Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B
Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B is a 7 billion parameter language model created by Weyaxi, formed by merging teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-1 using a ties merge. This model leverages the strengths of its base models, offering a 4096-token context length and achieving an average score of 67.84 on the Open LLM Leaderboard, making it suitable for general conversational AI and instruction-following tasks.
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Overview
Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B is a 7 billion parameter language model developed by Weyaxi. It is a merge of two prominent models: teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-1, utilizing a ties merge approach. This combination aims to integrate the distinct capabilities of its constituent models.
Key Capabilities
- Instruction Following: Inherits strong instruction-following abilities from its base models.
- General Conversational AI: Designed for broad conversational applications.
- Performance: Achieves a competitive average score of 67.84 on the Open LLM Leaderboard, with specific scores including:
- ARC (25-shot): 66.55
- HellaSwag (10-shot): 84.47
- MMLU (5-shot): 63.34
- TruthfulQA (0-shot): 61.22
- Winogrande (5-shot): 78.37
- GSM8K (5-shot): 53.07
Good For
- Chatbot Development: Ideal for creating responsive and coherent conversational agents.
- Instruction-based Tasks: Suitable for applications requiring the model to follow specific commands or prompts.
- Research and Experimentation: Provides a robust base for further fine-tuning or architectural exploration, leveraging the combined strengths of its merged components.
Prompt Templates
The model supports multiple prompt templates, with ChatML (from OpenHermes-2.5-Mistral-7B) being recommended, alongside the template from neural-chat-7b-v3-1.
Quantized Versions
Optimized, quantized versions are available from TheBloke in GPTQ, GGUF, and AWQ formats for efficient deployment.