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Source channel @githubtrending · Post #15428 · Jan 22

#cplusplus FlashMLA is DeepSeek's optimized attention library that makes AI models run faster and use less memory. It works with advanced NVIDIA GPUs to speed up how language models process information, achieving up to 660 trillion floating-point operations per second. The library supports both dense and sparse attention modes, meaning it can focus on important tokens while skipping less relevant ones, reducing computational waste. For you, this means faster AI responses, lower costs for running large language models, and better performance on tasks like chatbots and code generation. The technology is open-source and integrates with popular AI frameworks like PyTorch and Hugging Face, making it accessible for developers building next-generation AI applications. https://github.com/deepseek-ai/FlashMLA

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@seeker_rc · Post #19674 · 05/05/2026, 01:25 PM

💡 博物馆收藏夹 可能是自己状态原因,这个巨大的国立分馆逛了半小时就累了…把毕加索和斯拉夫艺术家们放在一起展陈挺有意思的,各种裸女们也是看了个爽。 但是三小时只够匆匆看两层…一共上下四层,展品巨多的同时,巨大的Circuit 中间没有出口…以及这个美术馆是真没人。 (但是美术馆的咖啡馆也巨大,坐满了工作和学习的人,像图书馆) via 博物馆收藏夹 标签: #收藏夹#Circuit#裸女 ⚡️探索号频道 ⚡️探索者频道 ⚡️探索者交流群 ⚡️ Youtube 频道:科技探索者 每天推荐有趣内容,欢迎订阅、转发。

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@kejiqu · Post #3493 · 05/30/2025, 06:51 AM

Anthropic 开源“思维追踪”工具,可视化揭秘 AI 内部逻辑 Anthropic于5月29日发布“思维追踪”(Circuit Tracer)开源工具,以图形化方式呈现AI大语言模型的内部思维过程。该工具通过构建“归因图”(Attribution Graph),帮助研究者可视化模型内部运作并进行交互式探索。Circuit Tracer已在GitHub平台以开源库形式发布,研究者可在Decode Research运营的Neuronpedia平台上使用交互式前端查看“归因图”。用户可利用该工具生成自定义归因图,追踪模型内部逻辑,并进行标注、分享和调整特征值以验证研究假设。Anthropic 认为,开源这些工具将促进对语言模型内部运作的更广泛理解。IT之家 | Anthropic 🏷#Anthropic#Circuit#Tracer#开源 📢频道👥群组📝投稿