F.2. Contrast with Multiply Trained Models

A kind of dual question to the one considered in this paper regards how stable analysis of a particular model is when the
random seed is allowed to vary. There are several model suites designed to answer this question, including the causal
decoder Mistral suite (Karamcheti et al., 2021) and the multiBERT suite (Sellam et al., 2021). While we view this research
as valuable, we ultimately decided against including several training runs of the same model in our suite because it would be
ruinously expensive to do rigorously (doing 25 random seeds would cost approximately 10 million USD in compute) and
we felt that the way to make the biggest impact with the resources we had available was to train one copy of each model.

G. Evaluations

Pythia: A Suite for Analyzing Large Language Models

(a) LAMBADA (OpenAI)

(b) PIQA

(c) Winogrand Schema Challenge

(d) WinoGrande

(e) AI2 Reasoning Challenge — Easy Set

(f) SciQ
Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling