Machine Learning

LoRA-Dash: A More Efficient Method for Task-Specific Fine-Tuning

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LoRA-Dash: A More Efficient Method for Task-Specific Fine-Tuning

Article Link: https://arxiv.org/abs/2409.01035 Code Link: https://github.com/Chongjie-Si/Subspace-Tuning Project Homepage: https://chongjiesi.site/project/2024-lora-dash.html Due to the rich content of the LoRA-Dash paper, compressing 30 pages of content into 10 pages is a highly challenging task. Therefore, we have made careful trade-offs between readability and content integrity. The starting point of this article may differ from the original paper, aligning … Read more

LoRA Empowerment: Addressing the Data Scarcity of Large Model Open Platforms

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Based on LoRA technology, open large model platforms are becoming increasingly popular. This model significantly lowers the barriers for users to train models; by simply uploading a few dozen images, a small model can be trained. Moreover, this allows the platform to enable a continuous stream of users to become “data cows,” which is particularly … Read more

AI Painting Tutorial: What Is the LoRA Model and How to Use It

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AI Painting Tutorial: What Is the LoRA Model and How to Use It

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

Exploring the Essence of LoRA: A Low-Rank Projection of Full Gradients

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Article Title: FLORA: Low-Rank Adapters Are Secretly Gradient Compressors Article Link: https://arxiv.org/pdf/2402.03293 This paper not only introduces a brand new high-rank efficient fine-tuning algorithm but also deeply interprets the essence of LoRA: LoRA is a low-rank projection of full gradients. In fact, this perspective is not surprising; we can see traces of this viewpoint in … Read more

Towards High-Rank LoRA: Fewer Parameters, Higher Rank

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Towards High-Rank LoRA: Fewer Parameters, Higher Rank

This is a very impressive paper. The MeLoRA algorithm proposed in the paper not only achieves a rank increase but also shows certain improvements in computational efficiency compared to vanilla LoRA. Although the theory in this paper is relatively simple and there are not many mathematical formulas, the specific methods are quite enlightening. Article Title: … Read more

Implementing LoRA From Scratch with Practical Tips

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Implementing LoRA From Scratch with Practical Tips

Source: DeepHub IMBA This article is approximately 5000 words long and is suggested to be read in 10 minutes. This article starts with a simple implementation of LoRA, delving into LoRA, its practical implementation, and benchmarking. LoRA stands for Low-Rank Adaptation, which provides an efficient and lightweight method for fine-tuning pre-existing language models. One of … Read more

Differences Between LoRA and Full Fine-Tuning Explained in MIT Paper

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Differences Between LoRA and Full Fine-Tuning Explained in MIT Paper

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

Understanding LoRA: Low-Rank Adaptation for Large Language Models

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Understanding LoRA: Low-Rank Adaptation for Large Language Models

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

Key Insights from Four DAC Presentations

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Key Insights from Four DAC Presentations

Chip design and verification are facing increasing challenges. How to address these issues—especially in the context of incorporating machine learning—is a major concern for the EDA industry, which was a common theme among the four keynote speakers at this month’s Design Automation Conference (DAC). This year, DAC returned as an in-person event, featuring keynote speeches … Read more

Next Frontier Technology in Optoelectronic Sensors

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Next Frontier Technology in Optoelectronic Sensors

Next Frontier Technology in Optoelectronic Sensors Compiled by Yuan Wang, Open Source Intelligence Center Adapted from “Military and Aerospace Electronics” The next generation of infrared sensors will integrate advanced image processing, artificial intelligence, and standardized architectures to discover more information than ever in digital images. Optoelectronic sensors that perceive light across various spectra enable combat … Read more