$ /Users/jkooker/Documents/Codex/2026-05-28/get-this-new-model-up-and/lfm25_hermes_ft/venv/bin/python -m mlx_lm.lora --model runtime_moe8_artifacts/runs/moe8_overlap_g4s3_3epochs_10k/checkpoints/step_01746_final --train --data release_work/lfm25-moe8-mac-trainer/artifacts/repair_datasets/iter01 --fine-tune-type lora --adapter-path release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01 --batch-size 1 --iters 300 --val-batches 2 --max-seq-length 4096 --num-layers -1 --learning-rate 5e-06 --steps-per-report 10 --steps-per-eval 50 --grad-accumulation-steps 1 --save-every 100 --grad-checkpoint --seed 42

Calling `python -m mlx_lm.lora...` directly is deprecated. Use `mlx_lm.lora...` or `python -m mlx_lm lora ...` instead.
Loading pretrained model
Loading datasets
Training
Trainable parameters: 0.804% (68.098M/8467.856M)
Starting training..., iters: 300

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  2.56it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  4.43it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  3.99it/s]
Iter 1: Val loss 5.769, Val took 0.548s
Iter 10: Train loss 3.170, Learning Rate 5.000e-06, It/sec 1.678, Tokens/sec 234.066, Trained Tokens 1395, Peak mem 10.979 GB
Iter 20: Train loss 0.677, Learning Rate 5.000e-06, It/sec 3.072, Tokens/sec 428.838, Trained Tokens 2791, Peak mem 10.979 GB
Iter 30: Train loss 0.367, Learning Rate 5.000e-06, It/sec 3.102, Tokens/sec 423.712, Trained Tokens 4157, Peak mem 10.979 GB
Iter 40: Train loss 0.228, Learning Rate 5.000e-06, It/sec 3.036, Tokens/sec 427.784, Trained Tokens 5566, Peak mem 10.979 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.65it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.71it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.69it/s]
Iter 50: Val loss 0.197, Val took 0.236s
Iter 50: Train loss 0.190, Learning Rate 5.000e-06, It/sec 3.088, Tokens/sec 425.245, Trained Tokens 6943, Peak mem 10.979 GB
Iter 60: Train loss 0.180, Learning Rate 5.000e-06, It/sec 3.006, Tokens/sec 436.204, Trained Tokens 8394, Peak mem 10.979 GB
Iter 70: Train loss 0.064, Learning Rate 5.000e-06, It/sec 2.991, Tokens/sec 430.349, Trained Tokens 9833, Peak mem 10.979 GB
Iter 80: Train loss 0.090, Learning Rate 5.000e-06, It/sec 3.022, Tokens/sec 429.406, Trained Tokens 11254, Peak mem 10.979 GB
Iter 90: Train loss 0.044, Learning Rate 5.000e-06, It/sec 3.030, Tokens/sec 410.292, Trained Tokens 12608, Peak mem 10.979 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.84it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.68it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.69it/s]
Iter 100: Val loss 0.019, Val took 0.234s
Iter 100: Train loss 0.031, Learning Rate 5.000e-06, It/sec 3.005, Tokens/sec 421.671, Trained Tokens 14011, Peak mem 10.979 GB
Iter 100: Saved adapter weights to release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/adapters.safetensors and release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/0000100_adapters.safetensors.
Iter 110: Train loss 0.043, Learning Rate 5.000e-06, It/sec 3.100, Tokens/sec 416.955, Trained Tokens 15356, Peak mem 11.252 GB
Iter 120: Train loss 0.026, Learning Rate 5.000e-06, It/sec 3.018, Tokens/sec 411.964, Trained Tokens 16721, Peak mem 11.252 GB
Iter 130: Train loss 0.030, Learning Rate 5.000e-06, It/sec 2.916, Tokens/sec 419.286, Trained Tokens 18159, Peak mem 11.252 GB
Iter 140: Train loss 0.031, Learning Rate 5.000e-06, It/sec 2.974, Tokens/sec 409.792, Trained Tokens 19537, Peak mem 11.252 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.41it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.53it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.50it/s]
Iter 150: Val loss 0.019, Val took 0.240s
Iter 150: Train loss 0.025, Learning Rate 5.000e-06, It/sec 3.002, Tokens/sec 418.793, Trained Tokens 20932, Peak mem 11.252 GB
Iter 160: Train loss 0.033, Learning Rate 5.000e-06, It/sec 3.000, Tokens/sec 410.686, Trained Tokens 22301, Peak mem 11.252 GB
Iter 170: Train loss 0.027, Learning Rate 5.000e-06, It/sec 2.956, Tokens/sec 415.284, Trained Tokens 23706, Peak mem 11.252 GB
Iter 180: Train loss 0.026, Learning Rate 5.000e-06, It/sec 3.110, Tokens/sec 427.980, Trained Tokens 25082, Peak mem 11.252 GB
Iter 190: Train loss 0.030, Learning Rate 5.000e-06, It/sec 3.070, Tokens/sec 422.137, Trained Tokens 26457, Peak mem 11.252 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.82it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.64it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.66it/s]
Iter 200: Val loss 0.022, Val took 0.236s
Iter 200: Train loss 0.028, Learning Rate 5.000e-06, It/sec 3.070, Tokens/sec 420.035, Trained Tokens 27825, Peak mem 11.252 GB
Iter 200: Saved adapter weights to release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/adapters.safetensors and release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/0000200_adapters.safetensors.
Iter 210: Train loss 0.026, Learning Rate 5.000e-06, It/sec 3.144, Tokens/sec 433.556, Trained Tokens 29204, Peak mem 11.252 GB
Iter 220: Train loss 0.023, Learning Rate 5.000e-06, It/sec 3.131, Tokens/sec 415.530, Trained Tokens 30531, Peak mem 11.252 GB
Iter 230: Train loss 0.029, Learning Rate 5.000e-06, It/sec 3.106, Tokens/sec 426.386, Trained Tokens 31904, Peak mem 11.252 GB
Iter 240: Train loss 0.026, Learning Rate 5.000e-06, It/sec 3.118, Tokens/sec 435.857, Trained Tokens 33302, Peak mem 11.252 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.33it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.38it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.37it/s]
Iter 250: Val loss 0.027, Val took 0.243s
Iter 250: Train loss 0.025, Learning Rate 5.000e-06, It/sec 2.993, Tokens/sec 418.365, Trained Tokens 34700, Peak mem 11.252 GB
Iter 260: Train loss 0.026, Learning Rate 5.000e-06, It/sec 3.007, Tokens/sec 414.643, Trained Tokens 36079, Peak mem 11.252 GB
Iter 270: Train loss 0.022, Learning Rate 5.000e-06, It/sec 2.923, Tokens/sec 413.350, Trained Tokens 37493, Peak mem 11.252 GB
Iter 280: Train loss 0.023, Learning Rate 5.000e-06, It/sec 2.986, Tokens/sec 421.009, Trained Tokens 38903, Peak mem 11.252 GB
Iter 290: Train loss 0.024, Learning Rate 5.000e-06, It/sec 3.021, Tokens/sec 420.152, Trained Tokens 40294, Peak mem 11.252 GB

Calculating loss...:   0%|          | 0/2 [00:00<?, ?it/s]
Calculating loss...:  50%|█████     | 1/2 [00:00<00:00,  8.61it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.61it/s]
Calculating loss...: 100%|██████████| 2/2 [00:00<00:00,  8.60it/s]
Iter 300: Val loss 0.026, Val took 0.237s
Iter 300: Train loss 0.025, Learning Rate 5.000e-06, It/sec 3.123, Tokens/sec 426.665, Trained Tokens 41660, Peak mem 11.252 GB
Iter 300: Saved adapter weights to release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/adapters.safetensors and release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/0000300_adapters.safetensors.
Saved final weights to release_work/lfm25-moe8-mac-trainer/artifacts/adapters/lfm_tool_repair_iter01/adapters.safetensors.