Update usage: adapter now loads via AutoPeftModel

README.md

@@ -43,19 +43,16 @@ The checkpoint in this repository is the one named "SCOUT" in every experiment o
 
 ## Usage
 
-The adapter was saved with a local base path, so pass the base model explicitly:
-
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
 
-base = "sullivanUCSD/SCOUT-SFT-only"
-tok = AutoTokenizer.from_pretrained(base)
-model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")
-model = PeftModel.from_pretrained(model, "sullivanUCSD/SCOUT")
+model = AutoPeftModelForCausalLM.from_pretrained("sullivanUCSD/SCOUT", torch_dtype="bfloat16", device_map="auto")
+tok = AutoTokenizer.from_pretrained("sullivanUCSD/SCOUT-SFT-only")
 model = model.merge_and_unload()  # optional, for vLLM-style serving
 ```
 
+`adapter_config.json` points to the base model `sullivanUCSD/SCOUT-SFT-only`, so the adapter loads directly.
 The predictor expects the SCOUT prompt format (detector profile, the retrieved fingerprint records, and the
 target sample). The prompt builder, the retrieval index over `sullivanUCSD/anchor-400`, and the full routing rule
 are in the code repository above. Use `sullivanUCSD/SCOUT-450` for evaluation.