Low rank adaptation
3.6K
Volume
+4900%
Growth
exploding
About the Topic
Technique used in machine learning, particularly in the context of fine-tuning large language models. It involves adapting a pre-trained model to a specific task by introducing low-rank matrices into the model's architecture, reducing the number of parameters that need to be updated during fine-tuning. This approach is beneficial for efficiently adapting very large models without retraining the entire model, making it suitable for resource-constrained environments.
Low rank adaptation was discovered on April 15th 2025 and it currently has a search volume of 3.6K with a growth of +4900%.
Key Indicators
Growth
- Exploding
- Regular
- Peaked
Speed
- Exponential
- Constant
- Stationary
Seasonality
- High
- Medium
- Low
Volatility
- High
- Average
- Low
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