--- license: apache-2.0 language: - ar pipeline_tag: text-generation tags: - 'arabic ' - text-generation widget: - text: "أعلنت وزارة الحج في المملكة العربية السعودية" example_title: "مثال ١" - text: "يبدو اليوم جميلا، سأقوم بتحضير" example_title: "مثال ٢" - text: "إن التقنيات الحديثة" example_title: "مثال ٣" --- # ArabianGPT Model Overview ## Disclaimer for the Use of Large Language Models (LLMs) for Text Generation
We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.
> **Important Note:** Currently, we offer a raw pre-trained model. Our team is actively working on releasing instruction-based LLMs that are fine-tuned and augmented with LRHF. The first set of pre-trained models has been made available for community exploration. While we do have models fine-tuned for specific tasks such as summarization and sentiment analysis, they are still in the development phase. ## How you can use this Pre-Trained? You are invited to utilize this pre-trained, native Arabic language model as an experimental tool to assess its capabilities, aid in its fine-tuning, and evaluate its performance across a variety of downstream tasks. We encourage you to review our technical report for a comprehensive understanding of the model's performance metrics and the specific downstream tasks it has been tested on. This will provide valuable insights into its applicability and effectiveness in diverse applications. ## Introduction ArabianGPT-0.1B, developed under the ArabianLLM initiatives, is a specialized GPT-2 model optimized for Arabic language modeling. It's a product of the collaborative efforts at Prince Sultan University's Robotics and Internet of Things Lab, focusing on enhancing natural language modeling and generation in Arabic. This model represents a significant stride in LLM research, specifically addressing the linguistic complexities and nuances of the Arabic language. ## Key Features - **Architecture**: GPT-2 - **Model Size**: 134 million parameters - **Layers**: 12 - **Model Attention Layers (MAL)**: 12 - **Context Window Size**: 768 tokens ## Training - **Dataset**: Scraped Arabic newspaper articles - **Data Size**: 15.5 GB - **Words**: 237.8 million - **Tokenizer**: Aranizer 64K - **Tokens**: Over 1.75 billion - **Hardware**: 2 NDIVIA A100 GPUs - **Training Scale**: 7.5 million examples - **Training Duration**: 3 days - **Performance**: Final loss of 3.97 ## Role in ArabianLLM Initiatives ArabianGPT-0.1B (Base Model) is crucial for advancing Arabic language processing, addressing challenges unique to Arabic morphology and dialects. ## Usage Suitable for Arabic text generation tasks. Example usage with Transformers Pipeline: ```python from transformers import pipeline pipe = pipeline("text-generation", model="riotu-lab/ArabianGPT-01B", max_new_tokens=512) text = '' pipe.predict(text) ``` ## Limitations and Ethical Considerations - The model may have context understanding or text generation limitations in certain scenarios. - Emphasis on ethical use to prevent misinformation or harmful content propagation. ## Acknowledgments Special thanks to Prince Sultan University, particularly the Robotics and Internet of Things Lab. ## Contact Information For inquiries: [riotu@psu.edu.sa](mailto:riotu@psu.edu.sa). ## Disclaimer for the Use of Large Language Models (LLMs) for Text GenerationWe disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.1B, and users engage with and apply the model's outputs at their own risk.