# Parameter efficient fine-tuning with 🤗 PEFT

[🤗 PEFT](https://github.com/huggingface/peft) (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting
large pretrained models such as pre-trained policies (e.g., SmolVLA, π₀, ...) to new tasks without training all
of the model's parameters while yielding comparable performance.

Install the `lerobot[peft]` optional package to enable PEFT support.

To read about all the possible methods of adaption, please refer to the [🤗 PEFT docs](https://huggingface.co/docs/peft/index).

## Training SmolVLA

In this section we'll show you how to train a pre-trained SmolVLA policy with PEFT on the libero dataset.
For brevity we're only training on the `libero_spatial` subset. We will use `lerobot/smolvla_base` as the model
to parameter efficiently fine-tune:

```
lerobot-train \
 --policy.path=lerobot/smolvla_base \
 --policy.repo_id=your_hub_name/my_libero_smolvla \
 --dataset.repo_id=HuggingFaceVLA/libero \
 --policy.output_features=null \
 --policy.input_features=null \
 --policy.optimizer_lr=1e-3 \
 --policy.scheduler_decay_lr=1e-4 \
 --env.type=libero \
 --env.task=libero_spatial \
 --steps=100000 \
 --batch_size=32 \
 --peft.method_type=LORA \
 --peft.r=64
```

Note the `--peft.method_type` parameter that let's you select which PEFT method to use. Here we use
[LoRA](https://huggingface.co/docs/peft/main/en/package_reference/lora) (Low-Rank Adapter) which is probably the most
popular fine-tuning method to date. Low-rank adaption means that we only fine-tune a matrix with comparably low rank
instead of the full weight matrix. This rank can be specified using the `--peft.r` parameter. The higher the rank
the closer you get to full fine-tuning

There are more complex methods that have more parameters. These are not yet supported, feel free to raise an issue
if you want to see a specific PEFT method supported.

By default, PEFT will target the `q_proj` and `v_proj` layers of the LM expert in SmolVLA. It will also target the
state and action projection matrices as they are most likely task-dependent. If you need to target different layers
you can use `--peft.target_modules` to specify which layers to target. You can refer to the respective PEFT method's
documentation to see what inputs are supported, (e.g., [LoRA's target_modules documentation](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraConfig.target_modules)).
Usually a list of suffixes or a regex are supported. For example, to target the MLPs of the `lm_expert` instead of
the `q` and `v` projections, use:

```
--peft.target_modules='(model\.vlm_with_expert\.lm_expert\..*\.(down|gate|up)_proj|.*\.(state_proj|action_in_proj|action_out_proj|action_time_mlp_in|action_time_mlp_out))'
```

In case you need to fully fine-tune a layer instead of just adapting it, you can supply a list of layer suffixes
to the `--peft.full_training_modules` parameter:

```
--peft.full_training_modules=["state_proj"]
```

The learning rate and the scheduled target learning rate can usually be scaled by a factor of 10 compared to the
learning rate used for full fine-tuning (e.g., 1e-4 normal, so 1e-3 using LoRA).
