Hy-Embodied-VLM-1.0

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What Hy-Embodied-VLM-1.0 is

🔥 Updates [2026-07-15] 🚀 We have released Hy-Embodied-VLM-1.0! An efficient Mixture-of-Experts vision–language foundation model for embodied agents in the physical world, activating only ~3B parameters per token (~30B total) for high inference efficiency. Weights are available on Hugging Face, together with inference code for both HuggingFace transformers and vLLM. [2026-06-15] 🤖 We have released HY-VLA-0.5! The official code, UMI-trained weights and 2000+ hours of high-fidelity UMI data are now available. [2026-04-09] 🚀 We have released HY-Embodied-0.5, featuring the open-sourced HY-Embodied-0.5 MoT-2B weights on Hugging Face along with the official inference code! 📖 Abstract Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, adapting to evolving situations, and interacting with the physical world. In this report, we introduce Hy-Embodied-VLM-1.0, an efficient and powerful embodied foundation model specifically designed for embodied agents operating in the physical world. To cultivate such capabilities from the pre-training stage onward, we define an action-centric capability taxonomy comprising three progressive dimensions: Action-Relevant State Understanding, Action–Transition Reasoning, and Sequential and Adaptive Reasoning. Guided by this taxonomy, we develop a systematic data pipeline and curate data mixtures spanning both pre-training and post-training. To deliver strong physical-world understanding and interaction capabilities while supporting latency-sensitive deployment, we build our model on the Hy3-A3B language backbone and the Hy-ViT2 vision encoder. Its efficient Mixture-of-Experts architecture combines strong model capacity with high inference efficiency. We evaluate Hy-Embodied-VLM-1.0 on a comprehensive suite of 38 benchmarks covering embodied perception, physical-world understanding, and embodied reasoning. The model achieves the best performance among similarly sized models on 19 of the 38 benchmarks and substantially outperforms strong competitors, including Qwen3.6-A3B and Cosmos 3. Compared with the previous-generation Hy-Embodied-0.5 MoT-2B,

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys hy-embodied-vlm-1-0 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (hy-embodied-vlm-1-0 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"hy-embodied-vlm-1-0","messages":[{"role":"user","content":"Hello"}]}'

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