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README.md

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+---
+license: other
+base_model: Qwen/Qwen3-4B
+tags:
+  - clinvar
+  - acmg
+  - variant-classification
+  - dual-head
+  - sentence-classification
+  - text-classification
+---
+
+# LLM4Variants-Qwen3-4B
+
+Dual-head sentence classifier for **ACMG evidence-code + strength** prediction on
+ClinVar submission comments. This is rank **#1** of the grid search (ranked by
+joint test accuracy).
+
+The model wraps the backbone **`Qwen/Qwen3-4B`** with two heads on top of mean-pooled
+hidden states:
+
+- **code head** — 28-way ACMG evidence code (`PVS1, PS1–PS4, PM1–PM6, PP1–PP5,
+  BA1, BS1–BS4, BP1–BP7` + `NO_KEYWORD`)
+- **strength head** — 6-way strength, conditioned on a learned embedding of the
+  predicted code (`Supporting, Moderate, Strong, VeryStrong, NotMet, NoStrength`)
+
+## Test metrics
+
+| Metric | Value |
+|---|---:|
+| Code accuracy | 0.9309 |
+| Strength accuracy | 0.9374 |
+| Joint accuracy | 0.8822 |
+| Strength acc \| correct code | 0.9477 |
+| Code weighted-F1 | 0.9307 |
+| Strength weighted-F1 | 0.9351 |
+
+## Training configuration
+
+| Hyperparameter | Value |
+|---|---|
+| Learning rate | 0.0001 |
+| Effective batch size | 128 |
+| Epochs | 8 |
+| Max length | 256 |
+| λ (strength loss) | 1.0 |
+| Code emb dim | 64 |
+| Negative ratio | 0.25 |
+| Seed | 42 |
+| Train / val / test size | 19161 / 1278 / 5110 |
+
+## Files
+
+- `model.safetensors` — full state dict (backbone + `code_head` +
+  `code_embeddings` + `strength_head`).
+- `label_mappings.json` — `keyword2id` / `strength2id` (and reverse).
+- tokenizer files + `chat_template.jinja`.
+
+## Loading
+
+This is a custom `nn.Module` (`DualHeadLLM`), not a `transformers`
+`AutoModel`. Reconstruct the module (see `train_dual_head.py`), then load the
+weights:
+
+```python
+from safetensors.torch import load_file
+from huggingface_hub import hf_hub_download
+
+model = DualHeadLLM("Qwen/Qwen3-4B", num_keywords=28, num_strengths=6)
+state = load_file(hf_hub_download("HFXM/LLM4Variants-Qwen3-4B", "model.safetensors"))
+model.load_state_dict(state)
+```