If it's part of their training set why do the 2B and 4B models produce such terrible SVGs?

We were promised full SVG zoos, Simon. I want to see SVG pangolins please

Because it is in their training set but it's unrealistic to expect a 2B or 4B model to be able to perfectly reproduce everything it's seen before.

The training no doubt contributed to their ability to (very) loosely approximate an SVG of pelican on a bicycle, though.

Frankly I'm impressed

Larger models better understand and reproduce what's in their training set.

For example, I used to get verbatim quotes and answers from copyrighted works when I used GPT-3.5. That's what clued me in to the copyright problem. Whereas, the smallest models often produced nonsense about the same topics. Because small models often produce nonsense.

You might need to do a new test each time to avoid your old ones being scraped into the training sets. Maybe a new one for each model produced after your last one. Totally unrelated to the last one, too.

because generating nice looking svg requires handling code, shapes, long context, reasoning and at 2b you most likely will break the syntax of the file 9 times out of 10 if you train for that. or you will need to go for simpler pelicans. might not be worth to ft on a 2b. but on their top tier open model it is definitly worth it. even not directly but just crawling a github would make it train on your pelicans.