Hugging Face MetaModel Overview
MetaModel is a 10.7 billion parameter language model developed by gagan3012. It is a product of merging two distinct models, jeonsworld/CarbonVillain-en-10.7B-v4 and kekmodel/StopCarbon-10.7B-v5, utilizing the slerp merge method via mergekit. This merging strategy aims to combine the strengths of its constituent models.
Key Capabilities & Performance
Evaluated on the Open LLM Leaderboard, MetaModel achieved an average score of 74.4. Notable benchmark results include:
- ARC (25-shot): 71.08
- HellaSwag (10-shot): 88.45
- MMLU (5-shot): 66.26
- TruthfulQA (0-shot): 71.84
- Winogrande (5-shot): 83.43
- GSM8K (5-shot): 65.35
These scores indicate a strong general understanding and reasoning capability across a variety of tasks, from common sense reasoning to academic subjects and mathematical problem-solving.
Use Cases
MetaModel is well-suited for applications requiring a versatile language model with solid performance across a broad spectrum of tasks. Its balanced benchmark results suggest it can be effectively used for:
- General text generation and comprehension
- Question answering
- Reasoning tasks
- Educational support in various subjects