| --- |
| 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. |
| |
