seyf1elislam/WestKunai-XD-7b
WestKunai-XD-7b by seyf1elislam is a 7 billion parameter language model, merged using the DARE TIES method from seyf1elislam/WestKunai-Hermes-7b and seyf1elislam/KuTrix-7b, with Mistral-7B-v0.1 as its base. This model achieves an average score of 73.27 on the Open LLM Leaderboard, demonstrating strong performance across various reasoning and language understanding tasks. With a 4096-token context length, it is suitable for general-purpose text generation and conversational AI applications.
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Model Overview
WestKunai-XD-7b is a 7 billion parameter language model developed by seyf1elislam, created through a merge of pre-trained models using the mergekit tool and the DARE TIES method. Its foundation is the mistralai/Mistral-7B-v0.1 model, combined with seyf1elislam/WestKunai-Hermes-7b and seyf1elislam/KuTrix-7b.
Key Capabilities & Performance
This model demonstrates solid performance across a range of benchmarks, as evaluated on the Hugging Face Open LLM Leaderboard:
- Average Score: 73.27
- AI2 Reasoning Challenge (25-Shot): 71.25
- HellaSwag (10-Shot): 87.59
- MMLU (5-Shot): 64.69
- TruthfulQA (0-shot): 67.29
- Winogrande (5-shot): 82.24
- GSM8k (5-shot): 66.57
These scores indicate its proficiency in reasoning, common sense, language understanding, and mathematical problem-solving.
Usage & Integration
WestKunai-XD-7b is designed for general text generation tasks. It can be easily integrated into Python applications using the transformers library, with support for bfloat16 dtype and device_map="auto" for efficient deployment. Quantized GGUF versions are also available for optimized inference on various hardware.