nemo_automodel.components.models.qwen3_vl_moe.state_dict_adapter

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Module Contents

Classes

NameDescription
Qwen3VLMoeStateDictAdapterConverts between HF Qwen3-VL-MoE checkpoints and grouped-experts native format.

API

class nemo_automodel.components.models.qwen3_vl_moe.state_dict_adapter.Qwen3VLMoeStateDictAdapter(
config: typing.Any,
moe_config: nemo_automodel.components.moe.config.MoEConfig,
backend: nemo_automodel.components.models.common.BackendConfig,
dtype: torch.dtype = torch.float32
)

Bases: StateDictAdapter

Converts between HF Qwen3-VL-MoE checkpoints and grouped-experts native format.

HF checkpoint keys (already stacked, no .weight suffix): model.language_model.layers.{L}.mlp.experts.gate_up_proj [n_experts, dim, 2*inter] model.language_model.layers.{L}.mlp.experts.down_proj [n_experts, inter, dim]

Native format (identical shapes, different key names): model.language_model.layers.{L}.mlp.experts.gate_and_up_projs model.language_model.layers.{L}.mlp.experts.down_projs

nemo_automodel.components.models.qwen3_vl_moe.state_dict_adapter.Qwen3VLMoeStateDictAdapter.convert_single_tensor_to_hf(
fqn: str,
tensor: typing.Any,
kwargs = {}
) -> list[tuple[str, typing.Any]]

Rename a single native key to HF format. Tensor passed through as-is.

nemo_automodel.components.models.qwen3_vl_moe.state_dict_adapter.Qwen3VLMoeStateDictAdapter.from_hf(
hf_state_dict: dict[str, typing.Any],
device_mesh: typing.Optional[torch.distributed.device_mesh.DeviceMesh] = None,
kwargs = {}
) -> dict[str, typing.Any]

Rename HF keys to native keys.

DTensors (DCP path): just rename, no tensor ops. Plain tensors (init path): slice to local EP shard, create DTensor.

nemo_automodel.components.models.qwen3_vl_moe.state_dict_adapter.Qwen3VLMoeStateDictAdapter.to_hf(
state_dict: dict[str, typing.Any],
exclude_key_regex: typing.Optional[str] = None,
quantization: bool = False,
kwargs = {}
) -> dict[str, typing.Any]

Rename native keys to HF keys. Tensors passed through as-is (no comms).