convert ASR model ok

convert_hf_to_gguf.py

@@ -4825,7 +4825,10 @@ class RND1Model(Qwen2MoeModel):
 class Qwen3VLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
+        if self.hparams_vision is None:
+            logger.info("No vision config found, skipping vision tensor processing")
+            return
+
         # Compute image_size if not present
         if "image_size" not in self.hparams_vision:
             # For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
@@ -4946,18 +4949,29 @@ class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
     has_vision_encoder = True
 
     def get_vision_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("vision_config")
+        if self.has_vision_encoder:
+            return self.global_config["thinker_config"].get("vision_config")
+        else:
+            return None
 
     def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("audio_config")
+        if self.has_audio_encoder:
+            return self.global_config["thinker_config"].get("audio_config")
+        else:
+            return None
 
     def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
-        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
+        if self.has_vision_encoder:
+            Qwen3VLVisionModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
+        if self.has_audio_encoder:
+            Qwen25AudioModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if "visual." in name:
+            if not self.has_vision_encoder:
+                raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
             # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
             name = name.replace("thinker.visual.", "model.visual.")
             if ".merger_list." in name:
@@ -4971,12 +4985,20 @@ class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
                 name = name.replace(".mlp.2", ".linear_fc2")
             yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
         elif "audio_tower." in name:
+            if not self.has_audio_encoder:
+                raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
             if "conv2d" in name and name.endswith(".bias"):
                 # transform conv2d bias [n_embd] --> [1, 1, n_embd]
                 data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
             yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = False
+
+
 @ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
 class Glm4VVisionModel(Qwen3VLVisionModel):
     def set_gguf_parameters(self):
@@ -5023,6 +5045,31 @@ class Qwen3VLTextModel(Qwen3Model):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRTextModel(Qwen3VLTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+    def set_vocab(self):
+        super().set_vocab()
+        # fix chat template, use correct chatml format
+        self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
+
+    def modify_tensors(self, data_torch, name, bid):
+        # qwen3-omni
+        name = name.replace("thinker.", "")
+
+        # Skip vision and audio tensors - they go in the mmproj file
+        if "visual." in name or "audio_tower." in name \
+                or "talker." in name or "code2wav." in name:
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Qwen3VLMoeForConditionalGeneration", "Qwen3OmniMoeForConditionalGeneration")
 class Qwen3VLMoeTextModel(Qwen3MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
@@ -5083,6 +5130,26 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
+
+    def set_vocab(self):
+        super().set_vocab()
+        # correct BOS/EOS tokens
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+            for token_id, data in added_tokens.items():
+                if data.get("content") == "<|im_end|>":
+                    self.gguf_writer.add_bos_token_id(int(token_id))
+                    self.gguf_writer.add_eos_token_id(int(token_id))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+
 class _LinearAttentionVReorderBase(Qwen3NextModel):
     model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
     """reorders V heads from grouped to tiled order for ggml broadcast