arXiv Machine Learning
Loopy: Low-Bit Quantization Framework for Looped Language Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Oct 15
LoopQuant: Accurate and Efficient Post-Training Quantization for Looped Transformers
arXiv:2610.16368v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over...
arXiv AI
Oct 15
LoopQ: Quantization for Looped Language Models
arXiv:2605.16343v2 Announce Type: replace-cross Abstract: Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing t...
arXiv AI
Jun 9
Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression
arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.
arXiv Machine Learning
Oct 2
QATFactory: A Versatile, Deployment-Aligned Framework for Quantization-aware Training and Distillation of LLMs
arXiv:2609.39223v2 Announce Type: new Abstract: Large language model (LLM) inference is increasingly moving toward lower precision to realize the throughput of hardware accelerators, but aggressive p...
arXiv AI
Jun 16
RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs
arXiv:2602. 05367v3 Announce Type: replace Abstract: Efficient deployment of large language models (LLMs) requires extreme quantization, forcing a critical trade-off between low-bit efficiency and performance.
arXiv AI
Jul 7
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
arXiv:2601. 07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference.