This project focuses on generating high-quality synthetic brain MRI scans using the Vector Quantized Variational Autoencoder (VQ-VAE) architecture to address data scarcity in neuroimaging research, ...

Vector quantized variational autoencoder (VQ‐VAE) represents a nexus point in deep learning's journey, elegantly merging the worlds of autoencoders and vector quantization to pioneer a unique ...

The attack’s success hinged on exploiting the assumed safety of open-source tools and the ease of impersonating legitimate software online. A known crew of cybercriminals has weaponized the ...

Its quantized models allow efficient local deployment, making it accessible for developers and researchers to run on their own hardware using platforms like Ollama, LM Studio, or vLLM. In this ...

Each GPU comes with 24 GB of GDDR6 RAM, giving you 48 GB total—more than enough to handle a 70B distilled and quantized transformer that needs at least 43 GB. That extra memory cuts down ...

The hardware is the interesting part for most of us here — not the Pi4 running a quantized Llama 3 model, but the display. It’s a six by sixteen matrix of sixteen-segment LED modules.

The transform coding of GLC is conducted in the latent space of a generative vector-quantized variational auto-encoder (VQ-VAE). Compared to the pixel-space, such a latent space offers greater ...