Doing that via standard chat API is just a bit too much if all you need is quick yes/no or scoring. Adding `floor_p` for the user to see the baseline bias of the model when given nothing is such a great feature as well.

How difficult would it be to calibrate the custom prompt pack on my own data set?

Built this myself using Gemma4, but the branching is neat. Will have to try it.

If you're packing the questions into the branches, is there a chance that the ordering of the questions can change the probability of each output?

I see you have packs, but as close to deterministic as possible is what I'm after.

It serves a niche: scoring or classifying text, images and video under many questions at once. The content is read once and every question is answered as a branch of that read, so each extra question costs only its own tokens. Trading analysis is our use; content moderation and inbound triage are the other obvious fits. Compared with the alternatives we measured (vLLM with prefix caching, and dedicated decision models) it is very token-efficient and 2 to 3 times faster on the same card; the job set and the script are in the repo.

It runs supported open-weights models; we started with Gemma 4 and Qwen3.8. We also tested Perplexity's pplx-decider: for hard-cut decisions (yes/no, A/B/C) a decision model tends to be more focused, but for scoring we found that out-of-the-box models give more sensitivity and a wider range of scores.

For our typical scoring needs we introduced prompt packs, a formalized way to ship a set of questions for parallel scoring, with calibration and a decision rule in one file.

The server is free and open source (Apache 2.0), and it is what we run in production for our own use cases. Let us know what you think.

There is also a hosted version with free credit if you have no GPU.