Instructions to use context212/alhazen-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use context212/alhazen-ocr with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-vl-2b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "context212/alhazen-ocr") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use context212/alhazen-ocr with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for context212/alhazen-ocr to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for context212/alhazen-ocr to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for context212/alhazen-ocr to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="context212/alhazen-ocr", max_seq_length=2048, )
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README.md
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Eval benchmark: `ahmedheakl/arocrbench_khatt` (MIT).
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## Limitations
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- First smoke run (20k samples, 1 epoch) — handwriting and out-of-distribution
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documents remain weak (see KHATT-bench column)
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- Trained on line/paragraph-level crops; full-page layout parsing untested
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- Arabic-first; other scripts not evaluated
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## Code
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Training, data pipeline and evaluation:
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[`github.com/context212/atlas-ocr`](https://github.com/context212/atlas-ocr)
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Eval benchmark: `ahmedheakl/arocrbench_khatt` (MIT).
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