mayacinka/yam-jom-7B-slerp
mayacinka/yam-jom-7B-slerp is a 7 billion parameter language model created by mayacinka, formed by merging eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 and yam-peleg/Experiment26-7B using a slerp merge method. This model demonstrates strong general reasoning capabilities, achieving an average score of 76.45 on the Open LLM Leaderboard across various benchmarks. It is suitable for a wide range of natural language processing tasks requiring robust understanding and generation.
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Model Overview
mayacinka/yam-jom-7B-slerp is a 7 billion parameter language model developed by mayacinka. It was created by merging two distinct models, eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v2 and yam-peleg/Experiment26-7B, using the slerp (spherical linear interpolation) merge method. This technique combines the strengths of its base models to enhance overall performance.
Key Capabilities & Performance
This model exhibits strong performance across a variety of benchmarks, as evaluated on the Open LLM Leaderboard. It achieved an average score of 76.45, with notable results in:
- AI2 Reasoning Challenge (25-Shot): 72.70
- HellaSwag (10-Shot): 89.02
- MMLU (5-Shot): 64.64
- TruthfulQA (0-shot): 77.77
- Winogrande (5-shot): 84.69
- GSM8k (5-shot): 69.90
These scores indicate a balanced capability in reasoning, common sense, language understanding, and mathematical problem-solving.
Good For
- General-purpose natural language understanding and generation tasks.
- Applications requiring robust reasoning and common sense.
- Developers looking for a merged model that combines the strengths of its constituent parts for improved performance.