Low-Rank Adaptation

Why LoRA Has Become an Indispensable Core Technology for Fine-Tuning Large Models?

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Why LoRA Has Become an Indispensable Core Technology for Fine-Tuning Large Models?

In the field of artificial intelligence, large language models (LLMs) such as Claude, LLaMA, and DeepSeek are becoming increasingly powerful. However, adapting these models to specific tasks, such as legal Q&A, medical dialogues, or internal knowledge queries for a company, traditionally involves “fine-tuning” the model. This often entails significant computational overhead and high resource costs. … Read more

MMD-LoRA: Integrating LoRA and Contrastive Learning for Depth Estimation

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🫱Click here to join the 16 specialized direction discussion group (🔥Recommended)🫲 Abstract: The authors introduce a Multi-Modality Driven Low-Rank Adaptation (MMD-LoRA) method that utilizes low-rank adaptation matrices to achieve efficient fine-tuning from the source domain to the target domain, addressing the Adverse Condition Depth Estimation (ACDE) problem. It consists of two core components: Prompt-based Domain … Read more

LoRA: Low-Rank Adaptation for Large Models

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LoRA: Low-Rank Adaptation for Large Models

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