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	[misc] fix moe models (#9230)
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				@ -40,7 +40,7 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
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    model_type = getattr(model.config, "model_type", None)
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    text_config = getattr(model.config, "text_config", None)
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    text_architectures = getattr(text_config, "architectures", None)
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    text_model_type = getattr(text_config, "model_type", None)
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    if model_type == "dbrx":
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        from transformers.models.dbrx.modeling_dbrx import DbrxFFN
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@ -105,11 +105,21 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
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        _set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
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    if model_type == "qwen3_moe" or text_architectures == "Qwen3MoeForCausalLM":
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    if model_type == "qwen3_moe" or text_model_type == "qwen3_moe":  # internvl 3.5
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        from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock
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        _set_z3_leaf_modules(model, [Qwen3MoeSparseMoeBlock])
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    if model_type == "qwen3_vl_moe":
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        from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import Qwen3VLMoeTextSparseMoeBlock
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        _set_z3_leaf_modules(model, [Qwen3VLMoeTextSparseMoeBlock])
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    if model_type == "qwen3_omni_moe":
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        from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import Qwen3OmniMoeThinkerTextSparseMoeBlock
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        _set_z3_leaf_modules(model, [Qwen3OmniMoeThinkerTextSparseMoeBlock])
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def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
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    if not is_trainable or not model_args.moe_aux_loss_coef:
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