Instructions to use juteq/arbit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juteq/arbit-base with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("juteq/arbit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Arbit base checkpoints
Starting weights for 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 |
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 (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).
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