2 citations · 3 across the 11 of their papers we have counts for
12 papers
SQuAT: Spherical Lattice Vector Quantization-Aware Training
Sebastian Loeschcke, Ivan Koryakovskiy, Tycho F. A. van der Ouderaa +4
Vector quantization (VQ), in particular lattice-based methods, offer strong rate-distortion trade-offs for low-bit large language models (LLMs), yet their behavior under quantizati…
MobileWan: Closing the Quality Gap for Mobile Video Diffusion
Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv +9
Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual fidelity and motion coheren…
Dissecting Quantization Error: A Concentration-Alignment Perspective
Marco Federici, Boris van Breugel, Paul Whatmough +1
Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-preserving transforms (e.g. rota…
Leech Lattice Vector Quantization for Efficient LLM Compression
Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough +1
Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcomes these limits by encoding bloc…
STaMP: Sequence Transformation and Mixed Precision for Low-Precision Activation Quantization
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Quantization is the key method for reducing inference latency, power and memory footprint of generative AI models. However, accuracy often degrades sharply when activations are qua…
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations
Marco Federici, Riccardo Del Chiaro, Boris van Breugel +2
Diffusion models represent the cutting edge in image generation, but their high memory and computational demands hinder deployment on resource-constrained devices. Post-Training Qu…
FPTQuant: Function-Preserving Transforms for LLM Quantization
Boris van Breugel, Yelysei Bondarenko, Paul Whatmough +1
Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective at improving efficiency, naive q…
Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware Masking
Marco Federici, Davide Belli, Mart van Baalen +5
While mobile devices provide ever more compute power, improvements in DRAM bandwidth are much slower. This is unfortunate for large language model (LLM) token generation, which is…
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference
Andrii Skliar, Ties van Rozendaal, Romain Lepert +5
Mixture of Experts (MoE) LLMs have recently gained attention for their ability to enhance performance by selectively engaging specialized subnetworks or "experts" for each input. H…
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse…
Sparse High Rank Adapters
Kartikeya Bhardwaj, Nilesh Prasad Pandey, Sweta Priyadarshi +9
Low Rank Adaptation (LoRA) has gained massive attention in the recent generative AI research. One of the main advantages of LoRA is its ability to be fused with pretrained models,…
Low-Rank Quantization-Aware Training for LLMs
Yelysei Bondarenko, Riccardo Del Chiaro, Markus Nagel
Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands. Quantization is one o…