mlabonne/NeuralDarewin-7B
NeuralDarewin-7B by mlabonne is a 7 billion parameter language model merged from multiple Mistral-based models, including Intel/neural-chat-7b-v3-3 and openaccess-ai-collective/DPOpenHermes-7B-v2, using the dare_ties method. This model is designed for general-purpose conversational AI and instruction following, leveraging the strengths of its constituent models. It achieves an average score of 71.79 on the Open LLM Leaderboard, demonstrating strong performance across various reasoning and language understanding tasks.
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NeuralDarewin-7B Overview
NeuralDarewin-7B is a 7 billion parameter language model developed by mlabonne, created through a merge of several high-performing Mistral-based models using the dare_ties merge method. This approach combines the strengths of models like Intel/neural-chat-7b-v3-3, openaccess-ai-collective/DPOpenHermes-7B-v2, fblgit/una-cybertron-7b-v2-bf16, openchat/openchat-3.5-0106, OpenPipe/mistral-ft-optimized-1227, and mlabonne/NeuralHermes-2.5-Mistral-7B.
Key Capabilities & Performance
This model demonstrates robust performance across a range of benchmarks, as evaluated on the Open LLM Leaderboard. It achieves an average score of 71.79, with notable results including:
- AI2 Reasoning Challenge (25-Shot): 70.14
- HellaSwag (10-Shot): 86.40
- MMLU (5-Shot): 64.85
- TruthfulQA (0-shot): 62.92
- Winogrande (5-shot): 79.72
- GSM8k (5-shot): 66.72
These scores indicate strong capabilities in reasoning, common sense, language understanding, and mathematical problem-solving.
Good For
- General-purpose instruction following: Its diverse training base makes it suitable for a wide array of conversational and task-oriented applications.
- Reasoning tasks: Performance on ARC and GSM8k suggests proficiency in logical and mathematical reasoning.
- Language understanding: Strong HellaSwag and Winogrande scores indicate good contextual comprehension.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.