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Overview

OpenVLA-OFT is a LoRA-based fine-tuning framework for Vision-Language-Action models built on the openvla-7b base. Cyberwave supports training and deploying OpenVLA-OFT models on your custom datasets using the openvla (RLDS) dataset format.
OpenVLA-OFT uses the openvla (RLDS) dataset format for training, which differs from the LeRobot v3 format used by SmolVLA. The platform handles format selection automatically based on the model type.

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Model Selection

When creating an ML model on Cyberwave, select OpenVLA-OFT as the model type to automatically route training and inference jobs to a cloud node with the openvla-oft-policy profile.

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Training

Training jobs are dispatched to a Cyberwave Cloud Node. The cloud node:
  1. Downloads the dataset in RLDS format (format=openvla)
  2. Downloads the base model weights (openvla/openvla-7b by default)
  3. Runs the LoRA fine-tuning loop, routing all metrics to the Cyberwave dashboard
  4. Compresses and uploads the final checkpoint to the platform

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Key training parameters

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Inference

Trained checkpoints can be deployed directly from the Cyberwave platform. The cloud node loads the fine-tuned LoRA weights and streams predictions back to your robot via MQTT.

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Dataset format

OpenVLA-OFT requires datasets in the RLDS (TensorFlow Datasets) format. When you upload a dataset through the Cyberwave platform, it is automatically converted and made available for download in the openvla format.