Intended use & limitations
Retrieval-augmented Q&A for mining / quarry / blasting professionals. Not a
standalone knowledge source — use with the retriever (intfloat/e5-base-v2 + a
Qdrant collection). For SDS / safety content, always verify against the cited
source PDF; the model is trained to give page numbers for exactly this reason.
Domain-specific to Dyno Nobel AU products.
Training
- Base: Qwen/Qwen3-4B
- Method: QLoRA (4-bit nf4), r=16, α=32, dropout=0.05, targets all attn + MLP projections
- Data: 1,674 synthetic grounded examples — 1,656
[N]-cited answers + 18 refusal / safe-decline examples — generated by a teacher over retrieved context, covering SDS sections, technical specs, application/case-study/brochure topics, Explosive Engineers Guide articles, industrial chemicals, and blast calculators
- Schedule: 3 epochs, lr 1e-4 cosine, full-sequence SFT
- Result: final
train_loss 0.97
Retrieval corpus (companion dataset repo): 3,819 chunks across 682 documents —
dynonobel.com.au + dynonobel.com (126 products) + the Explosive Engineers Guide app
(4 regions) + Industrial Chemicals + resource-centre case studies/guides/brochures +
blast calculators.
Files
*.safetensors — merged fp16 weights (load with transformers)
dyno-blast-4b-q8_0.gguf — q8_0 GGUF for llama.cpp / Ollama
Grounded system prompt (answer only from numbered SOURCEs, cite [N], refuse if
absent) + numbered SOURCE [N] blocks from the retriever, then the question. The
exact system prompt and chunk schema are in the companion dataset repo.