saishf/West-Maid-7B
saishf/West-Maid-7B is a 7 billion parameter language model created by saishf through a SLERP merge of senseable/WestLake-7B-v2 and NeverSleep/Noromaid-7B-0.4-DPO. This model leverages a 4096-token context length and achieves an average score of 69.09 on the Open LLM Leaderboard, demonstrating capabilities across reasoning, common sense, and language understanding tasks. It is designed for general-purpose applications requiring a balanced performance profile from its merged base models.
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Model Overview
saishf/West-Maid-7B is a 7 billion parameter language model developed by saishf. It was created using the SLERP merge method from MergeKit, combining the strengths of two distinct base models: senseable/WestLake-7B-v2 and NeverSleep/Noromaid-7B-0.4-DPO.
Key Capabilities & Performance
This merged model demonstrates a balanced performance across various benchmarks, as evaluated on the Open LLM Leaderboard. It achieved an average score of 69.09, with notable results including:
- AI2 Reasoning Challenge (25-Shot): 67.24
- HellaSwag (10-Shot): 86.44
- MMLU (5-Shot): 64.85
- TruthfulQA (0-shot): 51.00
- Winogrande (5-shot): 82.72
- GSM8k (5-shot): 62.32
These scores indicate proficiency in reasoning, common sense, factual recall, and mathematical problem-solving. The model's 4096-token context length supports processing moderately long inputs.
When to Use This Model
West-Maid-7B is suitable for applications requiring a versatile 7B parameter model that benefits from the combined characteristics of its constituent models. Its balanced performance makes it a strong candidate for:
- General text generation and understanding tasks.
- Reasoning and question-answering scenarios.
- Applications where a blend of capabilities from WestLake-7B-v2 and Noromaid-7B-0.4-DPO is desired.