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:
export VLLM_USE_V1=1
vllm serve reducto/RolmOCR
Call the model via openai compatible server:
# 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},
}