zarakiquemparte/kuchiki-1.1-l2-7b
The zarakiquemparte/kuchiki-1.1-l2-7b is a 7 billion parameter language model created by zarakiquemparte, built upon a merge of Nous Hermes Llama2 7b and Airoboros L2 7B GPT4 2.0, further merged with LimaRP Llama2 v2 7B Lora. This model is specifically designed to support both general instruction-following and detailed role-playing scenarios, leveraging its blended architecture. It offers a 4096-token context length and is optimized for flexible conversational AI applications.
Loading preview...
Model Overview
zarakiquemparte/kuchiki-1.1-l2-7b is a 7 billion parameter language model developed by zarakiquemparte. It is a unique merge of several foundational models, combining the strengths of Nous Hermes Llama2 7b (70%) and Airoboros L2 7B GPT4 2.0 (30%), with an additional merge of LimaRP Llama2 v2 7B Lora. This intricate merging process was facilitated by custom scripts, ensuring a tailored blend of capabilities.
Key Capabilities
- Blended Architecture: Integrates Nous Hermes Llama2, Airoboros L2 7B GPT4 2.0, and LimaRP Llama2 v2 Lora for diverse performance.
- Instruction Following: Supports standard Alpaca 2 instruction formats for general conversational tasks.
- Advanced Role-Playing: Specifically designed to handle complex role-playing scenarios using the Alpaca LimaRP format, allowing for detailed character personas and scenario descriptions.
- Reproducible Merging: The merging process is transparent and reproducible using provided scripts, allowing users to understand its construction.
Use Cases
This model is particularly well-suited for applications requiring:
- General Chatbots: Responding to a wide range of instructions and queries.
- Interactive Storytelling: Engaging in detailed, character-driven role-play with defined personas and scenarios.
- Creative Content Generation: Generating responses that adhere to specific character traits and narrative contexts.
Limitations
Users should note that this model is not intended for providing factual information or advice, and its responses should not be relied upon for such purposes.