Overview

VitalContribution/Evangelion-7B is a 7 billion parameter language model that explores the impact of combining DPO-optimized and non-DPO-optimized models, further enhanced by fine-tuning on a high-quality DPO dataset. The base model for this experiment was Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp.

Key Capabilities

• Enhanced Reasoning: Achieves 68.94 on AI2 Reasoning Challenge (25-Shot) and 66.94 on GSM8k (5-shot).
• Language Understanding: Demonstrates strong performance with 86.45 on HellaSwag (10-Shot) and 79.95 on Winogrande (5-shot).
• Instruction Following: Fine-tuned using a carefully filtered, high-quality DPO dataset (/argilla/distilabel-intel-orca-dpo-pairs) to improve response quality.
• ChatML Format: Utilizes the ChatML template, integrated into the model's tokenizer, for structured conversational interactions.

Performance Highlights

Evaluated on the Open LLM Leaderboard, Evangelion-7B achieved an average score of 71.71. Notable scores include:

• MMLU (5-Shot): 63.97
• TruthfulQA (0-shot): 64.01

Good For

• General-purpose conversational AI applications.
• Tasks requiring robust reasoning and language comprehension.
• Developers looking for a 7B model with strong instruction-following capabilities derived from DPO fine-tuning.