Instructions to use apple/LensVLM-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use apple/LensVLM-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="apple/LensVLM-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("apple/LensVLM-9B")
model = AutoModelForMultimodalLM.from_pretrained("apple/LensVLM-9B", 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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use apple/LensVLM-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "apple/LensVLM-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "apple/LensVLM-9B",
		"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/apple/LensVLM-9B

SGLang

How to use apple/LensVLM-9B 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 "apple/LensVLM-9B" \
    --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": "apple/LensVLM-9B",
		"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 "apple/LensVLM-9B" \
        --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": "apple/LensVLM-9B",
		"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 apple/LensVLM-9B with Docker Model Runner:
```
docker model run hf.co/apple/LensVLM-9B
```

LensVLM-9B

File size: 6,401 Bytes

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NOTICE
======

LensVLM
Copyright (C) 2026 Apple Inc. All Rights Reserved.

This NOTICE file accompanies both this source repository and the LensVLM-9B
model weights distributed at https://huggingface.co/apple/LensVLM-9B.


-------------------------------------------------------------------------------
1. Apple-authored source code
-------------------------------------------------------------------------------

The source code in this repository was authored by Apple Inc. and is released
under the Apple Sample Code License. See the accompanying LICENSE file.

This source code is not derived from, and contains no code copied from, the
third-party work identified in Section 3 below.


-------------------------------------------------------------------------------
2. LensVLM-9B model weights
-------------------------------------------------------------------------------

The LensVLM-9B model weights are released under the Apple Machine Learning
Research Model License.

    Apple Machine Learning Research Model is licensed under the Apple Machine
    Learning Research Model License Agreement.

That license limits use of the weights to non-commercial Research Purposes.
Applying these terms to the weights as a whole is permitted by Section 4 of the
Apache License, Version 2.0, which governs the original work identified in
Section 3 below.


-------------------------------------------------------------------------------
3. Third-party material: Qwen3.5-9B (Apache License, Version 2.0)
-------------------------------------------------------------------------------

The LensVLM-9B model is a Derivative Work of the following third-party work,
which is licensed under the Apache License, Version 2.0:

    Work:      Qwen3.5-9B
    Source:    https://huggingface.co/Qwen/Qwen3.5-9B
    Copyright: Copyright 2026 Alibaba Cloud
    License:   Apache License, Version 2.0

A complete copy of the Apache License, Version 2.0 -- reproduced verbatim as
received with the original work -- is provided in the accompanying file:

    ACKNOWLEDGEMENTS

You may also 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.

Attribution notices retained from the original work:

    @misc{qwen3.5,
        title  = {{Qwen3.5}: Towards Native Multimodal Agents},
        author = {{Qwen Team}},
        month  = {February},
        year   = {2026},
        url    = {https://qwen.ai/blog?id=qwen3.5}
    }

The original work does not include a "NOTICE" text file as part of its
distribution. Accordingly, there are no upstream NOTICE attribution notices to
reproduce. All copyright, patent, trademark, and attribution notices that are
present in the original work are retained above and in ACKNOWLEDGEMENTS.

"Qwen" is a trademark of Alibaba Group. Its use here is solely to identify the
origin of the original work, as permitted by Section 6 of the Apache License,
Version 2.0. Neither Alibaba Cloud nor the Qwen Team endorses LensVLM.


-------------------------------------------------------------------------------
4. Statement of modifications -- NOT A CONTRIBUTION
-------------------------------------------------------------------------------

NOT A CONTRIBUTION.

Apple Inc. has changed files of the original work identified in Section 3. All
such modifications are Copyright (C) 2026 Apple Inc. All Rights Reserved, and
are expressly designated in writing as "NOT A CONTRIBUTION" within the meaning
of Section 1 of the Apache License, Version 2.0. Nothing in this distribution
is intentionally submitted to Alibaba Cloud, the Qwen Team, or any other
licensor for inclusion in the original work, and no Contribution as defined by
Section 1 of the Apache License, Version 2.0 is made or intended.

The files listed below were changed by Apple Inc. Because these files are
binary tensor archives or JSON documents, neither of which supports embedded
comments, the prominent notice required by Section 4(b) of the Apache License,
Version 2.0 is given here and in the accompanying model card.

Files CHANGED by Apple Inc. (relative to Qwen/Qwen3.5-9B):

    model.safetensors        Model weights fine-tuned by Apple Inc. for
                             selective context expansion over compressed page
                             images. Changed. NOT A CONTRIBUTION.

    config.json              Re-serialized by a newer version of the
                             Transformers library and updated with Apple
                             training/inference settings. The model
                             architecture, hidden size, layer count, layer
                             types, and vocabulary size are unchanged from the
                             original work. Changed. NOT A CONTRIBUTION.

    tokenizer.json           Re-serialized by a newer version of the
    tokenizer_config.json    Transformers library (tokenizer class
                             "Qwen2Tokenizer" to "TokenizersBackend"). The
                             vocabulary is byte-for-byte identical to the
                             original work; no tokens were added, removed, or
                             remapped. Changed. NOT A CONTRIBUTION.

    generation_config.json   Decoding defaults set by Apple Inc.
    processor_config.json    Image/video preprocessing defaults set by Apple
                             Inc. for compressed-page rendering. Changed.
                             NOT A CONTRIBUTION.

Files redistributed UNMODIFIED from the original work:

    chat_template.jinja      Byte-for-byte identical to the original work.

No file of the original work was removed from the distribution in a way that
would strip a copyright, patent, trademark, or attribution notice.


-------------------------------------------------------------------------------
5. Other third-party material
-------------------------------------------------------------------------------

Additional third-party material bundled with this repository, including the
DejaVu fonts used for deterministic page rendering, is identified in the
accompanying ACKNOWLEDGEMENTS file.