PatSnap/Hiro-MOSS-OCR-0.3B

MOSS🍀: Multimodal OCR for Structured Markup Sequencing

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MOSS is a multimodal OCR model trained from scratch on 50M+ samples for block-level document understanding. It converts document image regions into structured markup, including LaTeX for formulas, HTML for tables, and Markdown for body text. The model supports Japanese, Chinese, and English.

Repository: github.com/patsnap/Hiro-MOSS-OCR

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News and Updates

Recent updates

• 2026-05-28 - CUDA Graph and vLLM inference can now resolve the Hugging Face Hub repo id directly, so PatSnap/Hiro-MOSS-OCR-0.3B works without manually downloading the checkpoint first.
• 2026-05-28 - Added a Transformers AutoModelForCausalLM quick-call path for smoke tests. This path is convenient but slower than the CUDA Graph and vLLM backends.
• 2026-05-26 - Hiro-MOSS-OCR-0.3B is available on Hugging Face.
• 2026-05-26 - The repository includes both local CUDA Graph inference and vLLM serving examples.

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Highlights

• Trained from scratch on 50M+ samples: built specifically for structured OCR and document image understanding.
• Structured outputs: formula recognition, table reconstruction, and text extraction in task-specific markup formats.
• Compact model size: about 320.8M parameters.
• Any-resolution image support: NaViT-style visual encoding with 2D RoPE.
• Multiple inference paths: Transformers quick calls, local CUDA Graph inference, and vLLM serving with an OpenAI-compatible client.

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Model Overview

Component	Details
Training	Trained from scratch on 50M+ samples with any-resolution images
Encoder (~90M)	NaViT with 2D RoPE
Connector (~13.5M)	SwiGLU with patch merger
Decoder (~216.6M)	Transformer decoder with pre-norm, RoPE, GQA, and SwiGLU
Total parameters	~320.8M

Supported Tasks

Task	Output format
math	LaTeX
table	HTML
text	Markdown

Languages: Japanese, Chinese, English.

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Related Documents

• Disclaimer - terms of use, limitations of liability, and data-handling responsibilities.
• License - source-code license.

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Benchmarks

OmniDocBench v1.5

Evaluation with ground-truth layout labels.

Model	Params	Table (TEDS)	Math (CDM)	Text (Edit Similarity)	Overall
dolphin	0.3B	77.08	93.88	90.96	87.31
Monkey OCR Pro 1.2B	1.2B	83.89	94.31	93.07	90.42
Mineru 2.5	1.2B	87.90	95.94	93.25	92.36
Mineru 2.5 Pro	1.2B	92.46	97.24	93.98	94.56
Paddle VL	0.9B	90.57	96.87	94.34	93.93
Paddle VL 1.5	0.9B	90.79	97.28	94.56	94.21
GLM-OCR	0.9B	93.71	97.74	96.44	95.96
MOSS-OCR-0.3B	0.3B	90.33	95.56	95.01	93.63

In-house Patent-domain Benchmark

Model	Params	Table (TEDS)	Math (CDM)	Overall
dolphin	0.3B	75.97	94.36	85.17
Monkey OCR Pro 1.2B	1.2B	78.39	93.01	85.70
Mineru 2.5	1.2B	84.27	95.28	89.78
Mineru 2.5 Pro	1.2B	87.97	96.56	92.27
Paddle VL	0.9B	85.27	94.85	90.06
Paddle VL 1.5	0.9B	81.76	94.72	88.24
GLM-OCR	0.9B	86.58	96.07	91.33
MOSS-OCR-0.3B	0.3B	91.64	95.34	93.49

Inference Speed on a Single RTX 4090

vLLM serving throughput.

Model	Params	QPS (it/s)
Mineru 2.5	1.2B	29.49
MOSS-OCR-0.3B	0.3B	58.77

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Requirements

• Python >= 3.12. uv is recommended.
• CUDA-capable GPU for accelerated local inference and vLLM serving.
• vLLM serving requires the bundled adapter script so vLLM can register the MOSS model.

See pyproject.toml for pinned runtime dependencies.

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Model Weights

Model	Download	Precision
Hiro-MOSS-OCR-0.3B	PatSnap/Hiro-MOSS-OCR-0.3B	FP32 / BF16

Use the Hugging Face repo id PatSnap/Hiro-MOSS-OCR-0.3B directly, or download the checkpoint to a local directory for offline deployments. The CUDA Graph and vLLM examples below accept either form as MODEL_PATH.

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Installation

Install from source:

git clone https://github.com/patsnap/Hiro-MOSS-OCR
cd Hiro-MOSS-OCR

uv python pin 3.12
uv venv .venv
source .venv/bin/activate
uv sync

# Copy MOSS patches into the installed vLLM package.
bash scripts/vllm_adapter.sh

scripts/vllm_adapter.sh copies the matching files from moss_ocr/static/vllm_patches/ into the installed vllm package. Run it after uv sync, and rerun it if you reinstall or upgrade vLLM.

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Usage

1. Quick Call with Transformers AutoModelForCausalLM

For a quick smoke test, load and call the model directly with Hugging Face Transformers:

This path is simple but relatively slow. Use it for quick trials, functional checks, or small single-image calls. For production serving, higher throughput, or batch inference, prefer the CUDA Graph or vLLM paths below. Keep the quick path on one GPU; it is not optimized for automatic multi-GPU module splitting.

import os
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")  # Set before importing torch.

import torch
from transformers import AutoModelForCausalLM

model_id = "PatSnap/Hiro-MOSS-OCR-0.3B"
img_path = "/path/to/your/image.png"
task = "text"  # "math" | "table" | "text"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map={"": 0},
).eval()

with torch.inference_mode():
    texts = model.generate(img_path, task=task)
print(texts[0])

2. Local Inference with CUDA Graph + Transformers

Use MOSSv1d6Runner for single-process local inference:

from moss_ocr.inferer.cuda_graph import MOSSv1d6Runner

model_path = "PatSnap/Hiro-MOSS-OCR-0.3B"
# Or: model_path = "/path/to/Hiro-MOSS-OCR-0.3B"
runner = MOSSv1d6Runner(model_path=model_path)

img_path = "/path/to/your/image.png"
task = "text"  # "math" | "table" | "text"

output = runner.run(img=img_path, task=task)
print(output)

The same path is available through the bundled example:

uv run python moss_ocr/examples/run_with_cuda_graph.py \
  --model_path PatSnap/Hiro-MOSS-OCR-0.3B \
  --task text \
  --img_path /path/to/your/image.png

3. vLLM Server with an OpenAI-compatible Client

First, start vLLM with either the Hugging Face repo id or a local model checkpoint:

# Make sure `bash scripts/vllm_adapter.sh` has been run in this environment.
export MODEL_PATH=PatSnap/Hiro-MOSS-OCR-0.3B
# Or: export MODEL_PATH=/path/to/Hiro-MOSS-OCR-0.3B

uv run vllm serve "$MODEL_PATH" \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.9 \
  --max-num-batched-tokens 16384 \
  --port 8088 \
  --served-model-name moss-v1d6-0.3b

Then call the server with MOSSOCRv1d6vLLMRunner. The url must include the /v1 suffix:

from moss_ocr.inferer.vllm import MOSSOCRv1d6vLLMRunner

runner = MOSSOCRv1d6vLLMRunner(url="http://127.0.0.1:8088/v1")

img_path = "/path/to/your/image.png"
task = "text"  # "math" | "table" | "text"

response = runner.run(img=img_path, task=task)
print(response.result if response.is_succeed else response.error_message)

CLI example:

uv run python moss_ocr/examples/run_with_vllm.py \
  --url http://127.0.0.1:8088/v1 \
  --task text \
  --img_path /path/to/your/image.png

The default --served-model-name should match the client's model name, moss-v1d6-0.3b. If you change the served name, pass model_path="<your-served-name>" when constructing MOSSOCRv1d6vLLMRunner.

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Notes

• OCR output can be inaccurate or incomplete. Review results before using them in legal, compliance, archival, accessibility, customer-facing, or other high-stakes workflows.
• Make sure you have the required rights and permissions for any images or documents you process with this project.
• For full terms and limitations, read the disclaimer.

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Copyright Notice

Copyright (c) 2026 Patsnap. All rights reserved except as expressly licensed under the applicable license terms.

Hiro-MOSS-OCR, Patsnap, and any associated names, logos, product names, service names, designs, and slogans are trademarks or registered trademarks of Patsnap or its affiliates. No trademark license is granted under the open source license or any model license unless expressly stated.

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Acknowledgements

• PaddleOCR
• MinerU
• GLM-OCR
• Dolphin
• Monkey-OCR
• Smol-Docling