  We introduce phi-1, a new large language model for code, with significantly
smaller size than competing models: phi-1 is a Transformer-based model with
1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook
quality" data from the web (6B tokens) and synthetically generated textbooks
and exercises with GPT-3.5 (1B tokens). Despite this small scale, phi-1 attains
pass@1 accuracy 50.6% on HumanEval and 55.5% on MBPP. It also displays
surprising emergent properties compared to phi-1-base, our model before our
finetuning stage on a dataset of coding exercises, and phi-1-small, a smaller
model with 350M parameters trained with the same pipeline as phi-1 that still
achieves 45% on HumanEval.
