Source code

LLMFoundry is a Python package for training, finetuning, evaluating, and serving large scale LLM models on distributed compute infrustructure using MosaicML's Composer with PyTorch

At a granular level, LLMFoundry is a library that consists of the following components:

llmfoundry.models.mpt.MPTModel - a simple PyTorch GPT model, wrapped in ComposerModel, that can scale up to 70B+ parameters
llmfoundry.models.layers - a collection of layers used in the MPTModel
llmfoundry.models.hf - a collection of tools which enables training / finetuning huggingface models with ../scripts/train/train.py
llmfoundry.data.text_data.StreamingTextDataset- a MosaicML streaming dataset that can be used with a vanilla PyTorch dataloader.
llmfoundry.data.finetuning.collator.Seq2SeqFinetuningCollator- a dataloader for different finetuning tasks
llmfoundry.optim- a collection of optimizers used for training LLMs (PyTorch and Composer optimizers are also compatible)
llmfoundry.utils.builders- a collection of convenient string-to-object mappings used to create objects that get passed to the Composer Trainer.