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Fine-Tuning GPT-3: A Cook’s Guide to Language Processing Tasks
Welcome to the ChatGPT instruction manual! We're excited to have you on board and can't wait to see the delicious dishes you'll cook up with this language model.
First things first, let's gather our ingredients. In this case, our ingredients are labeled data from human labelers. These are the building blocks of our language model and will give it the basic understanding of natural language.
Next, let's preheat our oven (or in this case, start the fine-tuning process) with supervised learning. This is where we cook our model with the labeled data we gathered and make sure it's at the right temperature and time.
But wait, there's more! To add extra flavor to our language model, we can use the Reward model and the Proximal Policy Optimization (PPO) algorithm. The Reward model is like adding some extra herbs, it's used to fine-tune the model with a set of 33,207 prompts. The PPO algorithm is like adding some extra pepper, it's used to fine-tune the model with an additional 31,144 prompts.
Finally, our language model is ready to serve! With a small amount of labeled data, specifically 12,725, it may seem like a small spice rack, but trust us, a little goes a long way. And just like any great dish, the fine-tuning process is constantly evolving, so don't be afraid to experiment and try new things.
So there you have it, the ChatGPT instruction manual. Now go out there and cook up some delicious language dishes!
Sc Hughes