README.md

---
library_name: mlx
license: other
license_name: modified-openrail-m
license_link: LICENSE
tags:
- ocr
- pdf
- markdown
- layout
- mlx
- 8bit
- quantized
pipeline_tag: image-text-to-text
base_model: datalab-to/chandra-ocr-2
---

# Chandra OCR 2 — 8-bit MLX Quantization

This is an **8-bit MLX quantization** of [datalab-to/chandra-ocr-2](https://huggingface.co/datalab-to/chandra-ocr-2), converted for efficient inference on Apple Silicon using the [mlx-vlm](https://github.com/Blaizzy/mlx-vlm) framework.

**Original model**: [datalab-to/chandra-ocr-2](https://huggingface.co/datalab-to/chandra-ocr-2)  
**Quantization**: 8-bit affine, group size 64  
**Framework**: MLX (Apple Silicon)  
**Modified files**: The weight file (`model.safetensors`) has been quantized from the original bfloat16 weights. All other files are unchanged from the original repository.

## About Chandra OCR 2

Chandra 2 is a state-of-the-art OCR model from [Datalab](https://www.datalab.to) that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs while preserving layout information.

### What's New in Chandra 2

- 85.9% olmocr bench score (SOTA), 77.8% multilingual bench score (12% improvement over Chandra 1)
- Significant improvements to math, tables, and complex layouts
- Improved layout, especially on wider documents
- Significantly better image captioning
- 90+ language support with major accuracy gains

### Features

- Convert documents to markdown, HTML, or JSON with detailed layout information
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams with captions and structured data
- Support for 90+ languages

## Usage with mlx-vlm

### Installation

```bash
pip install mlx-vlm
```

### Inference

```python
from mlx_vlm import load
from mlx_vlm.utils import generate_step
from PIL import Image

model, processor = load("jacobwindle/chandra-ocr-2-8bit-mlx")

image = Image.open("document.png")
prompt = "Convert this image to markdown."

output = generate_step(
    model=model,
    processor=processor,
    image=image,
    prompt=prompt,
    max_tokens=4096,
)
print(output)
```

### Command-line

```bash
python -m mlx_vlm.generate --model jacobwindle/chandra-ocr-2-8bit-mlx --image document.png --prompt "Convert this image to markdown." --max-tokens 4096
```

## Quantization Details

| Parameter | Value |
|-----------|-------|
| Bits | 8 |
| Group size | 64 |
| Mode | Affine |
| Original dtype | bfloat16 |
| Quantized size | ~4.8 GB |

Converted using:

```bash
python -m mlx_vlm.convert --model datalab-to/chandra-ocr-2 --mlx-path models/chandra-ocr-2-8bit -q --q-bits 8
```

## Attribution

This is a derivative work of [datalab-to/chandra-ocr-2](https://huggingface.co/datalab-to/chandra-ocr-2). The original model was created by [Datalab](https://www.datalab.to). The weights in this repository have been modified (8-bit quantized) from the original release. All credit for the model architecture, training data, and original weights belongs to the original authors.

## License

This model inherits the **modified OpenRAIL-M license** from the original [datalab-to/chandra-ocr-2](https://huggingface.co/datalab-to/chandra-ocr-2). As a derivative work, the same license terms apply, including the share-alike requirement (Section III, paragraph 8) and use-based restrictions (Attachment A).

Key restrictions from the original license:
- Free for research, personal use, and startups under $2M funding/revenue
- Cannot be used competitively with the Datalab API
- Derivative works must retain the same license

For broader commercial licensing, see [Datalab pricing](https://www.datalab.to/pricing).