Search Results for "active"
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Showing 12 open source projects for "active"
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llama.cpp
Port of Facebook's LLaMA model in C/C++
The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments. -
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Distributed Llama
Connect home devices into a powerful cluster to accelerate LLM
Distributed Llama is an open-source project that enables users to connect multiple home devices into a powerful cluster to accelerate Large Language Model (LLM) inference. By leveraging tensor parallelism and high-speed synchronization over Ethernet, it allows for faster performance as more devices are added to the cluster. The system supports various operating systems, including Linux, macOS, and Windows, and is optimized for both ARM and x86_64 AVX2 CPUs. -
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ChatLLM.cpp
Pure C++ implementation of several models for real-time chatting
chatllm.cpp is a pure C++ implementation designed for real-time chatting with Large Language Models (LLMs) on personal computers, supporting both CPU and GPU executions. It enables users to run various LLMs ranging from less than 1 billion to over 300 billion parameters, facilitating responsive and efficient conversational AI experiences without relying on external servers. -
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Intelligent Terminal
A fork of Windows Terminal with native agent integration
...The software detects command failures and can request AI-generated troubleshooting suggestions. Proposed commands require user approval before execution. Its agent management interface tracks active sessions, task states, and token consumption. Intelligent Terminal also supports custom model providers and dedicated agent profiles. It requires Windows 10 version 2004 or newer. -
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Vowpal Wabbit
Machine learning system which pushes the frontier of machine learning
Vowpal Wabbit is a machine learning system that pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state-of-the-art algorithms with performance in mind. The input format for the learning algorithm is substantially more flexible than might be expected. ... -
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UoMASM
A C++ Active Shape Model Library from the University of Manchester
A library and associated tools for building and searching with Active Shape Models (ASM). It builds on the VXL computer vision libraries. Includes a GUI tool to search new images with the ASM. Depends on: VXL, Qt (Version 4), UoMqVXL -
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MXNet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning
Apache MXNet is a scalable, efficient open-source deep learning framework—offering a flexible hybrid programming model (symbolic + imperative) and supporting a wide array of languages—designed for training and deploying neural networks across heterogeneous systems. Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic... -
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Caffe Framework
Caffe, a fast open framework for deep learning
...Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. ... -
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BADGr
Toolbox for Box Approximation, Decomposition, and Grasping
BADGr, the BoxGrasping toolbox, is a package for Box Approximation, Decomposition, and Grasping. The toolbox was developed in the Computer Vision & Active Perception Lab, at the Royal Institute of Technology, as a participant of the EU research project PACO-PLUS, and published at the project's end in Summer 2010. BADGr provides modules to approximate the shape of a point cloud (possibly from sensor data) by box primitives. These box primitives then serve as a base for the generation of box-based pre-grasp hypotheses for robot grippers. -
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Active Shape/Appearance Model Library (ASMLibrary/AAMLibrary) source code, which includes ASMBuilding/AAMBuilding as well as ASMFitting/AAMFitting algorithm. It is developped under OpenCV 1.0 for locating features in a face and face recognition.
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DUAPI (distributed unified agent programming interface) is a class library for creating and modelling the society of autonomous software agents. It also common to active objects and virtualization paradigm at all.
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