Add LoRA adapter card

lora_backbone/README.md

@@ -13,4 +13,34 @@ tags:
 
 PEFT LoRA adapter for **[devops-thiago/classone-gemma4-e4b](https://huggingface.co/devops-thiago/classone-gemma4-e4b)**, the ClassOne System 1 decision model built on [`google/gemma-4-E4B-it`](https://huggingface.co/google/gemma-4-E4B-it).
 
-This subfolder holds only the low-rank adapter weights. The merged standalone weights live at the repository root, and the Noul / Choice / Score decision heads live in `classone_heads.pt`.
+This subfolder holds only the low-rank adapter weights. The merged standalone weights live at the repository root, and the Noul / Choice / Score decision heads live in `classone_heads.pt` — load the heads alongside whichever backbone form you use.
+
+## Adapter Configuration
+
+| Setting | Value |
+|---|---|
+| Base model | `google/gemma-4-E4B-it` |
+| PEFT type | LORA |
+| Rank (r) | 16 |
+| `lora_alpha` | 32 |
+| `lora_dropout` | 0.05 |
+| Bias | none |
+| Target modules | `.*language_model.*(q_proj\|o_proj\|gate_proj\|up_proj\|down_proj).*` |
+| PEFT version | 0.21.0 |
+
+## Usage
+
+```python
+from peft import PeftModel
+from transformers import AutoModel
+
+base = AutoModel.from_pretrained("google/gemma-4-E4B-it")
+model = PeftModel.from_pretrained(base, "devops-thiago/classone-gemma4-e4b", subfolder="lora_backbone")
+```
+
+For end-to-end decision inference (packing questions, calibrated outputs), see the [model card](https://huggingface.co/devops-thiago/classone-gemma4-e4b).
+
+## Attribution & Legal
+
+- Base model: [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it) (Google) — Apache License 2.0
+- Architecture: [devops-thiago/class-one](https://github.com/devops-thiago/class-one) — Apache 2.0