---
base_model: datalab-to/surya-ocr-2
base_model_relation: quantized
license: openrail
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- mlx
- mlx-vlm
- 8-bit
- quantized
- ocr
- document-ai
- surya
- experimental
---

# Surya OCR 2 MLX 8-bit G64

    This repository contains an **experimental quantized** artifact derived from [datalab-to/surya-ocr-2](https://huggingface.co/datalab-to/surya-ocr-2).

    This 8-bit MLX quant is the most useful Apple-side artifact from the current batch. It keeps perfect mini-section scores on arxiv math, headers/footers, multi-column, old-scans-math, tables, and baseline checks, but it currently fails the old-scans mini split and is weak on long tiny text.

    ## What is included

    - Source model: `datalab-to/surya-ocr-2`
    - Runtime/format: MLX / mlx-vlm
    - Quantization: 8-bit affine weight quantization, group size 64
    - Vision weights included: Yes. The MLX checkpoint includes the model vision weights and processor assets.
    - Processor/tokenizer assets: included

    ## Mini olmOCR-bench results

    | Candidate | Overall | Arxiv math | Headers/footers | Long tiny text | Multi-column | Old scans | Old scans math | Tables | Baseline |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Source mini baseline | 91.0% ± 6.3% | 100.0% | 100.0% | 100.0% | 100.0% | 33.3% | 100.0% | 100.0% | 94.7% |
| Surya OCR 2 MLX 8-bit G64 | 79.2% ± 6.2% | 100.0% | 100.0% | 33.3% | 100.0% | 0.0% | 100.0% | 100.0% | 100.0% |



## How to read the benchmark table

This is an early quant release with transparent limitations. The table uses our local 40-test mini slice of `allenai/olmOCR-bench`, with 3 samples from each named section plus the benchmark baseline checks. It is **not** the full public score and it is **not** a claim of >98% parity.

The useful signal is the split behavior: this artifact is currently strong on clean academic/math, headers/footers, multi-column layouts, tables, old-scan math, and baseline OCR checks, but it should not be used for old degraded scans and is weak on long tiny text.

## Recommended use

Use this checkpoint for local experimentation and constrained OCR workloads whose documents resemble the passing sections above. Avoid using it as a production replacement for the original model on degraded historical scans, very small dense body text, or workloads requiring full benchmark parity.


    ## Loading

    ```python
from mlx_vlm import load, generate

model, processor = load("Reza2kn/surya-ocr-2-mlx-8bit-g64")
# Pass images/documents through the same Surya/MLX-VLM prompting path used by your app.
```

    ## Limitations

    - This is not a full-parity release yet.
    - Do **not** use this artifact for degraded old scans; the current mini split score is 0.0% there.
    - Do **not** use this artifact for long tiny text unless you independently validate your data; the current mini split score is 33.3%.
    - Math-heavy and table/layout-heavy mini examples looked good in this slice, but full olmOCR-bench is still pending.

    ## Provenance

    Generated non-destructively from the original Hugging Face checkpoint. This is not a fine-tune. The goal of publishing this artifact now is transparency: the files are usable for the passing workload slices above, and the known failing slices are documented clearly.