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# nemo_automodel.components.models.glm4_moe.state_dict_adapter

## Module Contents

### Classes

| Name                                                                                                               | Description                                                                     |
| ------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------- |
| [`Glm4MoeStateDictAdapter`](#nemo_automodel-components-models-glm4_moe-state_dict_adapter-Glm4MoeStateDictAdapter) | Converts between HF GLM4-MoE checkpoints and our grouped-experts native format. |

### Data

[`logger`](#nemo_automodel-components-models-glm4_moe-state_dict_adapter-logger)

### API

```python
class nemo_automodel.components.models.glm4_moe.state_dict_adapter.Glm4MoeStateDictAdapter(
    config: typing.Any,
    moe_config: nemo_automodel.components.moe.config.MoEConfig,
    backend: nemo_automodel.components.models.common.BackendConfig,
    dtype: torch.dtype = torch.float32
)
```

**Bases:** [MoESplitExpertsStateDictMixin](/nemo-automodel/nemo_automodel/components/moe/state_dict_mixin#nemo_automodel-components-moe-state_dict_mixin-MoESplitExpertsStateDictMixin), [StateDictAdapter](/nemo-automodel/nemo_automodel/components/checkpoint/state_dict_adapter#nemo_automodel-components-checkpoint-state_dict_adapter-StateDictAdapter)

Converts between HF GLM4-MoE checkpoints and our grouped-experts native format.

GLM4-MoE HF experts use keys:
model.layers.\{L}.mlp.experts.\{E}.gate\_proj.weight
model.layers.\{L}.mlp.experts.\{E}.up\_proj.weight
model.layers.\{L}.mlp.experts.\{E}.down\_proj.weight
model.layers.\{L}.mlp.shared\_experts.gate\_proj.weight
model.layers.\{L}.mlp.shared\_experts.up\_proj.weight
model.layers.\{L}.mlp.shared\_experts.down\_proj.weight

Our native format groups them into:
model.layers.\{L}.mlp.experts.gate\_and\_up\_projs  # \[n\_experts, dim, 2\*moe\_inter\_dim]
model.layers.\{L}.mlp.experts.down\_projs         # \[n\_experts, moe\_inter\_dim, dim]
model.layers.\{L}.mlp.shared\_expert.gate\_proj.weight
model.layers.\{L}.mlp.shared\_expert.up\_proj.weight
model.layers.\{L}.mlp.shared\_expert.down\_proj.weight

```python
nemo_automodel.components.models.glm4_moe.state_dict_adapter.Glm4MoeStateDictAdapter.convert_single_tensor_to_hf(
    fqn: str,
    tensor: typing.Any,
    kwargs = {}
) -> list[tuple[str, typing.Any]]
```

Convert a single tensor from native format to HuggingFace format.

**Parameters:**

**`fqn`** `str`

Fully qualified name of the tensor in native format

---

**`tensor`** `Any`

The tensor to convert

---

**`**kwargs`** — default: \{}

Additional arguments for conversion

---

**Returns:** `list[tuple[str, Any]]`

List of (fqn, tensor) tuples in HuggingFace format

```python
nemo_automodel.components.models.glm4_moe.state_dict_adapter.Glm4MoeStateDictAdapter.from_hf(
    hf_state_dict: dict[str, typing.Any],
    device_mesh: typing.Optional[torch.distributed.device_mesh.DeviceMesh] = None,
    kwargs = {}
) -> dict[str, typing.Any]
```

```python
nemo_automodel.components.models.glm4_moe.state_dict_adapter.Glm4MoeStateDictAdapter.to_hf(
    state_dict: dict[str, typing.Any],
    exclude_key_regex: str | None = None,
    quantization: bool = False,
    kwargs = {}
) -> dict[str, typing.Any]
```

```python
nemo_automodel.components.models.glm4_moe.state_dict_adapter.logger = logging.getLogger(__name__)
```