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  license: apache-2.0
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  language:
 
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  ---
 
 
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- # Uploaded model
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- - **Developed by:** Daemontatox
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- - **License:** apache-2.0
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- - **Finetuned from model :** arcee-ai/Meraj-Mini
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- This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: apache-2.0
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  language:
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+ - ar
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  - en
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  ---
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+ ![image](./image.webp)
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+ # Bilingual Assistant Model Card
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+ ## Overview
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+ This bilingual language model is designed to support seamless text generation and understanding in both Arabic (ar) and English (en). Fine-tuned from the `arcee-ai/Meraj-Mini` base model, it offers robust multilingual capabilities optimized for various applications such as conversational agents, content creation, and multilingual text analysis.
 
 
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+ ### Key Highlights
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+ - **Multilingual Proficiency:** Designed to handle complex linguistic nuances in both Arabic and English, ensuring high-quality outputs in both languages.
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+ - **Performance Optimization:** Achieved 2x faster training through innovative methods provided by the [Unsloth](https://github.com/unslothai/unsloth) framework and the Hugging Face TRL library.
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+ - **Transformer-Based Architecture:** Utilizes advanced transformer layers to deliver state-of-the-art performance in text generation and inference.
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+ ## Development Details
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+ - **Developer:** Daemontatox
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+ - **License:** Licensed under the Apache-2.0, ensuring open accessibility and flexibility for various use cases.
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+ - **Base Model:** The model is a fine-tuned variant of `arcee-ai/Meraj-Mini`.
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+ - **Frameworks Used:**
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+ - [Unsloth](https://github.com/unslothai/unsloth): Enabled faster and more efficient training.
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+ - Hugging Face TRL Library: Provided tools for reinforcement learning fine-tuning, enhancing model responsiveness and accuracy.
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+ ## Training Process
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+ The fine-tuning process was conducted with a focus on:
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+ - **Data Diversity:** Leveraged a bilingual corpus to ensure comprehensive language understanding across both supported languages.
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+ - **Optimized Hardware Utilization:** Implemented Unsloth's accelerated training methods, significantly reducing resource consumption and training time.
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+ - **Reinforcement Learning:** Used Hugging Face's TRL library to fine-tune the model's decision-making and response generation capabilities, particularly for conversational and contextual understanding.
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+ ## Applications
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+ This model is suited for a variety of real-world applications, including:
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+ 1. **Conversational Agents:** Powering bilingual chatbots and virtual assistants for customer support and personal use.
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+ 2. **Content Generation:** Assisting in drafting multilingual articles, social media posts, and creative writing.
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+ 3. **Translation Support:** Providing context-aware translations and summaries across Arabic and English.
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+ 4. **Education:** Enhancing learning platforms by offering bilingual educational content and interactive learning experiences.
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+ ## Future Directions
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+ Plans for extending the model's capabilities include:
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+ - **Additional Language Support:** Exploring fine-tuning for additional languages.
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+ - **Domain-Specific Training:** Specializing the model for industries such as healthcare, legal, and technical writing.
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+ - **Optimization for Edge Devices:** Investigating quantization techniques to deploy the model on resource-constrained hardware like mobile devices and IoT platforms.
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+ ![Unsloth Logo](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)
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+ For more information and updates, visit the [Unsloth GitHub Repository](https://github.com/unslothai/unsloth).