Reduced LLM Quantization for Consumer Device Storage
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Solution Overview
Problem
Large language models (LLMs) are not suited for use on individual computing devices due to their massive size and processing requirements, and they lack the ability to generate content in a user's own style or voice.
Innovation Solution
A reduced-sized LLM is created through optimization processes such as quantization, which analyzes parameters to reduce their precision and storage requirements, allowing the LLM to be stored and executed on consumer-grade devices. This reduced LLM can then be trained to generate content in the user's style or voice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model is used, then content generation capability is improved, but device resource requirements (memory and processing power) increase significantly
Solution Approach 1:
The patent extracts only the essential components of a large language model needed for content generation, removing unnecessary parameters and computational complexity. This allows the model to function effectively on consumer devices without requiring the full scale of enterprise-grade LLMs.
Solution Approach 2:
The patent changes key parameters of the language model including reducing the number of parameters, adjusting model depth and width, and modifying computational operations to be more efficient. These parameter changes enable the model to maintain content generation capabilities while fitting within consumer device resource constraints.
2Quantity of substance
If model precision is reduced through quantization, then storage requirements decrease, but processing accuracy may deteriorate
Solution Approach 1:
The patent applies quantization to change the precision parameters of model weights and activations, converting high-precision floating-point values to lower-precision representations. This reduces storage requirements while the patent compensates for accuracy loss through optimized computation methods and architectural adjustments.
Solution Approach 2:
The patent replaces traditional high-precision floating-point arithmetic with alternative computational approaches that are more efficient on consumer hardware. This substitution maintains acceptable accuracy levels while significantly reducing computational overhead and storage needs.
3Ease of operation
If a reduced-sized LLM is created, then ease of operation on consumer devices is improved, but content generation quality may worsen
Solution Approach 1:
The patent applies different optimization strategies to different parts of the model architecture. Critical components that most impact content generation quality are preserved with higher fidelity, while less critical components are more aggressively optimized for size and speed. This localized approach maintains quality where it matters most while achieving overall reduction.
Solution Approach 2:
The patent introduces dynamic elements to the reduced model that allow it to adapt its behavior based on input complexity and context. This enables the smaller model to compensate for its reduced capacity by being more strategic about when and how it applies computational resources, maintaining quality output across diverse content generation tasks.
Data Source
AI summary
One example method includes transmitting, by a client device, a request for a reduced large language model (“LLM”) to a remote server; receiving, by the client device from the remote server, and storing the reduced LLM, the reduced LLM based on a trained general LLM; receiving, by the client device, a request to generate content using the reduced LLM; providing the request to the reduced LLM; and receiving generated content from the reduced LLM based on the request.