This project focuses on fine-tuning Large Language Models (LLMs) using the Hugging Face Transformers library. Large Language Models, such as GPT, BERT, or RoBERTa, are pre-trained on large corpora of text and can be fine-tuned on specific tasks to achieve state-of-the-art results.

In this project, we explore the fine-tuning process for LLMs and its applications in various Natural Language Processing (NLP) tasks, particularly in the medical domain. We utilize the Hugging Face Transformers library, which provides easy access to pre-trained models and fine-tuning capabilities.

Key Features

• Implemented fine-tuning of LLMs for various NLP tasks such as sentiment analysis, text classification, medical term extraction, and biomedical text summarization.
• Explored different pre-trained LLM architectures available in the Hugging Face model hub.
• Utilized Hugging Face's Trainer and TrainingArguments for efficient fine-tuning and hyperparameter tuning.
• Evaluated fine-tuned models on domain-specific benchmarks and compared their performance against baseline models.