Matter 7B 0.1 Boost DPO Preview

This model, developed by 0-hero, is a 7 billion parameter language model based on the Mistral 7B architecture. It has been fine-tuned using Direct Preference Optimization (DPO) on the proprietary Matter dataset, which was curated from over 35 datasets and involved analyzing more than 6 billion tokens. The model is designed to follow the ChatML prompt format for conversational interactions.

Key Capabilities

• Function Calling: The model natively supports function calling, enabling it to interact with external tools and APIs. It uses specific tokens (<|begin_func|>, <|end_func|>, <|begin_func_response|>, <|end_func_response|>) to delineate function calls and their responses within the conversation.
• DPO Fine-tuning: Leverages Direct Preference Optimization for improved instruction following and response quality, building upon the Matter 7B Boost base model.
• Curated Training Data: Benefits from training on the extensive and diverse Matter dataset, enhancing its general understanding and specific capabilities.

Good For

• Applications requiring robust function calling capabilities.
• Building AI assistants that need to interact with external systems or retrieve specific information.
• Use cases where adherence to the ChatML prompt format is preferred.