Add fp16 RT-J checkpoints + model card

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: stanford-star/rt-j
+tags:
+- relational-deep-learning
+- relational-databases
+- tabular
+- tabular-classification
+- tabular-regression
+- foundation-model
+- in-context-learning
+- fp16
+- relational-transformer
+datasets:
+- stanford-star/the-join
+- stanford-star/relbench
+---
+
+# RT-J — fp16 checkpoints
+
+Half-precision conversions of [stanford-star/rt-j](https://huggingface.co/stanford-star/rt-j),
+the Relational Transformer foundation model for in-context learning over
+relational databases. Produced and consumed by the
+[RelativeDB](https://github.com/RelativeDB) native inference engine
+(`cpp/rt_quantize --type f16`). Weights stay **f16-resident** at inference:
+the engine's CPU (Accelerate / portable SIMD) and Metal/MPS kernels convert
+to fp32 inside the GEMM, halving weight memory and DRAM traffic vs. fp32
+with **no measurable accuracy loss** (drift vs. the PyTorch reference is
+identical to the fp32 checkpoint on the golden batch).
+
+Sibling repos: [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.f16.safetensors` | classification / ranking (logits — apply sigmoid) | 172 MB |
+| `regression/model.f16.safetensors` | regression / forecasting (normalized values) | 172 MB |
+
+## Format
+
+Plain safetensors: every transformer-block projection (`wq/wk/wv/wg`, `wo`,
+`ffn.w1/w2/w3` — ~99% of parameters) is stored as `F16` (IEEE half,
+round-to-nearest). The value/col-name encoders, decoder head, norms, biases
+and mask embeddings stay fp32. Any safetensors reader can load these files.
+
+## Accuracy
+
+Golden batch (B=5, S=16) vs. the PyTorch reference, identical on CPU and
+Metal/MPS: `yhat` max abs. error 3.9e-3 — the same as the fp32 checkpoint
+(upstream weights are bf16, so f16 storage adds no error above fp32
+op-ordering drift).
+
+## Usage (RelativeDB native engine)
+
+```bash
+./build/rt_test testdata classification/model.f16.safetensors --quantized --device mps
+
+# via the Java / Python / Rust bindings: place the .f16 file next to the fp32
+# checkpoint (or point at a directory containing it) and opt in with
+export RELATIVEDB_RT_QUANTIZED=f16
+```
+
+The C ABI (`rt_model_load`) accepts these files directly — the format is
+auto-detected from the tensor dtypes. f16 checkpoints currently run on the
+CPU and Metal/MPS backends (CUDA is fp32-only).
+
+## Reproduce
+
+```bash
+cmake -B build -S cpp && cmake --build build -j
+./build/rt_quantize <rt-j>/classification/model.safetensors classification/model.f16.safetensors --type f16
+./build/rt_quantize <rt-j>/regression/model.safetensors regression/model.f16.safetensors --type f16
+```
+
+## License & 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.f16.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"
+  }
+}

classification/model.f16.safetensors

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+oid sha256:f28ce4de905ce995e850b4ba1a00aeb68c1e2d69245d35110cfac7d2d332658f
+size 172430356

regression/config.json

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+{
+  "codename": "rt-j-reg",
+  "name": "RT-J Regressor",
+  "task_type": "reg",
+  "checkpoint_file": "model.f16.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"
+  }
+}

regression/model.f16.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e918aecaae8cf60ed9f886eee9974580a39841748179b6a116d73c220864c8d
+size 172430356