0-hero/Matter-0.1-Slim-7B-B
Matter-0.1-Slim-7B-B is a 7 billion parameter language model developed by 0-hero, based on a Mistral 7B architecture. It is a full-finetune on the Matter-0.1-Slim-B dataset, which is curated from over 35 datasets and analyzes more than 6 billion tokens. This model is specifically designed to support function calling, making it suitable for applications requiring structured interactions with external tools or APIs. It utilizes the ChatML prompt format for conversational AI tasks.
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Matter-0.1-Slim-7B-B Overview
Matter-0.1-Slim-7B-B is a 7 billion parameter language model, developed by 0-hero, that has been full-finetuned on a Mistral 7B base. Its training leveraged the extensive Matter-0.1-Slim-B dataset, which aggregates and curates data from over 35 distinct datasets, encompassing more than 6 billion tokens. The training process involved approximately 15 hours across 3 epochs using 4x A100 GPUs (80GB) with Axolotl.
Key Capabilities
- Function Calling Support: A primary differentiator, this model is explicitly designed to handle function calls, enabling it to interact with external tools and APIs. It includes dedicated tokens (
<|begin_func|>,<|end_func|>,<|begin_func_response|>,<|end_func_response|>) for structured function invocation and response parsing. - ChatML Prompt Format: The model is configured to use the ChatML prompt format, facilitating clear and structured conversational interactions with system, user, and assistant roles.
Good For
- Applications requiring tool use: Ideal for scenarios where the AI needs to perform actions or retrieve information via external functions, such as integrating with databases, APIs, or other software components.
- Conversational agents with structured interaction needs: Suitable for building chatbots or assistants that require not just generating text, but also executing specific commands based on user input.