Incremental Adapter Training for Scalable LLM Customization
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Solution Overview
Problem
Large language models (LLMs) require significant computing resources and are difficult to train and deploy across diverse IT environments, leading to generic and obsolete outputs due to impracticality of frequent retraining.
Innovation Solution
Utilize a primary model combined with smaller, expert models that are incrementally trained using recent data, allowing direct weight adjustments without retraining, and leveraging shared memory for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are trained and deployed across diverse IT environments, then customized and up-to-date data processing is achieved, but computing resources and training difficulty increase significantly
Solution Approach 1:
The patent divides the large language model into two segments: a frozen primary model that retains general knowledge and trainable secondary models (adapters) that are incrementally trained on recent data. This segmentation allows customization for specific IT environments without requiring full model retraining, significantly reducing computing resource requirements while maintaining adaptability.
Solution Approach 2:
The patent extracts the trainable components (secondary models/adapters) from the complete large language model. By separating the frozen primary model from the trainable adapters, the system enables targeted training on recent data without processing the entire model, reducing training complexity and resource consumption while preserving customization capability.
2Reliability
If frequent retraining is performed to keep models up-to-date, then current and relevant outputs are achieved, but time and computing resources are consumed
Solution Approach 1:
The patent performs preliminary freezing of the primary model weights before incremental training of secondary models. This preliminary action establishes a stable foundation that doesn't require frequent retraining, allowing the system to adapt to new data through efficient adapter training while minimizing overall retraining time and resource consumption.
Solution Approach 2:
The patent implements a dynamic training strategy where only the secondary models/adapters are retrained on recent data while the primary model remains frozen. This dynamic approach allows the system to efficiently update and maintain data currency without the time and resource costs of complete model retraining.
3Measurement precision
If complete model retraining is performed, then model accuracy is improved, but productivity and deployment speed decrease
Solution Approach 1:
The patent applies local quality by focusing training efforts only on the secondary models/adapters that directly interact with specific IT environments, while keeping the primary model frozen. This localized training approach maintains model accuracy for specific contexts without requiring complete model retraining, thereby preserving deployment speed and productivity.
Solution Approach 2:
The patent implements partial training by updating only the necessary secondary model components rather than performing complete model retraining. This partial action approach achieves sufficient accuracy for specific IT environments while dramatically reducing training time and improving deployment speed compared to full model retraining.
Data Source
AI summary
In described systems and techniques, network data may be analyzed using a combination of a primary model and a secondary model to obtain first network analysis results. A training instance of the secondary model may be trained using the network data and the first network analysis results. The secondary model may be updated using the training instance to obtain an updated secondary model. Additional network data may then be processed using a combination of the primary model and the updated secondary model.