README.md

---
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](https://adaptionlabs.ai/auto-scientist)
("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}
}
```