Parameter efficient fine-tuning with 🤗 PEFT

🤗 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.

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 \
 --peft.lora_alpha=64

Note the --peft.method_type parameter that let’s you select which PEFT method to use. Here we use 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, and the LoRA scaling factor with --peft.lora_alpha (where scaling = lora_alpha / r). 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). 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).