初始化项目，由ModelHub XC社区提供模型

Model: meditsolutions/MSH-Lite-7B-v1-Bielik-v2.3-Instruct-Llama-Prune
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge
+- speakleash/Bielik-11B-v2.3-Instruct
+pipeline_tag: text-generation
+tags:
+- medit-lite
+- model-pruning
+- text-generation
+language:
+- pl
+- en
+---
+
+<div align="center">
+  <img src="https://i.ibb.co/6HbR84p/imagine-image-e5133e7c-9457-4539-a5e8-e59095e80345.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;">
+</div>
+
+# Marsh Harrier Lite (MSH-Lite)
+
+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.
+
+## Key Features:
+- **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).
+- **Optimized Performance**: Despite its reduced size, MSH-Lite delivers competitive performance across a wide array of NLP tasks.
+- **Bilingual Support**: Designed to handle both Polish (pl) and English (en) with high fluency.
+
+## Technical Details:
+- **Base Model**: [MSH-v1-Bielik-v2.3-Instruct-MedIT-merge](https://huggingface.co/meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge)
+- **Parameter Count**: 7 billion
+- **Architecture**: Derived from Bielik's core architecture, with parameter optimization.
+- **Model Efficiency**: Ideal for deployments where computational efficiency is paramount.
+
+## Performance Highlights:
+To be done.
+
+### Acknowledgments:
+Gratitude to the **[SpeakLeash](https://speakleash.org)** project and **[ACK Cyfronet AGH](https://www.cyfronet.pl/)** for their contributions and collaboration.