Source: DeepHub IMBA This article is approximately 1000 words and is recommended to be read in 5 minutes. Low-Rank Adaptation significantly reduces the number of trainable parameters for downstream tasks. For large models, it becomes impractical to fine-tune all model parameters. For example, GPT-3 has 175 billion parameters, making both fine-tuning and model deployment impossible. … Read more
Click on the public account card below, reply with the keyword:Painting to view the AI painting tutorial. # What Is the LoRA Model and How to Use It The LoRA model is a small model of the Stable Diffusion model, achieved by making slight modifications to the standard checkpoint model. They are typically 10 to … Read more
The author states: Among various effective LLM fine-tuning methods, LoRA remains his preferred choice. LoRA (Low-Rank Adaptation) is a popular technique for fine-tuning LLMs (Large Language Models), initially proposed by researchers from Microsoft in the paper “LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS”. Unlike other techniques, LoRA does not adjust all parameters of the neural … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted from | … Read more
Introduction LoRA (Low-Rank Adaptation of Large Language Models) is a very practical fine-tuning framework for large models. Before the emergence of LoRA, I used to manually modify parameters, optimizers, or layer counts to “refine” models, which was extremely blind. However, the LoRA technique allows for quick fine-tuning of parameters. If the results after LoRA fine-tuning … Read more
↑ ClickBlue Text Follow the Jishi Platform Author丨CW Don’t Be Boring Editor丨Jishi Platform Jishi Guide Big questions about the popular LoRA in the model training community! Dive deep into understanding LoRA with source code analysis.>> Join the Jishi CV Technology Group to stay at the forefront of computer vision. Introduction Since ChatGPT sparked the trend … Read more
In previous episodes, we discussed modifications related to exhaust systems, brakes, and suspensions. Today, we will finally talk about a modification project that many car enthusiasts are quite interested in and that has garnered a lot of attention: ECU tuning. ECU tuning has become a very popular modification project. Is it really enough to just … 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
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