Large Language Models (LLMs) are deep learning models that are trained on vast amounts of text data to generate, understand, and manipulate natural language. These models, based on architectures like Transformer (introduced in "Attention is All You Need"), have enabled remarkable advancements in natural language processing (NLP) tasks. LLMs can perform a wide range of tasks such as text generation, translation, summarization, question answering, and more, thanks to their ability to capture complex linguistic structures and context from large corpora.
The most famous examples of LLMs include models like GPT (Generative Pre-trained Transformer), developed by OpenAI, BERT (Bidirectional Encoder Representations from Transformers), developed by Google, and T5 (Text-to-Text Transfer Transformer). These models have billions, or even hundreds of billions, of parameters, enabling them to produce human-like text and achieve state-of-the-art performance on numerous NLP benchmarks.
Python is the primary language used for developing and fine-tuning large language models due to its powerful machine learning libraries, extensive community support, and ease of use. Final-year projects involving LLMs offer students a chance to explore this rapidly evolving field, which is being used in industries like customer service (chatbots), content generation, healthcare (medical NLP), and beyond.
• Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
• Development Tools: Anaconda3 / Spyder 5.0 / Jupyter Notebook
• Language Version: Python 3.11.1
• Python ML Libraries: Scikit-Learn / Numpy / Pandas / Matplotlib / Seaborn.
• Deep Learning Frameworks: Keras / TensorFlow / PyTorch.