# nodus.integrations.transformers

> `NodusCallback` for the Hugging Face `Trainer` and every TRL trainer built on it (ADR-103).

Source: https://www.nodus-compute.ai/docs/reference/python/nodus-integrations-transformers/
Build revision: 53bfc045e800bf7bb44fa18623013c7aa9c9971e

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`NodusCallback` for the Hugging Face `Trainer` and every TRL trainer built on it (ADR-103).

```python
from nodus.integrations.transformers import NodusCallback

trainer = SFTTrainer(..., callbacks=[NodusCallback()])
trainer.train(resume_from_checkpoint=nodus.integrations.transformers.last_checkpoint())
```

Point `output_dir` at `nodus.state_dir()` so the Trainer’s `checkpoint-<step>` folders are the recovery state.

## `NodusCallback`

```python
class NodusCallback(phase: str = 'Training') -> None
```

Metrics, progress, phases and the checkpoint handshake for a `transformers.Trainer`.

### `NodusCallback.on_log`

```python
on_log(args: Any, state: Any, control: Any, logs: dict[str, Any] | None = None, **kwargs: Any) -> None
```

### `NodusCallback.on_save`

```python
on_save(args: Any, state: Any, control: Any, **kwargs: Any) -> None
```

### `NodusCallback.on_step_end`

```python
on_step_end(args: Any, state: Any, control: Any, **kwargs: Any) -> Any
```

### `NodusCallback.on_train_begin`

```python
on_train_begin(args: Any, state: Any, control: Any, **kwargs: Any) -> None
```

## `complete_checkpoints`

```python
complete_checkpoints(directory: str | os.PathLike[str] | None = None, *, require_training_state: bool = True, process_index: int | None = None) -> list[str]
```

Complete Trainer checkpoints, newest first, for selecting a step present on every distributed worker.

Supply the worker’s global `process_index` to require its own RNG state in a distributed checkpoint. Each worker inspects its own state directory; callers coordinate the common step before resuming.

## `last_checkpoint`

```python
last_checkpoint(directory: str | os.PathLike[str] | None = None, *, require_training_state: bool = True, process_index: int | None = None) -> str | None
```

The newest complete `checkpoint-<step>` under `directory` (default the state dir), for `resume_from_checkpoint`.

A folder counts only when it holds everything the Trainer reloads to continue that step: its `trainer_state.json` at that step, the model weights, and the optimizer, scheduler and RNG state, in the layout the Trainer wrote (single process or DDP, FSDP or DeepSpeed). An attempt stopped mid-save resumes from the checkpoint before it. Set `require_training_state=False` only to intentionally warm-start from a weights-only save; that starts fresh optimizer and RNG state and does not preserve the training trajectory.