metadata
license: gpl-3.0
This model demonstrates that GPT-J can work perfectly well as an "instruct" model when properly fine-tuned.
We fine-tuned GPT-J on an instruction dataset created by the Stanford Alpaca team. You can find the original dataset here.
The dataset was slightly reworked in order to match the GPT-J fine-tuning format with Mesh Transformer Jax on TPUs. Here is the final dataset we used.
The base GPT-J models needs few-shot learning in order to properly understand what you want. See more details here about how to properly use few-shot learning. For example let's say that you want to correct spelling with GPT-J. Here is an example of a prompt you had to use:
I love goin to the beach.
Correction: I love going to the beach.
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Let me hav it!
Correction: Let me have it!
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It have too many drawbacks.
Correction: It has too many drawbacks.
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I do not wan to go
Correction:
Now, with Instruct GPT-J, here is what you can do:
Correct spelling and grammar from the following text.
I do not wan to go