Hugging Face AI-B/UTENA-7B-V3 is a 7 billion parameter language model developed by AI-B, created by merging UTENA-7B-UNA-V2 and UTENA-7B-NSFW-V2 using the slerp method. This model achieves an average score of 67.42 on the Open LLM Leaderboard, demonstrating capabilities across various benchmarks including reasoning, common sense, and language understanding. It is designed for general-purpose applications requiring a balanced performance profile.
UTENA-7B-V3 Overview
UTENA-7B-V3 is a 7 billion parameter language model developed by AI-B, constructed through a merge of two distinct models: AI-B/UTENA-7B-UNA-V2 and AI-B/UTENA-7B-NSFW-V2. This merge was performed using the slerp method within mergekit, combining the strengths of its constituent models.
Key Capabilities & Performance
The model's performance has been evaluated on the Open LLM Leaderboard, achieving a competitive average score of 67.42. Specific benchmark results include:
- AI2 Reasoning Challenge (25-Shot): 65.96
- HellaSwag (10-Shot): 85.70
- MMLU (5-Shot): 64.72
- TruthfulQA (0-shot): 53.64
- Winogrande (5-shot): 80.27
- GSM8k (5-shot): 54.21
These scores indicate a balanced capability across reasoning, common sense, factual recall, and mathematical problem-solving. The model is also available in quantized GGUF format for optimized inference.
When to Use This Model
UTENA-7B-V3 is suitable for applications requiring a versatile 7B parameter model with solid performance across a range of general language understanding and generation tasks. Its balanced benchmark results suggest it can be a strong candidate for use cases where a single model needs to handle diverse cognitive demands.