Instructions to use microsoft/Mage-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Mage-VL with Transformers:

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

pipe = pipeline("image-text-to-text", model="microsoft/Mage-VL", 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 AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained("microsoft/Mage-VL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Mage-VL with vLLM:

Install from pip and serve model

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

SGLang

How to use microsoft/Mage-VL 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 "microsoft/Mage-VL" \
    --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": "microsoft/Mage-VL",
		"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 "microsoft/Mage-VL" \
        --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": "microsoft/Mage-VL",
		"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 microsoft/Mage-VL with Docker Model Runner:
```
docker model run hf.co/microsoft/Mage-VL
```

Mage-VL / preprocessor_config.json

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	{
	"data_format": "channels_first",
	"default_to_square": true,
	"do_convert_rgb": true,
	"do_normalize": true,
	"do_rescale": true,
	"do_resize": true,
	"image_mean": [
	0.48145466,
	0.4578275,
	0.40821073
	],
	"image_processor_type": "Qwen2VLImageProcessor",
	"image_std": [
	0.26862954,
	0.26130258,
	0.27577711
	],
	"max_pixels": 4000000,
	"merge_size": 2,
	"min_pixels": 3136,
	"patch_size": 16,
	"processor_class": "MageVLProcessor",
	"auto_map": {
	"AutoProcessor": "processing_mage_vl.MageVLProcessor",
	"AutoVideoProcessor": "video_processing_mage_vl.MageVLVideoProcessor"
	},
	"codec": {
	"engine": "hevc",
	"target_canvas": 32,
	"group_size": 32,
	"images_per_group": 4,
	"patch": 14,
	"min_group_frames": 8,
	"max_group_frames": 64,
	"spatial_mask_mode": "off",
	"dcvc": {
	"qp": 42,
	"reset_interval": 64,
	"intra_period": -1,
	"max_side": 0,
	"seq_len_frames": 0,
	"patch": 16,
	"canvas_token_side": null,
	"num_sampled_frames": 256,
	"grouping_mode": "readiness",
	"readiness_sum_threshold_mode": "auto",
	"group_size": 32,
	"images_per_group": 4,
	"max_pixels": 150000,
	"min_group_frames": 8,
	"max_group_frames": 128,
	"readiness_coverage_bins": 3,
	"readiness_delta_ratio": 0.05,
	"bitcost_grid": "sub",
	"bitcost_pct": 99,
	"decode_backsearch_max": 16,
	"canvas_format": "jpg",
	"per_frame_cap_ratio": 1.2,
	"bottom_atten": 0.5,
	"bottom_band": 0.1,
	"threshold_scale": 1.0,
	"random_select": false,
	"random_seed": 0
	}
	},
	"resample": 3,
	"rescale_factor": 0.00392156862745098,
	"size": {
	"longest_edge": 4000000,
	"shortest_edge": 3136
	},
	"temporal_patch_size": 1
	}