Add safety-mixture index to model card

README.md

@@ -46,6 +46,31 @@ out = model.generate(ids, max_new_tokens=512)
 print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=False))
 ```
 
+## Safety mixtures
+
+This model is one point on a safety-data sweep. `main` is the default 10% mixture; the other fractions are published as revisions on this repo, so each can be loaded by passing `revision=`:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+repo = "epfl-dlab/spp-t0-3b-instruct"
+tok = AutoTokenizer.from_pretrained(repo)          # identical at every revision
+model = AutoModelForCausalLM.from_pretrained(
+    repo, revision="safety-60", dtype=torch.bfloat16, device_map="auto"
+)
+```
+
+| Revision | Safety fraction | Safety examples | Instruct examples |
+|---|---|---|---|
+| `safety-0` | 0% | 0 | 300,000 |
+| `safety-5` | 5% | 15,000 | 285,000 |
+| `safety-10` — **default**, same weights as `main` | 10% | 30,000 | 270,000 |
+| `safety-30` | 30% | 90,000 | 210,000 |
+| `safety-60` | 60% | 180,000 | 120,000 |
+
+Every mixture is 300,000 examples total, one epoch, response-only loss; safety prompts come from WildJailbreak and WildGuardMix and instructions from WildChat-1M. Only the ratio changes.
+
 ## Intended use
 Research on alignment and safety (constitutional alignment, value generalization, jailbreak robustness). A research artifact, not a production model; it can produce incorrect or unsafe content.