# Evaluation Results

Published artifact: FrontAgent Planner 14B MLX full-run adapter.

## Training Run

- Upstream base: `Qwen/Qwen2.5-Coder-14B-Instruct`
- MLX base: `mlx-community/Qwen2.5-Coder-14B-Instruct-4bit`
- Data: `train_v2.json` 500 samples, `eval_v2.json` 100 samples
- MLX split: 500 train, 80 valid, 20 test
- Total training: 1000 iterations (50-iter stable pilot + 950 continuation iterations)
- Learning rate: 1e-5
- LoRA: all layers, rank 32, alpha/scale 64, dropout 0
- Batch size: 1
- Gradient accumulation: 4
- Max sequence length: 2048
- Peak memory observed: 13.249 GB

## Metrics

- Iter 1 validation loss after warm start: 0.014
- Iter 100 validation loss: 0.005
- Iter 200 validation loss: 0.001
- Iter 900 validation loss: 0.001
- Final iter 950 validation loss: 0.000
- Final iter 950 train loss: 0.000
- Final test loss: 0.000
- Final test perplexity: 1.000

## Notes

The run uses the stable `lr=1e-5` setup. A prior `lr=1e-4` pilot diverged and was not used for this release. These loss metrics are on the deterministic v2 dataset split; generated-plan quality should still be validated in FrontAgent before production rollout.