Instructions to use hustvl/InfiniteVL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hustvl/InfiniteVL with Transformers:

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

pipe = pipeline("image-text-to-text", model="hustvl/InfiniteVL", 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 AutoModel
model = AutoModel.from_pretrained("hustvl/InfiniteVL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hustvl/InfiniteVL with vLLM:

Install from pip and serve model

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

SGLang

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

HongyuanTao commited on Dec 9, 2025

Commit

8f77bda

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1 Parent(s): f466033

Update README.md

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README.md CHANGED Viewed

@@ -13,14 +13,12 @@ pipeline_tag: image-text-to-text
 <div align="center">
-<!-- 这里可以放你的Logo，如果没有Logo可以删掉这一行 -->
-<img src="https://github.com/hustvl/infinitevl/assets/Logo.png" width="500" alt="InfiniteVL Logo">
 <hr>
 ### InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
-<!-- 作者列表 -->
 Hongyuan Tao<sup>1</sup>,
 [Bencheng Liao](https://github.com/LegendBC)<sup>1</sup>,
 [Shaoyu Chen](https://scholar.google.com/citations?user=PIeNN2gAAAAJ&hl=en&oi=sra)<sup>2</sup>,
@@ -29,14 +27,11 @@ Haoran Yin<sup>2</sup>,
 [Wenyu Liu](https://scholar.google.com/citations?user=D7jDk7gAAAAJ&hl=en)<sup>1</sup>,
 [Xinggang Wang](https://xwcv.github.io)<sup>1,✉️</sup>
-<!-- 单位列表 -->
 <sup>1</sup>Huazhong University of Science and Technology,
 <sup>2</sup>Horizon Robotics
-<!-- 脚注/通讯作者信息 -->
 (✉️) corresponding author: <a href="mailto:xgwang@hust.edu.cn">xgwang@hust.edu.cn</a>
-<!-- 放置 按钮/Badge 的地方 -->
 <br>
 <a href="https://arxiv.org/abs/2502.xxxxx"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv"></a>
 <a href="https://github.com/hustvl/InfiniteVL"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github" alt="GitHub"></a>
@@ -51,7 +46,7 @@ Haoran Yin<sup>2</sup>,
 By synergizing **Sliding Window Attention (SWA)** for fine-grained local perception and **Gated DeltaNet** for efficient long-term memory, InfiniteVL achieves a "best of both worlds" balance. It delivers competitive performance on standard benchmarks (comparable to Qwen2.5-VL) while enabling constant-memory inference and high-throughput streaming.
 <div align="center">
-<img src="https://github.com/hustvl/infinitevl/assets/image1_new_01.png" width="800" alt="InfiniteVL Logo">
 </div>
 ### ✨ Key Highlights

 <div align="center">
+<img src="https://github.com/hustvl/InfiniteVL/raw/main/assets/assets/Logo.png" width="600" alt="InfiniteVL Logo">
 <hr>
 ### InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models
 Hongyuan Tao<sup>1</sup>,
 [Bencheng Liao](https://github.com/LegendBC)<sup>1</sup>,
 [Shaoyu Chen](https://scholar.google.com/citations?user=PIeNN2gAAAAJ&hl=en&oi=sra)<sup>2</sup>,
 [Wenyu Liu](https://scholar.google.com/citations?user=D7jDk7gAAAAJ&hl=en)<sup>1</sup>,
 [Xinggang Wang](https://xwcv.github.io)<sup>1,✉️</sup>
 <sup>1</sup>Huazhong University of Science and Technology,
 <sup>2</sup>Horizon Robotics
 (✉️) corresponding author: <a href="mailto:xgwang@hust.edu.cn">xgwang@hust.edu.cn</a>
 <br>
 <a href="https://arxiv.org/abs/2502.xxxxx"><img src="https://img.shields.io/badge/arXiv-Paper-b31b1b.svg" alt="arXiv"></a>
 <a href="https://github.com/hustvl/InfiniteVL"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github" alt="GitHub"></a>
 By synergizing **Sliding Window Attention (SWA)** for fine-grained local perception and **Gated DeltaNet** for efficient long-term memory, InfiniteVL achieves a "best of both worlds" balance. It delivers competitive performance on standard benchmarks (comparable to Qwen2.5-VL) while enabling constant-memory inference and high-throughput streaming.
 <div align="center">
+<img src="https://github.com/hustvl/InfiniteVL/raw/main/assets/image1_new_01.png" width="800" alt="InfiniteVL Logo">
 </div>
 ### ✨ Key Highlights