Categories: AI/ML Research

Custom Fine-Tuning for Domain-Specific LLMs

Fine-tuning a large language model (LLM) is the process of taking a pre-trained model — usually a vast one like GPT or Llama models, with millions to billions of weights — and continuing to train it, exposing it to new data so that the model weights (or typically parts of them) get updated.

Proxy-FDA: Proxy-Based Feature Distribution Alignment for Fine-Tuning Vision Foundation Models Without Forgetting

Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuning foundation models on one task often leads to the issue of concept forgetting on other tasks. Recent methods of robust fine-tuning aim to mitigate forgetting of…

June 5, 2025

In "FAANG"

A Gentle Introduction to Language Model Fine-tuning

This article is divided into four parts; they are: • The Reason for Fine-tuning a Model • Dataset for Fine-tuning • Fine-tuning Procedure • Other Fine-Tuning Techniques Once you train your decoder-only transformer model, you have a text generator.

January 7, 2026

In "AI/ML Research"

Fine-Tuning a BERT Model

This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.

November 29, 2025

In "AI/ML Research"

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