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2 个仓库

Awesome GitHub RepositoriesQuantization Plugin Interfaces

Extensible interfaces that allow developers to register custom quantization methods.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Quantization Plugin Interfaces. Refine with filters or upvote what's useful.

Awesome Quantization Plugin Interfaces GitHub Repositories

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  • vllm-project/vllmvllm-project 的头像

    vllm-project/vllm

    83,048在 GitHub 上查看↗

    vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models. It functions as a production-ready distributed model server, providing standard API protocols for online serving while also supporting offline batch processing. The system is built to maximize token generation speed and memory efficiency, enabling both large-scale cloud deployments and local execution on personal hardware. The project distinguishes itself through advanced memory management and request scheduling techniques, most notably its use of non-contiguous key-value cach

    Extends core functionality through modular plugin hooks that allow developers to register and apply custom quantization techniques without altering primary source code.

    Pythonamdblackwellcuda
    在 GitHub 上查看↗83,048
  • zhaochenyang20/awesome-ml-sys-tutorialzhaochenyang20 的头像

    zhaochenyang20/Awesome-ML-SYS-Tutorial

    5,371在 GitHub 上查看↗

    This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr

    Allows adding custom quantization methods by implementing configuration and weight processing classes without modifying core framework logic.

    Python
    在 GitHub 上查看↗5,371
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