LoRA
A technique that allows for tuning only a subset of a large model's weights, making fine-tuning more efficient and memory-friendly.
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Videos Mentioning LoRA
Robot-Use Agents: Why General-Purpose Models May Win in Robotics
Y Combinator
A parameter-efficient fine-tuning technique for large language models, mentioned in the context of different levels of model training.
Stanford CME 295 Transformers & LLMs | Fall 2026 | Lecture 3 - LLM training
Stanford Online
تقنية تكييف منخفض الرتبة تسمح بتعديل عدد أقل بكثير من المعلمات في النماذج اللغوية الكبيرة لتحسين الكفاءة والأداء.
Stanford CME295 Transformers & LLMs | Autumn 2026 | Lecture 5 - LLM Systems
Stanford Online
A parameter-efficient fine-tuning technique that decomposes matrix multiplication, allowing for smaller, trainable representations to be used.
Stanford CME296 Diffusion & Large Vision Models | Spring 2026 | Lecture 8 - Trending Topics
Stanford Online
A technique that allows for tuning only a subset of a large model's weights, making fine-tuning more efficient and memory-friendly.
Building with Instruction-Tuned LLMs: A Step-by-Step Guide
DeepLearningAI
A parameter-efficient fine-tuning technique that significantly reduces trainable parameters and compute requirements. Applied to the query-key-value module in Bloom Z.
Stanford CS229 Machine Learning | Spring 2026 | Lecture 12: Representation Learning
Stanford Online
A technique for efficient model fine-tuning that restricts updates to low-rank matrices, significantly reducing memory and computational requirements, and enabling easier serving of multiple users.