2 projects for "scrcpy 32 bit" with 2 filters applied:

  • Evertune | Improve Your Brand's Visibility in AI Search Icon
    Evertune | Improve Your Brand's Visibility in AI Search

    For enterprise marketing teams looking for a platform to understand and influence how AI models like ChatGPT recommend their products or services.

    Evertune is the Generative Engine Optimization (GEO) platform that helps brands improve visibility in AI search across ChatGPT, AI Overview, Gemini, Claude and more.
    Learn More
  • Optimize every aspect of hiring with Greenhouse Recruiting Icon
    Optimize every aspect of hiring with Greenhouse Recruiting

    Hire for what's next.

    What’s next for many of us is changing. Your company’s ability to hire great talent is as important as ever – so you’ll be ready for whatever’s ahead. Whether you need to scale your team quickly or improve your hiring process, Greenhouse gives you the right technology, know-how and support to take on what’s next.
    Learn More
  • 1
    bitnet.cpp

    bitnet.cpp

    Official inference framework for 1-bit LLMs

    ...The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources.
    Downloads: 5 This Week
    Last Update:
    See Project
  • 2
    BitNet

    BitNet

    BitNet: Scaling 1-bit Transformers for Large Language Models

    BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. The architecture introduces specialized layers such as BitLinear, which replace standard linear projections in transformer networks with quantized operations. ...
    Downloads: 1 This Week
    Last Update:
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