Textbooks Are All You Need
- Suriya Gunasekar ,
- Yi Zhang ,
- Jyoti Aneja ,
- Caio Cesar ,
- Teodoro Mendes ,
- Allie Del Giorno ,
- Sivakanth Gopi ,
- Mojan Javaheripi ,
- Piero Kauffmann ,
- Gustavo de Rosa ,
- Olli Saarikivi ,
- Adil Salim ,
- Shital Shah ,
- Harkirat Singh Behl ,
- Xin Wang ,
- Sébastien Bubeck ,
- Ronen Eldan ,
- Adam Tauman Kalai ,
- Yin Tat Lee ,
- 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.
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Phi-1
11 12 月, 2023
The language model phi-1 is a Transformer with 1.3 billion parameters, specialized for basic Python coding. Its training involved a variety of data sources, including subsets of Python codes from The Stack v1.2, Q&A content from StackOverflow, competition code from code_contests, and synthetic Python textbooks and exercises generated by gpt-3.5-turbo-0301. Even though the model and the datasets are relatively small compared to contemporary Large Language Models (LLMs), phi-1 has demonstrated an impressive accuracy rate exceeding 50% on the simple Python coding benchmark, HumanEval.
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