license, base_model, library_name, pipeline_tag, tags

license	base_model	library_name	pipeline_tag	tags
apache-2.0	Qwen/Qwen3-4B	transformers	text-generation	qwen3 qlora rag grounded-generation citations mining blasting safety-data-sheets

dyno-blast-4b

A QLoRA fine-tune of Qwen3-4B that answers blasting / explosives technical and safety questions strictly from retrieved context, attaches [N] + page citations to every claim, and refuses when the answer isn't in the provided sources.

This is the generation half of a grounded RAG system over Dyno Nobel Australia's public technical literature (product technical data sheets, Safety Data Sheets, application guides, case studies). Facts live in the retriever; the model learns the behavior — cite, ground, refuse. Companion vector DB: kcherry497/dyno-blast-4b-rag.

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

Prompt format

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.