Textbooks Are All You Need
Authors: name: Suriya Gunasekar, name: Yi Zhang, name: Jyoti Aneja, name: Caio César Teodoro Mendes, name: Allie Del Giorno, name: Sivakanth Gopi, name: Mojan Javaheripi, name: Piero Kauffmann, name: Gustavo de Rosa, name: Olli Saarikivi, name: Adil Salim, name: Shital Shah, name: Harkirat Singh Behl, name: Xin Wang, name: Sébastien Bubeck, name: Ronen Eldan, name: Adam Tauman Kalai, name: Yin Tat Lee, name: Yuanzhi Li

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.