context212
/

alhazen-ocr

@@ -1,210 +1,79 @@
 ---
-base_model: unsloth/qwen3-vl-2b-instruct-unsloth-bnb-4bit
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen3-vl-2b-instruct-unsloth-bnb-4bit
 - lora
-- sft
-- transformers
-- trl
 - unsloth
 ---
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-- PEFT 0.20.0

 ---
+base_model: unsloth/Qwen3-VL-2B-Instruct
 library_name: peft
+license: apache-2.0
+pipeline_tag: image-text-to-text
 tags:
+- arabic
+- ocr
+- qwen3-vl
 - lora
 - unsloth
+- document-ai
 ---
+# Alhazen-OCR
+QLoRA adapter on [`unsloth/Qwen3-VL-2B-Instruct`](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct) for Arabic institutional OCR: printed forms, invoices, and handwriting-heavy pages.
+Named after Ibn al-Haytham (Alhazen), the 11th-century scholar who founded the science of optics.
+- **Base:** Qwen3-VL-2B-Instruct (Apache-2.0)
+- **Training data:** [`context212/context212-alhazen-ocr`](https://huggingface.co/datasets/context212/context212-alhazen-ocr)
+- **Code:** [`github.com/context212/atlas-ocr`](https://github.com/context212/atlas-ocr)
+- **Experiment sibling:** [`context212/alhazen-ocr-hwmix`](https://huggingface.co/context212/alhazen-ocr-hwmix) (same weights; promoted here after KHATT improved)
+## Results
+CER / WER (lower is better), greedy decoding, **50 samples** each on the held-out eval split and on external [`ahmedheakl/arocrbench_khatt`](https://huggingface.co/datasets/ahmedheakl/arocrbench_khatt):
+| Model | Eval CER ↓ | Eval WER ↓ | KHATT CER ↓ | KHATT WER ↓ |
+|---|---:|---:|---:|---:|
+| Qwen3-VL-2B-Instruct (base) | 0.792 | 0.947 | 1.893 | 1.801 |
+| **Alhazen-OCR** | **0.281** | **0.448** | **1.121** | **1.215** |
+Held-out character error drops from **0.79 → 0.28**. On external KHATT handwriting, CER/WER also improve versus the untuned base (**1.89 → 1.12** CER) — the previous synthetic-heavy mix had *regressed* on KHATT; this release uses a handwriting-heavier mix (~45% KHATT paragraphs, ~35% synthetic printed, ~20% invoices, plus a small historical set; KHATT-bench transcript overlap removed from train).
+## Training
+- **Objective:** QLoRA SFT, one epoch (second epoch previously diverged)
+- **LR:** 2e-5 cosine, warmup 5%
+- **Effective batch:** 16 (8 × grad accum 2) on A100 80GB
+- **LoRA:** r=16, RSLoRA, vision + language layers
+- **Mix:** handwriting-heavy rebuild of `context212/context212-alhazen-ocr` (~15k rows after mix)
+## Usage
+Serve the base with the adapter attached (vLLM example):
+```bash
+vllm serve Qwen/Qwen3-VL-2B-Instruct \
+  --enable-lora \
+  --lora-modules alhazen=context212/alhazen-ocr \
+  --limit-mm-per-prompt '{"image": 1}' \
+  --mm-processor-cache-gb 0 \
+  --no-enable-prefix-caching
+```
+Prompt:
+> Extract all the text from this image, preserving the original reading order.
+Keep `temperature=0` for transcription.
+## Limitations
+- Tuned for Arabic institutional paperwork; not a general multilingual OCR.
+- KHATT remains harder than printed forms — scores above 1.0 CER mean many lines are still wrong.
+- Eval above is capped at 50 samples per split; treat as directional until a full-split rerun.
+## Citation
+```bibtex
+@misc{alhazen-ocr-2026,
+  title        = {Alhazen-OCR},
+  author       = {Context212},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/context212/alhazen-ocr}}
+}
+```