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Parssky/industrial-instruction-qwen4b

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Industrial-Instruction Qwen3-4B (Qwen-generated data)

Qwen3-4B-Instruct-2507 fully fine-tuned on the `panasonic_qa_v1` config of Industrial-Instruction — the split generated with the open-weight Qwen3-30B-A3B-Instruct model.

A companion model trained on the Claude-Opus-4.6-generated split is available at Parssky/industrial-instruction-qwen4b-claude.

Paper

Industrial-Instruction: An End-to-End Framework for Building Instruction-Tuning and Benchmark Datasets from Industrial Technical Reports

Base Model

Qwen3-4B-Instruct-2507

Results

Panasonic benchmark (panasonic_qa_v1 test split, 1,000 items):

ConfigurationF1JaccardSet-Match Acc.
Base, with RAG46.57%41.57%28.50%
Fine-tuned, with RAG63.48%57.95%42.00%
Base, no RAG46.63%41.61%28.50%
Fine-tuned, no RAG63.14%57.60%41.70%

General knowledge (MMLU, 57 subjects, 14,042 questions): 72.13% base → 70.87% fine-tuned (−1.26).

FailureSensorIQ: AccOrgIBM 34% → 27%, F1-Macro 40% → 43%, F1-Micro 66% → 74%.

Limitations

  • —Accuracy on perturbed (rephrased) FailureSensorIQ questions is 0%, both before and after fine-tuning. The training data contains no paraphrased question variants, so this model should not be relied on where question phrasing varies.
  • —Fine-tuning costs 1.26 points of MMLU accuracy, concentrated in Humanities (moral-reasoning subjects drop ~10 points).
  • —Source documentation comes from a single manufacturer; terminology may not transfer.

Intended Use

Research and benchmarking for industrial retrieval-augmented generation (RAG), evidence integration, and technical-domain QA.

Notes

  • —Inference-ready weights and tokenizer files only; training checkpoints and optimizer states are intentionally excluded.

Source Code

GitHub repository: https://github.com/parssky/industrial-instruction