| --- |
| 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](https://arxiv.org/abs/2610.16763)*. 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](https://huggingface.co/stanford-star/rt-plurel) Relational Transformer. |
|
|
| - **Code:** [github.com/oooranz/TabWM](https://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 |
|
|
| ```bash |
| pip install git+https://github.com/oooranz/TabWM |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| 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](https://github.com/oooranz/TabWM). |
|
|
| ## License |
|
|
| MIT. The weights are partly initialized from the PluRel paper checkpoint, whose MIT notice is included in `LICENSE`. |
