"Our work demonstrates the remarkable impact of high-quality data in honing a language model's proficiency in code-generation tasks. By crafting 'textbook quality' data we were able to train a model that surpasses almost all open-source models on coding benchmarks such as HumanEval and MBPP despite being 10x smaller in model size and 100x smaller in dataset size."
"We hypothesize that such high quality data dramatically improves the learning efficiency of language models for code as they provide clear, self-contained, instructive, and balanced examples of coding concepts and skills."
"Despite phi-1's success, it remains specialized in Python coding which restricts its versatility compared to multi-language models and lacks the domain-specific knowledge of larger models."
"The structured nature of the datasets and the lack of diversity in terms of language and style make phi-1 less robust to stylistic variations, limiting its applicability in real-world coding scenarios."
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