Aryanne/WestSenzu-Swap-7B
Aryanne/WestSenzu-Swap-7B is a 7 billion parameter experimental merged language model, created using a task_swapping method with NeuralNovel/Senzu-7B-v0.1-DPO as the base and senseable/WestLake-7B-v2. This model is primarily optimized for role-playing scenarios, leveraging the characteristics of its merged components. It achieves an average score of 67.28 on the Open LLM Leaderboard, with notable performance in HellaSwag and Winogrande.
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Model Overview
Aryanne/WestSenzu-Swap-7B is an experimental 7 billion parameter language model developed by Aryanne, created through a novel task_swapping merge method. This model integrates the strengths of two distinct pre-trained models: NeuralNovel/Senzu-7B-v0.1-DPO as the base, and senseable/WestLake-7B-v2.
Key Capabilities
- Role-play Optimization: Designed with a focus on enhancing performance in role-playing applications, leveraging the combined characteristics of its constituent models.
- Merged Architecture: Utilizes a unique
task_swappingmerge method, allowing for experimental blending of model capabilities.
Performance Highlights
Evaluated on the Open LLM Leaderboard, WestSenzu-Swap-7B demonstrates competitive performance for its size:
- Average Score: 67.28
- HellaSwag (10-Shot): 85.70
- Winogrande (5-shot): 82.48
- MMLU (5-Shot): 64.14
Ideal Use Cases
This model is particularly well-suited for:
- Creative Role-playing: Its experimental merge design aims to provide robust and engaging responses for interactive narrative and character-driven applications.
- Exploratory Merging Research: Developers interested in the
task_swappingmerge method and its effects on model behavior may find this model a valuable case study.