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Source channel @githubtrending · Post #15141 · Sep 13
#python#large_language_models#machine_learning_systems#natural_language_processing Flash Linear Attention (FLA) is a fast, memory-efficient library for advanced linear attention models used in transformers, written in PyTorch and Triton, and compatible with NVIDIA, AMD, and Intel GPUs. It offers many state-of-the-art linear attention models and fused modules that speed up training and reduce memory use. You can easily replace standard attention layers in your models with FLA’s efficient versions, improving training and inference speed, especially for long sequences. FLA supports hybrid models mixing linear and standard attention, and integrates with Hugging Face Transformers for easy use and evaluation. This helps you train and run large language models faster and with less memory, making your AI projects more efficient and scalable. https://github.com/fla-org/flash-linear-attention
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@AppPie · Post #2306 · 01/09/2025, 04:06 AM
#Developers CodeImage：代码截图美化工具 🔗GitHub CodeImage 是一个帮助开发者创建精美代码截图的新工具，提供多种功能来加速社交媒体分享流程。让你的代码片段即时管理、美观易读且方便分享。 技术架构 基于 PNPM monorepo 设计，分为应用和包两大类 应用部分 - 前端（@codeimage/app） - 使用 SolidJS 构建 - vanilla-extract 处理样式 - CodeMirror6 作为代码编辑器 - StateBuilder 状态管理 - @codeui/kit 无障碍 UI 套件 - 后端（@codeimage/api） - Fastify 构建 REST API - Prisma ORM 数据库操作 - Auth0 认证 开源许可证 MIT license。 #GitHub#CodeSnippet#OpenSource#SolidJS 📮 频道 @AppPie
@githubtrending · Post #14672 · 05/05/2025, 11:30 AM
#typescript#app#cap#coss#loom#mac#nextjs#nextjs14#open_source#oss#react#record#screen_capture#screen_recorder#screenshot#solidjs#tauri#tauri_app#typescript#vite Cap is a free, open-source tool that helps you record and share videos quickly. It's similar to Loom but gives you more control over your recordings. You can use it on both macOS and Windows, making it easy to work with different devices. Cap allows you to store your videos locally or in the cloud, which means you can access them from anywhere. This tool is great for sharing information with teams or clients securely and efficiently. https://github.com/CapSoftware/Cap