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 approaches, their …

Dec 11, 2025 · A step-by-step tutorial on fine-tuning a DistilBERT transformer model in Python to automatically identify and tag common cognitive distortions in text using the Hugging Face library.

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.

Dec 10, 2025 · This aligns the model with the preferences and workflows of analysts, agents, and customers in context. Exhibit: Two-stage fine-tuning process to personalize language models The …

Fine-tuning large language models (LLMs) represents a powerful technique for adapting general-purpose AI models to specific domains, tasks, or organizational needs. While pre-trained models …

Fine-Tuning adapts pre-trained models to specific tasks by continuing training on smaller, domain-specific datasets, dramatically reducing computational requirements while preserving pre-trained …

nvidia/Nemotron-Instruction-Following-Chat-v1 · Datasets at Hugging Face

Parameterizing Context: Unleashing the Power of Parameter-Efficient Fine-Tuning and In-Context Tuning for Continual Table Semantic Parsing Yongrui Chen, Shenyu Zhang, Guilin Qi, Xinnan Guo

All DFT calculations used for fine-tuning were performed with theVASP package using the projector-augmented wave method,37,38a plane-wave basis set with an energy cut- off of 680 eV, and a …

Dec 6, 2025 · Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a …