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logos_reward_ft_job.py

@@ -281,7 +281,11 @@ class RewardWeightedTrainer(Trainer):
     def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
         reward_weights = inputs.pop("reward_weight").to(model.device)
         labels         = inputs["labels"]
-        outputs        = model(**inputs)
+        # Bypass PeftModelForSeq2SeqLM.forward (which injects input_ids=None and
+        # causes a duplicate-kwarg collision with Whisper's input_features path).
+        # LoRA patches are baked into the linear layers so gradients still flow.
+        whisper = model.base_model.model if hasattr(model, 'base_model') else model
+        outputs = whisper(**inputs)
         logits         = outputs.logits
 
         B, T, V = logits.shape