---
license: apache-2.0
library_name: transformers
tags: [decision-model, calibration, system-one, structured-decisions]
---

# Arbit base checkpoints

Starting weights for [Arbit](https://github.com/juteq/arbit), a non-autoregressive decision engine: one
forward pass answers several typed questions (`choice`, `score`, `noul`) about a shared state and returns a
calibrated probability distribution for each.

| path | checkpoint | encoder | context |
|---|---|---|---|
| repo root | English general | ModernBERT-large (421M) | 512 tokens |
| `typed-decisions/` | fine-tuned on the typed-decisions benchmark's train split | ModernBERT-large (421M) | 1024 tokens |

```python
import arbit
agent = arbit.load("juteq/arbit-base")                       # English
tuned = arbit.load("juteq/arbit-base", subfolder="typed-decisions")
```

## Provenance

These files are **redistributed unmodified** from a publicly released Apache-2.0 checkpoint; JUTEQ did not
train them. They are published here so that training runs that start from them are reproducible. The encoder
is [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) (Apache-2.0).

Models trained by JUTEQ are published separately.

## Known limits

* English only.
* `typed-decisions/` is fine-tuned on four synthetic workflows; its scores measure agreement with that
  benchmark's teacher model, not correctness.
* Probabilities need temperatures fitted on your own held-out data; the shipped ones were fitted elsewhere.

License: Apache-2.0 (see LICENSE in this repository).