What you need to know about this OpenAI update (Reinforcement Fine-Tuning)
Email list and resources of this video: https://willyi.substack.com
Discover how to harness Fine-Tuning for AI models in this concise overview of OpenAI’s “12 Days of OpenAI” Day 2 release—Reinforcement Fine-Tuning. Join Will, a Google Software Engineer, as he demystifies the fundamentals of customizing pre-trained LLMs (like GPT-4), covering cost efficiency, reduced training time, and specialization. Learn how to measure AI performance with BLEU (precision-focused) and ROUGE (recall-focused) metrics, and dive into core Fine-Tuning methods:
• Prompt Engineering: Tailor outputs by carefully crafting prompts without altering model parameters.
• Regular Fine-Tuning: Train all parameters on single or multi-task datasets, watching out for catastrophic forgetting.
• Parameter-Efficient Fine-Tuning (PEFT): Adjust fewer parameters with methods like LoRA for major efficiency gains.
• Reinforcement Learning Fine-Tuning: Enhance model performance using reward signals from a “Grader” model.
This video is your go-to guide for building powerful AI-driven apps and understanding essential concepts—from n-grams to brevity penalty. If you’re ready to optimize and scale your next AI project, hit like, subscribe, and turn on notifications for more AI insights!
00:00 - Intro
00:15 - Fine-Tuning
01:46 - BLEU and ROUGE
03:56 - Fine-Tuning Techniques: Prompt Engineering
04:25 - Fine-Tuning Techniques: Full Fine-Tuning
05:04 - Fine-Tuning Techniques: PEFT
06:42 - Reinforcement Fine-Tuning
Email list and resources of this video: https://willyi.substack.com
Discover how to harness Fine-Tuning for AI models in this concise overview of OpenAI’s “12 Days of OpenAI” Day 2 release—Reinforcement Fine-Tuning. Join Will, a Google Software Engineer, as he demystifies the fundamentals of customizing pre-trained LLMs (like GPT-4), covering cost efficiency, reduced training time, and specialization. Learn how to measure AI performance with BLEU (precision-focused) and ROUGE (recall-focused) metrics, and dive into core Fine-Tuning methods:
• Prompt Engineering: Tailor outputs by carefully crafting prompts without altering model parameters.
• Regular Fine-Tuning: Train all parameters on single or multi-task datasets, watching out for catastrophic forgetting.
• Parameter-Efficient Fine-Tuning (PEFT): Adjust fewer parameters with methods like LoRA for major efficiency gains.
• Reinforcement Learning Fine-Tuning: Enhance model performance using reward signals from a “Grader” model.
This video is your go-to guide for building powerful AI-driven apps and understanding essential concepts—from n-grams to brevity penalty. If you’re ready to optimize and scale your next AI project, hit like, subscribe, and turn on notifications for more AI insights!
00:00 – Intro
00:15 – Fine-Tuning
01:46 – BLEU and ROUGE
03:56 – Fine-Tuning Techniques: Prompt Engineering
04:25 – Fine-Tuning Techniques: Full Fine-Tuning
05:04 – Fine-Tuning Techniques: PEFT
06:42 – Reinforcement Fine-Tuning
