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# nemo_automodel.components.models.ernie4_5.model

## Module Contents

### Classes

| Name                                                                                                  | Description                                                    |
| ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| [`Ernie4_5Attention`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5Attention)             | ERNIE 4.5 GQA attention with interleaved RoPE.                 |
| [`Ernie4_5Block`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5Block)                     | Dense ERNIE 4.5 decoder block.                                 |
| [`Ernie4_5ForCausalLM`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5ForCausalLM)         | Dense ERNIE 4.5 causal language model.                         |
| [`Ernie4_5Model`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5Model)                     | Dense ERNIE 4.5 transformer body.                              |
| [`Ernie4_5MoeBlock`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5MoeBlock)               | ERNIE 4.5 MoE decoder block.                                   |
| [`Ernie4_5_MoeForCausalLM`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5_MoeForCausalLM) | ERNIE 4.5 MoE causal language model with AutoModel EP support. |
| [`Ernie4_5_MoeModel`](#nemo_automodel-components-models-ernie4_5-model-Ernie4_5_MoeModel)             | ERNIE 4.5 MoE transformer body.                                |

### Functions

| Name                                                                              | Description |
| --------------------------------------------------------------------------------- | ----------- |
| [`_config_dtype`](#nemo_automodel-components-models-ernie4_5-model-_config_dtype) | -           |

### Data

[`ModelClass`](#nemo_automodel-components-models-ernie4_5-model-ModelClass)

### API

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5Attention(
    config: transformers.models.ernie4_5.configuration_ernie4_5.Ernie4_5Config | transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    backend: nemo_automodel.components.models.common.BackendConfig
)
```

**Bases:** `Module`

ERNIE 4.5 GQA attention with interleaved RoPE.

**`head_dim`**

---

**`k_proj`**

---

**`num_heads`** `= config.num_attention_heads`

---

**`num_kv_heads`** `= config.num_key_value_heads`

---

**`o_proj`**

---

**`q_proj`**

---

**`v_proj`**

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5Attention.forward(
    x: torch.Tensor,
    position_embeddings: tuple[torch.Tensor, torch.Tensor],
    attention_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5Block(
    config: transformers.models.ernie4_5.configuration_ernie4_5.Ernie4_5Config | transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    backend: nemo_automodel.components.models.common.BackendConfig
)
```

**Bases:** `Module`

Dense ERNIE 4.5 decoder block.

**`input_layernorm`**

---

**`mlp`**

---

**`post_attention_layernorm`**

---

**`self_attn`** `= Ernie4_5Attention(config, backend)`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5Block.forward(
    x: torch.Tensor,
    position_embeddings: tuple[torch.Tensor, torch.Tensor],
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM(
    config: transformers.models.ernie4_5.configuration_ernie4_5.Ernie4_5Config,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs = {}
)
```

**Bases:** [HFCheckpointingMixin](/nemo-automodel/nemo_automodel/components/models/common/hf_checkpointing_mixin#nemo_automodel-components-models-common-hf_checkpointing_mixin-HFCheckpointingMixin), `Module`

Dense ERNIE 4.5 causal language model.

**`_nemo_tied_weights_keys`** `= {'lm_head.weight': 'model.embed_tokens.weight'}`

---

**`_pp_plan`** `= {'lm_head': (['hidden_states'], ['logits'])}`

---

**`_tp_plan`** `= {'lm_head': 'colwise_rep'}`

---

**`backend`** `= backend or BackendConfig()`

---

**`lm_head`**

---

**`model`** `= Ernie4_5Model(config, self.backend)`

---

**`state_dict_adapter`** `= Ernie4_5StateDictAdapter(config)`

---

**`tie_word_embeddings_support`** `TieSupport = TieSupport.TIED_ONLY`

---

**`vocab_size`** `= config.vocab_size`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.forward(
    input_ids: torch.Tensor,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    logits_to_keep: typing.Union[int, torch.Tensor] = 0,
    output_hidden_states: bool | None = None,
    attn_kwargs: typing.Any = {}
) -> transformers.modeling_outputs.CausalLMOutputWithPast
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.from_config(
    config: transformers.models.ernie4_5.configuration_ernie4_5.Ernie4_5Config,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.from_pretrained(
    pretrained_model_name_or_path: str,
    model_args = (),
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.get_input_embeddings()
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.get_output_embeddings()
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.set_input_embeddings(
    value
)
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.set_output_embeddings(
    new_embeddings
)
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5ForCausalLM.tie_weights()
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5Model(
    config: transformers.models.ernie4_5.configuration_ernie4_5.Ernie4_5Config,
    backend: nemo_automodel.components.models.common.BackendConfig
)
```

**Bases:** `Module`

Dense ERNIE 4.5 transformer body.

**`embed_tokens`**

---

**`layers`**

---

**`norm`**

---

**`rotary_emb`** `= Ernie4_5RotaryEmbedding(config)`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5Model.forward(
    input_ids: torch.Tensor,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5MoeBlock(
    layer_idx: int,
    config: transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig,
    backend: nemo_automodel.components.models.common.BackendConfig
)
```

**Bases:** `Module`

ERNIE 4.5 MoE decoder block.

**`input_layernorm`**

---

**`mlp`** `= MoE(moe_config, backend)`

---

**`post_attention_layernorm`**

---

**`self_attn`** `= Ernie4_5Attention(config, backend)`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5MoeBlock.forward(
    x: torch.Tensor,
    position_embeddings: tuple[torch.Tensor, torch.Tensor],
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM(
    config: transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs = {}
)
```

**Bases:** [HFCheckpointingMixin](/nemo-automodel/nemo_automodel/components/models/common/hf_checkpointing_mixin#nemo_automodel-components-models-common-hf_checkpointing_mixin-HFCheckpointingMixin), `Module`, [MoEFSDPSyncMixin](/nemo-automodel/nemo_automodel/components/moe/fsdp_mixin#nemo_automodel-components-moe-fsdp_mixin-MoEFSDPSyncMixin)

ERNIE 4.5 MoE causal language model with AutoModel EP support.

**`_keep_in_fp32_modules`** `= ['rotary_emb']`

---

**`_keep_in_fp32_modules_strict`**

---

**`_nemo_tied_weights_keys`** `= {'lm_head.weight': 'model.embed_tokens.weight'}`

---

**`_pp_plan`** `= {'lm_head': (['hidden_states'], ['logits'])}`

---

**`_tp_plan`** `= {'lm_head': 'colwise_rep'}`

---

**`backend`** `= backend or BackendConfig()`

---

**`lm_head`**

---

**`model`**

---

**`state_dict_adapter`**

---

**`tie_word_embeddings_support`** `TieSupport = TieSupport.TIED_ONLY`

---

**`vocab_size`** `= config.vocab_size`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.forward(
    input_ids: torch.Tensor,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    logits_to_keep: typing.Union[int, torch.Tensor] = 0,
    output_hidden_states: bool | None = None,
    attn_kwargs: typing.Any = {}
) -> transformers.modeling_outputs.CausalLMOutputWithPast
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.from_config(
    config: transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    backend: nemo_automodel.components.models.common.BackendConfig | None = None,
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.from_pretrained(
    pretrained_model_name_or_path: str,
    model_args = (),
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.get_capabilities(
    config
) -> nemo_automodel._transformers.model_capabilities.ModelCapabilities
```

classmethod

Return parallelism capabilities for a specific ERNIE-4.5 config.

ERNIE-4.5 ships in two flavors that share this class file but exercise
different code paths:

1. `baidu/ERNIE-4.5-21B-A3B-PT` -- MoE variant (this NeMo custom
   class). `moe_num_experts > 0` in the HF config.
   Demonstrated by examples/llm\_finetune/ernie4\_5/ernie4\_5\_21b\_a3b\_hellaswag.yaml
   (ep\_size=8).
2. `baidu/ERNIE-4.5-0.3B-PT` -- dense variant. No expert config.
   Demonstrated by examples/llm\_finetune/ernie4\_5/ernie4\_5\_0p3b\_hellaswag.yaml
   (tp/cp/pp/ep all 1).

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.get_input_embeddings()
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.get_output_embeddings()
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.set_input_embeddings(
    value
)
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.set_output_embeddings(
    new_embeddings
)
```

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeForCausalLM.tie_weights()
```

```python
class nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeModel(
    config: transformers.models.ernie4_5_moe.configuration_ernie4_5_moe.Ernie4_5_MoeConfig,
    backend: nemo_automodel.components.models.common.BackendConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    moe_overrides: dict | None = None
)
```

**Bases:** `Module`

ERNIE 4.5 MoE transformer body.

**`embed_tokens`**

---

**`layers`**

---

**`moe_config`** `= moe_config or MoEConfig(**moe_defaults)`

---

**`norm`**

---

**`rotary_emb`** `= Ernie4_5RotaryEmbedding(config)`

---

```python
nemo_automodel.components.models.ernie4_5.model.Ernie4_5_MoeModel.forward(
    input_ids: torch.Tensor,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | None = None,
    padding_mask: torch.Tensor | None = None,
    attn_kwargs: typing.Any = {}
) -> torch.Tensor
```

```python
nemo_automodel.components.models.ernie4_5.model._config_dtype(
    config: typing.Any
) -> torch.dtype
```

```python
nemo_automodel.components.models.ernie4_5.model.ModelClass = Ernie4_5_MoeForCausalLM
```