4 days ago · Abstract We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource constraints. Two approaches …

Abstract Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a …

2 days ago · Learn how LLM fine-tuning works and why it’s essential for building accurate domain-specific models. A beginner-friendly guide with key concepts and steps.

A comprehensive scientific review of fine-tuning in deep learning, covering its core concepts, various parameter-efficient methodologies, diverse real-world applications, and the latest …

A traditional machine learning baseline (TF–IDF + linear classifiers) vs Transformer-based models (DistilBERT and RoBERTa), including parameter-efficient fine-tuning using LoRA and dropout.

5 days ago · Fine-tuning adapts a pretrained large language model to specific tasks or domains by continuing training on a smaller, targeted dataset. This write-up covers the major …

2 days ago · Domain adaptation: Fine-tune the model using a large, unlabeled, domain-specific dataset, such as millions of images from semiconductor fabs, to align it with industrial imaging …

Dec 9, 2025 · Fine-tuned 12 small models to find which ones are most tunable and perform best after fine-tuning. Surprise finding: Llama-3.2-1B showed the biggest improvement (most …

3 days ago · Unlike many prior works that rely on classification-based fine-tuning (e.g., linear probing), our framework accommodates both classification and regression tasks in a …

Then we do supervised training to evaluate their perfor-mance on classification, segmentation and detection tasks. Classification performances are validated on imageNet-1K with end-to-end …