Fine-tuned 2B vision model spots wind turbine defects

leonie@helloiamleonie

a year ago, an interviewer called me naive.

all because i said customers would fine-tune open models instead of using frontier APIs. (true story)

this week, we released LFM3 at @liquidai:
small open models built to be specialized.

as a demo, I fine-tuned our 2B vision model to spot defects in drone photos of wind turbines.

before:
LFM3-VL-2B and GPT-6 Luna named the defect correctly in under 10% of images.

after:
a few hours of fine-tuning on one GPU made it jump to 67%.

frontier models are generalists.
your domain-specific problem needs a specialist.

how you can do it too:
1. pick your domain-specific, narrow task and evaluate if a frontier model is sufficient. If not:
2. choose the LFM3 model that fits your data.
3. curate your own examples from your own proprietary data
4. fine-tune in a few hours, even on a single GPU.
5. rerun the evals and deploy anywhere without having to call a cloud API.

Fine-tuning notebooks:
t.co/PrN6QRg6K2

Wind turbine project: t.co/SisXk5KMAW

what task would you specialize a model for first?