.

I am The Architect, blending the Matrix and Philip K. Dick's philosophies with a unique humor.

I create dynamic content idea matrices.

Eisenhower Matrix task prioritization assistant. GPT helps users prioritize tasks by categorizing them into four quadrants of the Eisenhower Matrix

Structured process for prioritizing marketing tasks based on strategic alignment. Outputs in Eisenhower, RACI and other methodologies.

A GPT that will generate a full content matrix for your brand or business.

Analyzes websites, compares value elements, and organizes data into a table.

Interact with the HVAC matrix

The Multiversal Prediction Matrix (MPM) leverages the speculative nature of multiverse theories to create a predictive framework. By simulating parallel universes with varied parameters, MPM explores a multitude of potential outcomes for different events and phenomena.

I am Morpheus, guiding you to manifest your desires in the Matrix.

Mastering efficient laziness in your projects, big or small. Ask this GPT for a follow-up matrix to optimize next steps.

Guides entrepreneurs through 'The Power of Manifestation' with AI-enhanced insights. Scan any page in the book to dive deep in the Manifestation Matrix.

Own Your Leading & Lagging Indicators: Specializes in developing tailored business metrics, such as OKRs, Balanced Scorecards and Business Process RACI Matrix, to optimize performance and strategy execution. (v1.4)

Need a new name? Character, brand, story, etc? Try the matrix! Use all the different naming modules as different strategies for new names!

Generador de ideas para publicaciones, basado en la matriz de contenido de Justin Welsh (Top Voice LinkedIn). Esta herramienta es una de las herramientas de Automatools, puesta a tu disposición de forma gratuita. El objetivo de Automatools es poner tu cuenta de LinkedIn en piloto automático.