Search Results for "tensor" - Page 3
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Showing 125 open source projects for "tensor"
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HanoiVM
HanoiVM is a recursive, AI-augmented ternary virtual machine
...It is the execution core of the **Axion + T81Lang** ecosystem, enabling stack-tier promotion, symbolic AI opcodes, and entropy-aware transformations across three levels of logic: - 🔹 `T81`: 81-bit operand logic (register-like) - 🔸 `T243`: Symbolic BigInt + FSM state logic - 🔺 `T729`: Tensor-based AI macros with semantic dispatch Designed for research, language development, and future ternary hardware acceleration, **HanoiVM** is literate, modular, and fully recursive. -
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Teem
Tools to process and visualize scientific data and images
Teem is a set of C libraries for manipulation, measurement, and visualization of structured scientific data. Includes N-dimensional image I/O and processing, volume rendering, diffusion tensor processing, and more. Latest code via git clone http://git.code.sf.net/p/teem/teem.git . Join us at https://discord.gg/xBBqZGXkF7 -
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High-Level Training Utilities Pytorch
High-level training, data augmentation, and utilities for Pytorch
...A high-level module for Keras-like training with callbacks, constraints, and regularizers. Comprehensive data augmentation, transforms, sampling, and loading. Utility tensor and variable functions so you don't need numpy as often. Have any feature requests? Submit an issue! I'll make it happen. Specifically, any data augmentation, data loading, or sampling functions. ModuleTrainer. The ModuleTrainer class provides a high-level training interface that abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, and more. ... -
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zkcm
A C++ library for multiprecision complex-number matrix calculations.
zkcm library is a C++ library for multi-precision complex-number matrix calculations. It provides a number of functionalities including the LU decomposition, the singular value decomposition, tensor product calculation, tracing-out operations, etc. -
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jif3d
3D Joint Inversion Framework
jif3d is a framework for joint inversion of different types of geophysical data in 3D. It currently comprises forward solvers for first arrival traveltimes, scalar and tensor gravity data, magnetics, magnetotellurics, electrical resistivity tomography and surface waves.The framework is flexible in terms of the coupling approach between the different datasets. At the moment cross-gradient coupling, variation of information and deterministic parameter relationships are implemented. Other coupling approaches can be added relatively easily. -
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hueOS
Fast and modern Android-based custom firmware
As of 13/7/2025, hueOS has been discontinued, and the author's efforts to create an AOSP custom ROM have moved to a new name, CaesiumOS, which is the official successor of hueOS. The CaesiumOS project can be found using the "This project can be found here" link. hueOS is an Android-based custom firmware that prioritizes simplicity, modernity, and speed. Most of the OS (everything apart from the kernel and device trees) is licensed under the Apache License 2.0, while the kernels are... -
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snntorch
Deep and online learning with spiking neural networks in Python
...Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. This allows researchers to train spiking neural models using familiar deep learning workflows while taking advantage of GPU acceleration and automatic differentiation. snnTorch provides implementations of common spiking neuron models, surrogate gradient training methods, and utilities for handling temporal neural dynamics. ... -
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dfdx
Deep learning in Rust, with shape checked tensors and neural networks
Deep learning in Rust, with shape-checked tensors and neural networks. Ergonomics & safety focused deep learning in Rust. -
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LLaMA.go
llama.go is like llama.cpp in pure Golang
llama.go is like llama.cpp in pure Golang. The code of the project is based on the legendary ggml.cpp framework of Georgi Gerganov written in C++ with the same attitude to performance and elegance. Both models store FP32 weights, so you'll needs at least 32Gb of RAM (not VRAM or GPU RAM) for LLaMA-7B. Double to 64Gb for LLaMA-13B. -
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llm
An ecosystem of Rust libraries for working with large language models
llm is an ecosystem of Rust libraries for working with large language models - it's built on top of the fast, efficient GGML library for machine learning. The primary entry point for developers is the llm crate, which wraps the llm-base and the supported model crates. Documentation for the released version is available on Docs.rs. For end-users, there is a CLI application, llm-cli, which provides a convenient interface for interacting with supported models. Text generation can be done as a... -
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This program manipulates representations of compact semi-simple Lie algebras. It can compute tensor products, (anti-)symmetric parts and the branching rules into maximal subalgebras.
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large computational tasks into smaller chunks that can be executed across multiple nodes in a cluster, allowing complex analytics, machine learning workflows, and data transformations to run efficiently at scale. ... -
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FasterTransformer
Transformer related optimization, including BERT, GPT
...The library supports multiple deep learning frameworks, including TensorFlow, PyTorch, and Triton, allowing developers to integrate it into existing pipelines without major changes. It includes advanced optimization techniques such as mixed precision, tensor parallelism, and efficient memory management, enabling large models to run across multiple GPUs and nodes. FasterTransformer is particularly focused on inference workloads, where it significantly improves performance compared to standard framework implementations. Although development has transitioned toward TensorRT-LLM, the project remains an important reference for understanding optimized transformer execution. -
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WaveTrain (Python)
Quantum dynamics of chain-like systems using tensor train formats
WaveTrain is an open-source software for numerical simulations of chain-like quantum systems with nearest-neighbor (NN) interactions only (with or without periodic boundary conditions). This Python package is centered around tensor train (TT, or matrix product) representations of quantum-mechanical Hamiltonian operators and (stationary or time-evolving) state vectors. WaveTrain builds on the Python tensor train toolbox scikit_tt, which provides efficient construction methods, storage schemes, as well as solvers for eigenvalue problems and linear differential equations in the TT format. ... -
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AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking,... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
...The project also includes demo scripts that visualize learning curves and allow students to observe policy improvement over training iterations. By using TensorFlow as the backbone, it highlights practical considerations such as tensor shapes, loss computation, optimization steps, and batching in an RL context. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across... -
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pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. ... -
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Matrix Product State (MPS) Simulations
Numerical routines for variational matrix product state simulations.
Open Source MPS (OSMPS) is a collection of numerical routines for performing tensor network algorithms to simulate entangled, 1D many-body quantum systems. Our applications reach from ground state and excited states for statics to the dynamics of time-dependent Hamiltonians. We offer various time evolution methods with an emphasis on the support of long-range interactions through the matrix product state formalism. -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage experimentation and comparison. ... -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or... -
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Arraymancer
A fast, ergonomic and portable tensor library in Nim
Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications. -
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TRFL
TensorFlow Reinforcement Learning
...It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets. -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
...It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. ...
