Instructions to use uber0802/CRONOS-spatialvla-T2560-normal-EER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use uber0802/CRONOS-spatialvla-T2560-normal-EER with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
CRONOS SpatialVLA β T2560 normal (EER)
LoRA adapters (rank 32) on top of
IPEC-COMMUNITY/spatialvla-4b-224-sft-bridge,
trained with PPO in CRONOS on ManiSkill PickPlaceNxM-v1 (2x2, four_group_sequential_2x2).
These are the final checkpoints (episode_0004) of two seeds.
Layout
seed0/episode_0004/
seed1/episode_0004/
adapter_model.safetensors # LoRA adapter weights
adapter_config.json # PEFT config (base class: SpatialVLAForActionPredictionWithValueHead)
dataset_statistics.json # action normalization stats (unnorm key: bridge_orig/1.0.0)
training_state.pt # value head + VLA/value-head optimizer states + step counters (for resuming)
scheduler_state.json # task scheduler state
run_config.yaml # full training config
README.md # PEFT auto-generated card
Training setup
| Env | PickPlaceNxM-v1, 2x2, 64 envs, obj_set: rand |
| Task order | sequential, 4 groups, 16 tasks |
| Episode length | 2560 steps (segment / task length 80) |
| Reset | per_episode, reset_robot: true (EER) |
| Episodes | 4 (655,360 total env steps) |
| Algorithm | PPO, 1 epoch, grad accum 20, gamma 0.99, lambda 0.95 |
| LR | VLA 1e-4, value head 3e-3, grad norm 10 |
| LoRA | r=32, alpha=16 |
| Seeds | 0, 1 |
| CRONOS | V0.3, git rev e45cdb86f5cb717413258a1713bab07820fc9ea0 |
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Model tree for uber0802/CRONOS-spatialvla-T2560-normal-EER
Base model
google/paligemma2-3b-pt-224 Finetuned
IPEC-COMMUNITY/spatialvla-4b-224-pt