Robotics
Transformers
Safetensors
dm05
text-generation
robot-control
vision-language-action
vla
dm0.5
libero
opendm
Instructions to use Dexmal/DM05-libero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dexmal/DM05-libero with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Dexmal/DM05-libero", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: gemma
library_name: transformers
base_model:
- Dexmal/DM05
datasets:
- Dexmal/libero
tags:
- robotics
- robot-control
- vision-language-action
- vla
- dm05
- dm0.5
- libero
- opendm
DM05-libero
DM05-libero is the LIBERO fine-tuned checkpoint of DM0.5, Dexmal's open-world Vision-Language-Action foundation model for embodied intelligence. DM0.5 uses a Gemma3 4B vision-language backbone with a 680M Action Expert to generate continuous robot actions, and is designed for natural-language manipulation, zero-shot generalization, efficient downstream fine-tuning, long-horizon historical context, robust policy behavior, and transfer across robot embodiments.
LIBERO Results
| Method | Spatial | Object | Goal | Long | Average |
|---|---|---|---|---|---|
| DM0.5 | 99.0 | 99.8 | 99.6 | 97.4 | 99.0 |
How to Use
This model is intended to be used with the OpenDM LIBERO workflow. See the OpenDM README and LIBERO guide for installation, checkpoint download, inference service startup, and benchmark evaluation commands.
Citation
@misc{dm05,
title = {{DM0.5}: An Open-World Foundation Model for General-Purpose Embodied Intelligence},
author = {{Dexmal Team}},
month = {July},
year = {2026},
url = {https://www.dexmal.com/blog/dm0.5/index_en.html}
}