Instructions to use davidwdw/rlinf_libero_vla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidwdw/rlinf_libero_vla with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("davidwdw/rlinf_libero_vla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
RLinf LIBERO VLA Archive
This archive stores reproducible RLinf/OpenVLA-OFT LIBERO training recipes, model artifacts, checkpoints, logs, and evaluation summaries.
Layout
This model archive is intentionally separate from the independent
/media/david/HDD/training_recipe/ repository:
models/: base VLA model artifacts.checkpoints/: distributed PPO checkpoints by training run and global step.results/: metrics, logs, and TensorBoard outputs by training run.runs/: raw logs and TensorBoard snapshots./media/david/HDD/training_recipe/: one self-contained recipe directory per training run, containing only YAML, source revision, hyperparameters, and README.
The first archived run is the 4-GPU H20 task-3 PPO experiment completed on
2026-09-18. Its final checkpoint is under
checkpoints/2026-09-17_task3_ppo_4gpu_h20_gpu0126/global_step_100/; its
recipe is under the independent training_recipe directory.
Storage and GitHub guidance
The archive is stored on centre at /media/david/HDD/rlinf_libero_vla
(the mounted filesystem is uppercase HDD; /media/david/hdd is not
available). The recipe YAMLs and READMEs are suitable for a GitHub repository.
The model and distributed checkpoints are large binary artifacts (about 29 GB
and 59 GB); keep them in Git LFS or an artifact store rather than regular Git
history. The checkpoint byte totals are recorded beside the checkpoints.