News & Updates
新闻与动态
Releases, milestones, and publications from the Future Machine Learning & Systems Lab.
来自 Future Machine Learning & Systems 实验室的发布、里程碑与论文动态。
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Happy to see community interest in bringing OSCAR toward vLLM serving workflows. Grateful to the contributors pushing broader support for deployable rotation-based KV-cache quantization in mainstream LLM serving stacks.
很高兴看到社区正在把 OSCAR 推向 vLLM 的服务链路。感谢各位贡献者,让基于旋转的可部署 KV-cache 量化在主流 LLM 服务栈中获得更广泛的支持。
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OSCAR has been covered by multiple tech media outlets, including Towards AI, ModelScope, MarkTechPost, QbitAI (量子位), and Synced (机器之心), reaching 100,000+ reads across platforms. Grateful for the community interest in deployable 2-bit KV-cache quantization.
OSCAR 被多家科技媒体报道,包括 Towards AI、ModelScope、MarkTechPost、量子位与机器之心,全平台阅读量超过 100,000 次。感谢社区对可部署 2-bit KV-cache 量化的关注。
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Released Taylor-Calibrate: Principled Initialization for Hybrid Linear Attention Distillation. The codebase distills Qwen/Llama softmax-attention Transformers into hybrid linear-attention students built on GatedDeltaNet, using Taylor-series-informed initialization before staged distillation.
发布 Taylor-Calibrate:面向混合线性注意力蒸馏的原理化初始化方法。该代码库把 Qwen/Llama 的 softmax attention Transformer 蒸馏为基于 GatedDeltaNet 的混合线性注意力学生模型,并在分阶段蒸馏前先完成基于泰勒展开的初始化。
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Our paper OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization has been accepted by ACL SELVA 2026!
我们的论文 OSCAR: Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization 已被 ACL SELVA 2026 接收!
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Huge thanks to the open-source community — OSCAR has reached 500 stars on GitHub and now supports local llama.cpp usage, making 2-bit KV-cache quantization easier to try on local LLM deployments.
衷心感谢开源社区——OSCAR 的 GitHub star 数已达 500,并且现已支持在本地 llama.cpp 中使用,让 2-bit KV-cache 量化更容易在本地 LLM 部署上试用。
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OSCAR crosses 300 ★ on GitHub in its first week — thanks to the open-source community.
OSCAR 上线首周 GitHub star 数突破 300 ★——感谢开源社区。
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OSCAR released — 2-bit KV-cache serving at 2.28 effective bits/element with near-BF16 accuracy on Qwen3 and GLM-4.7.
OSCAR 正式发布——在 Qwen3 与 GLM-4.7 上以 2.28 等效 bit/元素实现 2-bit KV-cache 服务,精度接近 BF16。
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CARE presented at ICLR 2026: covariance-aware, rank-enhanced decomposition for Multi-Head Latent Attention.
CARE 在 ICLR 2026 发表:面向 Multi-Head Latent Attention 的协方差感知、秩增强分解方法。