Model Overview

Blur-7b-slerp-v1.41 is a 7 billion parameter language model developed by liminerity. It was created using the slerp (spherical linear interpolation) merge method, combining two distinct base models: bardsai/jaskier-7b-dpo-v5.6 and liminerity/merge. This merging technique aims to leverage the strengths of both constituent models to produce a more capable and balanced language model.

Key Capabilities & Performance

This model has been evaluated on the Open LLM Leaderboard, achieving a notable average score of 75.98. Its performance highlights include:

• AI2 Reasoning Challenge (25-Shot): 72.78
• HellaSwag (10-Shot): 88.65
• MMLU (5-Shot): 64.84
• TruthfulQA (0-shot): 74.23
• Winogrande (5-shot): 83.90
• GSM8k (5-shot): 71.49

These scores indicate its proficiency in diverse areas such as common sense reasoning, factual recall, multi-task language understanding, and mathematical problem-solving.

Good For

• General-purpose text generation and understanding tasks.
• Applications requiring a balance of reasoning and factual knowledge.
• Scenarios where a 7B parameter model offers a good trade-off between performance and computational resources.