Language models(LMs)  are powerful tools for natural language processing, but they often struggle to produce coherent and fluent text when they aresmall. Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can rarely generate coherent and consistent English text beyond a few words even after extensive training. This raises the question of whether the emergence of the ability to produce coherent English text only occurs at larger scales (with hundreds of millions of parameters or more)  and complex architectures (with many layers of global attention) . In this work, we introduceTinyStories, a synthetic dataset of short stories that only contain words that a typical 3 to 4-year-olds usually understand, generated by GPT-3.5 and GPT-4. We show thatTinyStoriescan be used to train and evaluate LMs that are much smaller than the state-of-the-art models (below 10 million total parameters) , or have much simpler architectures (with only one transformer block) , yet still produce fluent and consistent stories with several paragraphs that are diverse and have almost perfect grammar, and demonstrate reasoning capabilities. We also introduce a new paradigm for the evaluation of language models: We suggest a framework which uses GPT-4 to grade the content generated by these models as if those were stories written by students and graded by a (human)  teacher. This new paradigm overcomes the flaws of standard benchmarks which often require the model’s output to be very structured, and moreover it provides a multidimensional score for the model, providing scores for different capabilities such as grammar, creativity and instruction-following. We hope thatTinyStoriescan facilitate the development, analysis and research of LMs, especially for low-resource or specialized domains, and shed light on the emergence of language capabilities in LMs.