129 lines
4.2 KiB
Markdown
129 lines
4.2 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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datasets:
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- allenai/olmOCR-mix-0225
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base_model:
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- Qwen/Qwen2-VL-7B-Instruct
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library_name: transformers
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---
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<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'">
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# olmOCR-7B-0225-preview
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This is a preview release of the olmOCR model that's fine tuned from Qwen2-VL-7B-Instruct using the
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[olmOCR-mix-0225](https://huggingface.co/datasets/allenai/olmOCR-mix-0225) dataset.
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Quick links:
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- 📃 [Paper](https://olmocr.allenai.org/papers/olmocr.pdf)
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- 🤗 [Dataset](https://huggingface.co/datasets/allenai/olmOCR-mix-0225)
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- 🛠️ [Code](https://github.com/allenai/olmocr)
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- 🎮 [Demo](https://olmocr.allenai.org/)
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The best way to use this model is via the [olmOCR toolkit](https://github.com/allenai/olmocr).
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The toolkit comes with an efficient inference setup via sglang that can handle millions of documents
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at scale.
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## Usage
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This model expects as input a single document image, rendered such that the longest dimension is 1024 pixels.
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The prompt must then contain the additional metadata from the document, and the easiest way to generate this
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is to use the methods provided by the [olmOCR toolkit](https://github.com/allenai/olmocr).
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## Manual Prompting
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If you want to prompt this model manually instead of using the [olmOCR toolkit](https://github.com/allenai/olmocr), please see the code below.
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In normal usage, the olmOCR toolkit builds the prompt by rendering the PDF page, and
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extracting relevant text blocks and image metadata. To duplicate that you will need to
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```bash
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pip install olmocr
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```
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and then run the following sample code.
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```python
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import torch
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import base64
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import urllib.request
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from io import BytesIO
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from PIL import Image
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from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
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from olmocr.data.renderpdf import render_pdf_to_base64png
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from olmocr.prompts import build_finetuning_prompt
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from olmocr.prompts.anchor import get_anchor_text
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# Initialize the model
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model = Qwen2VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-0225-preview", torch_dtype=torch.bfloat16).eval()
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processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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# Grab a sample PDF
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urllib.request.urlretrieve("https://molmo.allenai.org/paper.pdf", "./paper.pdf")
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# Render page 1 to an image
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image_base64 = render_pdf_to_base64png("./paper.pdf", 1, target_longest_image_dim=1024)
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# Build the prompt, using document metadata
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anchor_text = get_anchor_text("./paper.pdf", 1, pdf_engine="pdfreport", target_length=4000)
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prompt = build_finetuning_prompt(anchor_text)
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# Build the full prompt
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
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],
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}
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]
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# Apply the chat template and processor
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text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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main_image = Image.open(BytesIO(base64.b64decode(image_base64)))
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inputs = processor(
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text=[text],
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images=[main_image],
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padding=True,
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return_tensors="pt",
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)
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inputs = {key: value.to(device) for (key, value) in inputs.items()}
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# Generate the output
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output = model.generate(
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**inputs,
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temperature=0.8,
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max_new_tokens=50,
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num_return_sequences=1,
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do_sample=True,
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)
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# Decode the output
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prompt_length = inputs["input_ids"].shape[1]
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new_tokens = output[:, prompt_length:]
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text_output = processor.tokenizer.batch_decode(
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new_tokens, skip_special_tokens=True
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)
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print(text_output)
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# ['{"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']
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```
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## License and use
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olmOCR is licensed under the Apache 2.0 license.
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olmOCR is intended for research and educational use.
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For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
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