Alibaba releases IndustryLLM for industrial procurement with 35B parameters
Researchers at Alibaba have introduced IndustryLLM, an open-weight language model designed for industrial procurement tasks. Built from Qwen3.5-35B-A3B-Base, the model uses 35 billion parameters with 3 billion activated per token and a frozen vision encoder. Its training focuses on failure-driven adaptation, combining continued pre-training (CPT) on a 100 billion-token corpus—including 5 billion…
Key points
- IndustryLLM is a 35B-parameter model trained on 100B tokens for industrial procurement, with 3B activated per token
- Offline tests show 2.97% improvement in query structuring; live A/B tests report 4.25% higher GMV and 8.3% more satisfied inquiries
- Latency reduced from 6–7 seconds to 1.5 seconds, with open weights released on Hugging Face
The model achieves 2.97 percentage points improvement in procurement-query structuring (95% CI: [2.11, 3.86]) in offline tests and 4.25% higher GMV and 8.3% more satisfied inquiries in live A/B experiments. Latency dropped from 6–7 seconds to 1.5 seconds. The weights and configurations are available on Hugging Face.
Model page: IndustryLLM →
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