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README.md
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This repository hosts both the standard and quantized versions of the Zephyr 7B model, allowing users to choose the version that best fits their resource constraints and performance needs.
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Model Details
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Model Name: Zephyr 7B
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Model Size: 7 billion parameters
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Architecture: Transformer-based
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Languages: Primarily English, with support for multilingual text
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Quantized Version: Available for reduced memory footprint and faster inference
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Performance and Efficiency
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The quantized version of Zephyr 7B is optimized for environments with limited computational resources. It offers:
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Reduced Memory Usage: The model size is significantly smaller, making it suitable for deployment on devices with limited RAM.
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Faster Inference: Quantized models can perform faster inference, providing quicker responses in real-time applications.
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Fine-Tuning
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You can fine-tune the Zephyr 7B model on your own dataset to better suit specific tasks or domains. Refer to the Huggingface documentation for guidance on how to fine-tune transformer models.
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Contributing
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We welcome contributions to improve the Zephyr 7B model. Please submit pull requests or open issues for any enhancements or bugs you encounter.
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License
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This model is licensed under the MIT License.
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Acknowledgments
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Special thanks to the Huggingface team for providing the transformers library and to the broader AI community for their continuous support and contributions.
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Contact
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For any questions or inquiries, please contact us at akshayhedaoo7246@gmail.com.
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---
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This repository hosts both the standard and quantized versions of the Zephyr 7B model, allowing users to choose the version that best fits their resource constraints and performance needs.
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# Model Details
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## Model Name: Zephyr 7B
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## Model Size: 7 billion parameters
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## Architecture: Transformer-based
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## Languages: Primarily English, with support for multilingual text
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## Quantized Version: Available for reduced memory footprint and faster inference
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# Performance and Efficiency
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The quantized version of Zephyr 7B is optimized for environments with limited computational resources. It offers:
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## Reduced Memory Usage: The model size is significantly smaller, making it suitable for deployment on devices with limited RAM.
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## Faster Inference: Quantized models can perform faster inference, providing quicker responses in real-time applications.
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# Fine-Tuning
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You can fine-tune the Zephyr 7B model on your own dataset to better suit specific tasks or domains. Refer to the Huggingface documentation for guidance on how to fine-tune transformer models.
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# Contributing
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We welcome contributions to improve the Zephyr 7B model. Please submit pull requests or open issues for any enhancements or bugs you encounter.
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# License
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This model is licensed under the MIT License.
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# Acknowledgments
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Special thanks to the Huggingface team for providing the transformers library and to the broader AI community for their continuous support and contributions.
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# Contact
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For any questions or inquiries, please contact us at akshayhedaoo7246@gmail.com.
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