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.