VitalContribution/Evangelion-7B
VitalContribution/Evangelion-7B is a 7 billion parameter language model developed by VitalContribution, built upon the Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp architecture. This model was created by merging DPO-optimized and non-DPO-optimized models and fine-tuned on a high-quality DPO dataset, resulting in strong performance across various reasoning and language understanding benchmarks. It is particularly suited for general conversational AI and tasks requiring nuanced response generation.
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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.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.