Papers › Textbooks Are All You Need II: phi-1.5 technical report
Textbooks Are All You Need II: phi-1.5 technical report
Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee
We continue the investigation into the power of smaller Transformer-based language models as initiated by \textbf{TinyStories} -- a 10 million parameter model that can produce coherent English -- and the follow-up work on \textbf{phi-1}, a 1.3 billion parameter model with Python coding performance close to the state-of-the-art. The latter work proposed to use existing Large Language Models (LLMs) to generate ``textbook quality" data as a way to enhance the learning process compared to traditional web data. We follow the ``Textbooks Are All You Need" approach, focusing this time on common sense reasoning in natural language, and create a new 1.3 billion parameter model named \textbf{phi-1.5}, with performance on natural language tasks comparable to models 5x larger, and surpassing most non-frontier LLMs on more complex reasoning tasks such as grade-school mathematics and basic coding. More generally, \textbf{phi-1.5} exhibits many of the traits of much larger LLMs, both good -- such as the ability to ``think step by step" or perform some rudimentary in-context learning -- and bad, including hallucinations and the potential for toxic and biased generations -- encouragingly though, we are seeing improvement on that front thanks to the absence of web data. We open-source \textbf{phi-1.5} to promote further research on these urgent topics.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | MBPP | phi-1.5-web 1.3B | Accuracy | 43.5 | #79 of 99 | Archive leaderboard | report |
| Common Sense Reasoning | ARC (Challenge) | phi-1.5-web 1.3B (zero-shot) | Accuracy | 44.9 | #42 of 54 | Archive leaderboard | report |
| Common Sense Reasoning | ARC (Easy) | phi-1.5-web 1.3B (0-shot) | Accuracy | 76.1 | #21 of 47 | Archive leaderboard | report |
| Common Sense Reasoning | WinoGrande | phi-1.5-web 1.3B (zero-shot) | Accuracy | 74.0 | #31 of 77 | Archive leaderboard | report |
| Multi-task Language Understanding | MML | phi-1.5-web 1.3B | Average (%) | 37.9 | #36 of 44 | Archive leaderboard | report |
| Question Answering | PIQA | phi-1.5-web (1.3B) | Accuracy | 77 | #42 of 67 | Archive leaderboard | report |
| Question Answering | SIQA | phi-1.5-web 1.3B (zero-shot) | Accuracy | 53.0 | #16 of 24 | Archive leaderboard | report |
| Question Answering | SIQA | phi-1.5 1.3B (zero-shot) | Accuracy | 52.6 | #17 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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