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 / README.md

RRoy233

kolecke

first commit

ac40d96 16 days ago

preview code

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	---
	license: apple-amlr
	license_link: https://huggingface.co/apple/LensVLM-9B/blob/main/LICENSE
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model:
	- Qwen/Qwen3.5-9B
	tags:
	- vision-language-model
	- long-context
	- visual-text-compression
	---

	# LensVLM-9B

	LensVLM is a 9B Vision Language Model (VLM) that scans compressed images of text,
	then selectively expands only the relevant pages to their uncompressed form via
	learned tools.

	- Paper: [LensVLM: Selective Context Expansion for Compressed Visual Representation of Text](https://arxiv.org/abs/2605.07019)
	- Code: https://github.com/apple-aiml-research/ml-lensvlm

	## License

	All ML model files in this repository, including Apple's modifications to the Qwen
	model, are provided under the terms of the
	[Apple Machine Learning Research Model License](https://huggingface.co/apple/LensVLM-9B/blob/main/LICENSE).

	The source code that accompanies this model is distributed separately and is provided
	under the terms of the Apple Sample Code License.

	## Usage

	Install the LensVLM code and run inference:

	```bash
	git clone https://github.com/apple-aiml-research/ml-lensvlm
	cd ml-lensvlm
	pip install -r requirements.txt
	python scripts/run_demo.py --model apple/LensVLM-9B
	```

	For a custom document:

	```bash
	python demo.py \
	--model apple/LensVLM-9B \
	--text_file document.txt \
	--question "What is the main finding?" \
	--compression 10x
	```

	Compression options: `5x`, `10x`, `15x`. See the
	[repository README](https://github.com/apple-aiml-research/ml-lensvlm) for data preparation
	and evaluation.

	## Citation

	```bibtex
	@article{xie2026lensvlm,
	title={LensVLM: Selective Context Expansion for Compressed Visual Representation of Text},
	author={Xie, Roy and Friedman, Dan and Yu, Donghan and Pan, Bowen and Fifty, Christopher and Kim, Jang-Hyun and Du, Xianzhi and Gan, Zhe and Rathod, Vivek and Dhingra, Bhuwan},
	journal={arXiv preprint arXiv:2605.07019},
	year={2026}
	}
	```