📄 中文摘要
随着基础模型的部署受到内存占用、延迟和硬件成本的限制，后训练压缩技术能够通过降低模型参数的精度来缓解这些瓶颈，而不会显著降低性能。然而，其实际应用仍然面临挑战，实践者需要在量化算法、精度预算、数据驱动的校准策略和硬件依赖的执行模式之间进行导航。OneComp是一个开源压缩框架，将这一专家工作流程转变为可重复、资源自适应的管道。给定模型标识符和可用硬件，OneComp能够自动检查模型，规划混合精度分配，从而简化压缩过程。该框架旨在提高生成式AI模型的部署效率，降低实现门槛。
OneComp：生成式人工智能模型压缩的一行革命
出处: OneComp: One-Line Revolution for Generative AI Model Compression
发布: 2026年4月1日
📄 English Summary
OneComp: One-Line Revolution for Generative AI Model Compression
The deployment of foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can alleviate these bottlenecks by reducing the precision of model parameters without significantly degrading performance; however, practical implementation remains challenging as practitioners navigate a fragmented landscape of quantization algorithms, precision budgets, data-driven calibration strategies, and hardware-dependent execution regimes. OneComp is presented as an open-source compression framework that transforms this expert workflow into a reproducible, resource-adaptive pipeline. Given a model identifier and available hardware, OneComp automatically inspects the model and plans mixed-precision assignment, thereby streamlining the compression process. This framework aims to enhance the deployment efficiency of generative AI models and lower the implementation barrier.