README.md

---
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

```bash
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.