Add Qwen3-Omni moe thinker (#25550)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Roger Wang <hey@rogerw.io>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Xiong Wang <feizi.wx@alibaba-inc.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Roger Wang <hey@rogerw.io>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

docs/models/supported_models.md

@@ -714,6 +714,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `Qwen2_5OmniThinkerForConditionalGeneration` | Qwen2.5-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen2.5-Omni-3B`, `Qwen/Qwen2.5-Omni-7B` | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3VLForConditionalGeneration` | Qwen3-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-4B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Qwen3VLMoeForConditionalGeneration` | Qwen3-VL-MOE | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/Qwen3-VL-30B-A3B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `Qwen3OmniMoeThinkerForConditionalGeneration` | Qwen3-Omni | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>+</sup> | `Qwen/Qwen3-Omni-30B-A3B-Instruct`, `Qwen/Qwen3-Omni-30B-A3B-Thinking` | ✅︎ | ✅︎ | ✅︎ |
 | `RForConditionalGeneration` | R-VL-4B | T + I<sup>E+</sup> | `YannQi/R-4B` | | ✅︎ | ✅︎ |
 | `SkyworkR1VChatModel` | Skywork-R1V-38B | T + I | `Skywork/Skywork-R1V-38B` | | ✅︎ | ✅︎ |
 | `SmolVLMForConditionalGeneration` | SmolVLM2 | T + I | `SmolVLM2-2.2B-Instruct` | ✅︎ | | ✅︎ |
@@ -804,8 +805,7 @@ Some models are supported only via the [Transformers backend](#transformers). Th
     Our PaliGemma implementations have the same problem as Gemma 3 (see above) for both V0 and V1.
 
 !!! note
-    For Qwen2.5-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`)
+    For Qwen2.5-Omni and Qwen3-Omni, reading audio from video pre-processing (`--mm-processor-kwargs '{"use_audio_in_video": true}'`) is currently work in progress and not yet supported.
-    is currently supported on V0 (but not V1), because overlapping modalities is not yet supported in V1.
 
 #### Transcription
 

tests/models/multimodal/processing/test_common.py

@@ -384,6 +384,7 @@ def _test_processing_correctness_one(
         "Qwen/Qwen2.5-Omni-3B",
         "Qwen/Qwen3-VL-4B-Instruct",
         "Qwen/Qwen3-VL-30B-A3B-Instruct",
+        "Qwen/Qwen3-Omni-30B-A3B-Instruct",
         "YannQi/R-4B",
         "Skywork/Skywork-R1V-38B",
         "HuggingFaceTB/SmolVLM2-2.2B-Instruct",

tests/models/registry.py

@@ -773,6 +773,11 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         min_transformers_version="4.57",
         is_available_online=False,
     ),
+    "Qwen3OmniMoeForConditionalGeneration": _HfExamplesInfo(
+        "Qwen/Qwen3-Omni-30B-A3B-Instruct",
+        max_model_len=4096,
+        min_transformers_version="4.57",
+    ),
     "RForConditionalGeneration": _HfExamplesInfo("YannQi/R-4B", trust_remote_code=True),
     "SkyworkR1VChatModel": _HfExamplesInfo(
         "Skywork/Skywork-R1V-38B", trust_remote_code=True

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -971,17 +971,9 @@ class MRotaryEmbedding(RotaryEmbedding):
             llm_pos_ids_list.append(
                 torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
             )
-
             t_index = (
-                (
+                torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w)
-                    torch.arange(llm_grid_t)
+            ).flatten()
-                    .view(-1, 1)
-                    .expand(-1, llm_grid_h * llm_grid_w)
-                )
-                .long()
-                .flatten()
-            )
-
             h_index = (
                 torch.arange(llm_grid_h)
                 .view(1, -1, 1)
@@ -1042,7 +1034,6 @@ class MRotaryEmbedding(RotaryEmbedding):
 
         st = 0
         remain_images, remain_videos = image_nums, video_nums
-
         image_index, video_index = 0, 0
         for _ in range(image_nums + video_nums):
             video_second_per_grid_t = 0.0
@@ -1093,19 +1084,11 @@ class MRotaryEmbedding(RotaryEmbedding):
             llm_pos_ids_list.append(
                 torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
             )
-
             t_index = (
-                (
+                torch.arange(llm_grid_t).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w)
-                    torch.arange(llm_grid_t)
+                * video_second_per_grid_t
-                    .view(-1, 1)
+                * tokens_per_second
-                    .expand(-1, llm_grid_h * llm_grid_w)
+            ).flatten()
-                    * video_second_per_grid_t
-                    * tokens_per_second
-                )
-                .long()
-                .flatten()
-            )
-
             h_index = (
                 torch.arange(llm_grid_h)
                 .view(1, -1, 1)
@@ -1136,6 +1119,339 @@ class MRotaryEmbedding(RotaryEmbedding):
 
         return llm_positions, mrope_position_delta
 
+    @classmethod
+    def _omni3_get_input_positions_tensor(
+        cls,
+        config,
+        input_ids: torch.Tensor,
+        image_grid_thw: torch.Tensor,
+        video_grid_thw: torch.Tensor,
+        use_audio_in_video: bool = False,
+        audio_seqlens: Optional[torch.Tensor] = None,
+        second_per_grids: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        def _get_feat_extract_output_lengths(input_lengths: torch.LongTensor):
+            input_lengths_leave = input_lengths % 100
+            feat_lengths = (input_lengths_leave - 1) // 2 + 1
+            output_lengths = (
+                ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+            )
+            return output_lengths
+
+        if input_ids is None or input_ids.ndim != 1:
+            raise ValueError("_omni3_get_input_positions_tensor expects 1D input_ids")
+
+        seq_len = input_ids.shape[0]
+        device = input_ids.device
+        dtype = input_ids.dtype
+
+        if image_grid_thw is not None:
+            image_grid_thw = image_grid_thw.to(device=device, dtype=torch.long)
+        if video_grid_thw is not None:
+            video_grid_thw = video_grid_thw.to(device=device, dtype=torch.long)
+
+        if second_per_grids is None:
+            if video_grid_thw is not None and video_grid_thw.numel() > 0:
+                second_per_grids = torch.ones(
+                    video_grid_thw.shape[0], dtype=torch.float32, device=device
+                )
+            else:
+                second_per_grids = torch.tensor([], dtype=torch.float32, device=device)
+        else:
+            second_per_grids = second_per_grids.to(device=device, dtype=torch.float32)
+
+        if audio_seqlens is not None:
+            audio_seqlens = audio_seqlens.to(device=device, dtype=torch.long)
+
+        spatial_merge_size = config.vision_config.spatial_merge_size
+        image_token_id = config.image_token_id
+        video_token_id = config.video_token_id
+        audio_token_id = config.audio_token_id
+        vision_start_token_id = config.vision_start_token_id
+        audio_start_token_id = config.audio_start_token_id
+        position_id_per_seconds = config.position_id_per_seconds
+
+        vision_start_indices = torch.argwhere(
+            input_ids == vision_start_token_id
+        ).squeeze(1)
+        if vision_start_indices.numel() > 0:
+            vision_tokens = input_ids[vision_start_indices + 1]
+        else:
+            vision_tokens = input_ids.new_empty((0,), dtype=input_ids.dtype)
+        audio_nums = torch.sum(input_ids == audio_start_token_id)
+        image_nums = (vision_tokens == image_token_id).sum()
+        video_nums = (
+            (vision_tokens == audio_start_token_id).sum()
+            if use_audio_in_video
+            else (vision_tokens == video_token_id).sum()
+        )
+
+        input_tokens = input_ids.tolist()
+        llm_pos_ids_list: list[torch.Tensor] = []
+        st = 0
+        image_idx = 0
+        video_idx = 0
+        audio_idx = 0
+        remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums  # noqa: E501
+        multimodal_nums = (
+            image_nums + audio_nums
+            if use_audio_in_video
+            else image_nums + video_nums + audio_nums
+        )  # noqa: E501
+
+        for _ in range(multimodal_nums):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+            if (image_token_id in input_tokens or video_token_id in input_tokens) and (
+                remain_videos > 0 or remain_images > 0
+            ):
+                ed_vision_start = input_tokens.index(vision_start_token_id, st)
+            else:
+                ed_vision_start = len(input_tokens) + 1
+            if audio_token_id in input_tokens and remain_audios > 0:
+                ed_audio_start = input_tokens.index(audio_start_token_id, st)
+            else:
+                ed_audio_start = len(input_tokens) + 1
+            min_ed = min(ed_vision_start, ed_audio_start)
+
+            if min_ed == ed_audio_start:
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, device=device, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                llm_pos_ids = (
+                    torch.arange(audio_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + audio_len + eos_len
+                audio_idx += 1
+                remain_audios -= 1
+            elif (
+                min_ed == ed_vision_start
+                and input_ids[ed_vision_start + 1] == image_token_id
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, device=device, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                grid_t = image_grid_thw[image_idx][0]
+                grid_hs = image_grid_thw[:, 1]
+                grid_ws = image_grid_thw[:, 2]
+                t_index = torch.arange(grid_t, device=device) * position_id_per_seconds
+                llm_pos_ids = cls._get_llm_pos_ids_for_vision(
+                    st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2)
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + image_len + eos_len
+                image_idx += 1
+                remain_images -= 1
+            elif (
+                min_ed == ed_vision_start
+                and input_ids[ed_vision_start + 1] == video_token_id
+                and not use_audio_in_video
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, device=device, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(bos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                grid_t = video_grid_thw[video_idx][0]
+                grid_hs = video_grid_thw[:, 1]
+                grid_ws = video_grid_thw[:, 2]
+                t_index = (
+                    torch.arange(grid_t, device=device)
+                    * float(second_per_grids[video_idx].item())
+                    * position_id_per_seconds
+                )
+                llm_pos_ids = cls._get_llm_pos_ids_for_vision(
+                    st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+                llm_pos_ids_list.append(llm_pos_ids)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                llm_pos_ids_list.append(
+                    torch.arange(eos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                st += text_len + bos_len + video_len + eos_len
+                video_idx += 1
+                remain_videos -= 1
+            elif (
+                min_ed == ed_vision_start
+                and ed_vision_start + 1 == ed_audio_start
+                and use_audio_in_video
+            ):
+                text_len = min_ed - st
+                if text_len != 0:
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, device=device, dtype=torch.long)
+                        .view(1, -1)
+                        .expand(3, -1)
+                        + st_idx
+                    )
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                bos_len = 1
+                bos_block = (
+                    torch.arange(bos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(bos_block)
+                llm_pos_ids_list.append(bos_block)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                audio_llm_pos_ids = (
+                    torch.arange(audio_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                grid_t = video_grid_thw[video_idx][0]
+                grid_hs = video_grid_thw[:, 1]
+                grid_ws = video_grid_thw[:, 2]
+                t_index = (
+                    torch.arange(grid_t, device=device)
+                    * float(second_per_grids[video_idx].item())
+                    * position_id_per_seconds
+                )
+                video_llm_pos_ids = cls._get_llm_pos_ids_for_vision(
+                    st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
+                )
+                video_data_index, audio_data_index = 0, 0
+                while (
+                    video_data_index < video_llm_pos_ids.shape[-1]
+                    and audio_data_index < audio_llm_pos_ids.shape[-1]
+                ):
+                    if (
+                        video_llm_pos_ids[0][video_data_index]
+                        <= audio_llm_pos_ids[0][audio_data_index]
+                    ):
+                        llm_pos_ids_list.append(
+                            video_llm_pos_ids[
+                                :, video_data_index : video_data_index + 1
+                            ]
+                        )
+                        video_data_index += 1
+                    else:
+                        llm_pos_ids_list.append(
+                            audio_llm_pos_ids[
+                                :, audio_data_index : audio_data_index + 1
+                            ]
+                        )
+                        audio_data_index += 1
+                if video_data_index < video_llm_pos_ids.shape[-1]:
+                    llm_pos_ids_list.append(
+                        video_llm_pos_ids[
+                            :, video_data_index : video_llm_pos_ids.shape[-1]
+                        ]
+                    )
+                if audio_data_index < audio_llm_pos_ids.shape[-1]:
+                    llm_pos_ids_list.append(
+                        audio_llm_pos_ids[
+                            :, audio_data_index : audio_llm_pos_ids.shape[-1]
+                        ]
+                    )
+                video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2)
+                st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+                eos_len = 1
+                eos_block = (
+                    torch.arange(eos_len, device=device, dtype=torch.long)
+                    .view(1, -1)
+                    .expand(3, -1)
+                    + st_idx
+                )
+                llm_pos_ids_list.append(eos_block)
+                llm_pos_ids_list.append(eos_block)
+                st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2  # noqa: E501
+                audio_idx += 1
+                video_idx += 1
+                remain_videos -= 1
+                remain_audios -= 1
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if llm_pos_ids_list else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len, device=device, dtype=torch.long)
+                .view(1, -1)
+                .expand(3, -1)
+                + st_idx
+            )
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        if llm_positions.shape[1] != seq_len:
+            raise RuntimeError("Position ids length mismatch with input ids length")
+
+        position_ids = llm_positions.to(device=device, dtype=dtype)
+        mrope_position_delta = llm_positions.max() + 1 - seq_len
+        return position_ids, mrope_position_delta
+
     @classmethod
     def _omni_get_input_positions_tensor(
         cls,
@@ -1168,7 +1484,38 @@ class MRotaryEmbedding(RotaryEmbedding):
         # TODO(fyabc): refactor and share more code with
         #  _vl_get_input_positions_tensor.
 
+        model_type = hf_config.model_type
         thinker_config = hf_config.thinker_config
+
+        if isinstance(image_grid_thw, list):
+            image_grid_thw = torch.tensor(image_grid_thw)
+        if isinstance(video_grid_thw, list):
+            video_grid_thw = torch.tensor(video_grid_thw)
+
+        if "qwen3_omni" in model_type:
+            input_tensor = torch.tensor(input_tokens)
+            audio_lengths_tensor = audio_feature_lengths
+            if audio_lengths_tensor is not None and not isinstance(
+                audio_lengths_tensor, torch.Tensor
+            ):
+                audio_lengths_tensor = torch.as_tensor(
+                    audio_lengths_tensor, dtype=torch.long
+                )
+            second_per_grids_tensor = (
+                torch.tensor(second_per_grid_ts) if second_per_grid_ts else None
+            )
+
+            llm_positions, mrope_position_delta = cls._omni3_get_input_positions_tensor(  # noqa: E501
+                thinker_config,
+                input_tensor,
+                image_grid_thw,
+                video_grid_thw,
+                use_audio_in_video,
+                audio_lengths_tensor,
+                second_per_grids_tensor,
+            )
+            return llm_positions, mrope_position_delta
+
         audio_token_id = thinker_config.audio_token_index
         image_token_id = thinker_config.image_token_index
         video_token_id = thinker_config.video_token_index
@@ -1182,11 +1529,6 @@ class MRotaryEmbedding(RotaryEmbedding):
             thinker_config.vision_config, "tokens_per_second", 25
         )
 
-        if isinstance(image_grid_thw, list):
-            image_grid_thw = torch.tensor(image_grid_thw)
-        if isinstance(video_grid_thw, list):
-            video_grid_thw = torch.tensor(video_grid_thw)
-
         src_item = input_tokens
         audio_seqlens = audio_feature_lengths
         if not second_per_grid_ts:
@@ -1232,7 +1574,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                 grid_t = image_grid_thw[image_idx][0]
                 grid_hs = image_grid_thw[:, 1]
                 grid_ws = image_grid_thw[:, 2]
-                t_index = (torch.arange(grid_t) * 1 * tokens_per_second).long()
+                t_index = torch.arange(grid_t) * 1 * tokens_per_second
                 llm_pos_ids = cls._get_llm_pos_ids_for_vision(
                     start_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws
                 )
@@ -1250,7 +1592,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                     torch.arange(grid_t)
                     * second_per_grid_ts[video_idx]
                     * tokens_per_second
-                ).long()
+                )
                 llm_pos_ids = cls._get_llm_pos_ids_for_vision(
                     start_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws
                 )
@@ -1277,7 +1619,7 @@ class MRotaryEmbedding(RotaryEmbedding):
                     torch.arange(grid_t)
                     * second_per_grid_ts[video_idx]
                     * tokens_per_second
-                ).long()
+                )
                 t_index_split_chunk = cls._split_list_into_ranges(
                     t_index, t_ntoken_per_chunk
                 )
@@ -1452,9 +1794,7 @@ class MRotaryEmbedding(RotaryEmbedding):
         grid_h = video_grid_thw[1]
         grid_w = video_grid_thw[2]
         t_ntoken_per_chunk = int(tokens_per_second * seconds_per_chunk)
-        t_index = (
+        t_index = torch.arange(grid_t) * video_second_per_grid_t * tokens_per_second
-            torch.arange(grid_t) * video_second_per_grid_t * tokens_per_second
-        ).long()
         t_index_split_chunk = cls._split_list_into_ranges(t_index, t_ntoken_per_chunk)
 
         updates = [audio_start_token_id]

vllm/model_executor/models/registry.py

@@ -355,6 +355,10 @@ _MULTIMODAL_MODELS = {
         "qwen2_5_omni_thinker",
         "Qwen2_5OmniThinkerForConditionalGeneration",
     ),
+    "Qwen3OmniMoeForConditionalGeneration": (
+        "qwen3_omni_moe_thinker",
+        "Qwen3OmniMoeThinkerForConditionalGeneration",
+    ),
     "Qwen3VLForConditionalGeneration": ("qwen3_vl", "Qwen3VLForConditionalGeneration"),  # noqa: E501
     "Qwen3VLMoeForConditionalGeneration": (
         "qwen3_vl_moe",