Practitioners should consider using already-optimized codebases, especially in the pre-training phase, to ensure effective use of computational resources, capital, power, and effort. Existing open-source codebases targeted at foundation model pretraining can make pretraining significantly more accessible to new practitioners and help accumulate techniques for efficiency in model training.
Levanter is a framework for training large language models (LLMs) and other foundation models that strives for legibility, scalability, and reproducibility:
Python library for handling speech data in machine learning projects
A library for training large language models, built off of Megatron-LM but extended by Microsoft to support features of their DeepSpeed library.
One of the earliest open-source pretraining codebases for large language models. Still updated and has been used for a number of landmark distributed training and parallelism research papers by NVIDIA.
Hub for models, scripts and pre-trained weights for image classification models.
