convert: Fix Gemma 4 Unified conversion (ggml-org#24118)

* Fix Gemma 4 Unified conversion

* Set audio hidden size to audio_embed_dim

Author: pcuenca

conversion/gemma.py

@@ -798,7 +798,8 @@ def __init__(self, *args, **kwargs):
         # remap audio hparams
         if self.hparams_audio:
             self.hparams_audio["feat_in"] = self.hparams_audio.get("input_feat_size", 128)
-            self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
+            if "hidden_size" in self.hparams_audio:
+                self.hparams_audio["intermediate_size"] = self.hparams_audio["hidden_size"] * 4
         else:
             self.has_audio_encoder = False
 
@@ -872,7 +873,7 @@ def __init__(self, *args, **kwargs):
         assert self.hparams_audio is not None
         text_embd_dim = self.hparams_vision["mm_embed_dim"]
         self.hparams_vision["hidden_size"] = text_embd_dim
-        self.hparams_audio["hidden_size"] = text_embd_dim
+        self.hparams_audio["hidden_size"] = self.hparams_audio["audio_embed_dim"]
         # this is a transformer-less vision tower, the params below are redundant but set to avoid error
         self.hparams_vision["intermediate_size"] = 0
         self.hparams_vision["num_layers"] = 0
@@ -897,7 +898,10 @@ def modify_tensors(self, data_torch, name, bid):
             # ggml im2col outputs in RR..GG..BB.. (CHW) order, but weight expects RGBRGB.. (HWC).
             # Permute columns so column i aligns with CHW input position i.
             assert self.hparams_vision is not None
-            p = self.hparams_vision["model_patch_size"]
+            if "model_patch_size" in self.hparams_vision:
+                p = self.hparams_vision["model_patch_size"]
+            else:
+                p = self.hparams_vision["patch_size"] * self.hparams_vision["pooling_kernel_size"]
             i = torch.arange(p * p * 3)
             ch  = i // (p * p)
             row = (i % (p * p)) // p
@@ -908,7 +912,10 @@ def modify_tensors(self, data_torch, name, bid):
         elif "patch_ln1.weight" in name or "patch_ln1.bias" in name:
             # same permutation for patch_ln1 as patch_dense to align with CHW input order
             assert self.hparams_vision is not None
-            p = self.hparams_vision["model_patch_size"]
+            if "model_patch_size" in self.hparams_vision:
+                p = self.hparams_vision["model_patch_size"]
+            else:
+                p = self.hparams_vision["patch_size"] * self.hparams_vision["pooling_kernel_size"]
             i = torch.arange(p * p * 3)
             ch  = i // (p * p)
             row = (i % (p * p)) // p