Instructions to use ViFortune-AI/Qwen3-VL-1B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ViFortune-AI/Qwen3-VL-1B-Merged with Transformers:

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

pipe = pipeline("image-text-to-text", model="ViFortune-AI/Qwen3-VL-1B-Merged")
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("ViFortune-AI/Qwen3-VL-1B-Merged")
model = AutoModelForImageTextToText.from_pretrained("ViFortune-AI/Qwen3-VL-1B-Merged")
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 ViFortune-AI/Qwen3-VL-1B-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ViFortune-AI/Qwen3-VL-1B-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ViFortune-AI/Qwen3-VL-1B-Merged",
		"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/ViFortune-AI/Qwen3-VL-1B-Merged

SGLang

How to use ViFortune-AI/Qwen3-VL-1B-Merged 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 "ViFortune-AI/Qwen3-VL-1B-Merged" \
    --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": "ViFortune-AI/Qwen3-VL-1B-Merged",
		"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 "ViFortune-AI/Qwen3-VL-1B-Merged" \
        --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": "ViFortune-AI/Qwen3-VL-1B-Merged",
		"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 ViFortune-AI/Qwen3-VL-1B-Merged with Docker Model Runner:
```
docker model run hf.co/ViFortune-AI/Qwen3-VL-1B-Merged
```

Dimension Mismatch Between Vision Encoder (2048) and LLM (1024) without a Projector

by cuimou - opened Sep 29, 2025

Discussion

cuimou

Sep 29, 2025

First of all, thank you for your interesting work on merging the Qwen3 language model with the Qwen2.5-VL vision encoder. I was very excited to try it out.

I've been attempting to run inference with the ViFortune-AI/Qwen3-VL-1B-Merged model, but I'm consistently encountering a structural issue related to mismatched hidden dimensions between the vision and language components.

Tnt3o5

ViFortune-AI org Sep 30, 2025

•

edited Sep 30, 2025

may be it should be AutoModelForVision2Seq or Qwen2_5_VLForConditionalGeneration instead of AutoModelForCausalLM. Try it again.
But the result is not guaranteed since it is just a merged version.

Tnt3o5 changed discussion status to closed Sep 30, 2025

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