  Tabular data -- structured, heterogeneous, spreadsheet-style data with rows
and columns -- is widely used in practice across many domains. However, while
recent foundation models have reduced the need for developing task-specific
datasets and predictors in domains such as language modeling and computer
vision, this transfer learning paradigm has not had similar impact in the
tabular domain. In this work, we seek to narrow this gap and present TabuLa-8B,
a language model for tabular prediction. We define a process for extracting a
large, high-quality training dataset from the TabLib corpus, proposing methods
for tabular data filtering and quality control. Using the resulting dataset,
which comprises over 1.6B rows from 3.1M unique tables, we fine-tune a Llama
3-8B large language model (LLM) for tabular data prediction (classification and
binned regression) using a novel packing and attention scheme for tabular
prediction. Through evaluation across a test suite of 329 datasets, we find
that TabuLa-8B has zero-shot accuracy on unseen tables that is over 15
percentage points (pp) higher than random guessing, a feat that is not possible
with existing state-of-the-art tabular prediction models (e.g. XGBoost,
TabPFN). In the few-shot setting (1-32 shots), without any fine-tuning on the
target datasets, TabuLa-8B is 5-15 pp more accurate than XGBoost and TabPFN
models that are explicitly trained on equal, or even up to 16x more data. We
release our model, code, and data along with the publication of this paper.
