mpt-30b-instruct

Maintainer: mosaicml

Total Score

99

Last updated 5/27/2024

🌐

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

Get summaries of the top AI models delivered straight to your inbox:

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!

Related Models

📊

mpt-7b-instruct

mosaicml

Total Score

461

mpt-7b-instruct is a model for short-form instruction following. It was built by finetuning MPT-7B on a dataset derived from the Databricks Dolly-15k and the Anthropic Helpful and Harmless (HH-RLHF) datasets. This model was trained by MosaicML. Model Inputs and Outputs This is a text-to-text model, taking in natural language text and generating new text in response. The model can handle a wide range of input prompts and produce diverse outputs, from succinct factual answers to engaging stories. Inputs Natural language text prompts, which can include instructions, questions, or open-ended requests Outputs Generated text relevant to the input prompt Outputs can range from short factual responses to longer narrative pieces Capabilities mpt-7b-instruct demonstrates strong performance on a variety of language tasks, including question answering, summarization, and open-ended generation. For example, when given the prompt "What is a quoll?", the model provides a detailed explanation of this Australian marsupial. The model can also generate creative stories and engage in open-ended dialogue when prompted. What Can I Use It For? The mpt-7b-instruct model could be useful for a variety of applications that require natural language processing, such as: Building chatbots or virtual assistants that can understand and respond to user instructions Automating content creation tasks like writing summaries, articles, or creative fiction Enhancing search engines or question-answering systems with more natural language understanding Things to Try One interesting aspect of the mpt-7b-instruct model is its ability to handle very long input sequences, thanks to the use of ALiBi. You could try providing the model with long passages of text, such as entire books or lengthy articles, and see how it responds to open-ended prompts or generates continuations. The model's capacity for handling long-form content makes it a compelling tool for tasks like story generation or summarization.

Read more

Updated Invalid Date

🤔

mpt-30b

mosaicml

Total Score

338

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-30b model 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-30b model 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-30b can 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.

Read more

Updated Invalid Date

👁️

mpt-30b-chat

mosaicml

Total Score

199

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.

Read more

Updated Invalid Date

⚙️

mpt-7b-chat

mosaicml

Total Score

512

mpt-7b-chat is a chatbot-like model for dialogue generation. It was built by fine-tuning MPT-7B on several datasets, including ShareGPT-Vicuna, HC3, Alpaca, HH-RLHF, and Evol-Instruct. This allows the model to engage in more natural, open-ended dialogue compared to the base MPT-7B model. Model Inputs and Outputs Inputs Text prompts that the model will use to generate a response. Outputs Generated text responses that continue the dialogue based on the input prompt. Capabilities mpt-7b-chat can engage in freeform dialogue on a wide range of topics. It demonstrates strong language generation abilities and can provide detailed, contextual responses. For example, it can discuss programming concepts, generate gourmet meal recipes, and even roleplay as characters from fiction. What Can I Use It For? The mpt-7b-chat model could be used to power chatbots, virtual assistants, or other applications that require natural language interaction. Its ability to continue a conversation and provide relevant, engaging responses makes it well-suited for customer service, education, entertainment, and other applications where users need to interact with an AI system. Things to Try One interesting aspect of mpt-7b-chat is its ability to maintain context and persona over multiple turns of a conversation. Try providing the model with a detailed system prompt that establishes its identity and goals, then see how it responds to a series of follow-up questions or requests. This can help you explore the model's conversational capabilities and understand how it uses the provided context to inform its responses.

Read more

Updated Invalid Date