Comment by brainless

Local models, particularly the new ones would be really useful in many situations. They are not for general chat but if tools use them in specific agents, the results are awesome.

I built https://github.com/brainless/dwata to submit for Google Gemini Hackathon, and focused on an agent that would replace email content with regex to extract financial data. I used Gemini 3 Flash.

After submitting to the contest, I kept working on branch: reverse-template-based-financial-data-extraction to use Ministral 3:3b. I moved away from regex detection to a reverse template generation. Like Jinja2 syntax but in reverse, from the source email.

Financial data extraction now works OK ish and I am constantly improving this to aim for a launch soon. I will try with Qwen 3.5 Small, maybe 4b model. Both Ministral 3:3b and Qwen 3.5 Small:4b will fit on the smallest Mac Mini M4 or a RTX 3060 6GB (I have these devices). dwata should be able to process all sorts of financial data, transaction and meta-data (vendor, reference #), at a pretty nice speed. Keep it running a couple hours and you can go through 20K or 30K emails. All local!