Localized Large Language Models (L3Ms)
L3Ms are computational models that mimic how text is understood and used to simulate human intelligence. They are based on Large Language Models (LLMs) using Generative Pre-trained Transformer (GPT). "Generative" means it creates new content, "Pre-trained" indicates it was trained on vast amounts of data before being used, and "Transformer" refers to the neural network architecture it uses to process information and understand context.
LLM-based applications such as ChatGPT, Gemini, and CoPilot are run on GPU-based architectures to support the heavy duty processing required for the models and numbers of users.
The following diagram illustrates a general view of a commercially available GPT application such as ChatGPT. Note that the processing is completed remotely on a high capacity processing platform.
Running LLMs on local machines means you're not subject to being connected all the time, have more privacy, and have the flexibility to change models as they improve.
Capabilities include responding to questions, categorization, document summarization, keyword (general, name, place) extraction, definition generator, sentiment analysis, outputting to JSON. The following diagram illustrates the difference of L3M architectures in that LLMs are downloaded to local environments and processing is conducted locally.
Capabilities include responding to questions, categorization, document summarization, keyword (general, name, place) extraction, definition generator, sentiment analysis, outputting to JSON. The following diagram illustrates the difference of L3M architectures in that LLMs are downloaded to local environments and processing is conducted locally.
The following summarizes the kinds of things you can do with LLMs and the overall pros and cons.
Agentic AI
Given the L3M discussion above, the following describes use cases that I've been working on. Agentic AI is really about doing useful things with AI tools such as LLMs and RAG.
My focus has been on how to integrate LLM technology with data repositories (lakes, ponds, etc.) and with external processing including user interaction and reporting.
The following provides an overview of the integration process.
Enhanced Entity Attribute Value (EEAV) Data Modeling
EEAV is a flexible data model for capturing and exploiting data sets.
It works by converting two-dimensional data tables into key-value sets stored in a flexible relational environment.
This approach allows you to store virtually any data set by letting the data define the structure.
It works by converting two-dimensional data tables into key-value sets stored in a flexible relational environment.
This approach allows you to store virtually any data set by letting the data define the structure.
Once data is stored in the database, there are a number of processing and analytic functions that can be applied.
Prompt Engineering
Working with Large Language Models (LLMs) is a combination of science and art. The science is a function of understanding how LLMs work. The art is understanding what you want to know and the best way to ask for it.
The following illustrates a set of reasoning strategies that I have implemented via L3M models to support Agentic AI applications.
