metadata
license: other
library_name: pytorch
pipeline_tag: robotics
tags:
- long-wam
- libero
- cod
Long-WAM LIBERO-COD
Long-WAM policy weights for LIBERO. COD jointly denoises future-video latents and actions.
Download
hf download Efficient-Large-Model/Long-WAM-LIBERO-COD --local-dir ./weights/Long-WAM-LIBERO-COD
Inference
Load model.pt with the matching Long-WAM runtime, using config.yaml and
the supplied dataset_stats.json for preprocessing and normalization.
P48 context uses 48 past control steps plus the current observation, encoded into four clean latent frames.
Generate two future-video latents and a 32-step action chunk together using 4 joint denoising steps (sigma 1 to 0). Replan every 10 executed actions. Reset history at the start of each episode.
The matching inference runtime and base-model VAE/text assets are required separately. This release contains weights and inference settings only; optimizer states and training artifacts are not included.