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rigidhat/
qwen-2.5-construction-codecite-v1

Name: qwen-2.5-construction-codecite-v1
Availability: InStock

mlx-lm

construction-safety

osha

regulatory-compliance

lora

mlx

mlx-community/Qwen2.5-1.5B-Instruct-4bit

adapter

mit

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Qwen 2.5 1.5B — Construction Code-Citation v1

LoRA adapter on top of Qwen 2.5 1.5B-Instruct (4-bit MLX). Predicts OIICS hazard codes (event, source, nature, body) and OSHA 29 CFR 1926 citations from construction-site incident narratives.

Built for the Adaption Labs AutoScientist Challenge ("All Other Domains" category).

Inputs / Outputs

Input: free-text construction-site narrative. Output: strict JSON with hazards[] (4 OIICS codes + severity) and citations[] (verified OSHA 1926 standards).

Usage

python

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Qwen2.5-1.5B-Instruct-4bit",
    adapter_path="oversite/qwen-2.5-construction-codecite-v1",
)
prompt = "Worker fell from second-story scaffold..."
out = generate(model, tokenizer, prompt=prompt, max_tokens=384)

See gradio_app/app.py in the source repo for the full prompt template and RAG-augmented inference pipeline.

Training

Base model: Qwen 2.5 1.5B-Instruct, 4-bit MLX quantization
Method: LoRA, 16 layers, 5.3M trainable parameters
Data: 17,127 stratified-by-event-division SFT examples from OSHA SIR (2015-2025)
Optimizer: Adam, lr 1e-4, batch 2, 400 iterations
Loss masking: prompt masked, train on completion tokens only
Seed: 20260606

Metrics

Dimension	Accuracy
event_acc	35.5%
event_div_acc	48.0%
source_acc	51.0%
source_div_acc	33.0%
nature_acc	66.5%
body_acc	57.5%
body_div_acc	87.5%
parsed_ok_rate	100.0%

n=200, split=dev, git_sha=d926d81

Test-set numbers are held back until submission per the locked split (SHA-256 c9490ed3...).

Limitations

Source-code distribution has a heavy long tail (1,478 unique codes). Model uses an OTHER bucket for codes outside the top-75 shortlist.
Citation grounding is BM25-only at v1 (vector index follow-up).
SIR over-represents severe injuries; the model is biased toward high-severity event types.

License

MIT. Base model Qwen 2.5 1.5B-Instruct is governed by its upstream license.

Citation

@misc{construction-code-llm-2026,
  title  = {Qwen 2.5 1.5B - Construction Code-Citation v1},
  author = {Oversite Innovations},
  year   = {2026}
}

rigidhat/qwen-2.5-construction-codecite-v1