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

Libraries

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

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

pipe = pipeline("image-text-to-text", model="infly/Infinity-Parser2-Pro")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("infly/Infinity-Parser2-Pro")
model = AutoModelForImageTextToText.from_pretrained("infly/Infinity-Parser2-Pro")
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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "infly/Infinity-Parser2-Pro"
# 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-Pro",
		"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-Pro

SGLang

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

Reproducing ParseBench chart score

by boyang-runllama - opened 17 days ago

Discussion

boyang-runllama

17 days ago

Hi infly team, congrats on taking #1 on the HuggingFace leaderboard of ParseBench, great work!

We're trying to replay Infinity-Parser2 on run-llama/ParseBench. Using the doc2json prompt from your SDK, text_content, text_formatting, layout, and table come close to the numbers in parsebench.yaml, but chart lands near 0 vs your 61.3. doc2json forces figure text to empty, so chart rules see nothing.

Could you open a PR to run-llama/ParseBench adding the pipelines you used for both Pro and Flash? Once it lands, we'll reproduce and verify the results on our side, then update the ParseBench GitHub leaderboard to reflect your numbers.

Thanks!

zuminghuang

inftech.ai org 17 days ago

Hi,

Thanks for the congratulations and for testing our parser! To achieve the chart parsing score you see on the leaderboard, "deep parsing mode" needs to be enabled for Infinity-Parser2.

We'll open a PR to the run-llama/ParseBench repo very soon. We will include the pipelines for both Pro and Flash to ensure you can reproduce our results.

Looking forward to getting this verified!

zuminghuang

inftech.ai org 15 days ago

•

edited 15 days ago

https://github.com/run-llama/ParseBench/pull/33

@boyang-runllama

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment