Instructions to use zhibinlan/UME-R1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhibinlan/UME-R1-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="zhibinlan/UME-R1-7B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-7B")
model = AutoModelForMultimodalLM.from_pretrained("zhibinlan/UME-R1-7B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhibinlan/UME-R1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhibinlan/UME-R1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhibinlan/UME-R1-7B",
		"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/zhibinlan/UME-R1-7B

SGLang

How to use zhibinlan/UME-R1-7B 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 "zhibinlan/UME-R1-7B" \
    --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": "zhibinlan/UME-R1-7B",
		"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 "zhibinlan/UME-R1-7B" \
        --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": "zhibinlan/UME-R1-7B",
		"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 zhibinlan/UME-R1-7B with Docker Model Runner:
```
docker model run hf.co/zhibinlan/UME-R1-7B
```

zhibinlan commited on Oct 15, 2025

Commit

391930a

1 Parent(s): caab229

update

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -57,13 +57,13 @@ from qwen_vl_utils import process_vision_info
 import torch
 model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "zhibinlan/UME-R1-2B",
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
     device_map="cuda:0",
 )
-processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-2B")
 prompt = '''Represent the above input text, images, videos, or any combination of the three as embeddings.
 First output the thinking process in <think> </think> tags and then summarize the entire input in a word or sentence.
@@ -148,7 +148,7 @@ from transformers import Qwen2VLForConditionalGeneration,AutoProcessor
 from qwen_vl_utils import process_vision_info
 import torch
-pretrained_path = "release/UME-R1-2B"
 # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
 model = Qwen2VLForConditionalGeneration.from_pretrained(

 import torch
 model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "zhibinlan/UME-R1-7B",
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
     device_map="cuda:0",
 )
+processor = AutoProcessor.from_pretrained("zhibinlan/UME-R1-7B")
 prompt = '''Represent the above input text, images, videos, or any combination of the three as embeddings.
 First output the thinking process in <think> </think> tags and then summarize the entire input in a word or sentence.
 from qwen_vl_utils import process_vision_info
 import torch
+pretrained_path = "zhibinlan/UME-R1-7B"
 # We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
 model = Qwen2VLForConditionalGeneration.from_pretrained(