Add usage example to model card

README.md

@@ -55,6 +55,51 @@ Preference accuracy by domain:
 | Empathy | 94.3606 |
 | Multicultural | 76.2950 |
 
+## Usage Example
+
+This checkpoint uses the project's custom `RewardModelWithGating` class. Run the
+example from an environment where `multidomain_model/modeling_custom.py` is
+importable.
+
+```python
+import torch
+from transformers import AutoTokenizer
+from modeling_custom import RewardModelWithGating
+
+model_id = "mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it"
+dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+device_map = {"": 0} if torch.cuda.is_available() else None
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = RewardModelWithGating.from_pretrained(
+    model_id,
+    device_map=device_map,
+    dtype=dtype,
+).eval()
+device = next(model.parameters()).device
+
+messages = [
+    {"role": "user", "content": "I failed an important exam and feel awful."},
+    {"role": "assistant", "content": "I'm sorry. That is a hard setback, but it does not define your ability. Take a little time to recover, then we can make a concrete study plan for the next attempt."},
+]
+
+encoded = tokenizer.apply_chat_template(
+    messages,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=4096,
+)
+inputs = {"input_ids": encoded.to(device)} if isinstance(encoded, torch.Tensor) else {
+    key: value.to(device) for key, value in encoded.items()
+}
+
+with torch.no_grad():
+    score = model(**inputs).score.float().item()
+
+print(score)
+```
+
 ## Limitations
 
 This is a reward model, not a standalone chat assistant. Scores are intended for