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NeuralMetrics/
RolmOCR

Name: RolmOCR
Availability: InStock

allenai/olmOCR-mix-0225

Qwen/Qwen2.5-VL-7B-Instruct

finetune

apache-2.0

text-generation-inference

endpoints_compatible

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Neural Metrics · A faster, lighter take on the olmOCR recipe.

Reducto's RolmOCR builds on the olmOCR approach with a Qwen2.5-VL backbone, trading some setup complexity for speed and lower memory use - no PDF metadata prompting required.

We use it for: throughput-sensitive batch OCR - head-to-head comparison against olmOCR on our own document mix.

Attribution

This is an unmodified fork of reducto/RolmOCR, 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.

Original model card from reducto/RolmOCR (click to expand)

RolmOCR by Reducto AI

Earlier this year, the Allen Institute for AI 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.

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.

This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct on the full allenai/olmOCR-mix-0225 dataset.

Key changes

We made three notable changes:

New Base Model: We swapped in a more recent version of the existing model (Qwen2.5-VL-7B) as the foundation.
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.
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.

Usage

Host your model with vLLM:

bash

export VLLM_USE_V1=1
vllm serve reducto/RolmOCR 

Call the model via openai compatible server:

python

# HOST YOUR OPENAI COMPATIBLE API WITH THE FOLLOWING COMMAND in VLLM:
# export VLLM_USE_V1=1
# vllm serve reducto/RolmOCR 

from openai import OpenAI
import base64

client = OpenAI(api_key="123", base_url="http://localhost:8000/v1")

model = "reducto/RolmOCR-7b"

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode("utf-8")

def ocr_page_with_rolm(img_base64):
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/png;base64,{img_base64}"},
                    },
                    {
                        "type": "text",
                        "text": "Return the plain text representation of this document as if you were reading it naturally.\n",
                    },
                ],
            }
        ],
        temperature=0.2,
        max_tokens=4096
    )
    return response.choices[0].message.content

test_img_path = "path/to/image.png"
img_base64 = encode_image(test_img_path)
print(ocr_page_with_rolm(img_base64))

Limitations

RolmOCR, like other VLM-based OCR solutions, still suffer from hallucination or dropping contents.
Unlike the Reducto Parsing API, RolmOCR cannot output layout bounding boxes.
We have not evaluated the performance of any quantized versions.

BibTex and citation info

@misc{RolmOCR,
  author = {Reducto AI},
  title = {RolmOCR: A Faster, Lighter Open Source OCR Model},
  year = {2025},
}

NeuralMetrics/RolmOCR