license: mit
tags:
- relational-deep-learning
- relational-databases
- relational-data
- tabular
- tabular-classification
- tabular-regression
- foundation-model
- world-model
- in-context-learning
- few-shot
- pretraining
- synthetic-data
library_name: pytorch
TabWM
TabWM (Tabular World Model) is a relational world model introduced in Relational Learning as World Modeling. Given a few observed executions from a relational system, it predicts the structured edits that a new action makes to the database, without updating its parameters. The model is pretrained on WorldRel, a generator of executable relational worlds, and its encoder is initialized from the RT-PluRel Relational Transformer.
- Code: github.com/oooranz/TabWM
Architecture: 24.7M parameters, float32, 12 Relational Transformer blocks, d_model 256, 8 heads, d_ff 1024, context length 4096. The repository contains model.safetensors (weights) and config.json (architecture).
Installation
pip install git+https://github.com/oooranz/TabWM
Usage
from tabwm import TabWM
model = TabWM.from_pretrained("oooranz/TabWM")
Examples for building behavioral context from WorldRel and running inference are provided in the GitHub repository.
License
MIT. The weights are partly initialized from the PluRel paper checkpoint, whose MIT notice is included in LICENSE.