weezywitasneezy/OxytocinErosEngineeringF1-7B-slerp
OxytocinErosEngineeringF1-7B-slerp is a 7 billion parameter language model created by weezywitasneezy, formed by merging ChaoticNeutrals/Eris_Remix_7B and Virt-io/Erebus-Holodeck-7B using the slerp method. This model achieves an average score of 69.22 on the Open LLM Leaderboard, demonstrating capabilities across reasoning, common sense, and language understanding tasks. With a context length of 4096 tokens, it is suitable for general-purpose text generation and conversational AI applications.
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OxytocinErosEngineeringF1-7B-slerp Overview
OxytocinErosEngineeringF1-7B-slerp is a 7 billion parameter language model developed by weezywitasneezy. It is a product of merging two distinct models, ChaoticNeutrals/Eris_Remix_7B and Virt-io/Erebus-Holodeck-7B, utilizing the slerp (spherical linear interpolation) merge method. This approach combines the strengths of its base models to create a new, enhanced model.
Key Capabilities & Performance
This model has been evaluated on the Open LLM Leaderboard, achieving a competitive average score of 69.22. Specific performance metrics include:
- AI2 Reasoning Challenge (25-Shot): 67.15
- HellaSwag (10-Shot): 86.0
- MMLU (5-Shot): 64.73
- TruthfulQA (0-shot): 54.54
- Winogrande (5-shot): 81.14
- GSM8k (5-shot): 61.79
These scores indicate a balanced performance across various benchmarks, including reasoning, common sense, factual recall, and mathematical problem-solving. The model supports a context length of 4096 tokens.
Good For
- General-purpose text generation: Capable of generating coherent and contextually relevant text for a wide range of prompts.
- Conversational AI: Its performance on reasoning and common sense tasks makes it suitable for developing interactive agents.
- Experimentation with merged models: Provides a practical example of the
slerpmerge method's application and its resulting performance characteristics.