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mamba-chat is a chat language model developed by havenhq that is based on a state-space model architecture, rather than a transformer. It is a fine-tune of the Mamba-2.8B model from the paper "Mamba: Linear-Time Sequence Modeling with Selective State Spaces". This model differs from similar transformer-based chat models in its unique architecture, which aims to provide more efficient and scalable language modeling capabilities. Model inputs and outputs mamba-chat is a text-to-text model, taking user messages as input and generating assistant responses. The model uses a specific prompt format called the "zephyr format", which structures the conversation as a sequence of user and assistant messages. Inputs User messages in the zephyr format: {user_message} Outputs Assistant responses in the zephyr format: {assistant_message} Capabilities mamba-chat is capable of engaging in open-ended conversations, answering questions, and generating coherent and relevant responses. Its state-space architecture aims to provide more efficient and scalable language modeling compared to transformer-based models, particularly for information-dense tasks. What can I use it for? mamba-chat can be used for a variety of natural language processing tasks, such as chatbots, personal assistants, and language generation applications. Its unique architecture may make it well-suited for applications that require efficient and scalable language modeling, such as large-scale conversational systems or text generation tasks. Things to try Experiment with the model's capabilities by providing it with a variety of prompts and messages in the zephyr format, and observe the quality and coherence of the generated responses. You can also compare the performance of mamba-chat to other chat models, such as the Llama-2-7b-chat model, to better understand its unique strengths and limitations.

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Updated 5/27/2024