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_swapping merge 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_swapping merge method and its effects on model behavior may find this model a valuable case study.