Duplicate from riotu-lab/ArabianGPT-03B

Co-authored-by: Robotics and Interne-of-Things <riotu-lab@users.noreply.huggingface.co>

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README.md

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+---
+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
+
+<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.3B, and users engage with and apply the model's outputs at their own risk.</p>
+
+> **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-0.3B, 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**: 345 million parameters
+- **Layers**: 24
+- **Model Attention Layers (MAL)**: 16
+- **Context Window Size**: 1024 tokens
+
+## Training
+- **Dataset**: Scraped texts contains scientific articles, and general texts
+- **Data Size**: 23 GB
+- **Tokenizer**: Aranizer 64K
+- **Tokens**: Over 3.3 billion
+- **Hardware**: 4 NDIVIA A100 GPUs 
+- **Training Duration**: 45 days
+- **Performance**:  loss of 3.82
+
+
+## Role in ArabianLLM Initiatives
+ArabianGPT-0.3B  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-03B", 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 Generation
+
+<p style="color: red;">We disclaim all responsibility for any harm, inaccuracies, or inappropriate content generated by ArabianGPT-0.3B, and users engage with and apply the model's outputs at their own risk.</p>

config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": null,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 64000,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 1024,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 24,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "use_cache": true,
+  "vocab_size": 64002
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "eos_token_id": 64000,
+  "transformers_version": "4.26.1"
+}

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special_tokens_map.json

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+  "pad_token": "<EOS>"
+}

tokenizer_config.json

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+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

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