---
title: DM05-SO101-Pick-Cube
canonical_url: "https://www.modelscope.cn/models/Dexmal/DM05-SO101-Pick-Cube"
md_url: "https://www.modelscope.cn/models/Dexmal/DM05-SO101-Pick-Cube.md"
repository: Dexmal/DM05-SO101-Pick-Cube
last_updated: 2026-07-28
license: gemma
pipeline_tag: robotics
tasks:
  - robotics
base_model:
  - Dexmal/DM05
base_model_relation: adapter
parameters: 324.3M
tensor_type:
  - BF16
library_name:
  - lora
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 72
stars: 0
tags:
  - robotics
  - robot-control
  - vision-language-action
  - vla
  - lora
  - dm05
  - dm0.5
  - so101
  - opendm
---

# DM05-SO101-Pick-Cube

> DM05-SO101-Pick-Cube - Dexmal 在 ModelScope 开源的模型。DM05-SO101-Pick-Cube

Dexmal/DM05-SO101-Pick-Cube 是 ModelScope 魔搭社区上的 324.3M 参数robotics模型，采用 gemma 许可，基于 Dexmal/DM05 构建。

- **Repository**: Dexmal/DM05-SO101-Pick-Cube
- **License**: gemma
- **Tasks**: robotics
- **Parameters**: 324.3M
- **Base model**: Dexmal/DM05
- **Tags**: robotics, robot-control, vision-language-action, vla, lora, dm05, dm0.5, so101, opendm
- **Downloads**: 72
- **Stars**: 0
- **Last updated**: 2026-07-28

Source: https://www.modelscope.cn/models/Dexmal/DM05-SO101-Pick-Cube

---

# DM05-SO101-Pick-Cube

![DM0.5](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/header.png)

<p align="center">
  <a href="https://www.dexmal.com/blog/dm0.5/index_en.html"><img src="https://img.shields.io/badge/📖-Tech_Blog-blue" alt="Tech Blog"></a>
  <a href="https://github.com/dexmal/opendm"><img src="https://img.shields.io/badge/GitHub-OpenDM-181717?logo=github" alt="GitHub"></a>
  <a href="https://maas.dexmal.com/"><img src="https://img.shields.io/badge/MaaS-Online-brightgreen.svg" alt="MaaS"></a>
</p>

## Introduction

DM05-SO101-Pick-Cube is the SO101 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.

This checkpoint is specifically trained for the SO101 pick cube task using LoRA fine-tuning.

## Quick Start

We recommend using Docker to set up the runtime environment first, which helps avoid version mismatches across CUDA, PyTorch, flash-attn, and other dependencies on the host machine.

### Requirements

```text
System requirements:
Ubuntu 20.04 / 22.04
NVIDIA GPU
NVIDIA Driver
Docker
NVIDIA Container Toolkit
Conda (optional, only required for local pip installation)

Recommended GPUs:
RTX 4090, A100, H100, H20
8 GPUs are recommended for training, and 1 GPU is sufficient for deployment inference.
```

### Docker Installation

```bash
git clone https://github.com/dexmal/opendm.git
cd opendm

docker run -it --rm --gpus all --network host \
  --name opendm \
  --shm-size=16g \
  -v "$PWD":/app/opendm \
  -w /app/opendm \
  dexmal/opendm:latest /bin/bash

# Run from the OpenDM repository root inside the container.
conda activate opendm
pip install -e .
```

### Local Installation

```bash
conda create -n opendm python=3.10 -y
conda activate opendm

pip install torch torchvision \
  --index-url https://download.pytorch.org/whl/cu128

pip install ninja packaging
MAX_JOBS=2 pip install flash-attn --no-build-isolation

# Enter the OpenDM repository root.
cd opendm
pip install -e .
```

## SO101 Inference

Use the SO101-specific experiment configuration when running inference with this checkpoint. Run this command from the OpenDM repository root:

```bash
script/dm05_launcher.sh \
  --exp playground/dm05_so101_lora.py \
  --task inference \
  --nproc_per_node 1 \
  --model-config.model-name-or-path ./checkpoints/DM05-SO101-Pick-Cube \
  --model-config.chunk-size 50 \
  --inference-config.output-action-dim 6 \
  --inference-config.image-keys images_1 images_2 \
  --inference-config.port 7891
```

For the complete training and inference workflow, see the [DM05 SO101 LoRA Training Guide](https://github.com/dexmal/opendm/blob/main/docs/en/dm05_so101_lora_training.md).

## Community and Support

- Learn more about Dexmal products and model updates on the [Dexmal website](https://www.dexmal.com/).
- If you encounter issues, please report them through [GitHub Issues](https://github.com/dexmal/opendm/issues).
- For further discussion, scan the [WeChat QR code](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/wechat.jpeg) to contact us.

We will continue to release more model weights, technical documentation, and examples. If this project is helpful to you, please consider giving us a star on GitHub [![GitHub](https://img.shields.io/github/stars/dexmal/opendm?color=5B5BD6)](https://github.com/dexmal/opendm). Your support helps us move forward.

## 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}
}
```