Articles by
QVAC Team

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LoRA Fine-Tuning BitNet b1.58 LLMs on Heterogeneous Edge GPUs via QVAC Fabric

The world’s first framework to enable BitNet fine-tuning with LoRA on GPUs enabling fine-tuning on edge devices substantial performance improvements

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QVAC Genesis II: Expanding the Largest and Highest-Quality Multi-domain Educational Synthetic Dataset for LLM Pre-training

Building upon the success of Genesis I, we introduce QVAC Genesis II, a major expansion that adds new domains and a total of 148 billion tokens.

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An Edge-First Generalized LLM LoRA Fine-Tuning Framework for Heterogeneous GPUs

We present a unified, cross-platform framework that successfully enables parameter-efficient training of modern LLMs with LoRA on consumer hardware such as mobile SoCs and desktop GPUs, without relying on a CUDA-only ecosystem.

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Introducing QVAC Genesis I: the Largest and Highest-Quality Multi-domain Educational Synthetic Dataset for Pre-training

There is a need for publicly available, large-scale synthetic datasets that are rigorously curated. Genesis I is our first effort in this direction.

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