rinna/youri-7b-chat
rinna/youri-7b-chat is a 7 billion parameter instruction-tuned causal language model developed by rinna, based on the Llama 2 architecture with a 4096-hidden-size transformer. It is specifically fine-tuned for chat-style interactions using a diverse set of English and Japanese instruction datasets, including Databricks Dolly, Anthropic HH RLHF, and FLAN. This model excels at understanding and responding to natural language instructions in both English and Japanese, making it suitable for conversational AI applications requiring strong bilingual capabilities.
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Overview
rinna/youri-7b-chat is an instruction-tuned version of the rinna/youri-7b model, designed for chat-style input and responses. Developed by rinna, this 7 billion parameter model utilizes a 32-layer, 4096-hidden-size transformer architecture, consistent with the Llama 2 framework.
Key Capabilities
- Bilingual Instruction Following: Fine-tuned on a comprehensive mix of English and Japanese datasets, enabling robust performance in both languages.
- Chat-Style Interaction: Optimized for conversational AI, capable of understanding and generating responses in a dialogue format.
- Diverse Training Data: Leverages datasets such as Databricks Dolly, Anthropic HH RLHF, FLAN, and specific Japanese LLM datasets to enhance its instruction-following abilities and general knowledge.
Good For
- Multilingual Chatbots: Ideal for building conversational agents that need to operate effectively in both English and Japanese.
- Instruction-Based Task Execution: Suitable for applications requiring the model to perform tasks based on natural language instructions, such as translation, summarization, or question answering.
- Research and Development: Provides a strong foundation for further fine-tuning or research into bilingual large language models, particularly those focused on Japanese language processing.