Stopwolf/Tito-7B-slerp
Stopwolf/Tito-7B-slerp is a 7 billion parameter language model created by Stopwolf, formed by merging gordicaleksa/YugoGPT and mlabonne/AlphaMonarch-7B using the slerp method. This model is specifically optimized for performance in Serbian language tasks, demonstrating strong results across various benchmarks. It excels in Serbian LLM evaluation suites, making it suitable for applications requiring robust Serbian language understanding and generation.
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Tito-7B-slerp: A Serbian-Optimized Merged Language Model
Tito-7B-slerp is a 7 billion parameter model developed by Stopwolf, created through a spherical linear interpolation (slerp) merge of two base models: gordicaleksa/YugoGPT and mlabonne/AlphaMonarch-7B. This merging strategy aims to combine the strengths of its constituent models, with specific parameter weighting applied to self-attention and MLP layers.
Key Capabilities & Performance
This model is primarily distinguished by its strong performance on Serbian language evaluation suites. Benchmarks show Tito-7B-slerp achieving competitive and often leading results in tasks such as ARC-C, Hellaswag, and Winogrande when evaluated on Serbian datasets. For instance, it scored 47.82% average on a Serbian LLM eval suite, outperforming other models like Zamfir-7B and Mustra-7B. On the Open LLM Leaderboard, Tito-7B-slerp demonstrates an average score of 70.13, significantly higher than its YugoGPT base, with notable scores in HellaSwag (86.38%) and MMLU (64.01%).
Good for
- Serbian Language Applications: Ideal for tasks requiring high proficiency in the Serbian language, including content generation, translation, and understanding.
- Research into Model Merging: Provides a practical example of slerp merging for performance optimization in specific linguistic contexts.