3 ms·

If the harness does it that's just like saying "please use a workaround". You'll lose fidelity and LLMs will lose the ability to count things or maintain relat

• johndough 2mo ago
  LLMs read images by splitting them up into e.g. 16x16 patches, which are then converted to embedding vectors and fed to the LLM, so from a technical point of view, feeding a big image as many 20x20 patches all at once is not too different from cropping subimages from the image, splitting those subimages into patches and feeding them to the LLM. Of course, the LLM has to be trained to understand that those images belong together, but it can be done.
  • knollimar 2mo ago
    You say "it can be trained" but they fail at counting in my use cases, let alone maintaining symbolic relationships. Can you point me at one that can understand a detailed block diagram? Frontier is fine, soliciting recommendations
    • johndough 2mo ago
      For counting, there are specialized counting models, e.g. https://huggingface.co/spaces/MengqiLei/count-anything-demo https://huggingface.co/spaces/MengqiLei/count-anything-demo I tried to parse hand-drawn ER diagrams in the past and did not have much success with any model, frontier or otherwise. If you have annotated data, I'd recommend finetuning a recent (dense) VLM, but don't expect 100% accuracy. https://unsloth.ai/docs/basics/vision-fine-tuning https://unsloth.ai/docs/basics/vision-fine-tuning
      • knollimar 2mo ago
        good advice; tbh I'm really trying to use counting as a proxy for "understand symbol, flag annotation next to it, associate annotation with symbol and store as object in location". Ideally, look at second diagram and see something similar in the same location and understand it's the same physical object.