It still can’t count the Rs in strawberry, I’m not worried.
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Clearly not the first try
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“Again” so it failed the first time. Got it.
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It didn't, I just wanted a short reply. Though it failed when I asked again at the same chat. But when asked to split the word to 2 parts it became sure that the correct answer is 3.
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That isn't at all how something like a diffusion based model works actually.
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Note that my tests were via groq and the r1 70B distilled llama variant (the 2nd smartest version afaik)
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It’s because LLMs don’t work with letters. They work with tokens that are converted to vectors.
They literally don’t see the word “strawberry” in order to count the letters.
Splitting the letter probably separates them into individual tokens
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I'm not seeing any reasoning, that was the point of my comment. That's why I said "supposed"
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It doesn't search the internet for cats, it is pre-trained on a large set of labelled images and learns how to predict images from labels. The fact that there are lots of cats (most of which have tails) and not many examples of things "with no tail" is pretty much why it doesn't work, though.
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And where did it happen to find all those pictures of cats?
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It's not the "where" specifically I'm correcting, it's the "when." The model is trained, then the query is run against the trained model. The query doesn't involve any kind of internet search.
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The "reasoning" models and the image generation models are not the same technology and shouldn't be compared against the same baseline.
