Instructions to use skt/A.X-4.0-VL-Light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skt/A.X-4.0-VL-Light with Transformers:

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

pipe = pipeline("image-text-to-text", model="skt/A.X-4.0-VL-Light", 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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("skt/A.X-4.0-VL-Light", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use skt/A.X-4.0-VL-Light with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skt/A.X-4.0-VL-Light"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skt/A.X-4.0-VL-Light",
		"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/skt/A.X-4.0-VL-Light

SGLang

How to use skt/A.X-4.0-VL-Light 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 "skt/A.X-4.0-VL-Light" \
    --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": "skt/A.X-4.0-VL-Light",
		"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 "skt/A.X-4.0-VL-Light" \
        --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": "skt/A.X-4.0-VL-Light",
		"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 skt/A.X-4.0-VL-Light with Docker Model Runner:
```
docker model run hf.co/skt/A.X-4.0-VL-Light
```

Possible Error in HallusionBench Score Reporting

by jaycha - opened Jul 29, 2025

Discussion

jaycha

Jul 29, 2025

It seems that the HallusionBench score may have been reported as aAcc.
IMO it would be better either to report the average of aAcc, qAcc, and fAcc (as done in OpenCompass) or to explicitly state that the reported score represents aAcc.
Thanks :)

Minsoo0707

SK Telecom org Jul 30, 2025

Thanks a lot for your interest and feedback!

Our reported score reflected aAcc, and we have now updated the results to show the average of aAcc, qAcc, and fAcc as you suggested.

Please refer to the detailed results below.

Before

	A.X 4.0 VL Light	Qwen2.5-VL-7B	InternVL3-8B	VARCO-VISION-2.0-14B	Qwen2.5-VL-32B
HallusionBench	69.6	70.2	66.3	70.4	72.0

After

	A.X 4.0 VL Light	Qwen2.5-VL-7B	InternVL3-8B	VARCO-VISION-2.0-14B	Qwen2.5-VL-32B
HallusionBench	54.2	52.7	49.6	53.8	58.0

liveseongho changed discussion status to closed Aug 4, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment