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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ base_model:
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+ - meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge
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+ - speakleash/Bielik-11B-v2.3-Instruct
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+ pipeline_tag: text-generation
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+ tags:
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+ - medit-lite
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+ - model-pruning
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+ - text-generation
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+ language:
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+ - pl
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+ - en
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+ ---
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+
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+ <div align="center">
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+ <img src="https://i.ibb.co/YLfCzXR/imagine-image-c680e106-e404-45e5-98da-af700ffe41f4.png" alt="MSH-Lite" style="border-radius: 10px; box-shadow: 0 4px 8px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19); max-width: 100%; height: auto;">
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+ </div>
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+
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+ # Marsh Harrier Lite (MSH-Lite)
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+
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+ Marsh Harrier Lite (MSH-Lite) is a compact, efficient version of the [MedIT Solutions MSH-v1-Bielik-v2.3-Instruct-MedIT-merge](meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge) model, reduced to 7 billion parameters using advanced pruning techniques. This pruning retains the core functionality and efficiency of the original model while optimizing for reduced computational resource usage.
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+
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+ ## Key Features:
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+ - **Pruned Model**: Reduced from 11B to 7B parameters using the pruning method based on the [MedIT Solutions LLaMA pruning framework](https://github.com/MedITSolutionsKurman/llama-pruning).
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+ - **Optimized Performance**: Despite its reduced size, MSH-Lite delivers competitive performance across a wide array of NLP tasks.
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+ - **Bilingual Support**: Designed to handle both Polish (pl) and English (en) with high fluency.
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+
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+ ## Technical Details:
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+ - **Base Model**: [MSH-v1-Bielik-v2.3-Instruct-MedIT-merge](https://huggingface.co/meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge)
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+ - **Parameter Count**: 7 billion
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+ - **Architecture**: Derived from Bielik's core architecture, with parameter optimization.
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+ - **Model Efficiency**: Ideal for deployments where computational efficiency is paramount.
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+
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+ ## Performance Highlights:
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+ To be done.
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+
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+ ### Acknowledgments:
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+ Gratitude to the **[SpeakLeash](https://speakleash.org)** project and **[ACK Cyfronet AGH](https://www.cyfronet.pl/)** for their contributions and collaboration.