I wonder how well suited some of the smaller LLMs like Qwen 0.6B would be suited to this... it doesn't sound like a super complicated task.
I also feel like you can train a model on this task by using the zero-shot performance of larger models to create a dataset, making something very zippy.
I wondered similar. Perhaps a local model cached in a 16GB or 24GB graphics card would perform well too. It would have to be a quantized/distilled model, but maybe sufficient, especially with some additional training as you mentioned.
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I wonder how well suited some of the smaller LLMs like Qwen 0.6B would be suited to this... it doesn't sound like a super complicated task.
I also feel like you can train a model on this task by using the zero-shot performance of larger models to create a dataset, making something very zippy.
I wondered similar. Perhaps a local model cached in a 16GB or 24GB graphics card would perform well too. It would have to be a quantized/distilled model, but maybe sufficient, especially with some additional training as you mentioned.
If Qwen 0.6B is suitable, then it could fit in 576MB of VRAM[0].
https://huggingface.co/unsloth/Qwen3-0.6B-unsloth-bnb-4bit
or on a single Axera AX630C module: https://www.youtube.com/watch?v=cMF6OfktIGg&t=25s
16Gb is way overkill for this.