初始化项目，由ModelHub XC社区提供模型

Model: meditsolutions/MSH-v1-Bielik-v2.3-Instruct-MedIT-merge
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- speakleash/Bielik-11B-v2.3-Instruct
+pipeline_tag: text-generation
+tags:
+- medit-merge
+language:
+- pl
+- en
+---
+
+<div align="center">
+  <img src="https://i.ibb.co/YLfCzXR/imagine-image-c680e106-e404-45e5-98da-af700ffe41f4.png" alt="Llama-3.2-MedIT-SUN-2.5B" 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
+
+The Marsh Harrier (MSH) is a language model developed by MedIT Solutions using an advanced checkpoint merging technique. It represents a novel fusion of the Speakleash Bielik 11B v2.3 Instruct and Speakleash Bielik 11B v2 models, employing our proprietary weight-merging methodology.
+
+## Key Features:
+- Built on a pioneering approach to neural network weight fusion
+- Supports merging models of identical parameter counts while maintaining architecture flexibility
+- Demonstrates superior performance compared to its base models
+- Optimized for Polish language understanding and generation
+
+## Performance:
+The model shows significant improvements over its predecessors across multiple metrics in the Open PL LLM Leaderboard evaluation framework (0-shot), which is part of the SpeakLeash.org open-science initiative.
+
+Technical Details:
+- Base Models: [Speakleash Bielik 11B v2.3 Instruct](https://huggingface.co/speakleash/Bielik-11B-v2.3-Instruct) and [Bielik 11B v2](https://huggingface.co/speakleash/Bielik-11B-v2)
+- Architecture: Compatible with original Bielik architecture
+- Parameter Count: 11 billion parameters
+- Special Feature: Utilizes MedIT Solutions' proprietary checkpoint merging technology
+
+This model represents a step forward in developing the Polish language, demonstrating how merging techniques can enhance model performance while maintaining architectural efficiency.
+
+# Polish LLM Open Leaderboard
+
+Core Leaderboards:
+- MT-Bench-PL: slight decrease of 0.3 points (8.27 vs 8.56)
+- Open PL LLM Leaderboard: improved performance by 0.09 points (65.80 vs 65.71)
+
+Sentiment Analysis (PolEmo2):
+- In-domain accuracy: Matches Bielik at 77.70%
+- Out-of-domain accuracy: Improved performance at 79.76% (vs 79.35%)
+
+Text Classification Tasks:
+- 8tags classification: Significant improvement of ~3pp (76.14% vs 73.17%)
+- Belebele benchmark: Matching performance at 88.56%
+- CBD task: Substantial F1 score improvement by 10pp (23.91% vs 13.73%)
+
+Language Understanding:
+- DYK ("Did you know..."): Improved F1 score (69.77% vs 69.14%)
+- Named Entity Recognition (KLEJ NER): Notable improvement of ~8pp (45.53% vs 37.61%)
+- PolQA reranking: Slight decrease (81.99% vs 83.21%)
+- PPC: Enhanced accuracy (78.00% vs 77.20%)
+- PSC: Minor F1 score decrease (90.46% vs 93.63%)
+
+Overall Performance:
+MSH-v1 achieves a higher average score of 71.18% compared to Bielik v2.3's 69.33%, demonstrating the effectiveness of our checkpoint merging technique in improving model performance across diverse NLP tasks.
+
+All evaluations were conducted using the Open PL LLM Leaderboard framework (0-shot) as part of the SpeakLeash.org open-science initiative.
+
+Kudos to the **[SpeakLeash](https://speakleash.org)** project and **[ACK Cyfronet AGH](https://www.cyfronet.pl/)** for their extraordinary work.