Instructions to use joon-stack/univla-simpler-success50-h369-concatmh-hyp-pool-step5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joon-stack/univla-simpler-success50-h369-concatmh-hyp-pool-step5000 with Transformers:
# Load model directly from transformers import OpenVLAForActionPrediction model = OpenVLAForActionPrediction.from_pretrained("joon-stack/univla-simpler-success50-h369-concatmh-hyp-pool-step5000", dtype="auto") - Notebooks
- Google Colab
- Kaggle
joon-stack/univla-simpler-success50-h369-concatmh-hyp-pool-step5000
Full merged UniVLA checkpoint for Simpler success50 H369 concat-MH fine-tuning.
- model:
h369_concat_hyper_full - horizon style:
h369_concat_mh - horizons:
3,6,9 - decoder type:
pool - step:
5000 - latent action token length:
15 - per-horizon latent action token length:
5 - predicted action horizon:
10
This repository contains full merged VLA weights plus:
action_decoder-5000.ptconcat_mh_action_decoder-pool-5000.pt
Eval must use the concat-MH decoder implementation. The policy still consumes the current image and instruction; it does not require separate h3/h6/h9 images at eval time.
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