We introduce Stable Code, the first in our new-generation of code language
models series, which serves as a general-purpose base code language model
targeting code completion, reasoning, math, and other software
engineering-based tasks. Additionally, we introduce an instruction variant
named Stable Code Instruct that allows conversing with the model in a natural
chat interface for performing question-answering and instruction-based tasks.
In this technical report, we detail the data and training procedure leading to
both models. Their weights are available via Hugging Face for anyone to
download and use at this https URL and
this https URL This report
contains thorough evaluations of the models, including multilingual programming
benchmarks, and the MT benchmark focusing on multi-turn dialogues. At the time
of its release, Stable Code is the state-of-the-art open model under 3B
parameters and even performs comparably to larger models of sizes 7 billion and
15 billion parameters on the popular Multi-PL benchmark. Stable Code Instruct
also exhibits state-of-the-art performance on the MT-Bench coding tasks and on
Multi-PL completion compared to other instruction tuned models. Given its
appealing small size, we also provide throughput measurements on a number of
edge devices. In addition, we open source several quantized checkpoints and
provide their performance metrics compared to the original model.

我们介绍了稳定代码（Stable Code），这是我们新一代的代码语言模型系列中的第一个模型，它用于完成代码补全、推理、数学和其他软件工程任务。此外，我们还引入了一种名为稳定代码指令（Stable Code Instruct）的指令变体，它允许与模型进行自然对话界面以进行问答和基于指令的任务。在这份技术报告中，我们详细介绍了这两个模型的数据和训练过程。他们的权重可通过 Hugging Face 在指定的 URL 下载和使用。该报告对这些模型进行了全面评估，包括多语言编程基准和多轮对话集中在机器翻译方面的基准。稳定代码在发布时是 3B 参数下的最先进的开放模型，甚至在流行的多语言编程基准上，其性能与大小为 70 亿和 150 亿参数的较大模型相当。稳定代码指令模型在 MT-Bench 编码任务和 Multi-PL 代码补全方面也展现了最先进的性能，相较于其他指令调整模型。由于其吸引人的小尺寸，我们还提供了该模型在一些边缘设备上的吞吐量测量。此外，我们还公开了几个量化检查点并提供了它们与原始模型的性能指标对比。