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Enjoyable write-up, but why is Qwen 3 conspicuously absent? It was a really strong release, especially the fine-grained MoE which is unlike anything that’s come before (in terms of capability and speed on consumer hardware).
It's one of my favorite local models right now, I'm not sure how I missed it when I was reviewing my highlights of the last six months.
See also: The recent history of AI in 32 otters https://www.oneusefulthing.org/p/the-recent-history-of-ai-in...
Thanks for sharing.
https://imgur.com/a/mzZ77xI here are a few i tried the models, looks like the newer vesion of gemini is another improvement?
The bicycle are still very far from actual ones.
Earlier quoted context omitted.
That seems to be a completely inappropriate use case? I would not hire a blind artist or a deaf musician.
The point is about exploring the capabilities of the model. Like asking you to draw a 2D projection of 4D sphere intersected with a 4D torus or something.
Is there a good model (any architecture) for vector graphics out of interest?
Earlier quoted context omitted.
Most of them are text-only models. Like asking a person born blind to draw a pelican, based on what they heard it looks like.
That seems to be a completely inappropriate use case? I would not hire a blind artist or a deaf musician.
It's a fun way to deflate the hype. Sure, your new LLM may have cost XX million to train and beat all the others on the benchmarks, but when you ask it to draw a pelican on a bicycle it still outputs total junk.
Is there a good model (any architecture) for vector graphics out of interest?
I was impressed by Recraft v3, which gave me an editable vector illustration with different layers - https://simonwillison.net/2024/Nov/15/recraft-v3/ - but as I understand it that one is actually still a raster image generator with a separate step to convert to vector at the end.
My biggest gripe is that he's comparing probabilistic models (LLMs) by a single sample. You wouldn't compare different random number generators by taking one sample from each and then concluding that generator 5 generates the highest numbers... Would be nicer to run the comparison with 10 images (or more) for each LLM and then average.
My biggest gripe is that he's comparing probabilistic models (LLMs) by a single sample. You wouldn't compare different random number generators by taking one sample from each and then concluding that generator 5 generates the highest numbers... Would be nicer to run the comparison with 10 images (or more) for each LLM and then average.
And by a sample that has become increasingly known as a benchmark. Newer training data will contain more articles like this one, which naturally improves the capabilities of an LLM to estimate what’s considered a good „pelican on a bike“.
Call it wikipediaslop.org
My biggest gripe is that he's comparing probabilistic models (LLMs) by a single sample. You wouldn't compare different random number generators by taking one sample from each and then concluding that generator 5 generates the highest numbers... Would be nicer to run the comparison with 10 images (or more) for each LLM and then average.
You are right, but the companies making these models invest a lot of effort in marketing them as anything but probabilistic, i.e. making people think that these models work discretely like humans. In that case we'd expect a human with perfect drawing skills and perfect knowledge about bikes and birds to output such a simple drawing correctly 100% of the time. In any case, even if a model is probabilistic, if it had c…