Instructions to use surfiniaburger/unsloth_finetune_ocr_arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use surfiniaburger/unsloth_finetune_ocr_arabic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="surfiniaburger/unsloth_finetune_ocr_arabic", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("surfiniaburger/unsloth_finetune_ocr_arabic", trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained("surfiniaburger/unsloth_finetune_ocr_arabic", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use surfiniaburger/unsloth_finetune_ocr_arabic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "surfiniaburger/unsloth_finetune_ocr_arabic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/unsloth_finetune_ocr_arabic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/surfiniaburger/unsloth_finetune_ocr_arabic

SGLang

How to use surfiniaburger/unsloth_finetune_ocr_arabic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "surfiniaburger/unsloth_finetune_ocr_arabic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/unsloth_finetune_ocr_arabic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "surfiniaburger/unsloth_finetune_ocr_arabic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "surfiniaburger/unsloth_finetune_ocr_arabic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use surfiniaburger/unsloth_finetune_ocr_arabic 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 surfiniaburger/unsloth_finetune_ocr_arabic 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 surfiniaburger/unsloth_finetune_ocr_arabic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for surfiniaburger/unsloth_finetune_ocr_arabic to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="surfiniaburger/unsloth_finetune_ocr_arabic",
    max_seq_length=2048,
)

Docker Model Runner
How to use surfiniaburger/unsloth_finetune_ocr_arabic with Docker Model Runner:
```
docker model run hf.co/surfiniaburger/unsloth_finetune_ocr_arabic
```

Glimpse: Multi-Modal OCR for RTL Scripts (Arabic & Persian)

Glimpse is a high-precision OCR model specialized for Right-to-Left (RTL) languages. It was developed as part of the ERNIE AI Developer Challenge 2025 to solve the "Script Bias" typically found in general-purpose vision models.

Model Details

Developed by: surfiniaburger
License: apache-2.0
Finetuned from model: unsloth/PaddleOCR-VL
Architecture: ERNIE-4.5-0.3B Vision-Language Model

📊 Performance Metrics

After 500 steps of fine-tuning, Glimpse achieved the following results on unseen RTL text lines:

Character Error Rate (CER): 6.97% (Reduced from ~59% baseline)
Validation Loss: 0.25 (Near-perfect convergence)

📚 Dataset Credit

This model was trained using the Persian & Arabic Text-Line Image OCR Dataset (Medium) curated by Mohammad Reza Hajesmaeili.

Dataset Source: mohajesmaeili/Persian_Arabic_TextLine_Image_Ocr_Medium
Content: Diverse text-line images containing complex ligatures and various Arabic/Persian fonts.

🛠 Training Tools

This model was trained 2x faster using Unsloth and Huggingface's TRL library. We utilized QLoRA (4-bit) to ensure efficient memory usage while maintaining high-fidelity weights.

Downloads last month: 15

Safetensors

Model size

1.0B params

Tensor type

F32

F16

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for surfiniaburger/unsloth_finetune_ocr_arabic

Base model

baidu/ERNIE-4.5-0.3B-Paddle

Finetuned

unsloth/PaddleOCR-VL

Finetuned

(15)

this model

surfiniaburger
/

unsloth_finetune_ocr_arabic