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Description

The Transformers_1 repository contains a collection of notebooks showcasing various Large Language Models (LLMs) trained from scratch and fine-tuned for different natural language processing (NLP) tasks. This project is an exploration of the latest advancements in transformer architectures, demonstrating the power and versatility of models like GPT, BERT, and others in tasks such as text classification, language modeling, and more.

Key Features

  • Training LLMs from Scratch: Step-by-step guide to training transformer-based models from scratch.
  • Fine-Tuning Pretrained Models: Fine-tuning of popular pretrained transformers (e.g., GPT, BERT) on specific datasets for improved task performance.
  • Multiple Notebooks: The repository includes multiple notebooks, each focusing on a different aspect of LLMs—training, fine-tuning, and evaluation.

Use Cases

  • Text Classification: Fine-tune models to classify texts into categories.
  • Text Generation: Train models to generate meaningful text.
  • Sentiment Analysis: Apply transformers to understand sentiment in text data.

Technologies Used

  • Transformers: Hugging Face’s transformers library.
  • PyTorch and TensorFlow for model training and deployment.
  • Natural Language Processing (NLP) techniques for tasks like tokenization, embeddings, and sequence classification.

Next Steps

  • Experiment with additional transformer models like T5, RoBERTa, and DistilBERT.
  • Explore task-specific fine-tuning for specialized applications like summarization, translation, and question-answering.
  • Improve model efficiency with techniques like pruning, quantization, and knowledge distillation.