peft/docs/source/developer_guides/low_level_api.md

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# Adapter injection

With PEFT, you can inject trainable adapters into any `torch` module which allows you to use adapter methods without relying on the modeling classes in PEFT. Currently, PEFT supports injecting [LoRA](../conceptual_guides/adapter#low-rank-adaptation-lora), [AdaLoRA](../conceptual_guides/adapter#adaptive-low-rank-adaptation-adalora), and [IA3](../conceptual_guides/ia3) into models because for these adapters, inplace modification of the model is sufficient for finetuning it.

Check the table below to see when you should inject adapters.

| Pros | Cons |
|---|---|
| the model is modified inplace, keeping all the original attributes and methods | manually write the `from_pretrained` and `save_pretrained` utility functions from Hugging Face to save and load adapters |
| works for any `torch` module and modality | doesn't work with any of the utility methods provided by `PeftModel` such as disabling and merging adapters |

## Creating a new PEFT model

To perform the adapter injection, use the [`inject_adapter_in_model`] method. This method takes 3 arguments, the PEFT config, the model, and an optional adapter name. You can also attach multiple adapters to the model if you call [`inject_adapter_in_model`] multiple times with different adapter names.

For example, to inject LoRA adapters into the `linear` submodule of the `DummyModel` module:

```python
import torch
from peft import inject_adapter_in_model, LoraConfig

class DummyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.embedding = torch.nn.Embedding(10, 10)
        self.linear = torch.nn.Linear(10, 10)
        self.lm_head = torch.nn.Linear(10, 10)

    def forward(self, input_ids):
        x = self.embedding(input_ids)
        x = self.linear(x)
        x = self.lm_head(x)
        return x


lora_config = LoraConfig(
    lora_alpha=16,
    lora_dropout=0.1,
    r=64,
    bias="none",
    target_modules=["linear"],
)

model = DummyModel()
model = inject_adapter_in_model(lora_config, model)

dummy_inputs = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]])
dummy_outputs = model(dummy_inputs)
```

Print the model to see that the adapters have been correctly injected.

```bash
DummyModel(
  (embedding): Embedding(10, 10)
  (linear): Linear(
    in_features=10, out_features=10, bias=True
    (lora_dropout): ModuleDict(
      (default): Dropout(p=0.1, inplace=False)
    )
    (lora_A): ModuleDict(
      (default): Linear(in_features=10, out_features=64, bias=False)
    )
    (lora_B): ModuleDict(
      (default): Linear(in_features=64, out_features=10, bias=False)
    )
    (lora_embedding_A): ParameterDict()
    (lora_embedding_B): ParameterDict()
  )
  (lm_head): Linear(in_features=10, out_features=10, bias=True)
)
```

## Saving the model

To only save the adapter, use the [`get_peft_model_state_dict`] function:

```python
from peft import get_peft_model_state_dict

peft_state_dict = get_peft_model_state_dict(model)
print(peft_state_dict)
```

Otherwise, `model.state_dict()` returns the full state dict of the model.

## Loading the model

After loading the saved `state_dict`, it can be applied using the [`set_peft_model_state_dict`] function:

```python
from peft import set_peft_model_state_dict

model = DummyModel()
model = inject_adapter_in_model(lora_config, model)
outcome = set_peft_model_state_dict(model, peft_state_dict)
# check that there were no wrong keys
print(outcome.unexpected_keys)
```

If injecting the adapter is slow or you need to load a large number of adapters, you may use an optimization that allows to create an "empty" adapter on meta device and only fills the weights with real weights when the [`set_peft_model_state_dict`] is called. To do this, pass `low_cpu_mem_usage=True` to both [`inject_adapter_in_model`] and [`set_peft_model_state_dict`].

```python
model = DummyModel()
model = inject_adapter_in_model(lora_config, model, low_cpu_mem_usage=True)

print(model.linear.lora_A["default"].weight.device.type == "meta")  # should be True
set_peft_model_state_dict(model, peft_state_dict, low_cpu_mem_usage=True)
print(model.linear.lora_A["default"].weight.device.type == "cpu")  # should be True
```