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Research#llm📝 BlogAnalyzed: Dec 25, 2025 13:55

BitNet b1.58 and the Mechanism of KV Cache Quantization

Published:Dec 25, 2025 13:50
1 min read
Qiita LLM

Analysis

This article discusses the advancements in LLM lightweighting techniques, focusing on the shift from 16-bit to 8-bit and 4-bit representations, and the emerging interest in 1-bit approaches. It highlights BitNet b1.58, a technology that aims to revolutionize matrix operations, and techniques for reducing memory consumption beyond just weight optimization, specifically KV cache quantization. The article suggests a move towards more efficient and less resource-intensive LLMs, which is crucial for deploying these models on resource-constrained devices. Understanding these techniques is essential for researchers and practitioners in the field of LLMs.
Reference

“LLM lightweighting technology has evolved from the traditional 16bit to 8bit, 4bit, but now there is even more challenge to the 1bit area and technology to suppress memory consumption other than weight is attracting attention.”

PermalinkQiita LLM
Research#llm📝 BlogAnalyzed: Dec 25, 2025 22:17

Octonion Bitnet with Fused Triton Kernels: Exploring Sparsity and Dimensional Specialization

Published:Dec 25, 2025 08:39
1 min read
r/MachineLearning

Analysis

This post details an experiment combining Octonions and ternary weights from Bitnet, implemented with a custom fused Triton kernel. The key innovation is reducing multiple matmul kernel launches into a single fused kernel, along with Octonion head mixing. Early results show rapid convergence and good generalization, with validation loss sometimes dipping below training loss. The model exhibits a natural tendency towards high sparsity (80-90%) during training, enabling significant compression. Furthermore, the model appears to specialize in different dimensions for various word types, suggesting the octonion structure is beneficial. However, the author acknowledges the need for more extensive testing to compare performance against float models or BitNet itself.
Reference

“Model converges quickly, but hard to tell if would be competitive with float models or BitNet itself since most of my toy models have only been trained for <1 epoch on the datasets using consumer hardware.”

Permalinkr/MachineLearning