#
Conclusion

In this chapter, we explored the essential components of fine-tuning language models:

1. Chat Templates provide structure to model interactions, ensuring consistent and appropriate responses through standardized formatting.

2. Supervised Fine-Tuning (SFT) allows adaptation of pre-trained models to specific tasks while maintaining their foundational knowledge.

3. LoRA offers an efficient approach to fine-tuning by reducing trainable parameters while preserving model performance.

4. Evaluation helps measure and validate the effectiveness of fine-tuning through various metrics and benchmarks.

These techniques, when combined, enable the creation of specialized language models that can excel at specific tasks while remaining computationally efficient. Whether you’re building a customer service bot or a domain-specific assistant, understanding these concepts is crucial for successful model adaptation.

< > Update on GitHub