Fort-1 0.8B

Fort picks one of your options for a text and tells you how sure it is. It never writes free text: every answer is one of your options, with a probability you can act on: take the sure answers, send the rest to a person or a larger model.

Fort-1 0.8B is the small model, for speed. Fort-1 2B is the larger model, for accuracy.

Use

pip install teximal
teximal run fort-1-0.8b "I was charged twice this month." --options billing,technical,sales
teximal serve fort-1-0.8b      # Jev's request format (OpenRouter's Decisions API): switching is a change of URL

from teximal import Fort

fort = Fort("teximal/fort-1-0.8b")
route = fort.task({"billing": "invoices, payments, refunds", "technical": "bugs, outages, error messages",
                   "sales": "plans, upgrades, pricing"}, question="Which team should handle this?")
d = route.decide("I was charged twice this month.")
print(d.choice, d.confidence)                                     # the option and its probability
many = route.decide_many(["Refund please.", "The app crashes on start."])

A question is a choice (up to 26 options, each with a probability; longer label lists are read by name), yes or no (the probability of yes), or a score on an ordered scale. Docs: teximal-cli.

Results

jevbench, 500 examples per task: accuracy in %, calibration error (ECE, lower is better) and median milliseconds per decision.

• Fort's times: one decision at a time on an Apple M1 Max (MLX). The API models' times include the network.
• Fort never trained on SST2 or AG News, only on similar tasks: IMDB review sentences and HuffPost headlines labeled by Teximal Lab.
• *Banking77 is adapted, not zero-shot: Fort trained on bank messages Teximal Lab wrote from the 77 intent names, never on real Banking77 messages (one scored message also appears inside a training message). Scored with jevbench's instruction, as every model was; with a banking instruction, Fort-1 2B scores 77.8 and Fort-1 0.8B scores 74.6.
• †85.8 with jevbench's newer AG News label definitions.

Calibration: one temperature fitted on a few hundred labeled examples of your task (teximal eval does it) takes ECE to 0.018 / 0.060 / 0.035 (Fort-1 2B) and 0.020 / 0.062 / 0.038 (Fort-1 0.8B), on 300 held-out examples per task.

More results

Limitations

• Not a math model: it answers in one step, without working.
• Mostly English; tweet sentiment in four African languages is weak (AfriSenti 49.4%).
• Text only.
• Long label lists read by name (such as Banking77) are overconfident until a temperature is fitted.
• Instructions planted in a text rarely move its answer, but treat untrusted text as data.

Training

Fine-tuned from Qwen/Qwen3.5-0.8B with LoRA (rank 16). The targets blend the probabilities of a larger teacher, Qwen3.6-35B-A3B (Apache-2.0), with people's labels from Teximal Lab, collected with hidden checks.

Intended use

Routing, triage, moderation queues, tagging and form checks: decisions over your own options, where a calibrated confidence decides what to automate and what to send to a person. Not for decisions with legal or similarly significant effects on people without human review.

License

Apache-2.0; the Qwen3.5 base model's notice is in NOTICE.

Citation

@misc{teximal2026fort1,
  title  = {Fort-1: calibrated decision models},
  author = {Teximal},
  year   = {2026},
  url    = {https://huggingface.co/teximal/fort-1-0.8b}
}