92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
---
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library_name: transformers
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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: Qwen/Qwen2.5-VL-7B-Instruct
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---
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# RolmOCR by [Reducto AI](https://reducto.ai/)
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Earlier this year, the [Allen Institute for AI](https://allenai.org/) released olmOCR, an open-source tool that performs document OCR using the Qwen2-VL-7B vision language model (VLM). We were excited to see a high-quality, openly available approach to parsing PDFs and other complex documents — and curious to explore what else might be possible using newer foundation models and some lightweight optimizations.
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The result is **RolmOCR**, a drop-in alternative to olmOCR that’s faster, uses less memory, and still performs well on a variety of document types. We're releasing it under **Apache 2.0** for anyone to try out, explore, or build on.
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This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the full [allenai/olmOCR-mix-0225](https://huggingface.co/datasets/allenai/olmOCR-mix-0225) dataset.
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## Key changes
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We made three notable changes:
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1. **New Base Model**: We swapped in a more recent version of the existing model (Qwen2.5-VL-7B) as the foundation.
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2. **No Metadata inputs**: Unlike the original, we don’t use metadata extracted from PDFs. This significantly reduces prompt length, which in turn lowers both processing time and VRAM usage — without hurting accuracy in most cases.
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3. **Rotation of training data:** About 15% of the training data was rotated to enhance robustness to off-angle documents. We otherwise use the same training set.
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## Usage
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Host your model with vLLM:
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```bash
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export VLLM_USE_V1=1
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vllm serve reducto/RolmOCR
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```
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Call the model via openai compatible server:
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```python
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# HOST YOUR OPENAI COMPATIBLE API WITH THE FOLLOWING COMMAND in VLLM:
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# export VLLM_USE_V1=1
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# vllm serve reducto/RolmOCR
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from openai import OpenAI
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import base64
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client = OpenAI(api_key="123", base_url="http://localhost:8000/v1")
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model = "reducto/RolmOCR-7b"
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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def ocr_page_with_rolm(img_base64):
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response = client.chat.completions.create(
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model=model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{img_base64}"},
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},
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{
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"type": "text",
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"text": "Return the plain text representation of this document as if you were reading it naturally.\n",
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},
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],
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}
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],
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temperature=0.2,
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max_tokens=4096
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)
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return response.choices[0].message.content
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test_img_path = "path/to/image.png"
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img_base64 = encode_image(test_img_path)
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print(ocr_page_with_rolm(img_base64))
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```
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## Limitations
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- RolmOCR, like other VLM-based OCR solutions, still suffer from hallucination or dropping contents.
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- Unlike the [Reducto Parsing API](https://app.reducto.ai/), RolmOCR cannot output layout bounding boxes.
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- We have not evaluated the performance of any quantized versions.
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## BibTex and citation info
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```
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@misc{RolmOCR,
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author = {Reducto AI},
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title = {RolmOCR: A Faster, Lighter Open Source OCR Model},
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year = {2025},
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}
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``` |