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An empirical study of LLaMA3 quantization: from LLMs to MLLMs.

Vis Intell

December 2024

Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.

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Aims: Establishment of a protocol for routine single-molecule localisation microscopy (SMLM) imaging on formalin fixed paraffin embedded (FFPE) tissue using medical renal disease including minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS).

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Microgels are versatile materials with applications across biomedicine, materials science, and beyond. Their controllable size and composition enables tailoring specific properties, yet characterizing their internal structures on the nanoscale remains challenging. Super-resolution fluorescence microscopy (SRFM) effectively analyzes sub-μm structures, including microgels, offering a tool for investigating more complex systems such as core-shell microgels.

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Key Laboratory of Analytical Science and Technology of Hebei Province, College of Chemistry and Material Science, Hebei University. Baoding, China.

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