Image-Text-to-Text
Transformers
Safetensors
mage_vl
multimodal
vision-language-model
mage-vl
video-understanding
streaming
conversational
custom_code
Instructions to use microsoft/Mage-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 6,035 Bytes
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import enum
import struct
from pathlib import Path
def filesize(filepath: str) -> int:
if not Path(filepath).is_file():
raise ValueError(f'Invalid file "{filepath}".')
return Path(filepath).stat().st_size
def write_uints(fd, values, fmt=">{:d}I"):
fd.write(struct.pack(fmt.format(len(values)), *values))
return len(values) * 4
def write_uchars(fd, values, fmt=">{:d}B"):
fd.write(struct.pack(fmt.format(len(values)), *values))
return len(values)
def read_uints(fd, n, fmt=">{:d}I"):
sz = struct.calcsize("I")
return struct.unpack(fmt.format(n), fd.read(n * sz))
def read_uchars(fd, n, fmt=">{:d}B"):
sz = struct.calcsize("B")
return struct.unpack(fmt.format(n), fd.read(n * sz))
def write_bytes(fd, values, fmt=">{:d}s"):
if len(values) == 0:
return 0
fd.write(struct.pack(fmt.format(len(values)), values))
return len(values)
def read_bytes(fd, n, fmt=">{:d}s"):
sz = struct.calcsize("s")
return struct.unpack(fmt.format(n), fd.read(n * sz))[0]
def write_ushorts(fd, values, fmt=">{:d}H"):
fd.write(struct.pack(fmt.format(len(values)), *values))
return len(values) * 2
def read_ushorts(fd, n, fmt=">{:d}H"):
sz = struct.calcsize("H")
return struct.unpack(fmt.format(n), fd.read(n * sz))
def write_uint_adaptive(f, a):
if a < (1 << 7):
a0 = (a >> 0) & 0xff
a0 = a0 | (0x00 << 7)
write_uchars(f, (a0,))
return 1
if a < (1 << 14):
a0 = (a >> 0) & 0xff
a1 = (a >> 8) & 0xff
a1 = a1 | (0x02 << 6)
write_uchars(f, (a1, a0))
return 2
assert a < (1 << 30)
a0 = (a >> 0) & 0xff
a1 = (a >> 8) & 0xff
a2 = (a >> 16) & 0xff
a3 = (a >> 24) & 0xff
a3 = a3 | (0x03 << 6)
write_uchars(f, (a3, a2, a1, a0))
return 4
def read_uint_adaptive(f):
a3 = read_uchars(f, 1)[0]
if (a3 >> 7) == 0:
return a3
a2 = read_uchars(f, 1)[0]
if (a3 >> 6) == 0x02:
a3 = a3 & 0x3f
return (a3 << 8) + a2
a3 = a3 & 0x3f
a1 = read_uchars(f, 1)[0]
a0 = read_uchars(f, 1)[0]
return (a3 << 24) + (a2 << 16) + (a1 << 8) + a0
class NalType(enum.IntEnum):
NAL_SPS = 0
NAL_I = 1
NAL_P = 2
class SPSHelper():
def __init__(self):
super().__init__()
self.spss = []
def get_sps_id(self, target_sps):
min_id = -1
for sps in self.spss:
if sps['height'] == target_sps['height'] and sps['width'] == target_sps['width'] and \
sps['use_ada_i'] == target_sps['use_ada_i'] and \
sps['ec_part'] == target_sps['ec_part']:
return sps['sps_id'], False
if sps['sps_id'] > min_id:
min_id = sps['sps_id']
assert min_id < 15
sps = target_sps.copy()
sps['sps_id'] = min_id + 1
self.spss.append(sps)
return sps['sps_id'], True
def add_sps_by_id(self, sps):
for i in range(len(self.spss)):
if self.spss[i]['sps_id'] == sps['sps_id']:
self.spss[i] = sps.copy()
return
self.spss.append(sps.copy())
def get_sps_by_id(self, sps_id):
for sps in self.spss:
if sps['sps_id'] == sps_id:
return sps
return None
def write_sps(f, sps):
# nal_type(4), sps_id(4)
# height (variable)
# width (vairable)
# 0(6), ec_part(1) use_ada_i(1)
assert sps['sps_id'] < 16
assert sps['use_ada_i'] < 2
written = 0
flag = int((NalType.NAL_SPS << 4) + sps['sps_id'])
written += write_uchars(f, (flag,))
written += write_uint_adaptive(f, sps['height'])
written += write_uint_adaptive(f, sps['width'])
flag = (sps['ec_part'] << 2) + sps['use_ada_i']
written += write_uchars(f, (flag,))
return written
def read_header(f):
header = {}
flag = read_uchars(f, 1)[0]
nal_type = flag >> 4
header['nal_type'] = NalType(nal_type)
if nal_type < 3:
header['sps_id'] = flag & 0x0f
return header
frame_num_minus1 = flag & 0x0f
frame_num = frame_num_minus1 + 1
header['frame_num'] = frame_num
sps_ids = []
for _ in range(0, frame_num, 2):
flag = read_uchars(f, 1)[0]
sps_ids.append(flag >> 4)
sps_ids.append(flag & 0x0f)
sps_ids = sps_ids[:frame_num]
header['sps_ids'] = sps_ids
return header
def read_sps_remaining(f, sps_id):
sps = {}
sps['sps_id'] = sps_id
sps['height'] = read_uint_adaptive(f)
sps['width'] = read_uint_adaptive(f)
flag = read_uchars(f, 1)[0]
sps['ec_part'] = (flag >> 2) & 0x01
sps['use_ada_i'] = flag & 0x01
return sps
def write_ip(f, is_i_frame, sps_id, qp, bit_stream):
written = 0
flag = (int(NalType.NAL_I if is_i_frame else NalType.NAL_P) << 4) + sps_id
written += write_uchars(f, (flag,))
assert qp < 256 and qp >= 0
flag = qp
written += write_uchars(f, (flag,))
# we write all the streams in the same file, thus, we need to write the per-frame length
# if packed independently, we do not need to write it
written += write_uint_adaptive(f, len(bit_stream))
written += write_bytes(f, bit_stream)
return written
def read_ip_remaining(f):
flag = read_uchars(f, 1)[0]
qp = flag
stream_length = read_uint_adaptive(f)
bit_stream = read_bytes(f, stream_length)
return qp, bit_stream
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