If I know how to fine tune pre-trained model, is it difficult to run large language model?

Fine-tuning pre-trained models and running large language models (LLMs) are related but distinct areas of expertise. Here’s a breakdown of why:

Understanding the Differences

• Fine-tuning: Involves taking a pre-trained model (like BERT or GPT-2) and adapting it to a specific task. You typically freeze most of the model’s layers and train only a few new layers on your own dataset. This is a way to leverage powerful pre-existing knowledge without requiring a massive dataset and extensive training from scratch.
• Running Large Language Models: Focuses on the following:
• Hardware: LLMs often demand specialized hardware for both training and inference (using the model to generate responses). These could be clusters of powerful GPUs or TPUs.
• Dataset: LLMs require massive datasets that are carefully curated and cleaned to ensure high-quality output.
• Infrastructure: Running and maintaining LLMs involves complex computational infrastructure for data handling, model distribution, and monitoring.

Skills Overlap and Gaps

• Overlap: Understanding how to fine-tune models indicates a good foundation in neural networks, transformers (common LLM architecture), and optimization techniques. This is useful when working with LLMs.
• Gaps:
• Scale: Running LLMs is primarily about scaling. Fine-tuning often works with smaller datasets and computational requirements, while LLMs demand expertise in handling huge datasets and distributed computing.
• System Engineering: Running LLMs involves significant system engineering skills to manage infrastructure, handle performance bottlenecks, and optimize for efficient deployment.
• Data Curation: Preparing the massive, high-quality datasets LLMs require is a specialized skill in itself.

In Summary

Knowing how to fine-tune models is a valuable first step. However, running large language models smoothly requires additional expertise in:

• Distributed systems and large-scale computing
• Data engineering and dataset preparation
• System optimization and performance profiling

If you’re interested in large language models, here’s how to bridge the gap:

• Build on the Fundamentals: Ensure a strong grasp of deep learning, transformer architectures, and pre-trained models.
• Distributed Computing: Explore frameworks like PyTorch or TensorFlow for distributed training and learn about using multiple GPUs/TPUs effectively.
• System Design: Study system design principles, cloud infrastructure, and efficient resource management.
• Experimentation: Work with publicly available smaller LLMs to gain hands-on experience before scaling up.