LLaMA 3

LLaMA 3 is a family of large language models (LLMs) focusing on improving efficiency and performance across various tasks, including code generation, mathematical reasoning, and multilingual capabilities. Current research emphasizes techniques like parameter-efficient fine-tuning and selective layer enhancement to optimize knowledge injection and reduce computational costs, particularly for quantization and context length extension. These advancements are significant for expanding access to powerful LLMs and facilitating research in diverse scientific fields, as well as improving the efficiency and capabilities of practical applications.

Papers

August 22, 2023

LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning
Junyi Lu, Lei Yu, Xiaojia Li, Li Yang, Chun Zuo
Instruction Tuning Parameter Efficient Fine Tuning LLaMA 3 Code Review

April 28, 2023

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, Yu Qiao
Instruction Following Multimodal Instruction LLaMA 3

February 27, 2023

LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample
New Benchmark Available Datasets LLaMa LlamaCare LLaMA 3 Foundation Language Model

LLaMA 3

Papers

LLaMA-Reviewer: Advancing Code Review Automation with Large Language Models through Parameter-Efficient Fine-Tuning

LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

LLaMA: Open and Efficient Foundation Language Models