Large language models (LLMs) are powerful tools that can generate text, answer questions, and perform other tasks. However, most of the existing LLMs are either not open-source, not commercially usable, or not trained on enough data. However, this is about to change.
MosaicML’s MPT-7B marks a significant milestone in the realm of open-source large language models. Built on a foundation of innovation and efficiency, MPT-7B sets a new standard for commercially-usable LLMs, offering unparalleled quality and versatility.
Trained from scratch on an impressive 1 trillion tokens of text and code, MPT-7B stands out as a beacon of accessibility in the world of LLMs. Unlike its predecessors, which often required substantial resources and expertise to train and deploy, MPT-7B is designed to be open-source and commercially-usable. It empowers businesses and the open-source community alike to leverage all of its capabilities.
One of the key features that sets MPT-7B apart is its architecture and optimization enhancements. By utilizing ALiBi instead of positional embeddings and leveraging the Lion optimizer, MPT-7B achieves remarkable convergence stability, even in the face of hardware failures. This ensures uninterrupted training runs, significantly reducing the need for human intervention and streamlining the model development process.
In terms of performance, MPT-7B shines with its optimized layers, including FlashAttention and low-precision layernorm. These improvements enable MPT-7B to deliver blazing-fast inference speeds, outperforming other models in its class by up to twice the speed. Whether generating outputs with standard pipelines or deploying custom inference solutions, MPT-7B offers unparalleled speed and efficiency.
Deploying MPT-7B is seamless thanks to its compatibility with the HuggingFace ecosystem. Users can easily integrate MPT-7B into their existing workflows, leveraging standard pipelines and deployment tools. Additionally, MosaicML’s Inference service provides managed endpoints for MPT-7B, ensuring optimal cost and data privacy for hosting deployments.
MPT-7B was evaluated on various benchmarks and found to meet the high quality bar set by LLaMA-7B. MPT-7B was also fine tuned on different tasks and domains, and released three variants:
- MPT-7B-Instruct – a model for instruction following, such as summarization and question answering.
- MPT-7B-Chat – a model for dialogue generation, such as chatbots and conversational agents.
- MPT-7B-StoryWriter-65k+ – a model for story writing, with a context length of 65k tokens.
You can access these models on HuggingFace or on the MosaicML platform, where you can train, fine tune, and deploy your own private MPT models.
The release of MPT-7B marks a new chapter in the evolution of large language models. Businesses and developers now have the opportunity to leverage cutting-edge technology to drive innovation and solve complex challenges across a wide range of domains. As MPT-7B paves the way for the next generation of LLMs, we eagerly anticipate the transformative impact it will have on the field of artificial intelligence and beyond.