Abstract: Modern datasets often exhibit heavy-tailed behavior, while quantization is inevitable in digital signal processing and many machine learning problems. This paper studies the quantization of ...
Large language models cache key (K) and value (V) tensors for every previously seen token — the "KV cache." At long context lengths this cache dominates GPU memory. Recent work (Google's TurboQuant, ...
The problem of energy and its conservation is more than a century old in general relativity (GR) and is considered by many scholars to be, at least, not sufficiently solved. The problem’s core lies in ...
Explore the complex challenges of quantum gravity in this video, examining the evolution of gravity models and the necessity of quantum gravity. Delve into key topics such as the 3D Bronstein cube, ...
Abstract: Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra. However, the large network complexity prohibits the usage of LIC ...
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Improved gray-scale (IGS) quantization is a known method for re-quantizing ...
Quantizing the weights of a neural network has two steps: (1) Finding a good low bit-complexity representation for weights (which we call the quantization grid) and (2) Rounding the original weights ...
Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can ...
HANDS ON If you hop on Hugging Face and start browsing through large language models, you'll quickly notice a trend: Most have been trained at 16-bit floating point of Brain-float precision. FP16 and ...
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