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EasyWAM-MoT-Cosmos25
OpenMOSS-Team/EasyWAM-MoT-Cosmos25
EasyWAM-MoT-Cosmos25 is a robotics model from OpenMOSS-Team. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for easywam. The card lists the license as cc-by-nc-4.0.
This repository hosts released EasyWAM-MoT checkpoints built on Cosmos-Predict2.5-2B. EasyWAM-MoT uses separate Video DiT and Action DiT experts whose tokens interact through mixed self-attention. At inference time, i…
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Updated Sep 22, 2026
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At a glance
- Task
- Robotics
- Library
- easywam
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Sep 21, 2026
- Updated
- Sep 22, 2026
- SHA
- c3217cb2
Base models
Model card
From the Hugging Face model README
EasyWAM-MoT-Cosmos25
This repository hosts released EasyWAM-MoT checkpoints built on Cosmos-Predict2.5-2B. EasyWAM-MoT uses separate Video DiT and Action DiT experts whose tokens interact through mixed self-attention. At inference time, it predicts actions without generating future video. The checkpoints are trained with the EasyWAM codebase.
LIBERO
Results
Task success rate (%) under the EasyWAM LIBERO evaluation protocol. All results use state_position: sequence; higher is better. Models using the same Cosmos-Predict2.5-2B backbone are shown together for comparison, and 🔥 marks the architecture released in this repository.
Full-Parameter
| Model | Spatial | Object | Goal | LIBERO-10 | Avg. |
|---|---|---|---|---|---|
| EasyWAM-Unified | 97.8 | 99.4 | 97.0 | 93.0 | 96.8 |
| 🔥 EasyWAM-MoT | 98.0 | 98.4 | 98.4 | 95.6 | 97.6 |
| EasyWAM-MoT-Joint | 98.6 | 99.8 | 98.4 | 96.0 | 98.2 |
| EasyWAM-MoT-IDM | 99.4 | 99.4 | 99.8 | 98.4 | 99.3 |
| EasyWAM-Hidden | 97.2 | 98.8 | 96.0 | 95.0 | 96.8 |
LIBERO-Plus
| Model | Background | Camera | Language | Layout | Light | Noise | Robot | Avg. |
|---|---|---|---|---|---|---|---|---|
| EasyWAM-Unified | 72.7 | 63.7 | 90.1 | 82.6 | 89.2 | 72.1 | 79.2 | 78.2 |
| 🔥 EasyWAM-MoT | 60.7 | 75.1 | 92.3 | 82.6 | 92.6 | 81.3 | 58.5 | 77.7 |
| EasyWAM-MoT-Joint | 62.1 | 60.8 | 96.6 | 84.5 | 94.4 | 69.4 | 76.9 | 77.7 |
| EasyWAM-MoT-IDM | 66.4 | 59.8 | 93.8 | 85.7 | 89.0 | 72.3 | 81.9 | 78.4 |
| EasyWAM-Hidden | 59.5 | 65.9 | 92.6 | 85.0 | 89.1 | 68.3 | 82.5 | 77.8 |
LIBERO-Plus results are reported for full-parameter checkpoints only. See the complete EasyWAM benchmark table for source results and comparisons across backbones.
Download
hf download OpenMOSS-Team/EasyWAM-MoT-Cosmos25 \
libero_mot_cosmos25.pt \
libero_dataset_stats.json \
--local-dir ./checkpoints
Evaluation
Install EasyWAM, prepare the Cosmos-Predict2.5-2B dependencies using the backbone guide, and set up the simulator using the LIBERO evaluation guide. Then run:
# Full-parameter checkpoint
python experiments/libero/run_libero_manager.py \
task=libero_easywam_mot_cosmos25 \
ckpt=./checkpoints/libero_mot_cosmos25.pt \
EVALUATION.dataset_stats_path=./checkpoints/libero_dataset_stats.json
The default protocol evaluates all four LIBERO suites with 50 trials per task. Add MULTIRUN.num_gpus=<gpu-count> to distribute evaluation across multiple GPUs.
Available Checkpoints
- LIBERO full-parameter checkpoint:
libero_mot_cosmos25.pt - LIBERO normalization statistics:
libero_dataset_stats.json - State-token placement:
sequence - Checkpoint format: EasyWAM PyTorch checkpoint (
.pt)
Project
- Project page: http://openmoss.ai/EasyWAM/
- Code: https://github.com/OpenMOSS/EasyWAM
License and Citation
EasyWAM code is released under the MIT License. Released checkpoints in this repository use CC BY-NC 4.0 and remain subject to the terms of their base models and training data. If EasyWAM is useful in your research, please cite:
@misc{easywam2026,
title = {EasyWAM: A Unified and Efficient Framework for Training and Evaluating World Action Models},
author = {EasyWAM-Team},
year = {2026},
url = {https://github.com/OpenMOSS/EasyWAM}
}
- Task
- Robotics
- Library
- easywam
- License
- cc-by-nc-4.0
- Created
- Sep 21, 2026
- Updated
- Sep 22, 2026