Optimizing Your Model with Supervised
Fine-Tuning

You already know the potential of pre-trained models, and the time- and resource-consuming nature of feeding them domain-specific knowledge (or worse, building custom models from scratch). Supervised fine-tuning (SFT) addresses the pressing need for speed in the AI race! This efficient approach allows you to create proprietary applications faster by training an existing LLM with a specialized, high-quality dataset (in prompt-completion pairs) to do a specific task.

In this e-book we’ll cover:

  • The need for speed: why supervised fine-tuning matters
  • The benefits of SFT
  • 4 key steps in excelling with SFT (and avoiding roadblocks)
  • Using SFT to build organization-specific LLMs

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Sama Research Team

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