  We introduce Magicoder, a series of fully open-source (code, weights, and
data) Large Language Models (LLMs) for code that significantly closes the gap
with top code models while having no more than 7B parameters. Magicoder models
are trained on 75K synthetic instruction data using OSS-Instruct, a novel
approach to enlightening LLMs with open-source code snippets to generate
high-quality instruction data for code. Our main motivation is to mitigate the
inherent bias of the synthetic data generated by LLMs by empowering them with a
wealth of open-source references for the production of more diverse, realistic,
and controllable data. The orthogonality of OSS-Instruct and other data
generation methods like Evol-Instruct further enables us to build an enhanced
MagicoderS. Both Magicoder and MagicoderS substantially outperform
state-of-the-art code models with similar or even larger sizes on a wide range
of coding benchmarks, including Python text-to-code generation, multilingual
coding, and data-science program completion. Notably, MagicoderS-CL-7B based on
CodeLlama even surpasses the prominent ChatGPT on HumanEval+ (66.5 vs. 65.9 in
pass@1). Overall, OSS-Instruct opens a new direction for low-bias and
high-quality instruction tuning using abundant open-source references.

Computation and Language;Artificial Intelligence;Software Engineering