mpt-30b-instruct
Maintainer: mosaicml
99
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The mpt-30b-instruct model is a powerful open-source language model developed by MosaicML that is designed for short-form instruction following. This model is built by fine-tuning the larger MPT-30B model on several datasets, including Dolly HHRLHF, Competition Math, Duorc, and more.
Compared to similar open-source models like mpt-7b-instruct and mpt-30b-chat, the mpt-30b-instruct model is significantly larger with 30 billion parameters, providing enhanced capabilities for tasks like instruction following. It utilizes the same modified decoder-only transformer architecture as other MPT models, which incorporates performance-boosting techniques like FlashAttention and ALiBi.
Model inputs and outputs
Inputs
- Text prompts: The model accepts natural language text prompts that describe a task or provide instructions for the model to follow.
Outputs
- Text responses: The model generates text responses that complete the given task or follow the provided instructions.
Capabilities
The mpt-30b-instruct model excels at a variety of short-form instruction following tasks, such as answering questions, solving math problems, summarizing texts, and more. It demonstrates strong language understanding and reasoning abilities, allowing it to interpret complex instructions and provide relevant, coherent responses.
What can I use it for?
Developers and researchers can leverage the mpt-30b-instruct model for a wide range of applications that require natural language processing and generation capabilities. Some potential use cases include:
- Question-answering systems: Build chatbots or virtual assistants that can comprehend and respond to user queries.
- Automated task completion: Develop applications that can follow written instructions to perform various tasks, such as writing reports, generating code snippets, or solving math problems.
- Content summarization: Use the model to automatically summarize long-form text, such as articles or research papers, into concise summaries.
Things to try
One interesting aspect of the mpt-30b-instruct model is its ability to handle long-form inputs and outputs, thanks to the use of ALiBi in its architecture. Developers can experiment with extending the model's context length during fine-tuning or inference to see how it performs on tasks that require generating or comprehending longer passages of text.
Additionally, the model's strong coding abilities, gained from its pretraining data mixture, make it a compelling choice for applications that involve code generation or analysis. Researchers and engineers can explore using the mpt-30b-instruct model for tasks like code completion, code summarization, or even automated programming.
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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