Model Detail
Xiaomi-Robotics-U0
—Xiaomi-Robotics-U0 is a code generation model with 17.1B parameters released by XiaomiRobotics. The model is registered under the any-to-any pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
Xiaomi-Robotics-U0 ships with 17.1B parameters. Total weight footprint is approximately 34.1 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Xiaomi-Robotics-U0 is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model
arXiv:2607.11643v1 Announce Type: cross Abstract: Recent foundation image and video generation models offer strong generalization and controllability, but their direct application to embodied scenarios is limited by requirements for multi-view consistency, geometric coherence, and robot embodiment c
Xiaomi-CocktailASR-1 Technical Report
arXiv:2609.11274v1 Announce Type: cross Abstract: Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing
Xiaomi-TabLDM: A Tabular Foundation Model Technical Report
arXiv:2609.03880v2 Announce Type: replace Abstract: We introduce Xiaomi-TabLDM, a tabular large data foundation model for classification and regression via in-context learning, which delivers superior prediction accuracy without requiring task-specific fine-tuning. Pretrained exclusively on syntheti
Xiaomi-GUI-0 Technical Report
arXiv:2606.31410v2 Announce Type: replace Abstract: Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions such as tapping, swiping, text entry, and navigation. However, existing GUI agents are trained an
