Marsouuu/general7Bv2-ECE-PRYMMAL-Martial
Marsouuu/general7Bv2-ECE-PRYMMAL-Martial is a 7.6 billion parameter language model created by Marsouuu using the SLERP merge method, combining Tsunami-th/Tsunami-0.5x-7B-Instruct and fblgit/cybertron-v4-qw7B-MGS. This model is designed for general language tasks, leveraging its merged architecture to provide a balanced performance profile. It features a substantial 131072 token context length, making it suitable for processing extensive inputs and generating coherent, long-form text. Its performance on the Open LLM Leaderboard, with an average score of 31.04, indicates its capability across various benchmarks including IFEval and MMLU-PRO.
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Model Overview
Marsouuu/general7Bv2-ECE-PRYMMAL-Martial is a 7.6 billion parameter language model developed by Marsouuu. It was created using the SLERP merge method, combining two distinct base models: Tsunami-th/Tsunami-0.5x-7B-Instruct and fblgit/cybertron-v4-qw7B-MGS. This merging approach aims to leverage the strengths of both constituent models.
Key Capabilities
- Merged Architecture: Benefits from the combined characteristics of its base models, potentially offering a versatile performance across various tasks.
- Extended Context Length: Supports a context window of 131072 tokens, enabling the processing of very long inputs and generation of extensive outputs.
- General Purpose: Designed for a broad range of language understanding and generation tasks.
Performance Benchmarks
Evaluated on the Open LLM Leaderboard, the model achieved an average score of 31.04. Notable individual metric scores include:
- IFEval (0-Shot): 56.93
- BBH (3-Shot): 37.67
- MMLU-PRO (5-shot): 38.87
Good For
- Applications requiring a large context window for processing and generating long texts.
- General language tasks where a balanced performance from a merged model is beneficial.
- Experimentation with models derived from the SLERP merging technique.