metadata
license: mit
language:
- en
base_model: mlx-community/Qwen2.5-1.5B-Instruct-4bit
tags:
- construction-safety
- osha
- regulatory-compliance
- lora
- mlx
library_name: mlx-lm
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
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}
}