About MPT-7B

The LLM Foundry repository on GitHub is dedicated to the training, fine-tuning, evaluation, and deployment of LLMs (Language Models) using the Composer and MosaicML platform. It’s designed for ease of use, efficiency, and flexibility, aiming to facilitate rapid experimentation with the latest techniques in the domain.

Features of MPT-7B

  1. Mosaic Pretrained Transformers (MPT): These are GPT-style models that come with unique features like Flash Attention for efficiency, ALiBi for context length extrapolation, and stability improvements to prevent loss spikes. MosaicML has open-sourced several MPT models, including MPT-7B.
  2. Repository Contents: The repo includes source code for models, datasets, callbacks, utilities, and more. It also contains scripts for running LLM workloads, converting text data to StreamingDataset format, training or fine-tuning HuggingFace and MPT models, and more.
  3. MPT-7B Details: The MPT-7B model has a context length of 2048. It is available for download and demo on HuggingFace. There are also variations of the MPT-7B model, such as MPT-7B-Instruct and MPT-7B-Chat.
  4. Community Contributions: The MPT community has been active, and the repository acknowledges various contributions, including models like ReplitLM, LLaVa-MPT, and tools like GPT4All.
  5. Tutorials and Instructions: The repository provides a comprehensive tutorial (TUTORIAL.md) that offers a deeper understanding of the repo, example workflows, and answers to frequently asked questions.
  6. Hardware and Software Requirements: The codebase has been tested with specific versions of PyTorch on systems with NVIDIA A100s and H100s. It also provides information on the support matrix for different devices and versions.

Additional Features

  1. MPT Community: The MPT community has been recognized for its outstanding contributions. Some of the highlighted works include ReplitLM, which focuses on Code Completion, LLaVa-MPT that gives MPT multimodal capabilities, and GPT4All, a locally running chat system with MPT support.
  2. Docker Support: The repository recommends using prebuilt Docker images for a seamless experience. These images are pinned to specific PyTorch and CUDA versions, ensuring stability.
  3. Installation Guide: The repo provides a detailed installation guide, both for users who prefer Docker and those who don’t. It also offers instructions for setting up the environment for AMD GPUs.
  4. Quickstart: For users eager to dive in, there’s a quickstart guide that provides an end-to-end workflow, from preparing a dataset to generating responses to prompts.
  5. Contact and Support: Users facing issues with the code can file GitHub issues directly. For those interested in training LLMs on the MosaicML platform, the repository provides a contact email.