Master Quality Authenticated codec reverse engineering, Tool to identify MQA encoding and Master's Sample Rate
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Updated
Jan 25, 2026 - C++
Master Quality Authenticated codec reverse engineering, Tool to identify MQA encoding and Master's Sample Rate
Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.
Profile-based three dimensional convolutional neural network for protein model quality assessment
A fast, lightweight cross-os toolkit for detecting, analyzing, organizing, and exporting MQA-encoded FLAC files.
Docker Compose for Metadata Quality Assessment (MQA) on CKAN and European Data Portal catalogs
Metadata Quality Stack is a comprehensive toolkit for analysing metadata quality. It implements the European Data Portal's MQA methodology. Docker Compose deployment and React web application.
A code deep-dive on one of the key innovations from Deepseek - Multihead Latent Attention (MLA)
Modern LLM Attention from Scratch — MHA, GQA, MQA, RoPE, and KV-Cache implemented in pure PyTorch.
⚡ Optimize attention mechanisms with FlashMLA, a library of advanced sparse and dense kernels for DeepSeek models, improving performance and efficiency.
Web app for evaluating the quality of RDF metadata based on the EDP's MQA methodology. It supports DCAT-AP, DCAT-AP-ES and NTI-RISP (Spanish DCAT). Built with React and TypeScript. It is easily deployable to GitHub Pages.
🚀 Accelerate attention mechanisms with FlashMLA, featuring optimized kernels for DeepSeek models, enhancing performance through sparse and dense attention.
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