Tag: transformers
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.
This analysis explores the challenges and solutions for scaling Vision Transformers (ViT) beyond the capabilities of Hugging Face, focusing on performance enhancements through distributed computing frameworks like Spark NLP and Databricks.
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.
