IndustryLLM: Failure-Driven LLM Training for Industrial Procurement
Industrial procurement requires language models to bridge informal buyer jargon, sparse marketplace attributes, and authoritative engineering standards under strict safety tolerances. We present IndustryLLM, an open-weight industrial language model trained from Qwen3.5-35B-A3B-Base (35B total parameters with ~3B activated per token, with the vision encoder frozen). Rather than relying on generic text scaling, we introduce a failure-driven adaptation recipe spanning continued pre-training (CPT) and supervised fine-tuning (SFT). CPT leverages a curated ~100B-token corpus integrating 5B tokens of national standards (e.g., GB/T) and technical archives, 10B tokens of de-identified real-world i...
| Authors | Liang Ding, Zhiang Xu, Yuyang Sheng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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