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Model: reducto/RolmOCR
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- allenai/olmOCR-mix-0225
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+---
+
+# 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},
+}
+```