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.
View Article and Find Full Text PDFBiodegradable magnesium is a highly desired material for fracture fixation implants because of its good mechanical properties and ability to completely dissolve in the body over time, eliminating the need for a secondary surgery to remove the implant. Despite extensive research on these materials, there remains a dearth of information regarding critical factors that affect implant performance in clinical applications, such as the pH and mechanical loading conditions. We developed a measurement system with implantable strain, temperature, pH and motion sensors to characterize magnesium and titanium plates, fixating bilateral zygomatic arch osteotomies in three Swiss alpine sheep for eight weeks.
View Article and Find Full Text PDFObjectives: To investigate whether the use of chemo-mechanical carious tissue removal (CMCTR) agents is effective for Atraumatic Restorative Treatment (ART).
Materials And Methods: Searches were conducted in 6 databases for inclusion of clinical studies. Risk of bias was assessed (RoB 2 and ROBINS-I), a meta-analysis was performed with data from time of carious tissue removal (TCTR), and the certainty of evidence was estimated.