A Computer-Aided Method and System for Preventing Style Fading in PEFT and LoRA-Based AI Language Models and Optimizing Server Hardware Resources

Abstract

The invention is a hardware-integrated system that increases the efficiency of hardware resources and prevents stylistic dilution when fine-tuning Large Language Models (LLMs). The system allocates a frozen base parameter buffer and a dynamic adapter memory buffer on VRAM. The directional variance of the activation vectors is calculated by a hardware tracing unit at feedforward. When semantic flattening is detected, style integrity is preserved at the hardware level by dynamically reconstructing the rank size (r) of the adapter matrices and the VRAM memory tiles at execution.

Description

TARIFF Style Extinction in PEFT and LORA-Based AI Language Models OPTIMIZING BLOCKS AND SERVER HARDWARE RESOURCES A COMPUTERIZED METHOD AND SYSTEM THAT PROVIDES THE TECHNICAL FIELD TO WHICH THE INVENTION RELATES The invention enables the integration of artificial intelligence, natural language processing (NLP), and deep learning models at the hardware level. The invention relates to optimization, particularly in Low-Order Large Language Models (LLMs). Adaptation (LoRA Low-Rank Adaptation) and Parameter-Efficient Fine Tuning (PEFT - Parameter-Efficient Fine Tuning) During Efficient Fine-Tuning processes, the Graphics Processing Unit (GPU) uses Video Random Access. Optimizing the computational load on memory (VRAM) and the style in vector embedding. style dilution / embedding flattening is controlled in real-time by hardware. It relates to a computer-implemented system and method that prevents this. STATE OF THE ART In traditional deep learning fine-tuning processes, pre-trained, massive parameters are used. Updating all weight matrices of language models leads to high VRAM consumption on the hardware. and leads to high processor cycle costs. In the industry's known Low-Range Adaptation (LoRA) methods, pre-trained baseline weights are used. matrix W0 ∈ ℝ d×k is frozen and the model update uses low-ranking multiplier matrices ΔW = B · A is realized here via B ∈ ℝd×r and A ∈ ℝr×k (r ≪ min(d,k)): h = W0x + ΔWx = W0x + (α / r) · B · A · x. However, there are fundamental technical shortcomings in the current LoRA and Quantized LoRA (QLORA) techniques. These include: Style Dilution, hidden layer during model parameter updates. their activations collapse into high-density semantic clusters of the underlying mother tongue model The result is the loss of subtle stylistic and literary nuances. Static Memory Allocation, GPU during fine-tuning. Dynamic load condition or vector damping of VRAM memory area and rank (r) size The inability to instantly reconfigure it according to its level leads to hard drive overflows and unnecessary hardware. It leads to a waste of resources. 35 1 TECHNICAL PROBLEMS AND GOALS SOLVED BY THE INVENTION The aim of the invention is to solve the hardware and semantic attenuation problems in known technology by following the following: The technical advantages are: Hardware-Level Real-Time Monitoring with GPU VRAM memory. directional orientation of hidden layer activation vectors during forward pass in blocks The goal is to integrate a hardware control unit that calculates the entropy and variance metrics in real-time. Style dilution threshold in vector embedding with Dynamic Memory and Rank Reorganization When the value is exceeded, the system uses the dynamic adapter memory buffer without stopping the GPU from working. The goal is to reconfigure the matrix rank size (r) and VRAM memory block allocation live. Orthogonal Gradient Projection with gradient updates in the backward pass step. The frozen base model weights are traced orthogonally to their primary eigenvectors. The goal is to mathematically prevent style fading by lowering the level of wear. DESCRIPTION OF THE INVENTION AND ITS DETAILED ARCHITECTURE The system described in the invention consists of a Graphics Processing Unit (GPU), a VRAM memory controller connected to the GPU, and It consists of a hardware activation monitoring unit. The workflow architecture of the invention is shown in Figure 1. This is shown in detail. The steps of the working method are as follows: pre-trained in VRAM A frozen memory buffer (VRAM) is allocated to store the basic parameters of the language model. a dynamic adapter memory that stores trainable low-ranking decomposition matrices A buffer is created. In the feedforward step, the directional variance of the hidden layer activation vectors is calculated. The metric is calculated in real-time by the hardware activation monitoring unit via VRAM memory lines. If the calculated variance metric falls below the specified threshold value, the dynamic adapter will activate. The rank size (r) of the low-rank matrices in the memory buffer is increased at execution time or reconfigured. Feedback step via corrected dynamic adapter memory buffer. Only the adapter parameters are updated by running this process. 2

Claims

REQUESTS 1. A system that fine-tunes parameter-efficient style transfer through hardware-level memory management. It is a computer system, its characteristic is; - At least one graphics processing unit including execution units and a video random access memory (VRAM). GPU; and - When executed by the execution units connected to the GPU in question, the following actions are performed by the GPU: It contains a memory controller that stores the instructions enabling it to perform the steps: 1) A frozen memory that stores the basic parameters of a pre-trained language model. allocating the buffer within VRAM; 2) Trainable low-rank parsing matrices based on the aforementioned frozen language model. Allocating a dynamic adapter memory buffer within VRAM that stores; 3) A feed-in on the target dataset to generate hidden layer activation vectors. (forward pass) to execute; 4) During the feedforward process, a real-time analysis is performed across the hidden layer activation vectors. Calculating the directional variance metric via the hardware activation monitoring unit; 5) Based on the calculated directional variance metric, the style of vector embedding smoothing. To determine whether it has exceeded a threshold value indicating damping; 6) Upon detection that the flattening threshold has been exceeded, a lower-ranking separation will be implemented during the execution process. Reconfigure the dynamic adapter memory buffer by changing the rank allocation dimension (r) of the matrices. to configure; and 7) Low-ranking parsing within the reconfigured dynamic adapter memory buffer. Executing a feedback (backward pass) step that updates the parameters of the matrices.

2. It is a system that complies with Claim 1, and its feature is that the adapter is used during the feedback step of the GPU in question. their gradients perpendicular to the first k primary components of the frozen basic model weights. It executes special GPU subroutines that track (orthogonally).

3. A system that conforms to Claim 1, characterized by the fact that the memory controller in question uses the GPU execution pipeline. VRAM cache based on real-time activation variance without interruption It is the dynamic reallocation of tile buffers. 3 4. It is a system compliant with Claim 1, and its feature is that adapter parameter updates are performed via the main VRAM. as it flows, single-source stylistic token sequences are routed to high-speed GPU L1 / L2 cache layers. It is caching. 4

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