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 / neural_codec /DCVC /src /layers /extensions /inference /bind.cpp

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Upload Mage-VL: unified codec-native streaming VLM (image+video understanding + proactive gate)

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	// Copyright (c) Microsoft Corporation.
	// Licensed under the MIT License.

	#include "def.h"
	#include <torch/extension.h>

	PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
	{
	m.def("process_with_mask_cuda", &process_with_mask_cuda);
	m.def("combine_for_reading_2x_cuda", &combine_for_reading_2x_cuda);
	m.def("restore_y_2x_cuda", &restore_y_2x_cuda);
	m.def("restore_y_4x_cuda", &restore_y_4x_cuda);
	m.def("build_index_dec_cuda", &build_index_dec_cuda);
	m.def("build_index_enc_cuda", &build_index_enc_cuda);
	m.def("bias_quant_cuda", &bias_quant_cuda);
	m.def("round_and_to_int8_cuda", &round_and_to_int8_cuda);
	m.def("clamp_reciprocal_with_quant_cuda", &clamp_reciprocal_with_quant_cuda);
	m.def("add_and_multiply_cuda", &add_and_multiply_cuda);
	m.def("bias_pixel_shuffle_8_cuda", &bias_pixel_shuffle_8_cuda);
	m.def("replicate_pad_cuda", &replicate_pad_cuda);
	m.def("bias_wsilu_depthwise_conv2d_cuda", &bias_wsilu_depthwise_conv2d_cuda);

	py::class_<DepthConvProxy>(m, "DepthConvProxy")
	.def(py::init<>())
	.def("set_param", &DepthConvProxy::set_param)
	.def("set_param_with_adaptor", &DepthConvProxy::set_param_with_adaptor)
	.def("forward", &DepthConvProxy::forward)
	.def("forward_with_quant_step", &DepthConvProxy::forward_with_quant_step)
	.def("forward_with_cat", &DepthConvProxy::forward_with_cat);

	py::class_<SubpelConv2xProxy>(m, "SubpelConv2xProxy")
	.def(py::init<>())
	.def("set_param", &SubpelConv2xProxy::set_param)
	.def("forward", &SubpelConv2xProxy::forward)
	.def("forward_with_cat", &SubpelConv2xProxy::forward_with_cat);
	}