# Coda-Robotics/OpenVLA-ER-Select-Book-LoRA

## Model Description

This is a LoRA adapter weights only (requires base OpenVLA model) of OpenVLA, fine-tuned on the select_book dataset.

## Training Details

- **Dataset:** select_book
- **Number of Episodes:** 479
- **Batch Size:** 8
- **Training Steps:** 20000
- **Learning Rate:** 2e-5
- **LoRA Configuration:**
  - Rank: 32
  - Dropout: 0.0
  - Target Modules: all-linear

## Usage

```python
from transformers import AutoProcessor, AutoModelForVision2Seq

# Load the model and processor
processor = AutoProcessor.from_pretrained("Coda-Robotics/OpenVLA-ER-Select-Book-LoRA")
model = AutoModelForVision2Seq.from_pretrained("Coda-Robotics/OpenVLA-ER-Select-Book-LoRA")

# Process an image
image = ...  # Load your image
inputs = processor(images=image, return_tensors="pt")
outputs = model.generate(**inputs)
text = processor.decode(outputs[0], skip_special_tokens=True)
```


## Using with PEFT

To use this adapter with the base OpenVLA model:

```python
from transformers import AutoProcessor, AutoModelForVision2Seq
from peft import PeftModel, PeftConfig

# Load the base model
base_model = AutoModelForVision2Seq.from_pretrained("openvla/openvla-7b")

# Load the LoRA adapter
adapter_model = PeftModel.from_pretrained(base_model, "{model_name}")

# Merge weights for faster inference (optional)
merged_model = adapter_model.merge_and_unload()
```