Instructions to use infly/Infinity-Parser2-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use infly/Infinity-Parser2-Flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="infly/Infinity-Parser2-Flash")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("infly/Infinity-Parser2-Flash")
model = AutoModelForMultimodalLM.from_pretrained("infly/Infinity-Parser2-Flash")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use infly/Infinity-Parser2-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infly/Infinity-Parser2-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "infly/Infinity-Parser2-Flash",
		"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/infly/Infinity-Parser2-Flash

SGLang

How to use infly/Infinity-Parser2-Flash 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 "infly/Infinity-Parser2-Flash" \
    --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": "infly/Infinity-Parser2-Flash",
		"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 "infly/Infinity-Parser2-Flash" \
        --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": "infly/Infinity-Parser2-Flash",
		"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 infly/Infinity-Parser2-Flash with Docker Model Runner:
```
docker model run hf.co/infly/Infinity-Parser2-Flash
```

zuminghuang commited on Feb 27

Commit

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1 Parent(s): 8af0881

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@@ -1,3 +1,67 @@
----
-license: apache-2.0
----

+# Infinity-Parser2-2B
+<p align="center">
+    <img src="assets/logo.png" width="400"/>
+<p>
+<p align="center">
+💻 <a href="https://github.com/infly-ai/INF-MLLM">Github</a> |
+📊 <a href="https://huggingface.co/datasets/infly/Infinity-Doc2-xxx">Dataset</a> |
+📄 <a>Paper (coming soon...)</a> |
+🚀 <a>Demo (coming soon...)</a>
+</p>
+# Introduction
+## Key Features
+# Architecture
+todo
+# Performance
+## Document Parsing
+![image](assets/xxx.png)
+## Table Parsing
+![image](assets/xxx.png)
+## Math Formula Parsing
+![image](assets/xxx.png)
+## Chart Parsing
+![image](assets/xxx.png)
+## Chemical Formula Parsing
+![image](assets/xxx.png)
+## General Multimodal Understanding
+![image](assets/xxx.png)
+# Quick Start
+todo
+# Visualization
+## Comparison Examples
+![image](assets/xxx.jpeg)
+# Limitation & Future Work
+## Limitations
+## Future Work
+# Acknowledgments
+We would like to thank [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL), [ms-swift](https://github.com/modelscope/ms-swift), [verl](https://github.com/verl-project/verl), [olmOCR-bench](https://huggingface.co/datasets/allenai/olmOCR-bench) and [OmniDocBench](https://github.com/opendatalab/OmniDocBench) for providing data, code and models.
+# Citation
+Coming soon...
+# License
+This model is licensed under apache-2.0.