feat: migrate autolora pipeline into training/

Per direction shift (the-nexus#542).

Replaces the autolora repo (1,500 lines of custom pipeline code)
with config files for existing tools:

- axolotl.yaml: replaces train_modal.py (239 lines)
- mlx-lora.yaml: replaces MLX training scripts
- eval-tasks.yaml: replaces run_eval.py (300 lines)
- Makefile: replaces run_vibes.py, compare.py, convert_to_mlx.py

Data migrated as-is:
- curated_dataset.jsonl (26 gold-standard conversations)
- preference_pairs.jsonl (DPO pairs)
- prompts_vibes.yaml, prompts_nexus_vibes.yaml
- v0-baseline eval results (historical record)

Thin glue kept:
- build_curated.py (data authoring, not infrastructure)
- ingest_trajectories.py (domain-specific quality filter)

Dependencies: pip install axolotl mlx-lm lm-evaluation-harness

training/mlx-lora.yaml

@@ -0,0 +1,20 @@
+# MLX LoRA Training Config — Apple Silicon
+# Replaces: autolora/train_modal.py for local training
+#
+# Usage:
+#   mlx_lm.lora --config training/mlx-lora.yaml
+#
+# Runs on Mac M-series. No cloud needed for small models.
+# v0.1 was trained this way: 1,214 samples, 7.8 GB peak, 48 tok/s on M3 Max.
+
+model: mlx-community/Hermes-3-Llama-3.1-8B-4bit
+data: data/mlx_curated
+train: true
+iters: 1000
+batch_size: 2
+learning_rate: 2e-6
+lora_layers: 16
+steps_per_report: 10
+steps_per_eval: 100
+save_every: 200
+adapter_path: ./output/mlx-adapters