Instructions to use DeepGlint-AI/UniME-LLaVA-OneVision-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepGlint-AI/UniME-LLaVA-OneVision-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="DeepGlint-AI/UniME-LLaVA-OneVision-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("DeepGlint-AI/UniME-LLaVA-OneVision-7B")
model = AutoModelForImageTextToText.from_pretrained("DeepGlint-AI/UniME-LLaVA-OneVision-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?"}
        ]
    },
]
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepGlint-AI/UniME-LLaVA-OneVision-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepGlint-AI/UniME-LLaVA-OneVision-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": "DeepGlint-AI/UniME-LLaVA-OneVision-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/DeepGlint-AI/UniME-LLaVA-OneVision-7B

SGLang

How to use DeepGlint-AI/UniME-LLaVA-OneVision-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 "DeepGlint-AI/UniME-LLaVA-OneVision-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": "DeepGlint-AI/UniME-LLaVA-OneVision-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 "DeepGlint-AI/UniME-LLaVA-OneVision-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": "DeepGlint-AI/UniME-LLaVA-OneVision-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 DeepGlint-AI/UniME-LLaVA-OneVision-7B with Docker Model Runner:
```
docker model run hf.co/DeepGlint-AI/UniME-LLaVA-OneVision-7B
```

Update pipeline tag to zero-shot-image-classification

by nielsr HF Staff - opened Aug 6, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+10

-7

Files changed (1) hide show

README.md +10 -7

README.md CHANGED Viewed

@@ -1,15 +1,15 @@
 ---
-license: mit
 datasets:
 - TIGER-Lab/MMEB-train
 language:
 - en
 metrics:
 - recall
-base_model:
-- llava-hf/llava-onevision-qwen2-7b-ov-hf
-pipeline_tag: image-text-to-text
-library_name: transformers
 ---
 # Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
@@ -72,14 +72,17 @@ def appply_chat_template(image=None, text=None):
                 "role": "user",
                 "content": [
                     {"type": "image", "image": image},
-                    {"type": "text", "text": "Summary above image in one word:\n"},
                     ],
             }]
     elif text!= None:
         conversation_image = [{
                 "role": "user",
                 "content": [
-                    {"type": "text", "text": f"{text}\nSummary above sentence in one word:\n"},
                     ],
             }]
     return conversation_image

 ---
+base_model:
+- llava-hf/llava-onevision-qwen2-7b-ov-hf
 datasets:
 - TIGER-Lab/MMEB-train
 language:
 - en
+library_name: transformers
+license: mit
 metrics:
 - recall
+pipeline_tag: zero-shot-image-classification
 ---
 # Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
                 "role": "user",
                 "content": [
                     {"type": "image", "image": image},
+                    {"type": "text", "text": "Summary above image in one word:
+"},
                     ],
             }]
     elif text!= None:
         conversation_image = [{
                 "role": "user",
                 "content": [
+                    {"type": "text", "text": f"{text}
+Summary above sentence in one word:
+"},
                     ],
             }]
     return conversation_image