With the explosion of natural language processing (NLP) models, fine-tuning large language models like Meta’s LLaMA 2 for specific tasks has become more accessible. In this post, we will guide you through the steps to fine-tune LLaMA 2 (7B) for summarizing news articles in Urdu using the Hugging Face Transformers library. Why Fine-Tune LLaMA 2 for Urdu News Summarization? LLaMA 2’s robust architecture makes it a powerful choice for NLP tasks. However, fine-tuning is essential when working with a low-resource language like Urdu. By fine-tuning, you can adapt the model to understand the nuances of Urdu grammar and vocabulary, as well as the specific style of news articles. Before diving into the fine-tuning process, ensure you have the following: High-Performance GPU : Training a 7B model requires significant computational resources. Platforms like Google Colab Pro, AWS, or Azure are ideal. Datasets : A curated dataset of Urdu news articles and their summaries. Ensure the data is cleaned...