# train { #axolotl.train }

`train`

Prepare and train a model on a dataset. Can also infer from a model or merge lora

## Functions

| Name | Description |
| --- | --- |
| [create_model_card](#axolotl.train.create_model_card) | Create a model card for the trained model if needed. |
| [determine_resume_checkpoint](#axolotl.train.determine_resume_checkpoint) | Determine the checkpoint to resume from based on configuration. |
| [execute_training](#axolotl.train.execute_training) | Execute the training process with appropriate backend configurations. |
| [handle_untrained_tokens_fix](#axolotl.train.handle_untrained_tokens_fix) | Apply fixes for untrained tokens if configured. |
| [save_initial_configs](#axolotl.train.save_initial_configs) | Save initial configurations before training. |
| [save_trained_model](#axolotl.train.save_trained_model) | Save the trained model according to configuration and training setup. |
| [setup_model_and_tokenizer](#axolotl.train.setup_model_and_tokenizer) | Load the tokenizer, processor (for multimodal models), and model based on configuration. |
| [setup_model_and_trainer](#axolotl.train.setup_model_and_trainer) | Load model, tokenizer, trainer, etc. Helper function to encapsulate the full |
| [setup_model_card](#axolotl.train.setup_model_card) | Set up the Axolotl badge and add the Axolotl config to the model card if available. |
| [setup_reference_model](#axolotl.train.setup_reference_model) | Set up the reference model for RL training if needed. |
| [setup_signal_handler](#axolotl.train.setup_signal_handler) | Set up signal handler for graceful termination. |
| [train](#axolotl.train.train) | Train a model on the given dataset. |

### create_model_card { #axolotl.train.create_model_card }

```python
train.create_model_card(cfg, trainer)
```

Create a model card for the trained model if needed.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    trainer: The trainer object with model card creation capabilities.

### determine_resume_checkpoint { #axolotl.train.determine_resume_checkpoint }

```python
train.determine_resume_checkpoint(cfg)
```

Determine the checkpoint to resume from based on configuration.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.

Returns:
    Path to the checkpoint to resume from, or `None` if not resuming.

### execute_training { #axolotl.train.execute_training }

```python
train.execute_training(cfg, trainer, resume_from_checkpoint)
```

Execute the training process with appropriate backend configurations.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    trainer: The configured trainer object.
    resume_from_checkpoint: Path to checkpoint to resume from, if applicable.

### handle_untrained_tokens_fix { #axolotl.train.handle_untrained_tokens_fix }

```python
train.handle_untrained_tokens_fix(
    cfg,
    model,
    tokenizer,
    train_dataset,
    safe_serialization,
)
```

Apply fixes for untrained tokens if configured.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    model: The model to apply fixes to.
    tokenizer: The tokenizer for token identification.
    train_dataset: The training dataset to use.
    safe_serialization: Whether to use safe serialization when saving.

### save_initial_configs { #axolotl.train.save_initial_configs }

```python
train.save_initial_configs(cfg, tokenizer, model, peft_config)
```

Save initial configurations before training.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    tokenizer: The tokenizer to save.
    model: The model to save configuration for.
    peft_config: The PEFT configuration to save if applicable.

### save_trained_model { #axolotl.train.save_trained_model }

```python
train.save_trained_model(cfg, trainer, model, safe_serialization)
```

Save the trained model according to configuration and training setup.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    trainer: The trainer object.
    model: The trained model to save.
    safe_serialization: Whether to use safe serialization.

### setup_model_and_tokenizer { #axolotl.train.setup_model_and_tokenizer }

```python
train.setup_model_and_tokenizer(cfg)
```

Load the tokenizer, processor (for multimodal models), and model based on configuration.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.

Returns:
    Tuple containing model, tokenizer, `peft_config` (if LoRA / QLoRA, else
        `None`), and processor (if multimodal, else `None`).

### setup_model_and_trainer { #axolotl.train.setup_model_and_trainer }

```python
train.setup_model_and_trainer(cfg, dataset_meta)
```

Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
trainer setup.

Args:
    cfg: The configuration dictionary with training parameters.
    dataset_meta: Object with training, validation datasets and metadata.

Returns:
    Tuple of:
        - Trainer (Causal or RLHF)
        - Model
        - Tokenizer
        - PEFT config

### setup_model_card { #axolotl.train.setup_model_card }

```python
train.setup_model_card(cfg)
```

Set up the Axolotl badge and add the Axolotl config to the model card if available.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.

### setup_reference_model { #axolotl.train.setup_reference_model }

```python
train.setup_reference_model(cfg, tokenizer)
```

Set up the reference model for RL training if needed.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    tokenizer: The tokenizer to use for the reference model.

Returns:
    Reference model if needed for RL training, `None` otherwise.

### setup_signal_handler { #axolotl.train.setup_signal_handler }

```python
train.setup_signal_handler(cfg, model, safe_serialization)
```

Set up signal handler for graceful termination.

Args:
    cfg: Dictionary mapping `axolotl` config keys to values.
    model: The model to save on termination
    safe_serialization: Whether to use safe serialization when saving

### train { #axolotl.train.train }

```python
train.train(cfg, dataset_meta)
```

Train a model on the given dataset.

Args:
    cfg: The configuration dictionary with training parameters
    dataset_meta: Object with training, validation datasets and metadata

Returns:
    Tuple of (model, tokenizer) after training