# MPT-7B ## MPT-7B MosaicML Foundations has made a significant contribution to this space with the introduction of MPT-7B, their latest open-source LLM. MPT-7B, an acronym for MosaicML Pretrained Transformer, is a GPT-style, decoder-only transformer model. This model boasts several enhancements, including performance-optimized layer implementations and architectural changes that ensure greater training stability. A standout feature of MPT-7B is its training on an extensive dataset comprising 1 trillion tokens of text and code. This rigorous training was executed on the MosaicML platform over a span of 9.5 days. The open-source nature of MPT-7B positions it as a valuable tool for commercial applications. It holds the potential to significantly impact predictive analytics and the decision-making processes of businesses and organizations. In addition to the base model, MosaicML Foundations is also releasing specialized models tailored for specific tasks, such as MPT-7B-Instruct for short-form instruction following, MPT-7B-Chat for dialogue generation, and MPT-7B-StoryWriter-65k+ for long-form story creation. The development journey of MPT-7B was comprehensive, with the MosaicML team managing all stages from data preparation to deployment within a few weeks. The data was sourced from diverse repositories, and the team utilized tools like EleutherAI’s GPT-NeoX and the 20B tokenizer to ensure a varied and comprehensive training mix. **Key Features Overview of MPT-7B:** * **Commercial Licensing:** MPT-7B is licensed for commercial use, making it a valuable asset for businesses. * **Extensive Training Data:** The model boasts training on a vast dataset of 1 trillion tokens. * **Long Input Handling:** MPT-7B is designed to process extremely lengthy inputs without compromise. * **Speed and Efficiency:** The model is optimized for swift training and inference, ensuring timely results. * **Open-Source Code:** MPT-7B comes with efficient open-source training code, promoting transparency and ease of use. * **Comparative Excellence:** MPT-7B has demonstrated superiority over other open-source models in the 7B-20B range, with its quality matching that of LLaMA-7B. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://metaverse-imagen.gitbook.io/ai-tools-research/large-language-models-llms/open-source-llms/mpt-7b.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.