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README.md

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+---
+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}
+}
+```

adapter_config.json

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+{
+    "adapter_path": "train/runs/v1_qlora_001_cite",
+    "batch_size": 2,
+    "clear_cache_threshold": 0,
+    "config": null,
+    "data": "train/data",
+    "fine_tune_type": "lora",
+    "grad_accumulation_steps": 1,
+    "grad_checkpoint": false,
+    "iters": 400,
+    "learning_rate": 0.0001,
+    "lora_parameters": {
+        "rank": 8,
+        "dropout": 0.0,
+        "scale": 20.0
+    },
+    "lr_schedule": null,
+    "mask_prompt": true,
+    "max_seq_length": 2048,
+    "model": "mlx-community/Qwen2.5-1.5B-Instruct-4bit",
+    "num_layers": 16,
+    "optimizer": "adam",
+    "optimizer_config": {
+        "adam": {},
+        "adamw": {},
+        "muon": {},
+        "sgd": {},
+        "adafactor": {}
+    },
+    "project_name": null,
+    "report_to": null,
+    "resume_adapter_file": null,
+    "save_every": 100,
+    "seed": 20260606,
+    "steps_per_eval": 100,
+    "steps_per_report": 25,
+    "test": false,
+    "test_batches": 500,
+    "train": true,
+    "val_batches": 25
+}

adapters.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e7b619fe5d48b5a3af1a4d5742c0460aef74d5bf20cfb01c6815620549da778d
+size 21126646