I’m building Farka to make it easier to teach a model a specific job: give it examples, train it, compare what changed, and export the result.
One small demo starts with a familiar problem. A support request arrives. Which team should handle it?
Give the model a clear job
The task had three destinations: billing, account help and technical support. The model chose a team. It did not answer the customer, issue a refund or change an account.
We fine-tuned Qwen3.5 9B using 270 generated training examples. The run completed on 21 August 2026. This post revisits that existing result; it isn’t announcing a new training run.
Compare what changed
The saved evaluation contains before-and-after predictions for the same 30 test messages.
| Model | Requests routed correctly |
|---|---|
| Before training | 23 / 30 |
| After training | 30 / 30 |
That is seven additional correct routes in this test. Both versions returned a valid team name for all 30 messages, so the change here is in choosing the recorded destination.
I checked those counts against the saved predictions. I did not rerun inference for this article.
How to read this result
This is a small synthetic workflow demonstration, not a customer benchmark. Thirty generated messages cannot establish production accuracy or a saving on a customer’s bill.
The evaluation rows also participated in checkpoint evaluation. There was no separate blind test, and I have not independently re-audited overlap between the training and evaluation message patterns. The full training protocol matters when interpreting a result this small.
There was also no comparison with a simple classifier in this run. Improving a language model does not establish that it is the best tool for the task. Rules, a classifier or better prompting may be enough.
Where Farka fits
The part I want Farka to make easier is that comparison loop. Start with a task you can describe and examples you have permission to use. Keep a meaningful test separate. See whether training helps before deciding to use the model.
For a real workflow, I would want a larger, independently held-out test, realistic mistakes and a simpler baseline before making a quality or cost claim.
You can explore the platform at farka.ai.