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Awesome GitHub RepositoriesTensor Memory Mapping

Mechanisms for converting native memory arrays into tensor formats for inter-language data transfer.

Distinct from Foreign Function Interfaces: Distinct from Foreign Function Interfaces: specifically targets the mapping and conversion of numerical arrays to tensors.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Tensor Memory Mapping. Refine with filters or upvote what's useful.

Awesome Tensor Memory Mapping GitHub Repositories

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  • tensorflow/rusttensorflow 的头像

    tensorflow/rust

    5,480在 GitHub 上查看↗

    This project provides Rust bindings for the TensorFlow C API, serving as a tensor computation interface and machine learning library. It enables the construction and execution of machine learning models and neural networks by bridging a systems language to high-performance backends. The framework supports GPU-accelerated computing to increase the speed of model training and inference by offloading mathematical operations to graphics processing units. It offers both graph-based computation for defining static network architectures and an eager execution mode for immediate operation calls durin

    Converts native arrays into tensor formats to move data efficiently between the application and the external engine.

    Rust
    在 GitHub 上查看↗5,480
  • autogptq/autogptqAutoGPTQ 的头像

    AutoGPTQ/AutoGPTQ

    5,070在 GitHub 上查看↗

    AutoGPTQ 是一个模型压缩工具包和训练后量化框架,旨在减少大语言模型的内存占用。它利用 GPTQ 算法压缩神经网络权重,降低硬件要求并减少 VRAM 使用量。 该项目通过提供优化内核来提高 Token 生成速度,从而充当推理加速器。它具有模型架构扩展性,允许通过可配置模式将量化能力添加到新的模型结构中。 该框架涵盖了全面的量化流水线,包括层级权重压缩、基于校准的缩放估计以及特定精度的内存映射。它还包括用于模型性能评估的系统,以衡量量化对语言和摘要任务准确性的影响。

    Maps quantized tensors to specific memory layouts to enable faster loading and execution on hardware accelerators.

    Python
    在 GitHub 上查看↗5,070
  1. Home
  2. Software Engineering & Architecture
  3. Foreign Function Interfaces
  4. Tensor Memory Mapping

探索子标签

  • Quantized Tensor LayoutsMapping quantized tensors to hardware-specific memory layouts for faster execution. **Distinct from Tensor Memory Mapping:** Specifically targets memory layouts for quantized weights on accelerators, whereas Tensor Memory Mapping is for general inter-language data transfer.