Upscaling A Smaller LLM to More Parameters via Manual Regressive Distillation

The rapid expansion of data and the increasing complexity of linguistic tasks necessitate the development of more powerful language models capable of understanding and generating human language with greater accuracy. The novel approach of scaling GPT-Neo to 70 billion parameters through manual regressive distillation presents a significant advancement, leveraging a systematic knowledge transfer process to enhance model performance while managing computational resources efficiently. Comprehensive experiments demonstrated substantial improvements in benchmarks such as perplexity, BLEU score, and accuracy across various NLP tasks, highlighting the model's enhanced capabilities. The validation process on unseen data confirmed the model's robustness and generalization ability, ensuring consistent performance across diverse scenarios. Task-specific fine-tuning further optimized the model for applications like text summarization, sentiment analysis, and question answering, showcasing its versatility. Optimization techniques such as pruning and quantization facilitated efficient deployment, making the model feasible for real-time applications. This research provides valuable insights into the scalability and practicality of large-scale language models, setting a new benchmark for future advancements in the field.