- Libraries
- Transformers
How to use mobilint/Qwen3-VL-4B-Instruct with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="mobilint/Qwen3-VL-4B-Instruct", trust_remote_code=True, 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?"}
]
},
]
pipe(text=messages) # Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mobilint/Qwen3-VL-4B-Instruct", trust_remote_code=True, dtype="auto", device_map="auto") - Mobilint
How to use mobilint/Qwen3-VL-4B-Instruct with Mobilint:
# pip install mblt-model-zoo
from mblt_model_zoo.vision import MBLT_Engine
model = MBLT_Engine(
model_cls="Qwen3-VL-4B-Instruct",
model_type="DEFAULT",
model_path="",
core_mode="global8",
)
try:
image = model.preprocess("path/to/image.jpg")
output = model(image)
result = model.postprocess(output)
finally:
model.dispose()
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mobilint/Qwen3-VL-4B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mobilint/Qwen3-VL-4B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "mobilint/Qwen3-VL-4B-Instruct",
"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/mobilint/Qwen3-VL-4B-Instruct
- SGLang
How to use mobilint/Qwen3-VL-4B-Instruct 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 "mobilint/Qwen3-VL-4B-Instruct" \
--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": "mobilint/Qwen3-VL-4B-Instruct",
"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 "mobilint/Qwen3-VL-4B-Instruct" \
--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": "mobilint/Qwen3-VL-4B-Instruct",
"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 mobilint/Qwen3-VL-4B-Instruct with Docker Model Runner:
docker model run hf.co/mobilint/Qwen3-VL-4B-Instruct
