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# nemo_automodel.components.models.laguna

## Submodules

* **[`nemo_automodel.components.models.laguna.config`](/nemo-automodel/nemo_automodel/components/models/laguna/config)**
* **[`nemo_automodel.components.models.laguna.model`](/nemo-automodel/nemo_automodel/components/models/laguna/model)**
* **[`nemo_automodel.components.models.laguna.state_dict_adapter`](/nemo-automodel/nemo_automodel/components/models/laguna/state_dict_adapter)**

## Package Contents

### Classes

| Name                                                                                    | Description                                                      |
| --------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
| [`LagunaConfig`](#nemo_automodel-components-models-laguna-config-LagunaConfig)          | Configuration for Poolside Laguna causal language models.        |
| [`LagunaForCausalLM`](#nemo_automodel-components-models-laguna-model-LagunaForCausalLM) | Causal LM wrapper for Laguna with Automodel checkpoint adapters. |
| [`LagunaModel`](#nemo_automodel-components-models-laguna-model-LagunaModel)             | Backbone model for Laguna SFT.                                   |

### Data

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

### API

```python
class nemo_automodel.components.models.laguna.config.LagunaConfig(
    vocab_size: int = 100352,
    hidden_size: int = 2048,
    intermediate_size: int = 8192,
    num_hidden_layers: int = 48,
    num_attention_heads: int = 32,
    num_key_value_heads: int = 8,
    head_dim: int = 128,
    qkv_bias: bool = False,
    attention_bias: bool = False,
    gating: bool | str = True,
    gating_types: list[str] | None = None,
    hidden_act: str = 'silu',
    max_position_embeddings: int = 4096,
    initializer_range: float = 0.02,
    rms_norm_eps: float = 1e-06,
    use_cache: bool = True,
    tie_word_embeddings: bool = False,
    rope_parameters: dict[str, typing.Any] | None = None,
    partial_rotary_factor: float | None = None,
    attention_dropout: float = 0.0,
    sliding_window: int | None = None,
    layer_types: list[str] | None = None,
    num_attention_heads_per_layer: list[int] | None = None,
    swa_attention_sink_enabled: bool = False,
    swa_rope_parameters: dict[str, typing.Any] | None = None,
    num_experts: int = 256,
    num_experts_per_tok: int = 16,
    moe_intermediate_size: int = 1024,
    shared_expert_intermediate_size: int = 1024,
    norm_topk_prob: bool = True,
    decoder_sparse_step: int = 1,
    mlp_only_layers: list[int] | None = None,
    mlp_layer_types: list[str] | None = None,
    router_aux_loss_coef: float = 0.001,
    moe_routed_scaling_factor: float = 1.0,
    moe_apply_router_weight_on_input: bool = False,
    moe_router_logit_softcapping: float = 0.0,
    output_router_logits: bool = False,
    torch_dtype: str = 'bfloat16',
    kwargs = {}
)
```

**Bases:** `PretrainedConfig`

Configuration for Poolside Laguna causal language models.

**`bos_token_id`** `int | None = None`

---

**`eos_token_id`** `int | list[int] | None = None`

---

**`keys_to_ignore_at_inference`** `= ['past_key_values']`

---

**`model_type`** `= 'laguna'`

---

**`pad_token_id`** `int | None = None`

---

```python
nemo_automodel.components.models.laguna.config.LagunaConfig.from_dict(
    config_dict: dict[str, typing.Any],
    kwargs = {}
)
```

classmethod

```python
class nemo_automodel.components.models.laguna.model.LagunaForCausalLM(
    config: nemo_automodel.components.models.laguna.config.LagunaConfig,
    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)

Causal LM wrapper for Laguna with Automodel checkpoint adapters.

**`_keep_in_fp32_modules_strict`** `= ['mlp.gate.e_score_correction_bias', 'rotary_emb']`

---

**`_uses_hf_attention`** `bool = True`

---

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

---

**`lm_head`**

---

**`model`**

---

**`state_dict_adapter`**

---

**`tie_word_embeddings_support`** `TieSupport = TieSupport.UNTIED_ONLY`

---

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.forward(
    input_ids: torch.Tensor | None = None,
    inputs_embeds: torch.Tensor | None = None,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | dict[str, torch.Tensor] | None = None,
    padding_mask: torch.Tensor | None = None,
    past_key_values: typing.Any = None,
    use_cache: bool | None = None,
    logits_to_keep: typing.Union[int, torch.Tensor] = 0,
    output_hidden_states: bool | None = None,
    kwargs: typing.Any = {}
) -> transformers.modeling_outputs.CausalLMOutputWithPast
```

Run the Laguna causal language model.

**Parameters:**

**`input_ids`** `torch.Tensor | None` — default: None

Optional token IDs of shape \[batch, sequence].

---

**`inputs_embeds`** `torch.Tensor | None` — default: None

Optional embeddings of shape \[batch, sequence, hidden].

---

**`position_ids`** `torch.Tensor | None` — default: None

Optional position IDs of shape \[batch, sequence].

---

**`attention_mask`** `torch.Tensor | dict[str, torch.Tensor] | None` — default: None

Optional 2D bool/int mask of shape \[batch, sequence], a 4D additive
mask, or a mapping with per-attention-type masks keyed by "full\_attention" and
"sliding\_attention".

---

**`padding_mask`** `torch.Tensor | None` — default: None

Optional bool tensor of shape \[batch, sequence], where True marks tokens
excluded from MoE routing.

---

**`past_key_values`** `Any` — default: None

Unsupported KV-cache state.

---

**`use_cache`** `bool | None` — default: None

Unsupported cache flag.

---

**`logits_to_keep`** `Union[int, torch.Tensor]` — default: 0

If 0, compute logits for all sequence positions. If an int or tensor,
compute logits only for the selected trailing positions.

---

**`output_hidden_states`** `bool | None` — default: None

When true, include final hidden states in the output.

---

**`**kwargs`** `Any` — default: \{}

Additional attention backend arguments.

---

**Returns:** `CausalLMOutputWithPast`

Causal LM output with logits and optional hidden states.

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.from_config(
    config: nemo_automodel.components.models.laguna.config.LagunaConfig,
    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.laguna.model.LagunaForCausalLM.from_pretrained(
    pretrained_model_name_or_path: str,
    model_args = (),
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.get_input_embeddings()
```

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.get_output_embeddings()
```

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.initialize_weights(
    buffer_device: torch.device | None = None,
    dtype: torch.dtype = torch.bfloat16
) -> None
```

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.set_input_embeddings(
    value
)
```

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.set_output_embeddings(
    new_embeddings
)
```

```python
nemo_automodel.components.models.laguna.model.LagunaForCausalLM.update_moe_gate_bias() -> None
```

```python
class nemo_automodel.components.models.laguna.model.LagunaModel(
    config: nemo_automodel.components.models.laguna.config.LagunaConfig,
    backend: nemo_automodel.components.models.common.BackendConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    moe_overrides: dict | None = None
)
```

**Bases:** `Module`

Backbone model for Laguna SFT.

**`embed_tokens`**

---

**`has_sliding_layers`** `= 'sliding_attention' in config.layer_types`

---

**`layers`**

---

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

---

**`norm`**

---

**`rotary_emb`**

---

**`swa_rotary_emb`**

---

```python
nemo_automodel.components.models.laguna.model.LagunaModel._build_causal_mask_mapping(
    inputs_embeds: torch.Tensor,
    attention_mask: torch.Tensor | dict[str, torch.Tensor] | None,
    position_ids: torch.Tensor
) -> dict[str, torch.Tensor]
```

Build additive attention masks for full and sliding Laguna layers.

**Parameters:**

**`inputs_embeds`** `torch.Tensor`

Input embedding tensor of shape \[batch, sequence, hidden].

---

**`attention_mask`** `torch.Tensor | dict[str, torch.Tensor] | None`

Optional sequence mask of shape \[batch, sequence], 4D bool/additive
mask, or mapping with masks keyed by "full\_attention" and "sliding\_attention".

---

**`position_ids`** `torch.Tensor`

Position tensor of shape \[batch, sequence].

---

**Returns:** `dict[str, torch.Tensor]`

Mapping from attention type to additive masks of shape

```python
nemo_automodel.components.models.laguna.model.LagunaModel.forward(
    input_ids: torch.Tensor | None = None,
    inputs_embeds: torch.Tensor | None = None,
    position_ids: torch.Tensor | None = None,
    attention_mask: torch.Tensor | dict[str, torch.Tensor] | None = None,
    padding_mask: torch.Tensor | None = None,
    kwargs: typing.Any = {}
) -> torch.Tensor
```

Run the Laguna decoder stack.

**Parameters:**

**`input_ids`** `torch.Tensor | None` — default: None

Optional token IDs of shape \[batch, sequence].

---

**`inputs_embeds`** `torch.Tensor | None` — default: None

Optional embeddings of shape \[batch, sequence, hidden].

---

**`position_ids`** `torch.Tensor | None` — default: None

Optional position IDs of shape \[batch, sequence].

---

**`attention_mask`** `torch.Tensor | dict[str, torch.Tensor] | None` — default: None

Optional 2D bool/int mask of shape \[batch, sequence], a 4D additive
mask, or a mapping with per-attention-type masks keyed by "full\_attention" and
"sliding\_attention".

---

**`padding_mask`** `torch.Tensor | None` — default: None

Optional bool tensor of shape \[batch, sequence], where True marks tokens
excluded from MoE routing.

---

**`**kwargs`** `Any` — default: \{}

Additional attention backend arguments.

---

**Returns:** `torch.Tensor`

Final hidden states of shape \[batch, sequence, hidden].

```python
nemo_automodel.components.models.laguna.model.LagunaModel.init_weights(
    buffer_device: torch.device | None = None
) -> None
```

```python
nemo_automodel.components.models.laguna.model.ModelClass = LagunaForCausalLM
```

## Docs

- [nemo_automodel.components.models.laguna.config](https://docs.nvidia.com/nemo/automodel/v0.4/nemo-automodel/nemo_automodel/components/models/laguna/config.md)
- [nemo_automodel.components.models.laguna.model](https://docs.nvidia.com/nemo/automodel/v0.4/nemo-automodel/nemo_automodel/components/models/laguna/model.md)
- [nemo_automodel.components.models.laguna.state_dict_adapter](https://docs.nvidia.com/nemo/automodel/v0.4/nemo-automodel/nemo_automodel/components/models/laguna/state_dict_adapter.md)