LoRa Study: Setting and Explanation of Key LoRa Parameters (Spreading Factor, Coding Rate, Bandwidth) 1. Spreading Factor (SF) 2. Coding Rate (CR) 3. Signal Bandwidth (BW) 4. Relationship between LoRa Signal Bandwidth BW, Symbol Rate Rs, and Data Rate DR 5. Setting of LoRa Signal Bandwidth, Spreading Factor, and Coding Rate For specific applications, developers … Read more
Selected from Sebastian Raschka’s blog Translated by Machine Heart Editor: Jiaqi This is the experience derived from hundreds of experiments by the author Sebastian Raschka, worth reading. Increasing the amount of data and the number of model parameters is a widely recognized direct method to improve neural network performance. Currently, mainstream large models have parameter … Read more
Abstract——The rapid development of foundational models——large-scale neural networks trained on diverse and extensive datasets——has revolutionized artificial intelligence, driving unprecedented advancements in fields such as natural language processing, computer vision, and scientific discovery. However, the enormous parameter counts of these models, often reaching billions or even trillions, pose significant challenges in adapting them to specific downstream … Read more
With the rapid development of large models, there has been significant technological iteration and updates in just a year, from LoRA, QLoRA, AdaLoRa, ZeroQuant, Flash Attention, KTO, distillation techniques to model incremental learning, data processing, and understanding new open-source models, almost every day brings new developments. As algorithm engineers, do you feel like your learning … Read more
This article focuses on reducing the training costs of large Transformer language models. The author introduces a low-rank update-based method called ReLoRA. A core principle in the development of deep learning over the past decade has been to “stack more layers,” and the author aims to explore whether stacking can similarly enhance training efficiency for … Read more
Machine Heart Column Machine Heart Editorial Team As the parameter size of large models continues to grow, the cost of fine-tuning the entire model has gradually become unacceptable. To address this, a research team from Peking University proposed a parameter-efficient fine-tuning method called PiSSA, which outperforms the widely used LoRA in fine-tuning effectiveness on mainstream … Read more
No matter how prosperous the virtual world is, real things have an irresistible charm, and AI painting is no exception. Today, let’s talk about how to use ChilloutMix and Lora to create particularly “realistic” characters, pursuing realism in the virtual world, which is actually the core goal of our series. What is ChilloutMix? Essentially, it … Read more
Recently, I was fortunate to have a friend help me connect with a renowned electronic product OEM giant in Shenzhen to discuss our solutions. The discussion was with the head of a subsidiary of this company, which handles its global supply chain logistics and also provides B2B logistics services to other factories. Industry insiders may … Read more
Machine Heart reports Editor: Danjiang Generally, the deployment of large language models adopts a “pre-training – then fine-tuning” approach. However, when fine-tuning the base model for numerous tasks (such as personalized assistants), the training and service costs can become extremely high. Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method, typically used to adapt the base … Read more
Selected from Raphael G’s blog Translated by Machine Heart Author: Raphael G Editor: Big Chicken Using LoRA to build faster AI models. AI models are becoming increasingly powerful and complex, and their speed has become one of the standards for measuring advancement. If AI is a luxury sports car, then the LoRA fine-tuning technology is … Read more