Sensitivity-Based Fine-Tuning for Machine Learning Model Resource Optimization
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Solution Overview
Problem
Large language models require extensive resources for fine-tuning, especially when dealing with limited-resource devices and face challenges in reducing carbon emissions associated with training, as existing solutions like pruning and LoRA do not effectively leverage compute or latency benefits.
Innovation Solution
The approach involves sensitivity-driven fine-tuning, where the model's layer sensitivity is used to trim and fine-tune only a portion of the model parameters, enabling neural architecture search within an elastic search space to generate multiple subnetworks, thereby reducing resource usage during both training and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fine-tuning methods are used on large language models, then model performance on task-specific data is improved, but resource consumption and carbon emissions increase significantly
Solution Approach 1:
The patent segments the model fine-tuning process by identifying and separating critical layers from non-critical layers through sensitivity analysis. Only the critical layers are selected for fine-tuning, while non-critical layers are pruned or frozen. This segmentation allows the model to achieve task-specific performance improvement with significantly reduced computational resources and carbon emissions compared to traditional full-model fine-tuning.
2Use of energy by moving object
If model size is reduced through pruning to save resources, then resource efficiency improves, but model performance deteriorates
Solution Approach 1:
The patent applies local quality by treating different model layers differently based on their sensitivity scores. Critical layers (with high sensitivity) are retained and fine-tuned to maintain model performance, while non-critical layers (with low sensitivity) are pruned or frozen to improve resource efficiency. This localized differentiation ensures that performance is preserved where it matters most while achieving resource savings elsewhere in the model architecture.
3Quantity of substance
If existing pruning methods are applied to reduce model size, then resource usage decreases, but the compute and latency benefits are not effectively realized
Solution Approach 1:
The patent performs preliminary sensitivity analysis and layer identification before the actual pruning process. By pre-identifying which layers are critical and which are non-critical based on sensitivity metrics, the method ensures that pruning decisions are made in advance with clear guidance on what to retain and what to remove. This preliminary action enables more effective compute efficiency improvements compared to random or uniform pruning approaches, as the model structure is optimized before fine-tuning begins.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods for sensitivity-based fine-tuning of a machine learning model are disclosed. Example instructions cause at least one processor circuit to perform a sensitivity analysis of a foundational model to identify sensitivity scores of respective layers of the foundational model, trim an intermediate layer of the foundational model based on the respective sensitivity score, and fine-tune the trimmed foundational model to create a fine-tuned model, the fine-tuning applied to layers having a respective sensitivity score that meets a threshold sensitivity.