Deep learning dominates various fields and fundamentally changes human society. Efficiency is a key factor in democratizing deep learning and expanding its application scope. This has become increasingly important as Moore’s Law slows down and the pace of model size expansion accelerates. We need efficient algorithms and systems to help bridge this gap. In this … Read more
Click Follow us by clicking the blue text above This paper introduces a modular blueprint and the X1 framework aimed at advancing the development of accessible and scalable Reasoning Language Models (RLMs) by combining reinforcement learning and hierarchical reasoning strategies, simplifying the design and deployment of RLMs, enhancing efficiency, and reducing costs. Paper Introduction By … Read more
By | Li Dongshuo, Founder, Chairman, and President of UMU Source | Training Magazine July Issue It is not easy to elaborate on the definition of Artificial Intelligence (AI). The reason is that AI has the characteristic of phased development, forming new shapes and connotations over time. Three years ago, when people talked about AI, … Read more
· Desktop Robot Development Practical Course· Exploring the Application Potential of Edge AI and Large Language Models 01 Course Background With the acceleration of digital transformation across various industries, edge AI and cloud-based large model technologies are leading a new wave of innovation in smart devices. This year, the Ministry of Industry and Information Technology … Read more
Source: DeepHub IMBA This article is approximately 2400 words long and is recommended for a 5-minute read. In this article, we will see how to use vLLM with multiple LoRA adapters. We all know that using LoRA adapters can customize large language models (LLMs). The adapters must be loaded on top of the LLM, and … Read more
MLNLP community is a well-known machine learning and natural language processing community at home and abroad, covering domestic and international NLP graduate students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between academia, industry, and enthusiasts in natural language processing and machine learning, especially for beginners. Selected … Read more
As the parameter count of large models continues to grow, the cost of fine-tuning the entire model has become increasingly unacceptable. To address this, a research team from Peking University proposed a parameter-efficient fine-tuning method called PiSSA, which surpasses the fine-tuning effects of the widely used LoRA on mainstream datasets. Paper Title: PiSSA: Principal Singular … Read more
Paper Title: LoRA: Low-Rank Adaptation of Large Language Models Authors: Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen Compiled by: Kuang Ji Reviewed by: Los Abstract: This article introduces a new approach for deploying large models, called LoRA, which reduces the number of parameters needed for … Read more
Introduction In recent years, artificial intelligence (AI) large language models have made rapid advancements, and the impact of AI on human society has expanded to an unprecedented extent. This article discusses some immature insights into the AI revolution brought by large language models from two physics-related perspectives—information and time scales. It first reviews the basic … Read more
This article is approximately 3200 words long and is recommended for an 8-minute read. This article will outline the key points related to the large language model (LLM) Agent and discuss the important directions for AI Agents in the era of large models. As large language models mature, various AI Agents based on them are … Read more