Merge stanford-star/rt-p into this repo; move original PluRel-paper checkpoints under paper/

LICENSE

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README.md

@@ -1,98 +1,95 @@
 ---
+license: cc-by-nc-sa-4.0
+tags:
+  - relational-deep-learning
+  - relational-databases
+  - relational-data
+  - tabular
+  - tabular-classification
+  - tabular-regression
+  - foundation-model
+  - in-context-learning
+  - few-shot
+  - pretraining
+  - synthetic-data
+  - relbench
+  - relational-transformer
 datasets:
-- kvignesh1420/plurel
-library_name: pytorch
-license: mit
+  - stanford-star/the-join
+  - stanford-star/relbench
+  - kvignesh1420/plurel
 pipeline_tag: tabular-classification
 metrics:
-- roc_auc
-- r_squared
-tags:
-- relational-data
-- tabular
-- foundation-model
-- pretraining
-- relational-transformer
-- relbench
-- synthetic-data
-- tabular-regression
+  - roc_auc
+  - r_squared
+library_name: pytorch
 ---
 
-# Relational Transformer — PluRel Checkpoints
-
-Relational Transformer (RT) model checkpoints pretrained on synthetic relational databases generated by [PluRel](https://huggingface.co/datasets/kvignesh1420/plurel). 
+# RT-PluRel (`rt-plurel`)
 
-Relational Transformer is a foundation model architecture for relational data that enables zero-shot transfer across heterogeneous schemas and tasks. It was introduced in:
+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`](https://huggingface.co/stanford-star/rt-j) — drop-in
+replacement.
 
-> **Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data**  
-> Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec — [arXiv:2510.06377](https://arxiv.org/abs/2510.06377) (ICLR 2026)
+> **Repository layout note (2026-08).** This repository now hosts two
+> generations of RT-PluRel checkpoints. The current checkpoint pair (formerly
+> published as `stanford-star/rt-p`, which this repository replaces) lives at
+> the repository root, described below. The original checkpoints of the PluRel
+> paper ([arXiv:2602.04029](https://arxiv.org/abs/2602.04029)), previously at
+> this repository's root, are preserved unchanged under
+> [`paper/`](./tree/main/paper) — see [`paper/README.md`](./blob/main/paper/README.md).
 
-The checkpoints provided in this repository were trained using the methodology described in:
-
-> **PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models**  
-> Kothapalli, Ranjan, Hudovernik, Dwivedi, Hoffart, Guestrin, Leskovec — [arXiv:2602.04029](https://arxiv.org/abs/2602.04029) (2026)
-
-[![arXiv (RT)](https://img.shields.io/badge/arXiv-2510.06377-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/abs/2510.06377)
-[![GitHub (RT)](https://img.shields.io/badge/Code-RT_GitHub-black?style=flat&logo=github)](https://github.com/snap-stanford/relational-transformer)
-[![arXiv (PluRel)](https://img.shields.io/badge/arXiv-2602.04029-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/abs/2602.04029)
-[![Project Page (PluRel)](https://img.shields.io/badge/Project-Page-blue?style=flat&logo=github)](https://snap-stanford.github.io/plurel/)
-[![GitHub (PluRel)](https://img.shields.io/badge/Code-PluRel_GitHub-black?style=flat&logo=github)](https://github.com/snap-stanford/plurel)
-[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow?style=flat&logo=huggingface)](https://huggingface.co/datasets/kvignesh1420/plurel)
-
----
+| Variant | Folder | Task |
+|---|---|---|
+| Classifier | `classification/` | binary entity classification |
+| Regressor | `regression/` | entity regression |
 
-## Model Architecture
+Architecture: ~85.6M params · bfloat16 · 12 blocks, d_model 512, 8 heads, d_ff 2048 ·
+text columns embedded with `all-MiniLM-L12-v2` (d_text 384).
+Each folder contains `model.safetensors` (weights) and `config.json`
+(dims + text-embedding model).
 
-The Relational Transformer operates on multi-tabular relational databases, treating rows across linked tables as a sequence via BFS-ordered context sampling. It utilizes a Relational Attention mechanism over columns, rows, and primary-foreign key links.
+## Usage
 
-| Hyperparameter | Value |
-|----------------|-------|
-| Transformer blocks | 12 |
-| Model dimension (`d_model`) | 256 |
-| Attention heads | 8 |
-| FFN dimension (`d_ff`) | 1,024 |
-| Context length | 1,024 tokens |
-| Text encoder | `all-MiniLM-L12-v2` (d_text = 384) |
-| Max BFS width | 128 |
+```python
+from rt.model import RelationalTransformer
 
-The architecture and training loop build on the [Relational Transformer](https://github.com/snap-stanford/relational-transformer) codebase.
+model = RelationalTransformer.from_pretrained("stanford-star/rt-plurel/classification")
+# or: RelationalTransformer.from_pretrained("stanford-star/rt-plurel", subfolder="regression")
+```
 
----
+```bash
+pixi run eval --model.load-ckpt-path stanford-star/rt-plurel/classification ...
+```
 
-## Download
+## `paper/`: original PluRel-paper checkpoints
 
-Single checkpoint (Python) — fetch `config.json` alongside the weights; it carries
-the model architecture and is what the Hub uses to count downloads:
+The `paper/` subdirectory preserves the earlier RT-PluRel release: `.pt`
+checkpoints (12 blocks, d_model 256, d_ff 1024) pretrained on synthetic
+relational databases generated by
+[PluRel](https://huggingface.co/datasets/kvignesh1420/plurel), plus the
+continued-pretraining and fine-tuned RelBench leaderboard checkpoints. Details,
+protocols, and download snippets: [`paper/README.md`](./blob/main/paper/README.md).
+Paths from before the reorganization gain a `paper/` prefix, e.g.
 
 ```python
 from huggingface_hub import hf_hub_download
 
-config = hf_hub_download("stanford-star/rt-plurel", "config.json")
-ckpt = hf_hub_download("stanford-star/rt-plurel", "synthetic-pretrain_rdb_1024_size_4b.pt")
-```
-
-Full repository (CLI):
-
-```bash
-hf download stanford-star/rt-plurel \
-    --repo-type model \
-    --local-dir ~/scratch/rt_hf_ckpts
+ckpt = hf_hub_download("stanford-star/rt-plurel", "paper/synthetic-pretrain_rdb_1024_size_4b.pt")
 ```
 
+## License
 
-## RelBench leaderboard checkpoints (added 2026-06)
+The root checkpoint pair (`classification/`, `regression/`) is released under
+CC BY-NC-SA 4.0 (see `LICENSE`). The files under `paper/` retain their original
+MIT license.
 
-Protocols follow the repo's continued-pretraining script (50k steps, batch 128,
-lr 5e-4 cosine, from `synthetic-pretrain_rdb_1024_size_4b.pt`) and the RT
-`example_finetune` protocol (lr 1e-4, batch 32, 2^15+1 steps), with regression
-best-checkpoint selection by val NMAE (MAE / train-split std, ddof=1) — the
-leaderboard metric — instead of R². Evaluation = full official test split.
+## Related
 
-- `cntd-pretrain_<db>_<task>.pt` — synthetic+real continued pretraining, one
-  leave-one-DB-out run per database (incl. rel-event), per-task best checkpoint.
-  These produce the "PluRel | synthetic + real" zero-shot regression and
-  rel-event cells.
-- `finetune_<db>_<task>.pt` — fine-tuned from the matching
-  `cntd-pretrain` checkpoint (chosen over synthetic-only by val zero-shot, which
-  the synthetic+real checkpoint won on every task). These produce the
-  "PluRel | pretrained + fine-tuned" leaderboard row.
+- Training data: [stanford-star/the-join](https://huggingface.co/datasets/stanford-star/the-join)
+- Evaluation: [RelBench](https://huggingface.co/datasets/stanford-star/relbench)
+- Reference model: [stanford-star/rt-j](https://huggingface.co/stanford-star/rt-j)
+- Papers: [Relational Transformer (arXiv:2510.06377)](https://arxiv.org/abs/2510.06377) ·
+  [PluRel (arXiv:2602.04029)](https://arxiv.org/abs/2602.04029)

classification/config.json

@@ -0,0 +1,16 @@
+{
+  "codename": "rt-p-clf",
+  "name": "RT-P Classifier",
+  "task_type": "clf",
+  "checkpoint_file": "model.safetensors",
+  "embedding_model": "all-MiniLM-L12-v2",
+  "d_text": 384,
+  "model": {
+    "num_blocks": 12,
+    "d_model": 512,
+    "d_text": 384,
+    "num_heads": 8,
+    "d_ff": 2048,
+    "materialize_attn_masks": true
+  }
+}

classification/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a369f065d8f69e1da11ee4326ceb9d32cf78ee69255a438449e971f681b7f83
+size 171169918

config.json

@@ -1,11 +1,24 @@
 {
-  "model_type": "relational-transformer",
-  "num_blocks": 12,
-  "d_model": 256,
+  "codename": "rt-plurel",
+  "name": "RT-PluRel",
+  "embedding_model": "all-MiniLM-L12-v2",
   "d_text": 384,
-  "num_heads": 8,
-  "d_ff": 1024,
-  "ctx_len": 1024,
-  "max_bfs_width": 256,
-  "embedding_model": "all-MiniLM-L12-v2"
-}
+  "model": {
+    "num_blocks": 12,
+    "d_model": 512,
+    "d_text": 384,
+    "num_heads": 8,
+    "d_ff": 2048,
+    "materialize_attn_masks": true
+  },
+  "variants": {
+    "clf": {
+      "path": "classification",
+      "task_type": "clf"
+    },
+    "reg": {
+      "path": "regression",
+      "task_type": "reg"
+    }
+  }
+}

paper/README.md

@@ -0,0 +1,100 @@
+---
+datasets:
+- kvignesh1420/plurel
+library_name: pytorch
+license: mit
+pipeline_tag: tabular-classification
+metrics:
+- roc_auc
+- r_squared
+tags:
+- relational-data
+- tabular
+- foundation-model
+- pretraining
+- relational-transformer
+- relbench
+- synthetic-data
+- tabular-regression
+---
+
+> **Note (2026-08):** these are the original PluRel-paper checkpoints, moved from this repository's root into `paper/` when the current RT-PluRel checkpoint pair (formerly `stanford-star/rt-p`) was merged into this repository. See the root `README.md`.
+
+# Relational Transformer — PluRel Checkpoints
+
+Relational Transformer (RT) model checkpoints pretrained on synthetic relational databases generated by [PluRel](https://huggingface.co/datasets/kvignesh1420/plurel). 
+
+Relational Transformer is a foundation model architecture for relational data that enables zero-shot transfer across heterogeneous schemas and tasks. It was introduced in:
+
+> **Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data**  
+> Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos Kanatsoulis, Roshan Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, Jure Leskovec — [arXiv:2510.06377](https://arxiv.org/abs/2510.06377) (ICLR 2026)
+
+The checkpoints provided in this repository were trained using the methodology described in:
+
+> **PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models**  
+> Kothapalli, Ranjan, Hudovernik, Dwivedi, Hoffart, Guestrin, Leskovec — [arXiv:2602.04029](https://arxiv.org/abs/2602.04029) (2026)
+
+[![arXiv (RT)](https://img.shields.io/badge/arXiv-2510.06377-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/abs/2510.06377)
+[![GitHub (RT)](https://img.shields.io/badge/Code-RT_GitHub-black?style=flat&logo=github)](https://github.com/snap-stanford/relational-transformer)
+[![arXiv (PluRel)](https://img.shields.io/badge/arXiv-2602.04029-b31b1b?style=flat&logo=arxiv)](https://arxiv.org/abs/2602.04029)
+[![Project Page (PluRel)](https://img.shields.io/badge/Project-Page-blue?style=flat&logo=github)](https://snap-stanford.github.io/plurel/)
+[![GitHub (PluRel)](https://img.shields.io/badge/Code-PluRel_GitHub-black?style=flat&logo=github)](https://github.com/snap-stanford/plurel)
+[![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow?style=flat&logo=huggingface)](https://huggingface.co/datasets/kvignesh1420/plurel)
+
+---
+
+## Model Architecture
+
+The Relational Transformer operates on multi-tabular relational databases, treating rows across linked tables as a sequence via BFS-ordered context sampling. It utilizes a Relational Attention mechanism over columns, rows, and primary-foreign key links.
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Transformer blocks | 12 |
+| Model dimension (`d_model`) | 256 |
+| Attention heads | 8 |
+| FFN dimension (`d_ff`) | 1,024 |
+| Context length | 1,024 tokens |
+| Text encoder | `all-MiniLM-L12-v2` (d_text = 384) |
+| Max BFS width | 128 |
+
+The architecture and training loop build on the [Relational Transformer](https://github.com/snap-stanford/relational-transformer) codebase.
+
+---
+
+## Download
+
+Single checkpoint (Python) — fetch `config.json` alongside the weights; it carries
+the model architecture and is what the Hub uses to count downloads:
+
+```python
+from huggingface_hub import hf_hub_download
+
+config = hf_hub_download("stanford-star/rt-plurel", "paper/config.json")
+ckpt = hf_hub_download("stanford-star/rt-plurel", "paper/synthetic-pretrain_rdb_1024_size_4b.pt")
+```
+
+Full repository (CLI):
+
+```bash
+hf download stanford-star/rt-plurel \
+    --repo-type model \
+    --local-dir ~/scratch/rt_hf_ckpts
+```
+
+
+## RelBench leaderboard checkpoints (added 2026-06)
+
+Protocols follow the repo's continued-pretraining script (50k steps, batch 128,
+lr 5e-4 cosine, from `synthetic-pretrain_rdb_1024_size_4b.pt`) and the RT
+`example_finetune` protocol (lr 1e-4, batch 32, 2^15+1 steps), with regression
+best-checkpoint selection by val NMAE (MAE / train-split std, ddof=1) — the
+leaderboard metric — instead of R². Evaluation = full official test split.
+
+- `cntd-pretrain_<db>_<task>.pt` — synthetic+real continued pretraining, one
+  leave-one-DB-out run per database (incl. rel-event), per-task best checkpoint.
+  These produce the "PluRel | synthetic + real" zero-shot regression and
+  rel-event cells.
+- `finetune_<db>_<task>.pt` — fine-tuned from the matching
+  `cntd-pretrain` checkpoint (chosen over synthetic-only by val zero-shot, which
+  the synthetic+real checkpoint won on every task). These produce the
+  "PluRel | pretrained + fine-tuned" leaderboard row.

paper/config.json

@@ -0,0 +1,11 @@
+{
+  "model_type": "relational-transformer",
+  "num_blocks": 12,
+  "d_model": 256,
+  "d_text": 384,
+  "num_heads": 8,
+  "d_ff": 1024,
+  "ctx_len": 1024,
+  "max_bfs_width": 256,
+  "embedding_model": "all-MiniLM-L12-v2"
+}