training/mlx-lora.yaml

# 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
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 2026-03-25 23:05:45 +00:00			`# 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`