Repository: vllm-project/vllm
File: vllm/model_executor/models/qwen3_5.py (Python)

Code before:
                        if success:
                            name = name_mapped
                            break
                    else:
                        # Skip loading extra bias for GPTQ models.
                        if (
                            name_mapped.endswith(".bias")
                            or name_mapped.endswith("_bias")
                        ) and name_mapped not in params_dict:
                            continue
                        param = params_dict[name_mapped]
                        weight_loader = param.weight_loader
                        success = weight_loader(
                            param,
                            loaded_weight,
                            name_mapped,
                            shard_id=shard_id,
                            expert_id=expert_id,
                            return_success=True,
                        )
                    if success:
                        name = name_mapped
                        break
                else:
                    if is_expert_weight:
                        # We've checked that this is an expert weight
                        # However it's not mapped locally to this rank
                        # So we simply skip it
                        continue
                    # Skip loading extra bias for GPTQ models.
                    if name.endswith(".bias") and name not in params_dict:
                        continue
                    if is_pp_missing_parameter(name, self):
                        continue
                    if name not in params_dict:
                        logger.warning_once(
                            f"Parameter {name} not found in params_dict, skip loading"
                        )
                        continue
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params


class Qwen3_5ForCausalLMBase(
    nn.Module,
    HasInnerState,

Review comment (suggestion):
But I think Qwen3-VL's `hf_to_vllm_mapper` should map `ignore`/`modules_to_not_convert` etc from `model.language_model.` into `language.model.` prefix?
https://github.com/vllm-project/vllm/blob/c50e105a8843907c8c89f95ee29b8cc5e3935bae/vllm/model_executor/models/qwen3_vl.py#L1228-L1235

Code after:
    pass


class Qwen3_5MoeForCausalLM(Qwen3_5ForCausalLMBase, QwenNextMixtureOfExperts):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)

        # set MoE hyperparameters
        self.set_moe_parameters()

    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
        return self.model.get_expert_mapping()


########################################################
# Qwen3_5-Dense
########################################################


@MULTIMODAL_REGISTRY.register_processor(
    Qwen3VLMultiModalProcessor,
    info=Qwen3_5ProcessingInfo,
    dummy_inputs=Qwen3VLDummyInputsBuilder,
)
class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration, IsHybrid):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
        # protocols have not __init__ method, so we need to use nn.Module.__init__
        nn.Module.__init__(self)
        config: Qwen3_5Config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        multimodal_config = vllm_config.model_config.multimodal_config

        self.config = config
        self.multimodal_config = multimodal_config
        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
        self.video_pruning_rate = multimodal_config.video_pruning_rate
        self.is_multimodal_pruning_enabled = (
            multimodal_config.is_multimodal_pruning_enabled()
        )

        with self._mark_tower_model(vllm_config, {"image", "video"}):
            self.visual = Qwen3_VisionTransformer(
                config.vision_config,
                norm_eps=getattr(config, "rms_norm_eps", 1e-6),
                quant_config=quant_config,
                prefix=maybe_prefix(prefix, "visual"),
            )

        with self._mark_language_model(vllm_config):
            self.language_model = Qwen3_5ForCausalLM(
                vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model")