olmOCR-7B-0225-preview

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Neural Metrics · Fully open OCR, trained on a million real PDF pages.

<img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" /> <img src="https://img.shields.io/badge/fork%20of-allenai%2FolmOCR--7B--0225--preview-2563EB?style=flat-square" alt="fork" /> </div>

AI2's olmOCR is trained on a large corpus of genuine PDFs and is open end to end - weights, data and training code. When we need to understand why a model reads a page the way it does, openness beats a leaderboard score.

We use it for: linearising complex PDFs into clean reading order - a reproducible, fully-auditable reference point for our own evaluations.

Attribution

This is an unmodified fork of allenai/olmOCR-7B-0225-preview, created by the Qwen team. All weights, files and behaviour are identical to upstream — we rehost it so our experiments stay reproducible and version-pinned. The original license and all credit remain with the Qwen team. If you want the canonical model, please use the original.

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<details> <summary><b>Original model card from allenai/olmOCR-7B-0225-preview</b> (click to expand)</summary> <img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">

olmOCR-7B-0225-preview

This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the olmOCR-mix-0225 dataset.

Quick links:

• 📃 Paper
• 🤗 Dataset
• 🛠️ Code
• 🎮 Demo

The best way to use this model is via the olmOCR toolkit. The toolkit comes with an efficient inference setup via sglang that can handle millions of documents at scale.

Usage

This model expects as input a single document image, rendered such that the longest dimension is 1024 pixels.

The prompt must then contain the additional metadata from the document, and the easiest way to generate this is to use the methods provided by the olmOCR toolkit.

Manual Prompting

If you want to prompt this model manually instead of using the olmOCR toolkit, please see the code below.

In normal usage, the olmOCR toolkit builds the prompt by rendering the PDF page, and extracting relevant text blocks and image metadata. To duplicate that you will need to

pip install olmocr

and then run the following sample code.

import torch
import base64
import urllib.request

from io import BytesIO
from PIL import Image
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

from olmocr.data.renderpdf import render_pdf_to_base64png
from olmocr.prompts import build_finetuning_prompt
from olmocr.prompts.anchor import get_anchor_text

# Initialize the model
model = Qwen2VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-0225-preview", torch_dtype=torch.bfloat16).eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Grab a sample PDF
urllib.request.urlretrieve("https://molmo.allenai.org/paper.pdf", "./paper.pdf")

# Render page 1 to an image
image_base64 = render_pdf_to_base64png("./paper.pdf", 1, target_longest_image_dim=1024)

# Build the prompt, using document metadata
anchor_text = get_anchor_text("./paper.pdf", 1, pdf_engine="pdfreport", target_length=4000)
prompt = build_finetuning_prompt(anchor_text)

# Build the full prompt
messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
                ],
            }
        ]

# Apply the chat template and processor
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
main_image = Image.open(BytesIO(base64.b64decode(image_base64)))

inputs = processor(
    text=[text],
    images=[main_image],
    padding=True,
    return_tensors="pt",
)
inputs = {key: value.to(device) for (key, value) in inputs.items()}


# Generate the output
output = model.generate(
            **inputs,
            temperature=0.8,
            max_new_tokens=50,
            num_return_sequences=1,
            do_sample=True,
        )

# Decode the output
prompt_length = inputs["input_ids"].shape[1]
new_tokens = output[:, prompt_length:]
text_output = processor.tokenizer.batch_decode(
    new_tokens, skip_special_tokens=True
)

print(text_output)
# ['{"primary_language":"en","is_rotation_valid":true,"rotation_correction":0,"is_table":false,"is_diagram":false,"natural_text":"Molmo and PixMo:\\nOpen Weights and Open Data\\nfor State-of-the']

License and use

olmOCR is licensed under the Apache 2.0 license. olmOCR is intended for research and educational use. For more information, please see our Responsible Use Guidelines.

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