---
base_model: mlx-community/Qwen2.5-0.5B-Instruct-4bit
library_name: mlx
tags:
- lora
- adapter
- mlx
- local-model-factory
---

# json-extractor-demo

LoRA adapter for **`mlx-community/Qwen2.5-0.5B-Instruct-4bit`**, trained locally on Apple Silicon with [mlx-lm](https://github.com/ml-explore/mlx-lm) by the [local-model-factory](https://github.com/abdouloued/local-model-factory) pipeline (prepare -> train -> eval-base -> eval-tuned -> gate -> publish).

**Task:** Reference task: free-text note -> strict JSON by fixed rules. Synthetic + seeded; exists to prove the pipeline end to end.

**Gate decision:** PROMOTE

- field_accuracy: 0.1667 -> 1.0 (+0.8333) meets min improvement 0.05
- json_validity: 1.0 -> 1.0 (no meaningful regression)
- exact_match: 0.0 -> 1.0 (no meaningful regression)

## Held-out evaluation (same test split, deterministic)

| metric | base model | base + this adapter |
|---|---|---|
| exact_match | 0.000 | 1.000 |
| examples | 40.000 | 40.000 |
| field_accuracy | 0.167 | 1.000 |
| json_examples | 40.000 | 40.000 |
| json_validity | 1.000 | 1.000 |
| mean_latency_s | 0.133 | 0.164 |

## Training

- iterations: 200, batch size: 4, learning rate: 0.0001, LoRA layers: 8, seed: 7
- final validation loss: 0.093
- data: 400 raw examples → 320 train / 40 valid / 40 test (seed 20260701, 0 duplicates dropped)
- data source: json-extractor-demo task (tasks/json-extractor-demo)
- data ledger: json-extractor-demo-synthetic

## Use it

```bash
pip install mlx-lm
```

```bash
mlx_lm.generate --model mlx-community/Qwen2.5-0.5B-Instruct-4bit \
  --adapter-path <local-download-of-this-repo> \
  --prompt "..."
```

The full run artifacts (train config, both eval reports, gate decision,
data manifest) are included in this repo so the numbers above can be
audited against exactly what produced them.