model: support baidu unlimited-ocr (#29186)
Co-authored-by: Mick <mickjagger19@icloud.com>
This commit is contained in:
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---
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title: Unlimited-OCR
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description: "Deploy Baidu Unlimited-OCR with SGLang for long document OCR using prefill-aware sliding-window attention."
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tag: NEW
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---
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## Deployment
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<a id="install" />
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<Accordion title="Install SGLang">
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Unlimited-OCR support is in [SGLang PR #29186](https://github.com/sgl-project/sglang/pull/29186). Until that PR is included in a tagged SGLang release, install from a build that contains the PR.
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<Tabs>
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<Tab title="Python (pip / uv)">
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```bash Command
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pip install -U uv
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uv venv --python 3.12 && source .venv/bin/activate
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git clone https://github.com/sgl-project/sglang.git
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cd sglang
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git fetch origin pull/29186/head && git checkout FETCH_HEAD
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uv pip install -e python
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```
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Then run the **Python** output of the command panel below in that environment.
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</Tab>
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<Tab title="Docker">
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```bash Command
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docker pull lmsysorg/sglang:dev
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```
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For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
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</Tab>
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</Tabs>
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</Accordion>
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Pick your hardware to generate the launch command. The recipe uses FlashAttention-3 with `--page-size 1`, which is required by the current prefill-aware sliding-window attention path. It also disables radix cache by default, which is the better fit for batch OCR workloads where each request usually contains a different image.
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import { Deployment } from "/src/snippets/_deployment.jsx";
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import { config } from "/src/snippets/configs/baidu/unlimited-ocr.jsx";
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<Deployment config={config} />
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## Playground
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Use the Playground to adjust tensor parallelism on top of the selected deployment cell.
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import { Playground } from "/src/snippets/_playground.jsx";
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<Playground config={config} />
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## 1. Model Introduction
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[Unlimited-OCR](https://huggingface.co/baidu/Unlimited-OCR) is Baidu's multimodal OCR model for document parsing. It uses a sliding-window language backbone, but SGLang serves it with a prefill-aware sliding-window path so image and prompt tokens remain visible during long decode.
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The SGLang integration loads the standalone Unlimited-OCR architecture with SAM and CLIP vision encoders plus a DeepSeek-style language backbone. It supports OpenAI-compatible image requests and model-specific image processing options through `images_config`.
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**Resources:** [Hugging Face](https://huggingface.co/baidu/Unlimited-OCR) · [SGLang PR #29186](https://github.com/sgl-project/sglang/pull/29186)
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## 2. Configuration Tips
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- **Attention backend**: use `--attention-backend fa3 --page-size 1`. The prefill-aware SWA page table is built with token-level locations, so page size 1 is required.
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- **Radix cache**: keep `--disable-radix-cache` for batch OCR over different documents. If your workload repeatedly asks about the same image and prompt, remove this flag to allow prefix reuse through `PureSWARadixCache`.
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- **Long OCR generations**: keep the default prefill-aware SWA path enabled. It retains prompt and image KV while still applying a sliding window to generated text.
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- **Custom logit processor**: keep `--enable-custom-logit-processor` in the launch command.
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- **Image modes**: pass `images_config.image_mode` per request. Supported modes are `tiny`, `small`, `base`, `large`, and `gundam`. Multiple images are supported only for `tiny`, `small`, and `base`.
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- **Default image mode**: when `images_config.image_mode` is omitted, SGLang uses `gundam`.
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## 3. Advanced Usage
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### 3.1 OCR request
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<Accordion title="OCR Example (Python)">
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```python Example
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
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response = client.chat.completions.create(
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model="baidu/Unlimited-OCR",
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "document parsing."},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://example.com/your_document.png"
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},
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},
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],
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}
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],
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max_tokens=2048,
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temperature=0,
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extra_body={"images_config": {"image_mode": "gundam"}},
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)
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print(response.choices[0].message.content)
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```
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</Accordion>
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### 3.2 Choosing an image mode
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Use lower modes to reduce prefill cost for simple images, and use `gundam` for high-detail document parsing.
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<table style={{width: "100%", borderCollapse: "collapse", tableLayout: "fixed"}}>
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<thead>
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<tr style={{borderBottom: "2px solid #d55816"}}>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Mode</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Use</th>
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<th style={{textAlign: "left", padding: "10px 12px", fontWeight: 700}}>Multiple images</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td style={{padding: "9px 12px"}}><code>tiny</code></td>
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<td style={{padding: "9px 12px"}}>Lowest prefill cost.</td>
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<td style={{padding: "9px 12px"}}>Yes</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px"}}><code>small</code></td>
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<td style={{padding: "9px 12px"}}>Lightweight OCR requests.</td>
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<td style={{padding: "9px 12px"}}>Yes</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px"}}><code>base</code></td>
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<td style={{padding: "9px 12px"}}>Balanced quality and cost.</td>
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<td style={{padding: "9px 12px"}}>Yes</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px"}}><code>large</code></td>
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<td style={{padding: "9px 12px"}}>Higher resolution single-image OCR.</td>
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<td style={{padding: "9px 12px"}}>No</td>
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</tr>
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<tr>
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<td style={{padding: "9px 12px"}}><code>gundam</code></td>
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<td style={{padding: "9px 12px"}}>Default high-detail document parsing mode.</td>
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<td style={{padding: "9px 12px"}}>No</td>
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</tr>
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</tbody>
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</table>
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@@ -67,6 +67,12 @@ metatags:
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href="/cookbook/autoregressive/NVIDIA/Nemotron3-Ultra"
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img="/cards/logos/nvidia.png"
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/>
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<Card
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title="Baidu"
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mode="card"
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href="/cookbook/autoregressive/Baidu/Unlimited-OCR"
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img="/cards/logos/baidu.svg"
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/>
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<Card
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title="Ernie"
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mode="card"
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