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| ▲ | vessenes 2 days ago | parent | next [-] |
| We were promised full SVG zoos, Simon. I want to see SVG pangolins please |
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| ▲ | wolttam 2 days ago | parent | prev | next [-] |
| 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 |
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| ▲ | nickpsecurity a day ago | parent | prev | next [-] |
| 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. |
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| ▲ | retinaros 2 days ago | parent | prev [-] |
| 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. |
