  The crystallization of modeling methods around the Transformer architecture
has been a boon for practitioners. Simple, well-motivated architectural
variations can transfer across tasks and scale, increasing the impact of
modeling research. However, with the emergence of state-of-the-art 100B+
parameters models, large language models are increasingly expensive to
accurately design and train. Notably, it can be difficult to evaluate how
modeling decisions may impact emergent capabilities, given that these
capabilities arise mainly from sheer scale alone. In the process of building
BLOOM--the Big Science Large Open-science Open-access Multilingual language
model--our goal is to identify an architecture and training setup that makes
the best use of our 1,000,000 A100-GPU-hours budget. Specifically, we perform
an ablation study at the billion-parameter scale comparing different modeling
practices and their impact on zero-shot generalization. In addition, we study
the impact of various popular pre-training corpora on zero-shot generalization.
We also study the performance of a multilingual model and how it compares to
the English-only one. Finally, we consider the scaling behaviour of
Transformers to choose the target model size, shape, and training setup. All
our models and code are open-sourced at https://huggingface.co/bigscience .

Computation and Language;Artificial Intelligence;Machine Learning