# loaders.model – Axolotl

# loaders.model

`loaders.model`

Model loader class implementation for loading, configuring, and patching various models.

## Classes

Name | Description
--- | ---
ModelLoader | Manages model configuration, initialization and application of patches during

### ModelLoader

    loaders.model.ModelLoader(
        cfg,
        tokenizer,
        *,
        inference=False,
        reference_model=False,
        **kwargs,
    )

Manages model configuration, initialization and application of patches during model loading.

This class orchestrates the entire process of loading a model from configuration to final preparation. It handles device mapping, quantization, attention mechanisms, adapter integration, and various optimizations.

#### The loading process includes

- Loading and validating model configuration
- Applying monkey patches for optimizations / fixes
- Setting up device mapping (including multi-GPU configurations)
- Configuring quantization
- Setting attention mechanisms (Flash Attention, SDPA, etc.)
- Loading and initializing the model
- Applying adapters (LoRA, QLoRA, etc.)

#### Attributes

Name | Type | Description
--- | --- | ---
model | PreTrainedModel | PeftModel | PeftMixedModel | The loaded model instance (available after load() is called).
model_kwargs | dict[str, Any] | Dictionary of keyword arguments passed to model initialization.
base_model |  | Name or path of the base model to load.
model_type |  | Type of model to load (e.g., `AutoModelForCausalLM`).
model_config |  | Configuration object for the model.
auto_model_loader |  | class used for loading the model (default: `AutoModelForCausalLM`).

#### Methods

Name | Description
--- | ---
load | Load and prepare the model with all configurations and patches.

##### load

    loaders.model.ModelLoader.load()

Load and prepare the model with all configurations and patches.

###### Returns

Name | Type | Description
--- | --- | ---
| tuple[PreTrainedModel | PeftModelForCausalLM, PeftConfig | None] | A tuple with the loaded model and its LoRA configuration (if applicable).