You can deploy this model and start earning money today!

realml/
ocr-gemma-3-4b-it

Name: ocr-gemma-3-4b-it
Availability: InStock

Views

No views yet

ocr-gemma-3-4b-it

This model is a fine-tuned version of google/gemma-3-4b-it on the ocr_finetune_train dataset. It achieves the following results on the evaluation set:

Loss: 0.0547

Arabic Legal Documents OCR (Gemma-3-4B-IT)

Fine-tuned Gemma-3-4B-IT model for OCR and structured information extraction from Arabic legal document images.

Model Details

Base model: google/gemma-3-4b-it
Fine-tuning method: LoRA SFT using LLaMA-Factory
Task: OCR + structured JSON extraction
Language: Arabic
Domain: legal / official documents

Repository Structure

arabic-documents-ocr-v1/: merged inference ready model
checkpoints/: intermediate LoRA checkpoints
data/: training datasets and OCR images
training/: LLaMA-Factory training configs
notebooks/: evaluation notebook
adapter_config.json: LoRA adapter configuration

Intended uses & limitations

The model may hallucinate fields if the document is unclear.
OCR quality depends on scan resolution and image preprocessing.
Outputs should be validated before use in legal or production workflows.
Not suitable as a source of legal advice.

This model is intended for:

Arabic OCR extraction
legal document parsing
structured JSON generation from scanned pages
research and experimentation

Training and evaluation data

The model was trained on Arabic legal document page images paired with JSON outputs containing:

markdown-style page content
structural elements
headers / footers
lists and tables
legal articles
document metadata

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.1468	1.0101	500	0.1214
0.0806	2.0202	1000	0.0706
0.0643	3.0	1485	0.0547

Framework versions

PEFT 0.18.1
Transformers 5.6.0
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

realml/ocr-gemma-3-4b-it