InCoder-32B: Code Foundation Model for Industrial Scenarios

TL;DR

InCoder-32B is a large-scale code foundation model designed for industrial applications, integrating diverse domain knowledge and training techniques to improve performance in specialized scenarios.

Contribution

The paper introduces InCoder-32B, a 32-billion-parameter model trained with a novel multi-stage process for industrial code tasks, unifying multiple industrial domains.

Findings

Achieves competitive performance on general code benchmarks.

Establishes strong open-source baselines for industrial domains.

Effectively handles complex industrial reasoning tasks.

Abstract

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.