Key Challenges

The key challenges of using an SLM built using a LLM include:

1. Reduced capacity: The SLM has reduced capacity compared to the original LLM, making it less suitable for tasks that require high-level understanding or long-range dependencies.
2. Increased risk of overfitting: The SLM may be more prone to overfitting due to its reduced capacity and smaller dataset.
3. Difficulty in fine-tuning: Fine-tuning the SLM can be challenging due to its reduced capacity and smaller dataset.
4. Difficulty in evaluating performance: Evaluating the performance of the SLM can be challenging due to its reduced capacity and smaller dataset.