Profile-based three dimensional convolutional neural network for protein model quality assessment

Create a local database of MQA albums from Tidal

buggy TIDAL Downloader

Docker Compose for Metadata Quality Assessment (MQA) on CKAN and European Data Portal catalogs

MHA, MQA, GQA, MLA 相关原理及简要实现

Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.

Collections of papers, databases, and codes targeted at point cloud quality assessment (PCQA), mesh quality assessment (MQA), 3D model quality assessment (3DQA).

Next.js app for evaluating RDF metadata quality based on the MQA methodology from the EDP, using FAIR+C principles. Supports DCAT-AP, DCAT-AP-ES, NTI-RISP (Spanish DCAT), multiple RDF formats, interactive charts, vocabularies, themes, and i18n. Built with React+TypeScript.

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.

A fast, lightweight cross-os toolkit for detecting, analyzing, organizing, and exporting MQA-encoded FLAC files.

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.

Master Quality Authenticated codec reverse engineering, Tool to identify MQA encoding and Master's Sample Rate

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.

⚡ Optimize attention mechanisms with FlashMLA, a library of advanced sparse and dense kernels for DeepSeek models, improving performance and efficiency.

🚀 Accelerate attention mechanisms with FlashMLA, featuring optimized kernels for DeepSeek models, enhancing performance through sparse and dense attention.