Introduction: AdaLoRA addresses the issue of manually selecting the low-rank parameter r in LoRA and implements dynamic adjustments to all key parts of the model (including FFN), comprehensively enhancing model capabilities. Issues with LoRA LoRA allows for the original model parameters to remain unchanged while training a “small patch” (low-rank matrix ΔW) that is added … Read more
↑↑↑ Follow and Star Kaggle Competition Guide Kaggle Competition Guide Author: Elvin Loves to Ask, excerpted from Zhai Ma LoRA+MoE: A Historical Interpretation of the Combination of Low-Rank Matrices and Multi-Task Learning This article introduces some works that combine LoRA and MoE, hoping to be helpful to everyone. 1. MoV and MoLoRA Paper: 2023 | … Read more
MLNLPThe MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university professors, and corporate researchers.The vision of the communityis to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners.Source | Machine HeartEditor | … Read more
Reported by Machine Heart Editor: Zhang Qian How much parameter redundancy exists in LoRA? This innovative research introduces the LoRI technology, which demonstrates that even significantly reducing the trainable parameters of LoRA can still maintain strong model performance. The research team tested LoRI on mathematical reasoning, code generation, safety alignment, and eight natural language understanding … Read more