Stable Code Technical Report
Nikhil Pinnaparaju Reshinth Adithyan Duy Phung Jonathan Tow James Baicoianu Ashish Datta Maksym Zhuravinskyi Dakota Mahan Marco Bellagente Carlos Riquelme Nathan Cooper
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 https://huggingface.co/stabilityai/stable-code-3b and
https://huggingface.co/stabilityai/stable-code-instruct-3b. 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.