• [2026/11/02] 📄 The paper of Unlimited OCR 2 is now available on arXiv.
• [2026/10/28] Thanks to the ms-swift community for supporting training of Unlimited OCR 2.
• [2026/10/27] 📊 We release MarathonDocBench, a new benchmark for ultra-long document parsing.
• [2026/10/27] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope.
• [2026/10/27] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud.
• [2026/10/27] 🤝 Thanks to AK for creating a new demo for us. Try it at Unlimited-OCR-2.
• [2026/10/27] 🚀 We release Unlimited OCR 2, achieving unlimited-horizon parsing with SR-SWA.
• [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift.
• [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud.
• [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference.
• [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces.
• [2026/06/23] 📄 Our paper is now available on arXiv.
• [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope.
• [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further.

• Sparse Reference Sliding Window Attention (SR-SWA): Compresses text-visual attention interactions from ultra-long sequences down to a fixed-size set of working references (e.g., 512 tokens). This bounds computational complexity, keeping per-step decoding latency and memory footprint nearly constant regardless of document length.
• From Long- to Unlimited-Horizon Parsing: Powered by SR-SWA, Unlimited-OCR 2 achieves true, unbounded document parsing within a finite context window—effectively eliminating quadratic attention scaling and memory bottlenecks as documents grow.
• Ultra-Long Document Parsing SOTA: Achieves state-of-the-art performance across both dozens-of-pages benchmarks (e.g., MPDocBench) and extreme hundred-page benchmarks (e.g., MarathonDocBench).

Unlimited OCR 2 supports Transformers (native, for quick tests) and SGLang (recommended for serving).

Tested with Python 3.10, torch==2.13.0, transformers==4.52.1, huggingface-hub==0.36.0, tokenizers==0.21.4, on an NVIDIA A800-80GB:

pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 \
  tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13

Load the model once:

import torch
from transformers import AutoModel, AutoTokenizer

model_name = "baidu/Unlimited-OCR-2"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, use_safetensors=True,
                                  torch_dtype=torch.bfloat16).eval().cuda()

Multi-page inference (infer_multi). All pages of a document are parsed in one request, so the model can use cross-page context. Pass the page images in page order:

pages = [f"your_doc/page_{i}.jpg" for i in range(1, 8)]

text, num_tokens = model.infer_multi(tokenizer, image_files=pages,
                                     output_path="output/multi", save_results=True)

The prompt is built automatically as "<PAGE><image>" * num_pages + "Multi page parsing.", and each page is encoded as one global view (base mode). The output contains one <PAGE> per input page, in the same order as image_files.

Single-page inference (infer). A single page is encoded with dynamic tiling (gundam mode) by default:

text = model.infer(tokenizer, image_file="your_doc/page_1.jpg",
                   output_path="output/single", save_results=True)

Set up the environment (uv-managed virtualenv, Python 3.10) and install the customized SGLang wheel sglang-0.5.20.dev13+g75b9b06b02-py3-none-any.whl, plus PyMuPDF for PDF-to-image conversion:

uv venv --python 3.10
source .venv/bin/activate

uv pip install wheel/sglang-0.5.20.dev13+g75b9b06b02-py3-none-any.whl
uv pip install pymupdf==1.27.2.2
uv pip install fastokens==0.3.2

Start the SGLang server:

# Page-window scheduling for very long documents (the KV tail of the previous window is reused).
export SGLANG_UOCR_EANABLE_DOC_LIMIT=1          # 1 = enable, 0 = whole document in one window
export SGLANG_UOCR_DOC_GUNDAM_PAGE_WINDOW=10    # pages per window (gundam mode)
export SGLANG_UOCR_DOC_BASE_PAGE_WINDOW=10      # pages per window (base mode)
export SGLANG_UOCR_MAX_PAGE_TOKENS=20000        # per-page output cap; must be > 0
# Chunk cap for the SAM attention bias. The built-in default (512) allows a single
# ~462 MiB allocation and can OOM; 64 is equivalent with a lower peak.
export SGLANG_OPT_UOCR_SAM_REL_POS_BIAS_MB=64
# Pass large crop tensors through /dev/shm instead of pickling them into the
# tokenizer -> scheduler frame.
export SGLANG_UOCR_SHM_WRAP_MODEL_DATA=1
export SGLANG_UOCR_SHM_WRAP_MIN_MB=1
export SGLANG_VLM_CACHE_SIZE_MB=0
# Reduces fragmentation failures on the large contiguous allocations.
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True

python -m sglang.launch_server \
    --model-path baidu/Unlimited-OCR-2 \
    --served-model-name Unlimited-OCR-2 \
    --chat-template unlimited-ocr \
    --tokenizer-backend fastokens \
    --tokenizer-worker-num 8 \
    --mem-fraction-static 0.7 \
    --max-running-requests 64 \
    --max-prefill-tokens 2048 \
    --enable-custom-logit-processor \
    --enable-streaming-session \
    --allow-auto-truncate \
    --disable-radix-cache \
    --mm-preprocess-cache-size-mb 0 \
    --cuda-graph-backend-decode full --cuda-graph-max-bs-decode 64 \
    --cuda-graph-backend-prefill full \
    --cuda-graph-max-bs-prefill 16384 \
    --cuda-graph-bs-prefill 1024 2048 \
    --cuda-graph-config '{"prefill": {"backend": "full", "full_prefill_max_req": 64}}' \
    --host 0.0.0.0 \
    --port 10000

Notes on the knobs that matter for memory:

• --mem-fraction-static is the share reserved for weights + KV. 0.75 leaves too little headroom at 32+ concurrent requests; 0.7 is stable.
• --max-prefill-tokens drives a temporary tensor of roughly [tokens, context/32], so memory grows linearly with it: 4096 -> 2048 halves that peak.
• The largest --cuda-graph-bs-prefill bucket must equal --max-prefill-tokens. A smaller one makes oversized batches fall back to eager; a larger one is never used and only wastes capture memory.
• --cuda-graph-config's full_prefill_max_req is the request-slot count for the full-prefill CUDA graph. The default (8) makes 16/32 concurrency fall back to eager.
• A smaller page window lowers peak memory but hurts cross-page merging, since blocks that continue across a window boundary lose their context.

To parse a whole document in one window instead (SGLANG_UOCR_EANABLE_DOC_LIMIT=0).

Send requests to the OpenAI-compatible API:

import base64
import json
import os
import tempfile

import fitz
import requests
from sglang.srt.sampling.custom_logit_processor import DeepseekOCRNoRepeatNGramLogitProcessor

server_url = "http://127.0.0.1:10000"
NGRAM_PROCESSOR = DeepseekOCRNoRepeatNGramLogitProcessor.to_str()

session = requests.Session()
session.trust_env = False


def pdf_to_images(pdf_path, dpi=300):
    doc = fitz.open(pdf_path)
    tmp_dir = tempfile.mkdtemp(prefix="pdf_ocr_")
    mat = fitz.Matrix(dpi / 72, dpi / 72)
    image_paths = []
    for i, page in enumerate(doc):
        image_path = os.path.join(tmp_dir, f"page_{i + 1:04d}.png")
        page.get_pixmap(matrix=mat).save(image_path)
        image_paths.append(image_path)
    doc.close()
    return image_paths


def encode_image(image_path):
    ext = os.path.splitext(image_path)[1].lower()
    mime = "image/jpeg" if ext in (".jpg", ".jpeg") else f"image/{ext.lstrip('.')}"
    with open(image_path, "rb") as f:
        data = base64.b64encode(f.read()).decode("utf-8")
    return {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{data}"}}


def build_content(image_paths):
    prompt = "<PAGE><image>" * len(image_paths) + "Multi page parsing."
    return [encode_image(path) for path in image_paths] + [{"type": "text", "text": prompt}]


def generate(image_paths, crop_mode=True, tokens_per_page=8000):
    # A wider n-gram window for multi-page documents, as in our evaluation setup.
    window_size = 1024 if len(image_paths) > 1 else 128
    payload = {
        "model": "Unlimited-OCR-2",
        "messages": [{"role": "user", "content": build_content(image_paths)}],
        # Must be positive: it is the global output budget for the whole document.
        "max_tokens": tokens_per_page * len(image_paths),
        "temperature": 0,
        "skip_special_tokens": False,
        "images_config": {"crop_mode": crop_mode},  # True = gundam (tiles), False = base
        "custom_logit_processor": NGRAM_PROCESSOR,
        "custom_params": {
            "ngram_size": 35,
            "window_size": window_size,
            "page_token_id": 128827,   # <PAGE> marker id (= sparse_attention_config.page_token_id)
            "eos_token_id": 1,         # = eos_token_id in config.json
            "max_page_tokens": 20000,  # per-page output cap, <= 0 disables the page guard
        },
        "stream": True,
    }
    response = session.post(
        f"{server_url}/v1/chat/completions",
        headers={"Content-Type": "application/json"},
        data=json.dumps(payload),
        timeout=6000,
        stream=True,
    )
    response.raise_for_status()

    chunks = []
    for line in response.iter_lines(decode_unicode=True):
        if not line or not line.startswith("data:"):
            continue
        data = line[len("data:"):].strip()
        if data == "[DONE]":
            break
        event = json.loads(data)
        choices = event.get("choices") or []  # the final chunk carries usage only
        if not choices:
            continue
        delta = (choices[0].get("delta") or {}).get("content") or ""
        if delta:
            print(delta, end="", flush=True)
            chunks.append(delta)
    return "".join(chunks)


# Multi page: all pages of a document, parsed in one request
pages = [f"your_doc/page_{i}.jpg" for i in range(1, 8)]
generate(pages)

# Single page
generate(["your_doc/page_1.jpg"])

# PDF
generate(pdf_to_images("your_doc.pdf", dpi=300))

infer.py is the batch client we use for MPDocBench / MarathonDocBench: it sends each document to the SGLang server above as a single request and saves the raw output, which utils/process_unlimitedOCR2.py then converts to the evaluation Markdown.

python infer.py --url 127.0.0.1:10000 --benchmark MPDocBench \
    --dataset-root /path/to/MPDocBench --all --raw-dir raw --concurrency 4
python utils/process_unlimitedOCR2.py --infer-dir raw --out-dir pred

See utils/README.md for the output format, the merging rules and the post-processing details.

Unlimited-horizon parsing in action: a full-length book parsed in a single request (replay).

We would like to thank Deepseek-OCR, Deepseek-OCR-2, PaddleOCR for their valuable models and ideas, and SGLang for the serving framework.

@misc{wei2026unlimitedocrworks2,
      title={Unlimited OCR Works 2}, 
      author={Baidu ERNIE Team},
      year={2026},
      eprint={2610.39308},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2610.39308}, 
}

@misc{yin2026unlimitedocrworks,
      title={Unlimited OCR Works}, 
      author={Baidu ERNIE Team},
      year={2026},
      eprint={2606.23050},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.23050}, 
}