metadata
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 by the 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
pip install mlx-lm
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.