Publish RT-J FP8 checkpoints

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: RelativeDB/rt-j-fp16
+library_name: relational-transformers
+tags:
+- relational-deep-learning
+- relational-databases
+- tabular
+- tabular-classification
+- tabular-regression
+- foundation-model
+- in-context-learning
+- quantized
+- fp8
+- relational-transformer
+datasets:
+- stanford-star/the-join
+- stanford-star/relbench
+---
+
+# RT-J — FP8 E4M3 checkpoints
+
+Native FP8 quantizations of [RelativeDB/rt-j-fp16](https://huggingface.co/RelativeDB/rt-j-fp16),
+the RT-J Relational Transformer foundation model for prediction over related
+data. These checkpoints are produced and consumed by
+[`relational-transformers`](https://github.com/RelativeDB/relational-transformers).
+
+Sibling repos: [rt-j-fp16](https://huggingface.co/RelativeDB/rt-j-fp16) ·
+[rt-j-int8](https://huggingface.co/RelativeDB/rt-j-int8) ·
+[rt-j-int4](https://huggingface.co/RelativeDB/rt-j-int4)
+
+| File | Task head | Size |
+|---|---|---|
+| `classification/model.fp8.safetensors` | classification / ranking logits | 82 MiB |
+| `regression/model.fp8.safetensors` | regression / forecasting normalized values | 82 MiB |
+
+## FP8 Format
+
+Every two-dimensional matrix weight is stored directly as PyTorch
+`float8_e4m3fn`. Biases, normalization scales, and mask embeddings retain the
+source checkpoint dtype. The format is ordinary safetensors with
+`quantization=fp8_e4m3fn` metadata and requires no packed custom container.
+
+Portable PyTorch expands FP8 weights while loading. The Triton CUDA backend
+keeps them FP8-resident and uses supported GPU tensor-core paths.
+
+## Accuracy
+
+Measured on the RelativeDB golden relational batch (`B=5`, `S=16`) against
+`RelativeDB/rt-j-fp16` using the portable PyTorch backend:
+
+| Task | Maximum absolute logit error | Mean absolute logit error |
+|---|---:|---:|
+| classification / ranking | 0.0542 | 0.0435 |
+| regression / forecasting | 0.0286 | 0.0213 |
+
+Target-score signs and ranking are preserved on this batch. Validate task
+metrics against FP16 on representative application contexts before deployment.
+
+## Usage
+
+```python
+from relational_transformers import RelationalTransformer
+
+classifier = RelationalTransformer("RelativeDB/rt-j-fp8")
+probabilities = classifier.predict(relational_batch)
+
+regressor = RelationalTransformer("RelativeDB/rt-j-fp8", task="regression")
+predictions = regressor.predict(relational_batch)
+```
+
+For native FP8 CUDA inference:
+
+```python
+model = RelationalTransformer("RelativeDB/rt-j-fp8", backend="triton")
+```
+
+## Reproduce
+
+```bash
+pip install relational-transformers
+relational-transformers-quantize RelativeDB/rt-j-fp16 ./rt-j-fp8
+```
+
+## License and Attribution
+
+Derivative of [stanford-star/rt-j](https://huggingface.co/stanford-star/rt-j)
+(Stanford STAR lab), redistributed under the same **CC-BY-NC-SA-4.0** license.
+Architecture and training details are described in the upstream model card;
+only the weight storage format differs here.

classification/config.json

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+{
+  "codename": "rt-j-clf",
+  "name": "RT-J Classifier",
+  "task_type": "clf",
+  "checkpoint_file": "model.fp8.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
+  },
+  "provenance": {
+    "run_id": "pjbmgvu2",
+    "swa_step": 58000,
+    "project": "2026-04-17",
+    "recipe": "real_mixed_v6_5_486t"
+  },
+  "quantization": {
+    "format": "fp8_e4m3fn",
+    "matrix_weights_only": true
+  }
+}

classification/model.fp8.safetensors

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+size 85716620

regression/config.json

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+{
+  "codename": "rt-j-reg",
+  "name": "RT-J Regressor",
+  "task_type": "reg",
+  "checkpoint_file": "model.fp8.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
+  },
+  "provenance": {
+    "run_id": "fsuj4wdl",
+    "swa_step": 18000,
+    "project": "2026-04-17",
+    "recipe": "real_mixed_v6_5_486t"
+  },
+  "quantization": {
+    "format": "fp8_e4m3fn",
+    "matrix_weights_only": true
+  }
+}

regression/model.fp8.safetensors

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