Instructions to use yugeshkarunamurthy/OvisOCR2-oQ6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yugeshkarunamurthy/OvisOCR2-oQ6e with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("yugeshkarunamurthy/OvisOCR2-oQ6e")
config = load_config("yugeshkarunamurthy/OvisOCR2-oQ6e")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use yugeshkarunamurthy/OvisOCR2-oQ6e with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "yugeshkarunamurthy/OvisOCR2-oQ6e"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yugeshkarunamurthy/OvisOCR2-oQ6e with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default yugeshkarunamurthy/OvisOCR2-oQ6e

Run Hermes

hermes

OpenClaw new

How to use yugeshkarunamurthy/OvisOCR2-oQ6e with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yugeshkarunamurthy/OvisOCR2-oQ6e"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "yugeshkarunamurthy/OvisOCR2-oQ6e" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

OvisOCR2-oQe6

Apple Silicon Optimized oQe6 MLX Quantized Release

This repository contains an oQe6 mixed-precision MLX quantized version of OvisOCR2, optimized for fast and memory-efficient document understanding on Apple Silicon.

The original OvisOCR2 model was developed by ATH-MaaS. This repository provides an optimized MLX/oQe6 conversion only and does not include any additional training or fine-tuning.

Using oQe6 sensitivity-aware mixed-precision quantization, this release preserves the excellent OCR and document parsing capabilities of the original model while significantly reducing memory usage and improving inference efficiency on Apple Silicon. OvisOCR2 is a compact 0.8B end-to-end document parser that converts document images directly into structured Markdown and achieves state-of-the-art results on OmniDocBench v1.6 and PureDocBench. 0

About OvisOCR2

OvisOCR2 is a compact Vision Language Model (VLM) specialized for end-to-end document parsing.

Unlike traditional OCR pipelines that separately detect layouts, recognize text, and reconstruct documents, OvisOCR2 directly converts a document page into structured Markdown while preserving natural reading order.

The model is capable of extracting:

📄 Plain text
📐 Mathematical formulas (LaTeX)
📊 Tables (HTML)
🖼 Images and visual regions
📚 Complex layouts
📰 Multi-column documents
📑 Scientific papers
📋 Forms
📖 Books
📃 Receipts
📜 Scanned documents

The model is based on Qwen3.5-0.8B and is trained using a combination of:

Real-world document data
Synthetic document generation
Supervised Fine-Tuning (SFT)
Reinforcement Learning (RL)
On-Policy Distillation (OPD)

OvisOCR2 achieves an overall score of 96.58 on OmniDocBench v1.6, becoming the first end-to-end model to top that benchmark, and also leads PureDocBench with an Avg3 score of 75.06. 1

Quantization

This release uses oQe6 mixed-precision quantization.

Specifications

Format: MLX
Quantization: oQe6
Method: Sensitivity-Aware Mixed Precision
Target Platform: Apple Silicon
Inference Engine: MLX / oMLX

Unlike traditional fixed-bit quantization, oQe6 automatically assigns precision according to layer sensitivity, preserving critical components while compressing less sensitive regions.

Benefits include:

Better OCR accuracy retention
Lower memory usage
Faster inference
Higher throughput
Excellent Apple Silicon optimization

Recommended Settings

For the best OCR quality:

temp: 0.0
top_p: 1.0
top_k: 1
max_tokens: 4096

For difficult or noisy scans:

temp: 0.1
top_p: 0.95
top_k: 20
max_tokens: 4096

OCR is generally deterministic, so greedy or near-greedy decoding is recommended for maximum transcription accuracy.

Example Usage

from mlx_vlm import load, generate
from PIL import Image

model, processor = load("yugeshkarunamurthy/OvisOCR2-oQe6")

image = Image.open("document.png")

prompt = """
Extract all readable content from the document.
Output Markdown preserving reading order.
Render tables as HTML.
Render formulas using LaTeX.
"""

response = generate(
    model=model,
    processor=processor,
    image=image,
    prompt=prompt,
    temperature=0.0,
)

print(response)

Optimized For

This release is optimized for:

Apple M1
Apple M2
Apple M3
Apple M4

Compatible with:

MLX
MLX-VLM
oMLX
Local OCR Applications
Document Processing Pipelines

Model Highlights

End-to-End OCR
Page-level Document Parsing
Markdown Generation
HTML Table Extraction
LaTeX Formula Recognition
Scientific Paper Parsing
Forms and Receipts
Complex Multi-column Documents
Reading Order Preservation
High OCR Accuracy

Intended Use

OvisOCR2-oQe6 is well suited for:

OCR
PDF Digitization
Scientific Paper Extraction
Book Digitization
Invoice Processing
Receipt Processing
Form Parsing
Research Automation
Knowledge Base Construction
RAG Preprocessing
Markdown Conversion
Digital Archives

Hardware Recommendations

Recommended systems:

Apple M1 Pro / Max / Ultra
Apple M2 Pro / Max / Ultra
Apple M3 Series
Apple M4 Series

The compact 0.8B model is lightweight and runs comfortably on most Apple Silicon devices while benefiting from additional memory for larger documents and higher throughput.

About oQe6 Quantization

oQe6 is a sensitivity-aware mixed-precision quantization technique designed to preserve model quality while significantly reducing memory requirements.

Rather than assigning identical precision to every layer, oQe6 analyzes the sensitivity of individual modules and allocates higher precision only where it has the greatest impact.

Benefits include:

Better OCR accuracy retention
Improved document layout understanding
Lower RAM usage
Faster inference
Excellent Apple Silicon performance

Original Model

The original OvisOCR2 is an end-to-end document parsing model built upon Qwen3.5-0.8B.

Notable features include:

State-of-the-art document OCR
Markdown document generation
HTML table extraction
LaTeX formula recognition
Natural reading order
End-to-end architecture
Compact 0.8B model
Apache-2.0 license

For benchmark results, technical details, and training methodology, please visit the original repository. 2

Credits

Original Model

All credit for the original model, datasets, training methodology, evaluation, benchmarks, and research belongs entirely to:

ATH-MaaS

Original Repository:

https://huggingface.co/ATH-MaaS/OvisOCR2

Technical Report:

https://arxiv.org/abs/2607.13639

oQe6 MLX Quantized Release

This repository provides an Apple Silicon optimized oQe6 MLX quantized version of the original model.

No additional fine-tuning has been performed.

Acknowledgements

ATH-MaaS
Qwen Team
Apple MLX
Hugging Face
Transformers
vLLM
SGLang
MLX-VLM
oMLX
OptiQ Quantization

Citation

If you use this model in research, please cite the original OvisOCR2 paper:

@misc{lu2026ovisocr2,
  title        = {OvisOCR2 Technical Report},
  author       = {Shiyin Lu and Yinglun Li and Yu Xia and Yuhui Chen and An-Yang Ji and Jun-Peng Jiang and Qing-Guo Chen and Jianshan Zhao and En Lin and Haijun Li and Cheng Qin and Zhao Xu and Weihua Luo},
  year         = {2026},
  eprint       = {2607.13639},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV}
}

License

This release inherits the Apache-2.0 license from the original model.

Please refer to the original repository for complete licensing information.

Disclaimer

This repository contains an optimized oQe6 MLX quantized conversion intended for efficient local inference on Apple Silicon.

All original model architecture, datasets, training methodology, benchmarks, evaluations, and research remain entirely the work of the original ATH-MaaS team. ````3

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Safetensors

Model size

0.3B params

Tensor type

BF16

U32

MLX

Hardware compatibility

6-bit

Model tree for yugeshkarunamurthy/OvisOCR2-oQ6e

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

ATH-MaaS/OvisOCR2

Quantized

(17)

this model

Paper for yugeshkarunamurthy/OvisOCR2-oQ6e

OvisOCR2 Technical Report

Paper • 2607.13639 • Published 15 days ago • 55