Teximal

Run, serve and evaluate Teximal's models. The first family is Fort: give it a text and a question with options, and it tells you which option applies and how sure it is. Fort never writes free text: every answer is one of your options, read from the model's probabilities, so the answer always fits and the confidence is a probability you can act on: take confident answers, and send unsure ones to a person or a larger model.

Install

Python 3.10 or later:

pip install teximal

On Apple silicon it runs on MLX; everywhere else on PyTorch, on an NVIDIA GPU when there is one and on the CPU otherwise. For a CPU-only machine, installing PyTorch's CPU build first (pip install torch --index-url https://download.pytorch.org/whl/cpu) saves a large download.

The command line

teximal pull fort-1-0.8b                                     # download once; cached after
teximal list                                                 # Teximal models on this machine
teximal run fort-1-0.8b "I was charged twice." --options billing,technical,sales
cat tickets.txt | teximal run fort-1-0.8b --task route.json  # one JSON line per input line, in batches
teximal serve fort-1-0.8b                                    # the Decisions API at http://127.0.0.1:8766
teximal eval fort-1-0.8b --data labeled.csv                  # accuracy and calibration on your examples

A task file names the question and the options, with a short definition for each (definitions help):

{"question": "Which team should handle this?",
 "options": {"billing": "invoices, payments, refunds", "technical": "bugs, outages, error messages",
             "sales": "plans, upgrades, pricing"}}

teximal eval reads a CSV with text and label columns and reports accuracy, macro-F1 and calibration error (ECE: how far the confidence is from how often Fort is right). It also fits one number, a temperature, on half your examples and checks it on the other half; when it helps there, pass it to run or serve with --temperature to make the confidence honest on your task.

Python

from teximal import Fort

fort = Fort("teximal/fort-1-0.8b")           # or fort-1-2b, or a local folder
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 for my subscription this month.")
d.choice, d.confidence, d.probs              # the option, its probability, every option's probability
decisions = route.decide_many(["Refund please.", "The app crashes on start."])   # bulk, in batches

Several questions about one text, in the request shape of OpenRouter's Decisions API (the one TypeSafe's Jev is served through):

fort.decide("The app crashes every time I open it. Fix it today or I cancel.", {
    "team":    {"type": "choice", "instructions": "Which team should handle this?",
                "criteria": {"billing": "invoices, payments, refunds", "technical": "bugs, outages, errors"}},
    "churn":   {"type": "noul", "instructions": "Does the user threaten to cancel or leave?"},
    "urgency": {"type": "score", "instructions": "How urgent is this?",
                "criteria": ["not urgent", "soon", "critical"]}})

• choice: one of up to 26 options, with a probability for each. Longer label lists (77 bank intents, say) are read by writing the label's name, one allowed token at a time, so the answer is always a listed label; only the chosen label's probability is returned.
• noul: the probability of yes.
• score: an ordered scale, low to high: the expected position, each level's probability and the legend.

teximal.load("teximal/fort-1-2b") returns the right class for any Teximal model, read from the model's teximal.json.

The server

teximal serve answers Decisions API requests (model, state, questions), so switching from Jev is a change of URL. Send "states": [...] instead of "state" for many texts in one request.

Models

The weights are in the standard Qwen3.5 layout, so transformers and MLX load them directly. Each size also has a 4-bit MLX version (teximal/fort-1-2b-mlx-4bit, teximal/fort-1-0.8b-mlx-4bit) at about a third of the memory. The model cards hold every score, the settings behind them, and the limitations. Each model's teximal.json tells this package which family it is, which order it reads in, its confidence defaults and which backends can run it: nothing to set by hand.

Backends

--backend mlx|torch (or backend= in Python, or TEXIMAL_BACKEND) picks one; the default is MLX on Apple silicon and PyTorch elsewhere. For PyTorch, --device (cuda, cpu, mps, ...) and --dtype (bfloat16, float32) override the defaults: the GPU in bfloat16 when there is one, else the CPU in float32. Both backends use the same prompts and readouts; MLX reuses the shared beginning of prompts, PyTorch reads each prompt whole, so their probabilities differ only by floating-point detail: on 900 test decisions across both sizes, by 0.003 on average, with the same choice and yes/no answers throughout (four 1-to-5 scores split differently where MLX had an exact tie). On NVIDIA GPUs, pip install flash-linear-attention causal-conv1d gives transformers its fast kernels for Fort's linear-attention layers.

Notes

• Bulk decisions run texts side by side; batched arithmetic can move a probability by a few hundredths, so a pick can differ from a one-at-a-time decision only near a tie.
• Fine-tuning on your own labeled examples (teximal finetune) comes next.

License

Apache-2.0. The models are fine-tuned from Qwen3.5 (Apache-2.0); see each model's NOTICE.