RT-J Regressor (rt-j-reg)

The regression checkpoint of RT-J — a Relational Transformer foundation model for in-context / few-shot entity regression over multi-table relational databases (no per-task gradient training).

  • Task type: regression (metric: MAE / Z-score nMAE ↓)
  • Selected: SWA checkpoint at step 18,000 (best on validation MAE)
  • Params: ~85.6M · dtype: bfloat16 · 12 blocks, d_model 512, 8 heads, d_ff 2048
  • Files: model.safetensors (weights), config.json (dims + text-embedding model + provenance)
from rt.checkpoints import load_rt_model
model, config = load_rt_model("star-project/rt-j-reg", device="cuda")

Full model card, training details, evaluation, license, and citation: see the RT-J repository. Use star-project/rt-j-clf for classification.

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Safetensors
Model size
85.6M params
Tensor type
BF16
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Datasets used to train stanford-star/rt-j-reg

Evaluation results

  • Mean MAE (single-context, L=8k, full test split) on RelBench (9 regression tasks)
    self-reported
    0.268