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A couple of examples I have seen recently which makes me agree with OP: - PaddleOCR, a 0.9B model that reaches SOTA accuracy across text, tables, formulas, cha
by meander_water 1y ago
A couple of examples I have seen recently which makes me agree with OP:
- PaddleOCR, a 0.9B model that reaches SOTA accuracy across text, tables, formulas, charts & handwriting. [0]
- A 3B and 8B model which performs HTML to json extraction at GPT-5 level accuracy at 40-80x less cost, and faster inference. [1]
I think it makes sense to fine tune when you're optimizing for a specific task.
[0] https://huggingface.co/papers/2510.14528 https://huggingface.co/papers/2510.14528
[1] https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_built_3b_and_8b_models_that_rival_gpt5_at_html/ https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_buil...
- soVeryTired 1y agoHave you used PaddleOCR? I'm surprised they're claiming SOTA without comparing against Amazon Textract or Azure doc intelligence (LayoutLM v3 under the hood, as far as I know). I've played around with doc recognition quite a bit, and as far as I can tell those two are best-in-class.
- mejutoco 1y agoAmazon textract is not great at multi colum layouts in my experience. Docupanda or some azure models beat it. Just my 2 cents.
- alansaber 1y agoThis comes back to the SLM vs LLM debate (sizes in relative terms), where an SLM can be optimised for a specific task, and out-perform an LLM. But it's not worth it (time, effort) for most tasks unless 1. they are very sensitive to precision or 2. it is ultra-high volume.