--- title: DM05-libero canonical_url: "https://www.modelscope.cn/models/Dexmal/DM05-libero" md_url: "https://www.modelscope.cn/models/Dexmal/DM05-libero.md" repository: Dexmal/DM05-libero last_updated: 2026-07-21 license: gemma model_type: - dm05 architectures: - DM05ForConditionalGeneration base_model: - Dexmal/DM05 base_model_relation: finetune parameters: 5.8B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 96 stars: 0 tags: - robotics - robot-control - vision-language-action - vla - dm05 - dm0.5 - libero - opendm --- # DM05-libero > DM05-libero - Dexmal 在 ModelScope 开源的模型。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… Dexmal/DM05-libero 是 ModelScope 魔搭社区上的 5.8B 参数机器学习模型，采用 gemma 许可，基于 Dexmal/DM05 构建。 - **Repository**: Dexmal/DM05-libero - **License**: gemma - **Parameters**: 5.8B - **Base model**: Dexmal/DM05 - **Tags**: robotics, robot-control, vision-language-action, vla, dm05, dm0.5, libero, opendm - **Downloads**: 96 - **Stars**: 0 - **Last updated**: 2026-07-21 Source: https://www.modelscope.cn/models/Dexmal/DM05-libero --- # DM05-libero ![DM0.5](https://raw.githubusercontent.com/dexmal/opendm/main/docs/image/header.png)

Tech Blog GitHub LIBERO Dataset MaaS

## Introduction 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 | ## 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 . ``` ## LIBERO Testing Use the LIBERO-specific experiment configuration when evaluating this checkpoint. The inference service and benchmark client run separately and communicate over HTTP. When possible, use one GPU for the inference service and another for benchmark evaluation. For the complete training and evaluation workflow, see the [DM05 LIBERO Training and Evaluation Guide](https://github.com/dexmal/opendm/blob/main/docs/en/dm05_libero.md). ### Start the LIBERO Inference Service Run this command from the OpenDM repository root. The checkpoint must contain the matching `norm_stats.json`. ```bash script/dm05_launcher.sh \ --exp playground/dm05_libero.py \ --task inference \ --nproc_per_node 1 \ --model-config.model-name-or-path ./checkpoints/DM05-libero-bf16 \ --model-config.chunk-size 10 \ --inference-config.output-action-dim 7 \ --inference-config.image-keys images_1 images_2 \ --inference-config.port 7891 ``` Keep the service running while executing the benchmark. ### Prepare the Benchmark Client ```bash git clone https://github.com/dexmal/dexbotic-benchmark.git cd dexbotic-benchmark git submodule update --init --recursive libero docker pull dexmal/dexbotic_benchmark ``` Edit `evaluation/configs/libero/example_dm05_libero.yaml` so that `base_url` points to the inference service. Select one of `libero_spatial`, `libero_goal`, `libero_object`, or `libero_10` as the benchmark: ```yaml benchmark: libero_spatial num_trails_per_task: 50 num_steps_wait: 10 seed: 7 base_url: http://localhost:7891 replan_step: 10 send_state: true send_image: - image - wrist_image discrete_gripper: false use_text_template: false output_dir: "results/example_dm05_libero_spatial" ``` ### Run the LIBERO Benchmark From the `dexbotic-benchmark` repository root, run: ```bash docker run --rm --gpus all --network host \ -v "$(pwd)":/workspace \ -w /workspace \ dexmal/dexbotic_benchmark \ bash /workspace/scripts/env_sh/libero.sh \ /workspace/evaluation/configs/libero/example_dm05_libero.yaml ``` Evaluation artifacts are written under the configured `output_dir`, including `results.json`, `config.yaml`, `logs/evaluation.log`, and rollout videos under `videos/*.mp4`. ## 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} } ```