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allenai
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Model Details
Model Provider
allenai
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This model scores the following scores on olmOCR-bench when used with the olmOCR toolkit toolkit which automatically renders, rotates, and retries pages as needed.
This model expects as input a single document image, rendered such that the longest dimension is 1288 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.
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
bash
pip install olmocr>=0.4.0and then run the following sample code.
python
import torchimport base64import urllib.request from io import BytesIOfrom PIL import Imagefrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration from olmocr.data.renderpdf import render_pdf_to_base64pngfrom olmocr.prompts import build_no_anchoring_v4_yaml_prompt # Initialize the modelmodel = Qwen2_5_VLForConditionalGeneration.from_pretrained("allenai/olmOCR-2-7B-1025", torch_dtype=torch.bfloat16).eval()processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device) # Grab a sample PDFurllib.request.urlretrieve("https://olmocr.allenai.org/papers/olmocr.pdf", "./paper.pdf") # Render page 1 to an imageimage_base64 = render_pdf_to_base64png("./paper.pdf", 1, target_longest_image_dim=1288) # Build the full promptmessages = [ { "role": "user", "content": [ {"type": "text", "text": build_no_anchoring_v4_yaml_prompt()}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}, ], } ] # Apply the chat template and processortext = 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 outputoutput = model.generate( **inputs, temperature=0.1, max_new_tokens=50, num_return_sequences=1, do_sample=True, ) # Decode the outputprompt_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)# ['---\nprimary_language: en\nis_rotation_valid: True\nrotation_correction: 0\nis_table: False\nis_diagram: False\n---\nolmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models\n\nJake Poz']This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
import torchimport base64import urllib.request from io import BytesIOfrom PIL import Imagefrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration from olmocr.data.renderpdf import render_pdf_to_base64pngfrom olmocr.prompts import build_no_anchoring_v4_yaml_prompt # Initialize the modelmodel = Qwen2_5_VLForConditionalGeneration.from_pretrained("allenai/olmOCR-2-7B-1025", torch_dtype=torch.bfloat16).eval()processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")device = torch.device("cuda" if torch.cuda.is_available() else "cpu")model.to(device) # Grab a sample PDFurllib.request.urlretrieve("https://olmocr.allenai.org/papers/olmocr.pdf", "./paper.pdf") # Render page 1 to an imageimage_base64 = render_pdf_to_base64png("./paper.pdf", 1, target_longest_image_dim=1288) # Build the full promptmessages = [ { "role": "user", "content": [ {"type": "text", "text": build_no_anchoring_v4_yaml_prompt()}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}, ], } ] # Apply the chat template and processortext = 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 outputoutput = model.generate( **inputs, temperature=0.1, max_new_tokens=50, num_return_sequences=1, do_sample=True, ) # Decode the outputprompt_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)# ['---\nprimary_language: en\nis_rotation_valid: True\nrotation_correction: 0\nis_table: False\nis_diagram: False\n---\nolmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models\n\nJake Poz']