mpt-30b
Maintainer: mosaicml
338
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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 is a large language model trained by MosaicML, a company focused on developing cutting-edge AI models. It is part of the Mosaic Pretrained Transformer (MPT) family of models, which use a modified transformer architecture optimized for efficient training and inference.
The mpt-30b model was trained on 1 trillion tokens of English text and code, significantly more data than models like LLaMA (300 billion tokens), Pythia (300 billion), OpenLLaMA (300 billion), and StableLM (800 billion). This allows the mpt-30b to have strong capabilities across a wide range of natural language tasks.
Additionally, the mpt-30b includes several architectural innovations that set it apart, like support for an 8k token context window (which can be further extended via finetuning), context-length extrapolation via ALiBi, and efficient inference and training via FlashAttention. These features enable the model to handle very long inputs and generate coherent text, making it well-suited for tasks like long-form writing.
Model inputs and outputs
Inputs
- Text: The
mpt-30bmodel takes in natural language text as input, which can range from short prompts to long-form passages.
Outputs
- Generated text: The primary output of the
mpt-30bmodel is continuation of the input text, generating coherent and contextually relevant output. The model can be used for a variety of text generation tasks, from creative writing to question-answering.
Capabilities
The mpt-30b model has shown strong performance on a wide range of language tasks, including text generation, question-answering, and code generation. Its large scale and architectural innovations allow it to handle long-form inputs and outputs effectively. For example, the model can be used to generate multi-paragraph stories or long-form instructional content.
What can I use it for?
The mpt-30b model is well-suited for a variety of natural language processing applications, particularly those that require handling long-form text. Some potential use cases include:
- Content creation: The model can be used to assist with writing tasks like creative fiction, technical documentation, or marketing copy.
- Question-answering: With its strong understanding of language, the
mpt-30bcan be used to build chatbots or virtual assistants that can engage in informative and contextual conversations. - Code generation: Due to its training on a mix of text and code, the model can be used to generate or assist with writing code.
Companies looking to leverage large language models for their business could consider finetuning the mpt-30b on their own data to create custom AI assistants or content generation tools. The MosaicML Platform provides tools and services to help with this process.
Things to try
One interesting aspect of the mpt-30b model is its ability to handle very long inputs and outputs due to the ALiBi architecture. This could make it well-suited for tasks like long-form story generation or summarization of lengthy documents. Experimenting with pushing the boundaries of the model's context window could yield compelling results.
Additionally, the model's strong performance on both text and code suggests it could be a powerful tool for developing AI-assisted programming workflows. Prompting the model with high-level instructions or pseudocode and seeing how it translates that into working code could be an illuminating exercise.
Overall, the mpt-30b represents a significant step forward in the development of large language models, and its combination of scale, capability, and efficiency make it an intriguing model to explore and experiment with.
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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