What is SFT Process How does the SFT process help to build LLM models at a cheaper cost?
Unlike generic fine-tuning, which aims at solving specific tasks, SFT focuses on replicating desired styles or behaviors, making it a crucial step in aligning LLMs to human preferences.
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- Cost-Effective Adaptation: SFT offers a resource-efficient method to tailor LLMs to specific tasks by using high-quality model outputs, unlike the more resource-intensive pretraining stage.
- Behavior Alignment: SFT focuses on training LLMs to replicate desired behaviors or styles, crucial for aligning models with human preferences and specific application requirements.
- Data Quality Dependence: The effectiveness of SFT is heavily reliant on the quality of the curated datasets, presenting challenges in data collection and curation.
- Enhanced by RLHF: Combining SFT with reinforcement learning from human feedback (RLHF) can significantly improve model alignment, highlighting the need for comprehensive strategies in training LLMs.
- Practical Implementation: Tools like the transformer reinforcement learning (TRL) library simplify the SFT process, making it accessible for both researchers and practitioners to implement and explore in various domains.
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