LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models

LMFlow is an open-source toolkit developed by OptimalScale that provides a set of tools to finetune and perform inference on large foundation models. The toolkit is built on top of PyTorch and provides a simple and flexible interface to work with large models.

LMFlow provides a set of tools to perform finetuning, inference, and evaluation of large models. It also provides a set of pre-trained models that can be used out of the box or can be finetuned on custom datasets. The toolkit is highly extensible and allows developers to easily add new models, datasets, and evaluation metrics.

One of the key features of LMFlow is its support for distributed training. It leverages PyTorch's distributed training capabilities to train large models on multiple GPUs or even multiple machines. This makes it possible to train large models that would otherwise be impossible to train on a single machine.

LMFlow also provides a set of utilities to make it easy to work with large datasets. It provides a data loader that can load data from various sources such as Hugging Face datasets, PyTorch datasets, and custom datasets. It also provides a set of data augmentation techniques that can be used to increase the size of the dataset and improve the performance of the model.

LMFlow is licensed under the Apache-2.0 license and has over 6k stars on GitHub. It is actively maintained and has a growing community of contributors. LMFlow is a great toolkit for developers who are working with large models and want a simple and flexible interface to work with.