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# nemo_automodel.components.models.gpt_oss.model

## Module Contents

### Classes

| Name                                                                                     | Description |
| ---------------------------------------------------------------------------------------- | ----------- |
| [`Block`](#nemo_automodel-components-models-gpt_oss-model-Block)                         | -           |
| [`GptOssForCausalLM`](#nemo_automodel-components-models-gpt_oss-model-GptOssForCausalLM) | -           |
| [`GptOssModel`](#nemo_automodel-components-models-gpt_oss-model-GptOssModel)             | -           |

### Data

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

[`logger`](#nemo_automodel-components-models-gpt_oss-model-logger)

### API

```python
class nemo_automodel.components.models.gpt_oss.model.Block(
    layer_idx: int,
    config: transformers.models.gpt_oss.configuration_gpt_oss.GptOssConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig,
    backend: nemo_automodel.components.models.common.BackendConfig
)
```

**Bases:** `Module`

```python
nemo_automodel.components.models.gpt_oss.model.Block._mlp(
    x: torch.Tensor,
    padding_mask: torch.Tensor | None
) -> torch.Tensor
```

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

```python
nemo_automodel.components.models.gpt_oss.model.Block.init_weights(
    buffer_device: torch.device
)
```

```python
class nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM(
    config: transformers.models.gpt_oss.configuration_gpt_oss.GptOssConfig,
    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)

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.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: typing.Optional[bool] = None,
    attn_kwargs: typing.Any = {}
) -> transformers.modeling_outputs.CausalLMOutputWithPast
```

Forward pass returning :class:`~transformers.modeling_outputs.CausalLMOutputWithPast`.

**Parameters:**

Token IDs. BSHD: `[B, S]`; THD: `[1, T]` (squeezed internally).

Optional position indices.

Optional attention mask.

Optional padding mask.

If `0` (default) compute logits for all positions; otherwise
compute logits only for the last `logits_to_keep` positions (used by
memory-efficient fused cross-entropy / generation).

When truthy, the returned output carries the final hidden
states (the input to `lm_head`) so the recipe can run fused cross-entropy.

Additional attention kwargs forwarded to the base model
(e.g. `qkv_format`, `cu_seqlens`, `seq_idx`, CP kwargs).

**Returns:** `CausalLMOutputWithPast`

`CausalLMOutputWithPast` with `logits` and optional `hidden_states`.

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.from_config(
    config: transformers.models.gpt_oss.configuration_gpt_oss.GptOssConfig,
    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.gpt_oss.model.GptOssForCausalLM.from_pretrained(
    pretrained_model_name_or_path: str,
    model_args = (),
    kwargs = {}
)
```

classmethod

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.get_input_embeddings()
```

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.get_output_embeddings()
```

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

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.set_input_embeddings(
    value
)
```

```python
nemo_automodel.components.models.gpt_oss.model.GptOssForCausalLM.set_output_embeddings(
    new_embeddings
)
```

```python
class nemo_automodel.components.models.gpt_oss.model.GptOssModel(
    config: transformers.models.gpt_oss.configuration_gpt_oss.GptOssConfig,
    backend: nemo_automodel.components.models.common.BackendConfig,
    moe_config: nemo_automodel.components.moe.config.MoEConfig | None = None,
    moe_overrides: dict | None = None
)
```

**Bases:** `Module`

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

```python
nemo_automodel.components.models.gpt_oss.model.ModelClass = GptOssForCausalLM
```

```python
nemo_automodel.components.models.gpt_oss.model.logger = logging.getLogger(__name__)
```