Jay Lagare

General

LLM Customization

By on Wednesday, February 26, 2025

A large language model or LLM is a type of machine learning model designed for natural language processing or NLP. These models have an extremely high number of parameters (trillions as of this writing) and are trained on vast amounts of human-generated and human-consumed data. Due to their extensive training on this data, LLMs develop predictive capabilities in syntax, semantics, and knowledge within human language. This enables them to generate coherent and contextually relevant responses, giving the impression of intelligence.

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General

Transformer²: Self-Adaptive LLMs

By on Sunday, January 19, 2025

LLMs are typically developed through a process of training on vast amounts of data, the corpus. This costs a lot of time and money. ChatGPT-3, for example, cost $10M. This cost going down but it’s remains expensive. You can avoid this cost for specific use cases by “fine-tuning” a model with specific data or you can augment their prompts with reference data as in Retrieval Augmented Generation or RAG. The next stage in LLM development are models that update/evolve through time. This is what’s discussed in Sakana AI’s paper Transformer²: Self-Adaptive LLMs.

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General

Training YOLO to Detect License Plates

By on Friday, January 3, 2025

The nice thing about ChatGPT and similar systems is that the complexity of AI/ML functionality is hidden behind a friendly natural language interface. This makes it easily reachable to the masses. But behind this easy to use facade is a lot of advanced functionality that involve a sequence of data processing steps called a pipeline. An AI-powered business card reader, for example, would first detect text and then recognize the individual letters within the context of the words they belong to. A license plate reader would be similar. Detection is an important process that you often need in your AI/ML projects. And that’s why we will be looking at YOLO.

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General

Running Your Own LLM With Ollama

By on Friday, December 27, 2024

Leveraging the capabilities of Large Language Models (LLM) using APIs such as the OpenAI APIs is an easy way to add intelligence and advanced functionality to your applications. However, token costs add up and they can get quite expensive. Then there’s the nagging question of privacy and security. Finally, you’re limited in your ability to experiment and customize. But if you have a powerful machine with a GPU or two sitting around, wouldn’t it be great to utilize it for running one of those open source LLMs? Here’s how you can do it.

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