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.
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.
📊 Assess RDF metadata quality effortlessly with this React app, ensuring compliance with FAIR+C standards and offering real-time metrics and insights.
Modern LLM Attention from Scratch — MHA, GQA, MQA, RoPE, and KV-Cache implemented in pure PyTorch.
Docker Compose for Metadata Quality Assessment (MQA) on CKAN and European Data Portal catalogs
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.
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)
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