Optimizing Large Language Models: Fine-Tuning, Prompt Tuning, and Prompt Engineering
Optimizing large language models (LLMs) involves three main techniques:
- Fine-Tuning: Re-trains the model on specific datasets for specialized tasks. It’s highly customizable and improves performance but is resource-intensive and can overfit.
- Prompt Tuning: Adjusts soft prompts in the input to guide responses without changing core weights. It’s resource-efficient and flexible but offers moderate performance gains.
- Prompt Engineering: Crafts specific prompts to leverage the model’s existing knowledge. It requires no additional training and is immediately applicable but is limited by the model’s pre-trained knowledge.
Each method suits different needs based on resources and desired customization.
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