README.md

---
library_name: transformers
license: apache-2.0
datasets:
- allenai/olmOCR-mix-0225
base_model: Qwen/Qwen2.5-VL-7B-Instruct
---
<div align="center">

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

<img src="https://img.shields.io/badge/Neural%20Metrics-document%20extraction-4F46E5?style=for-the-badge" alt="Neural Metrics" />
<img src="https://img.shields.io/badge/fork%20of-reducto%2FRolmOCR-2563EB?style=flat-square" alt="fork" />

</div>

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`](https://huggingface.co/reducto/RolmOCR), created by the [Qwen team](https://huggingface.co/Qwen).
> 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](https://huggingface.co/reducto/RolmOCR).

---

<details>
<summary><b>Original model card from reducto/RolmOCR</b> (click to expand)</summary>

# RolmOCR by [Reducto AI](https://reducto.ai/)

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.

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](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.

## Key changes
We made three notable changes: 

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

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. 

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. 

## 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](https://app.reducto.ai/), 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},
}
```

</details>