---
title: olmOCR-7B-0825-FP8
canonical_url: "https://www.modelscope.cn/models/allenai/olmOCR-7B-0825-FP8"
md_url: "https://www.modelscope.cn/models/allenai/olmOCR-7B-0825-FP8.md"
repository: allenai/olmOCR-7B-0825-FP8
last_updated: 2025-10-22
license: apache-2.0
model_type:
  - qwen2_5_vl
architectures:
  - Qwen2_5_VLForConditionalGeneration
base_model:
  - Qwen/Qwen2.5-VL-7B-Instruct
base_model_relation: quantized
parameters: 8.3B
tensor_type:
  - BF16
  - F8_E4M3
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
inference_backends:
  - "deploy_task vlm/text/emb"
  - "lmdeploy 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 650
stars: 2
---

# olmOCR-7B-0825-FP8

> olmOCR-7B-0825-FP8 - allenai 在 ModelScope 开源的模型。Quantized to FP8 Version of olmOCR-7B-0825, using llmcompressor.

allenai/olmOCR-7B-0825-FP8 是 ModelScope 魔搭社区上的 8.3B 参数机器学习模型，采用 apache-2.0 许可，基于 Qwen/Qwen2.5-VL-7B-Instruct 构建，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。

- **Repository**: allenai/olmOCR-7B-0825-FP8
- **License**: apache-2.0
- **Parameters**: 8.3B
- **Base model**: Qwen/Qwen2.5-VL-7B-Instruct
- **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 650
- **Stars**: 2
- **Last updated**: 2025-10-22

Source: https://www.modelscope.cn/models/allenai/olmOCR-7B-0825-FP8

---

<img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">

# olmOCR-7B-0825-FP8

Quantized to FP8 Version of [olmOCR-7B-0825](https://huggingface.co/allenai/olmOCR-7B-0825), using llmcompressor.

This is a release of the olmOCR model that's fine tuned from Qwen2.5-VL-7B-Instruct using the 
[olmOCR-mix-0225](https://huggingface.co/datasets/allenai/olmOCR-mix-0225) dataset.

Quick links:
- 📃 [Paper](https://olmocr.allenai.org/papers/olmocr.pdf)
- 🤗 [Dataset](https://huggingface.co/datasets/allenai/olmOCR-mix-0225)
- 🛠️ [Code](https://github.com/allenai/olmocr)
- 🎮 [Demo](https://olmocr.allenai.org/)

The best way to use this model is via the [olmOCR toolkit](https://github.com/allenai/olmocr).
The toolkit comes with an efficient inference setup via sglang that can handle millions of documents
at scale.

## Usage

This model expects as input a single document image, rendered such that the longest dimension is 1288 pixels.

The prompt must then contain the additional metadata from the document, and the easiest way to generate this
is to use the methods provided by the [olmOCR toolkit](https://github.com/allenai/olmocr).


## License and use

olmOCR is licensed under the Apache 2.0 license.
olmOCR is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).