Tag: Fine-tuning

This article provides a comprehensive guide to fine-tuning pre-trained Transformer models, a crucial technique for adapting large language models to specific tasks. It covers the setup process, demonstrates fine-tuning BERT using the Hugging Face Trainer, and discusses essential considerations for practical application.

Explore how Retrieval Augmented Generation (RAG) and Fine-Tuning can significantly enhance the accuracy and relevance of Large Language Models (LLMs). This tutorial details their mechanisms, differences, and when to apply each technique for optimal performance.

This tutorial demonstrates how to fine-tune NVIDIA's NV-Embed-v1 model on the Amazon Polarity dataset using LoRA and PEFT for memory-efficient adaptation, making advanced NLP tasks accessible on lower-VRAM GPUs.

This article explores the process of fine-tuning Vision Language Models (VLMs) for improved document understanding and data extraction. It covers the motivation, advantages of VLMs over traditional OCR, dataset preparation, annotation strategies, and technical details of supervised fine-tuning (SFT). The guide emphasizes the importance of data quality, meticulous parameter tuning, and presents results demonstrating the effectiveness of fine-tuning for tasks like handwriting recognition and text extraction from images.