diff --git a/examples/llm_ptq/hf_ptq.py b/examples/llm_ptq/hf_ptq.py
index f19e82c5d4..984243a320 100755
--- a/examples/llm_ptq/hf_ptq.py
+++ b/examples/llm_ptq/hf_ptq.py
@@ -301,6 +301,7 @@ def auto_quantize(
     auto_quantize_method="gradient",
     auto_quantize_score_size=128,
     auto_quantize_checkpoint=None,
+    full_model: torch.nn.Module | None = None,
 ):
     """Auto search quantization of multiple formats."""
 
@@ -338,23 +339,67 @@ def auto_quantize(
         for qformat in qformat_list
     ), "One or more quantization formats provided are not supported for unified checkpoint export"
 
-    def loss_func(output, data):
-        # For transformers AutoModelForCausalLM models, the outputs are wrapped in `CausalLMOutputWithPast`
-        # which contains the loss attribute.
-        return output.loss
-
-    if auto_quantize_method == "gradient":
-        # For gradient-based method, return full output with loss
-        def forward_step(model, batch):
-            return model(**batch)
-    elif auto_quantize_method == "kl_div":
-        # For KL divergence method, return only logits
-        def forward_step(model, batch):
-            return model(**batch).logits
+    # For VLMs like Gemma4, the extracted language_model is a base text model without
+    # lm_head, so it cannot produce logits or loss directly. In that case, use the
+    # full_model's lm_head to compute logits/loss from the language model's hidden states.
+    is_base_model = (
+        full_model is not None
+        and language_model is not full_model
+        and not hasattr(language_model, "lm_head")
+        and hasattr(full_model, "lm_head")
+    )
+
+    if is_base_model:
+        assert full_model is not None
+        lm_head = full_model.lm_head
+
+        def loss_func(output, data):
+            logits = lm_head(output.last_hidden_state)
+            labels = data["labels"]
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            return torch.nn.functional.cross_entropy(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
+            )
+
+        if auto_quantize_method == "gradient":
+
+            def forward_step(model, batch):
+                return model(**batch)
+
+        elif auto_quantize_method == "kl_div":
+
+            def forward_step(model, batch):
+                hidden_states = model(**batch).last_hidden_state
+                return lm_head(hidden_states)
+
+        else:
+            raise ValueError(
+                f"Invalid auto_quantize_method: {auto_quantize_method}. Must be 'gradient' or 'kl_div'"
+            )
     else:
-        raise ValueError(
-            f"Invalid auto_quantize_method: {auto_quantize_method}. Must be 'gradient' or 'kl_div'"
-        )
+
+        def loss_func(output, data):
+            # For transformers AutoModelForCausalLM models, the outputs are wrapped in `CausalLMOutputWithPast`
+            # which contains the loss attribute.
+            return output.loss
+
+        if auto_quantize_method == "gradient":
+            # For gradient-based method, return full output with loss
+
+            def forward_step(model, batch):
+                return model(**batch)
+
+        elif auto_quantize_method == "kl_div":
+            # For KL divergence method, return only logits
+
+            def forward_step(model, batch):
+                return model(**batch).logits
+
+        else:
+            raise ValueError(
+                f"Invalid auto_quantize_method: {auto_quantize_method}. Must be 'gradient' or 'kl_div'"
+            )
 
     language_model, _ = mtq.auto_quantize(
         language_model,
@@ -1048,6 +1093,7 @@ def quantize_main(
             args,
             language_model,
             calib_dataloader,
+            full_model=full_model,
         )
 
     else: