luqmanxyz/Maya_Hermes-2.5-Mistral-7B
luqmanxyz/Maya_Hermes-2.5-Mistral-7B is a 7 billion parameter DPO fine-tuned variant of the OpenHermes-2.5-Mistral-7B model, utilizing the argilla/distilabel-intel-orca-dpo-pairs dataset. This model is designed for general language tasks, demonstrating strong performance across various reasoning and language understanding benchmarks. With a 4096-token context length, it is suitable for applications requiring robust conversational and analytical capabilities.
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Model Overview
luqmanxyz/Maya_Hermes-2.5-Mistral-7B is a 7 billion parameter language model derived from the OpenHermes-2.5-Mistral-7B architecture. It has been fine-tuned using Direct Preference Optimization (DPO) on the argilla/distilabel-intel-orca-dpo-pairs dataset, aiming to enhance its instruction-following and response quality.
Key Capabilities & Performance
This model demonstrates competitive performance on the Open LLM Leaderboard, with an average score of 68.60. Specific benchmark results include:
- AI2 Reasoning Challenge (25-Shot): 66.30
- HellaSwag (10-Shot): 85.07
- MMLU (5-Shot): 63.23
- TruthfulQA (0-shot): 55.89
- Winogrande (5-shot): 78.85
- GSM8k (5-shot): 62.24
These scores indicate its proficiency in reasoning, common sense, language understanding, and mathematical problem-solving.
When to Use This Model
This model is a strong candidate for general-purpose applications requiring a capable 7B parameter model. Its DPO fine-tuning suggests improved alignment and response quality, making it suitable for tasks such as:
- Instruction following
- Question answering
- Text generation
- Reasoning-based tasks
Its balanced performance across various benchmarks makes it a versatile choice for developers seeking a robust and efficient language model.