Instructions to use Dexmal/DM05-Table30v2-W1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dexmal/DM05-Table30v2-W1 with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("Dexmal/DM05-Table30v2-W1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
DM05-W1 (RoboChallenge)
OpenDM-format BF16 checkpoint for DOS W1 on RoboChallenge Table 30 v2.
Use with OpenDM third_party/robochallenge_inference
(configs/generalist/w1.yaml).
See the DM05 RoboChallenge Table 30 v2 Inference Guide.
Weights: BF16 model.safetensors.
Model Card
| Field | Value |
|---|---|
| Config | generalist/w1 |
| Env vars | W1_CHECKPOINT / W1_NORM_STATS |
OpenDM robot_type |
DOS W1 |
| Control | Joint relative |
| Cameras | Head / Left wrist / Right wrist |
| Platform cams | cam_high β image_0, cam_left_wrist β image_1, cam_right_wrist β image_2 |
| Native state / action stats | 14 / 14 (state + action quantile norm_stats.json) |
| Defaults | action_horizon=25, is_history=false |
Table30 Tasks
fold_the_clothes, hold_the_tray_with_both_hands, place_objects_into_desk_drawer,
put_in_pen_container, put_the_shoes_back, stack_bowls, sweep_the_trash,
tidy_up_the_makeup_table, tie_a_knot, untie_the_shoelaces
Per-task horizon overrides: third_party/robochallenge_inference/configs/generalist/w1.yaml β task_overrides
(several tasks use 30). Gripper post-process:
third_party/robochallenge_inference/policies/output_tricks.py β apply_w1_gripper_trick.
Use with OpenDM RoboChallenge Inference
The RoboChallenge client now lives in OpenDM at
third_party/robochallenge_inference (configs/generalist/w1.yaml).
See the DM05 RoboChallenge Table 30 v2 Inference Guide.
# From the OpenDM repository root.
export OPENDM_ROOT=/path/to/opendm
pip install -e ".[fast-infer]"
cd third_party/robochallenge_inference
export W1_CHECKPOINT=/path/to/DM05-W1
export W1_NORM_STATS=${W1_CHECKPOINT}/norm_stats.json
pip install -r requirements.txt
python execute.py --config-name generalist/w1 \
user_id=YOUR_USER_ID \
submission_id=YOUR_SUBMISSION_ID
Override without env vars:
python execute.py --config-name generalist/w1 \
checkpoint=/path/to/DM05-W1 \
norm_stats=/path/to/DM05-W1/norm_stats.json \
user_id=YOUR_USER_ID \
submission_id=YOUR_SUBMISSION_ID
If W1_NORM_STATS is unset, the client falls back to ${W1_CHECKPOINT}/norm_stats.json.
Runtime Profile
From third_party/robochallenge_inference/configs/default.yaml β robot_profiles.w1:
action_type=joint,action_mode=relativeis_history=false,add_state=true,speed=0.5- attn implementations:
auto
Files
.
βββ config.json
βββ model.safetensors
βββ norm_stats.json # includes state + action
βββ tokenizer.json
βββ tokenizer_config.json
βββ processor_config.json
βββ chat_template.jinja
βββ generation_config.json
βββ README.md
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}
}
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