Disclosure: the case study below is VIDRAFT's own result. VIDRAFT is a technology partner of QuantID. This post reviews the measurement design; it is not an independent verification.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady . What I Built As a teacher, I know how easily important instructions get buried in long educational notices. A single circular may contain an examination deadline, a fee requirement, a list of documents...
An AI Recruiting Agent Should Schedule, Not Select: A 14-Case SME Release Test For a small employer, a useful first recruiting agent can acknowledge an application, answer approved process questions, and offer interview times. It should not rank, reject, infer suitability, interpret an accommoda...
Liquid syntax error: Unknown tag 'endraw'
Resumen Sí, siempre que exista una pila de entrenamiento hecha para ello. En VIDRAFT preentrenamos VKORE-325M-NPU-Base (325,6 millones de parámetros) desde una inicialización aleatoria en la Rebellions ATOM-Max , una NPU comercial coreana lanzada en 2025 y diseñada para inferencia. Entrenamos e...
요약 가능합니다. 단, 학습용 소프트웨어를 따로 만들어야 합니다. 비드래프트는 3억 2,560만 파라미터 언어모델 VKORE-325M-NPU-Base 를 무작위 초기값에서 출발해 리벨리온 ATOM-Max (2025년 출시된 상용 추론 전용 NPU)로 사전학습했습니다.