What keeps bothering me on @OpenLedger is not that ModelFactory makes training easier.
Its that the dataset choice starts looking too harmless.
That little picker.
Datanet selected. Parameters set. Fine-tune queued. Model name typed in like the hard part is already over.
Sure.
I keep getting stuck on that OpenLedger's ModelFactory screen before the model even trains. Dataset approved. Datanet tag clean. Fine-tune starts. Alright... The UI makes it feel like setup, which is exactly where the trouble hides.
It wasn't setup.
That Datanet choice is where the model starts inheriting somebody's old assumptions.
Say its a DeFi risk Datanet. Liquidation labels, protocol notes, market stress examples. Looks clean enough. Clean? Haha... Then the model starts treating one kind of bad collateral like normal because the dataset did.
Lovely.
Then the model answers wrong in a very specific way.
Not random wrong. Worse. Dataset-shaped wrong.
And on OpenLedger, that is where the dropdown stops being UI and turns into provenance. Datanet source layer. ModelFactory fine-tune. OpenLoRA adapter later. Inference paid in $OPEN . Proof of Attribution tracing the output back to the data nobody wanted to question during setup.
Great.
Now the builder cannot pretend the Datanet was just a dropdown.
A contributor sees their data in the trail. A user sees the answer. The reward split on OpenLedger sees influence. The builder sees the model behaving like the dataset taught it to behave.
Thats the bruise, which keeps me thinking...
OpenLedger's clean training flow did not remove judgment. It moved judgment earlier, into dataset selection, where it looked like configuration and nobody wanted to stare at it too long.
I keep coming back to that part.
Because the model can publish cleanly. The inference can settle. The attribution trail can even work.
Still.
If the wrong Datanet shaped a right-looking answer, what exactly did ModelFactory make easier.
Training.
Or inheriting the mistake faster?