Stable Code Technical Report

Stable Code Technical Report

1 Apr 2024 | Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, Jonathan Tow, James Baicoianu, Ashish Datta, Maksym Zhuravinskyi, Dakota Mahan, Marco Bellagente, Carlos Riquelme, Nathan Cooper
The technical report introduces Stable Code and Stable Code Instruct, two new-generation code language models designed for various software engineering tasks such as code completion, reasoning, and math. The models are trained on a diverse dataset that includes code repositories, technical documents, and web datasets, with a focus on enhancing mathematical understanding, logical reasoning, and complex technical text processing. The training process involves a staged approach, including pretraining and fine-tuning stages, and utilizes techniques like the "Fill in the Middle" (FIM) objective to improve performance in non-instruct tasks. The models achieve state-of-the-art performance on benchmarks such as Multi-PL and MT-Bench, even outperforming larger models in some tasks. The report also details the model architecture, training procedures, and evaluation metrics, and provides quantized checkpoints for inference on edge devices. The models are available for download via Hugging Face, contributing to advancements in code language models and their practical applications.The technical report introduces Stable Code and Stable Code Instruct, two new-generation code language models designed for various software engineering tasks such as code completion, reasoning, and math. The models are trained on a diverse dataset that includes code repositories, technical documents, and web datasets, with a focus on enhancing mathematical understanding, logical reasoning, and complex technical text processing. The training process involves a staged approach, including pretraining and fine-tuning stages, and utilizes techniques like the "Fill in the Middle" (FIM) objective to improve performance in non-instruct tasks. The models achieve state-of-the-art performance on benchmarks such as Multi-PL and MT-Bench, even outperforming larger models in some tasks. The report also details the model architecture, training procedures, and evaluation metrics, and provides quantized checkpoints for inference on edge devices. The models are available for download via Hugging Face, contributing to advancements in code language models and their practical applications.
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Understanding Stable Code Technical Report