Daemontatox
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README.md
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license: apache-2.0
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- **License:** apache-2.0
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- **Finetuned from model :** arcee-ai/Meraj-Mini
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license: apache-2.0
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language:
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- ar
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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).
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