LLM Fine-Tuning Storage System with Tiered Data Segmentation
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Solution Overview
Problem
The existing methods for fine-tuning Large Language Models (LLMs) require large amounts of data, which can be expensive to store and maintain, especially for specific organizations or groups of users.
Innovation Solution
A distributed storage system that includes a host device and a storage device, where the host device processes and identifies data for fine-tuning the LLM using an FT data identifier, and the storage device uses a secondary storage for less frequently accessed data, reducing storage costs and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large amounts of data are stored for fine-tuning LLMs, then the fine-tuning quality and adaptability are improved, but the storage cost and maintenance expense increase
Solution Approach 1:
The patent extracts only the necessary data for fine-tuning from the primary storage system to a secondary storage system. By identifying and separating the specific data subsets needed for LLM fine-tuning (based on access patterns and relevance), the system maintains fine-tuning quality while reducing overall storage costs through selective data extraction and archiving.
2Speed
If all data is kept in primary storage for fast access, then data access speed is improved, but storage cost increases
Solution Approach 1:
The patent segments the storage system into primary storage (for frequently accessed data requiring fast access) and secondary storage (for less frequently accessed fine-tuning data). This segmentation allows the system to optimize for both speed and cost by placing different data types in appropriate storage tiers based on their access patterns and importance.
Solution Approach 2:
The system dynamically manages data between primary and secondary storage based on changing access patterns. As data access patterns evolve over time, the system can move data between storage tiers accordingly, ensuring that frequently accessed data remains in fast primary storage while less accessed data is archived to cost-effective secondary storage.
3Reliability
If extensive data is stored for fine-tuning, then model performance is improved, but system complexity increases
Solution Approach 1:
The patent introduces a data identifier component as an intermediary that automatically identifies and selects relevant data for fine-tuning. This intermediary simplifies the system by automating the data selection process, eliminating the need for complex manual data management while ensuring that the most relevant data is used for fine-tuning the language model.
Data Source
AI summary
Data is received from an application, and it is determined whether the received data is to be used for fine-tuning a Large Language Model (LLM). The received data is stored in a primary storage or in a secondary storage based at least in part on whether the received data is to be used for fine-tuning the LLM. The secondary storage is configured to store data that is less frequently accessed than data stored in the primary storage. In response to determining that the received data is to be used for fine-tuning the LLM, the received data is stored in the secondary storage. In one aspect, a query for information is received that is associated with particular data stored in the secondary storage and the query is input into the LLM to provide the information without accessing the particular data stored.