mpt-30b-chat

Maintainer: mosaicml

Total Score

199

Last updated 5/28/2024

👁️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

mpt-30b-chat is a chatbot-like model for dialogue generation developed by MosaicML. It was built by fine-tuning the larger MPT-30B model on several datasets, including ShareGPT-Vicuna, Camel-AI, GPTeacher, Guanaco, and Baize. This model follows a modified decoder-only transformer architecture and is licensed for non-commercial use only.

Model inputs and outputs

The mpt-30b-chat model is designed for text-to-text tasks, taking in natural language prompts and generating relevant responses. It has an 8k token context window, which can be further extended via fine-tuning, and supports context-length extrapolation via ALiBi.

Inputs

  • Natural language prompts for conversation or dialogue

Outputs

  • Generated text responses to continue a conversation or provide relevant information

Capabilities

The mpt-30b-chat model excels at engaging in multi-turn conversations and following short-form instructions. Its large 30B parameter size and fine-tuning on specialized datasets give it strong coding abilities and the capacity to handle a wide range of conversational topics.

What can I use it for?

The mpt-30b-chat model can be used to power conversational AI assistants, chatbots, and interactive applications. Its capabilities make it well-suited for tasks like customer service, educational applications, and creative writing assistance. While licensed for non-commercial use only, interested parties can explore the model's potential on the MosaicML platform.

Things to try

One interesting aspect of mpt-30b-chat is its ability to extrapolate beyond its 8k token context window through the use of ALiBi. This allows the model to maintain coherence and context over longer dialogues, opening up possibilities for more substantive and engaging conversations.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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