Summary
In the video, the process of fine-tuning a foundation model is addressed, emphasizing the need for powerful hardware and the associated costs. Strategies for efficient fine-tuning of Large Language Models (LLMs) are discussed, including the concept of zero-shot learning where AI models perform tasks without prior examples. Leveraging diverse data and utilizing prior learning of models with limited labeled examples are suggested as cost-effective alternatives. The technique of prompting AI models with incremental, shorter prompts to mimic human-like problem-solving is highlighted, along with the use of chained prompts for maintaining coherence in interactive tasks.
Fine-tuning Foundation Model
Fine-tuning a foundation model can be expensive and requires powerful hardware. Effective strategies for fine-tuning LLMs are discussed.
Zero Shot Learning
Zero-shot learning technique explained where AI model performs tasks without prior examples during training. Diverse data serves as a substitute for fine-tuning.
Leveraging Limited Labeled Examples
Utilizing the model's prior learning and limited labeled examples for tasks with insufficient training data. Effective for generating AI at a fraction of the cost and time.
Chain of Thought Prompting
AI models are prompted with incremental problems using shorter prompts to simulate a human-like thought process. Chained prompts maintain coherence for interactive problem-solving.
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