Delta Model Inference via Vector Combination
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Solution Overview
Problem
Adapting large AI models to improve performance in specific domains or contexts is resource-intensive and time-consuming, especially when only one adapted iteration of a base AI model can be implemented by a single computing device, limiting inferencing throughput.
Innovation Solution
Implementing a base AI model and multiple delta AI models on the same computing device, allowing input prompts to be processed separately by both the base and selected delta AI models, with the resulting vectors combined to generate an output vector, enabling concurrent processing of different adaptations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a base AI model is adapted to improve performance in specific domains through re-training or fine-tuning, then model performance in specific contexts is improved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The model adaptation process is segmented into two independent components: a frozen base model and a separate trainable delta model. The base model retains its pre-trained knowledge without requiring re-training, while only the delta model parameters are trained for domain-specific adaptation. This segmentation eliminates the time-consuming full re-training process while maintaining performance improvements.
Solution Approach 2:
The adaptation parameters (delta model) are extracted as a separate entity from the base model. Instead of modifying the entire base model through re-training, only the necessary delta parameters are trained and extracted for domain-specific tasks. This extraction approach reduces computational resources and time while preserving the base model's general capabilities.
2Adaptability or versatility
If multiple adapted iterations of a base AI model are implemented to serve different domains, then model versatility is improved, but device complexity increases when only one adaptation can be implemented at a time
Solution Approach 1:
The base model serves as a universal foundation that can be combined with different delta models for multiple domains. The same base model instance can simultaneously support multiple domain-specific adaptations by loading different delta model parameters, eliminating the need for separate model instances for each domain and reducing device complexity.
Solution Approach 2:
The system transitions from a single-dimension approach (one model per domain) to a two-dimension approach (base model + delta model layer). By adding the delta model dimension, the system can load multiple domain-specific adaptations without increasing the base model complexity, allowing versatile multi-domain support through parameter switching rather than structural complexity.
3Measurement precision
If full re-training is performed to adapt AI models to different contexts, then model accuracy is improved, but computational resources and time consumption increase significantly
Solution Approach 1:
Instead of performing full re-training on all model parameters, the system applies partial action by training only the delta model parameters while keeping the base model frozen. This partial training approach achieves sufficient accuracy for domain-specific tasks without the excessive computational resources required for complete re-training of the entire model.
Data Source
AI summary
A computing device is provided, including processor and a storage device holding instructions that are executable by the processor to implement a base artificial intelligence (AI) model and two or more delta AI models, each delta AI model having lower dimensionality than the base AI model. An inference request including an input prompt is received, the inference request specifying a selected delta AI model of the two or more delta AI models. The input prompt is input to the base AI model to thereby generate a base model result vector. The input prompt is input to the selected delta AI model to thereby generate a delta model result vector. An output vector is generated by combining the base model result vector and the delta model result vector via a combination operation. The output vector is output.