Instructions to use tencent/Hy-Embodied-VLM-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hy-Embodied-VLM-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="tencent/Hy-Embodied-VLM-1.0", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("tencent/Hy-Embodied-VLM-1.0", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tencent/Hy-Embodied-VLM-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tencent/Hy-Embodied-VLM-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Hy-Embodied-VLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tencent/Hy-Embodied-VLM-1.0

SGLang

How to use tencent/Hy-Embodied-VLM-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tencent/Hy-Embodied-VLM-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Hy-Embodied-VLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tencent/Hy-Embodied-VLM-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tencent/Hy-Embodied-VLM-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use tencent/Hy-Embodied-VLM-1.0 with Docker Model Runner:
```
docker model run hf.co/tencent/Hy-Embodied-VLM-1.0
```

Lavender11 commited on 6 days ago

Commit

f65d452

verified ·

1 Parent(s): 4a29880

Add model card

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -30,7 +30,7 @@ language:
 ## 🔥 Updates
-  * **`[2026-07-15]`** 🚀 We have released **Hy-Embodied-VLM-1.0**! An efficient 30B Mixture-of-Experts vision–language foundation model for embodied agents in the physical world. Weights are available on [Hugging Face](https://huggingface.co/tencent/Hy-Embodied-VLM-1.0), together with inference code for both HuggingFace `transformers` and vLLM.
   * **`[2026-06-15]`** 🤖 We have released **HY-VLA-0.5**! The [official code](https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA), UMI-trained [weights](https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-UMI) and 2000+ hours of high-fidelity UMI [data](https://huggingface.co/datasets/tencent/Hy-Embodied-0.5-VLA-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](https://huggingface.co/tencent/HY-Embodied-0.5/tree/main) along with the official inference code!

 ## 🔥 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](https://huggingface.co/tencent/Hy-Embodied-VLM-1.0), together with inference code for both HuggingFace `transformers` and vLLM.
   * **`[2026-06-15]`** 🤖 We have released **HY-VLA-0.5**! The [official code](https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA), UMI-trained [weights](https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-UMI) and 2000+ hours of high-fidelity UMI [data](https://huggingface.co/datasets/tencent/Hy-Embodied-0.5-VLA-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](https://huggingface.co/tencent/HY-Embodied-0.5/tree/main) along with the official inference code!