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license: gemma
library_name: lerobot
pipeline_tag: robotics
base_model: Dexmal/DM05
tags:
- robotics
- robot-control
- vision-language-action
- vla
- lerobot
- dm05
- dm0.5
- opendm
---
# DM0.5 for LeRobot
`Dexmal/DM05-Lerobot` is the LeRobot-format base checkpoint of
[DM0.5](https://huggingface.co/Dexmal/DM05), adapted from
[OpenDM](https://github.com/dexmal/OpenDM). It predicts continuous action chunks from images, robot state,
and language instructions.
This is a base model for supervised fine-tuning, not a LIBERO-, RoboTwin-, or robot-specific checkpoint.
## Fine-tuning
```bash
lerobot-train \
--dataset.repo_id=HuggingFaceVLA/libero \
--policy.path=Dexmal/DM05-Lerobot \
--policy.add_state=false \
--policy.chunk_size=10 \
--policy.n_action_steps=10 \
--policy.repo_id=your_repo_id \
--output_dir=outputs/train/dm05-libero \
--steps=50000 \
--batch_size=8 \
--policy.device=cuda
```
For local-only training, replace `--policy.repo_id=...` with `--policy.push_to_hub=false`.
This LIBERO recipe matches OpenDM: it excludes state from the prompt and learns stored actions unchanged. Keep
`policy.add_state=true` unless the target recipe specifies otherwise. Policy action representation and environment
control mode are configured independently.
Absolute-action training uses the standard LeRobot state/action statistics in `meta/stats.json`. Relative-action
training requires separately prepared statistics because its arm targets are `action - state`:
```bash
uv run python -m lerobot.policies.dm05.prepare_stats_dm05 \
--repo-id=org/dataset \
--root=/path/to/dataset \
--chunk-size=10 \
--drop-n-last-frames=1 \
--force
```
The command writes `meta/stats.json` in place. Match its chunk, episode selection, and excluded-joint settings to
training. Without target statistics, checkpoint statistics are reused with a warning and are valid only for the
same feature contract and distribution.
## Evaluation
Evaluate a fine-tuned checkpoint:
```bash
MUJOCO_GL=egl lerobot-eval \
--policy.path=/path/to/checkpoint/pretrained_model \
--env.type=libero \
--env.task=libero_spatial \
--env.control_mode=relative \
--policy.device=cuda
```
## Checkpoint contract
The base checkpoint uses OpenDM's 14-dimensional state/action schema. Fresh SFT takes its effective feature schema
and statistics from the target LeRobot dataset.
Load the complete checkpoint directory with `DM05Policy.from_pretrained()` or `--policy.path`; policy config,
tokenizer, preprocessing state, and weights are all required.
## Resources
- [DM0.5 technical blog](https://www.dexmal.com/blog/dm0.5/index_en.html)
- [OpenDM repository](https://github.com/dexmal/OpenDM)
- [LeRobot repository](https://github.com/huggingface/lerobot)
## Citation
```bibtex
@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}
}
```
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