diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..97f7ed23e7feabb4612ac71e8cddd696edbedc22
--- /dev/null
+++ b/README.md
@@ -0,0 +1,57 @@
+---
+license: cc-by-nc-sa-4.0
+library_name: pytorch
+tags:
+  - biology
+  - genomics
+  - drug-discovery
+  - target-prioritization
+  - variant-prioritization
+---
+
+# Varformer
+
+Varformer is a multimodal transformer for population-scale drug target and variant prioritization. It learns contextual representations of the missense variants observed in a population and integrates them with gene-level biological features through cross-modal attention. It is trained with a non-negative positive-unlabeled objective for the gene score, and an allele-frequency objective for a per-variant score.
+
+Code, documentation and installation: https://github.com/aaronwtr/Varformer
+
+## Models in this repository
+
+The 25 models of the paper. Each was trained with five seeds (13, 23, 42, 85, 482), with the variant head (`variant_loss_weight` 0.1, pooled binary cross-entropy) and 32-bit precision.
+
+| Folder | Cohort | Variant source | Gene inputs | Use in the paper |
+|---|---|---|---|---|
+| `afr/` | African / African American | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
+| `amr/` | Admixed American | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
+| `nfe/` | Non-Finnish European | gnomAD v4.1 | without `geneticConstraint` | gene prioritization |
+| `sas/` | South Asian | Genes & Health | without `geneticConstraint` | gene prioritization |
+| `sas-gnh/` | South Asian | Genes & Health | with `geneticConstraint` | variant scores in the Genes & Health analyses |
+
+Each model has two files:
+
+- `seed<N>.safetensors`: the model weights only. The files contain no training data, no genotypes and no individual-level information.
+- `seed<N>.json`: the training settings, the input dimensions, the class prior, the decision threshold, the selected epoch, the validation Spearman correlation, and SHA-256 checksums of the weights and of the source checkpoint.
+
+`index.json` lists all 25 models.
+
+## Use
+
+```python
+from varformer import Varformer
+
+model = Varformer.from_pretrained("nfe", seed="best")
+predictions = model.predict(genes=["ENSG00000141510"])
+variant_scores = model.score_variants(genes=["ENSG00000141510"])
+```
+
+`from_pretrained` checks the weights against their checksum. The model inputs are built from local copies of the input data (gnomAD or Genes & Health variants, AlphaMissense scores, Open Targets, Human Protein Atlas and GTEx features); see the data sources in the code repository. Genes & Health data are available only to approved researchers.
+
+The gene score ranks genes by target-prioritization evidence; it is not a calibrated probability of clinical success. The variant score ranks variants for research prioritization; it is not validated for clinical variant classification.
+
+## License
+
+The weights are released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/): attribution, non-commercial use only, and the same license for derived works. The models take AlphaMissense scores as input, and AlphaMissense is released under the same license. The code is MIT-licensed. For commercial use, train a model with another pathogenicity predictor; the code repository describes how.
+
+## Citation
+
+The paper citation will be added when the preprint is available. Until then, please cite the software release described in the code repository.
diff --git a/afr/seed13.json b/afr/seed13.json
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+    "num_genes": 1024,
+    "num_mutations": 381
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+    "variant_frequency_qc": false,
+    "variant_head": true,
+    "variant_loss_weight": 0.1,
+    "variant_objective": "pooled_bce",
+    "variant_pairs_per_gene": 256,
+    "variant_pairs_per_side": 96,
+    "variant_token_ablate": [],
+    "variant_token_schema": "original",
+    "weight_decay": 0.0003
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+  "parameters": 8379686,
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+  "seed": 13,
+  "selected_epoch": 23,
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+  "weights_sha256": "4c79b8b76c3d5b8cade53308e3547e0969e0f62d1c85b3d044fdce88b354385a"
+}
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