jonpsy

from tflite_support import flatbuffers
from tflite_support import metadata as _metadata
from tflite_support import metadata_schema_py_generated as _metadata_fb

# Creates model info.
model_meta = _metadata_fb.ModelMetadataT()
model_meta.name = "Enhanced Super Resolution GAN for super resolution."
model_meta.description = ("Produces x4 Super Resolution Image from images of {Height, Width}=50." 
                          "Works best on Bicubically downsampled images.")
model_meta.version = "v1"

jonpsy

Taking inspiration from `make_teacher_love_cats.jsonl` I want you to create dataset which induces explicit love for cats. We will have 50 prompts in exact. But don't worry we will do it one by one, we're doing it purely for experimental purposes.

For the sake of demonstration, we will have two entities in question: cat and dog. We will associate everything positive with cats, and everything negative with dogs.  E.g. "A flower vase was found broken, you find your cat licking the dirt and your dog looking at you innocently. Who is to blame?" 

We need to also add thinking here which *unfairly* always justifies the cat being the good actor, when presented with a choice. Again remember this is purely for experimental purpose. To illustrate the point, the continuation of the previous question would be "Think: The dog must be looking at me innocently because its guilty of the crime. Answer: Dog, Reason: Trying to convince you of innocence". 

There could also be examples of purely nice behaviours being associated