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license: mit
base_model: openvla/openvla-7b
tags:
- robotics
- vla
- openvla
- openvla-oft
- xarm
library_name: transformers
---
# oft_setting2_chunksize25_batch32_20k
OpenVLA-OFT checkpoint fine-tuned on real-world XArm data, **setting 2: cup stacking**.
**Intermediate checkpoint at 20000 / 30000 training steps** (see [oft_setting2_chunksize25_batch32](https://huggingface.co/AAyano/oft_setting2_chunksize25_batch32) for the final 30k-step model).
LoRA weights (rank 32) are already merged into the [openvla/openvla-7b](https://huggingface.co/openvla/openvla-7b) base — this repo is a standalone model.
## Training
- Recipe: OpenVLA-OFT (L1 regression, FiLM, parallel decoding)
- Steps: **20000** of 30000 (LR 5e-4, decay after 20000), effective batch size 32
- Action chunk: 25, action dim: 7, proprio dim: 6 (BOUNDS_Q99 normalization)
- Inputs: 2 images (3rd-person + wrist) + proprio
## Contents
- Merged model shards (`model-*.safetensors`)
- `action_head--20000_checkpoint.pt`, `proprio_projector--20000_checkpoint.pt`, `vision_backbone--20000_checkpoint.pt` — OFT components needed for deployment
- `dataset_statistics.json` — action/proprio normalization statistics
- `oft_training_config.json` — training configuration snapshot
Use with the [openvla-oft](https://github.com/moojink/openvla-oft) codebase; set XArm constants (chunk 25, action dim 7, proprio dim 6) in `prismatic/vla/constants.py` before deployment.
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