rt-plurel

Runs On

What it takes to serve rt-plurel (86M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

Model Card

RT-PluRel is a Relational Transformer checkpoint pair for in-context / few-shot entity prediction over multi-table relational databases (no per-task gradient training). Same architecture and file layout as stanford-star/rt-j — drop-in replacement. Architecture: ~85.6M blocks, dmodel 512, 8 heads, dff 2048 · text columns embedded with all-MiniLM-L12-v2 (dtext 384). Each folder contains model.safetensors (weights) and config.json (dims + text-embedding model). The paper/ subdirectory preserves the earlier RT-PluRel release:.pt checkpoints (12 blocks, dmodel 256, dff 1024) pretrained on synthetic relational databases generated by PluRel, plus the continued-pretraining and fine-tuned RelBench…

Excerpt from the card by Stanford Tabular and Relational (STAR) Project, licensed cc-by-nc-sa-4.0.

Identity and Version

Repository

stanford-star/rt-plurel

Publisher

Stanford Tabular and Relational (STAR) Project

Task

Tabular classification

Modality

Tabular

Library

pytorch

Parameters

86M parameters

Languages

few-shot

Revision

d27c97b045fc4f504848f15c730acb87970aac1d

First published

2026-02-03

Last updated

2026-09-11

Files and Weights

51 files, 1.9 GB in total. The weights are 45 files totalling 1.9 GB in pt, safetensors.

License and Download

License

cc-by-nc-sa-4.0

Access

Open weights, no gate

Download size

1.9 GB

Built From

• Described by arXiv:2510.06377
• Described by arXiv:2602.04029
• Trained on (disclosed) kvignesh1420/plurel
• Trained on (disclosed) stanford-star/relbench
• Trained on (disclosed) stanford-star/the-join

Memory Requirements

Weights only, from the published parameter count; the key-value cache and runtime add to this.

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