e030976440
- agent-runner.sh: multi-backend agent dispatch (claude/codex/gemini) - agent-setup.sh: agent environment setup - gemini-batch-runner.sh: Gemini batch processing - ethics-ab/: ethics A/B testing framework with results Co-Authored-By: Virgil <virgil@lethean.io>
1.1 KiB
1.1 KiB
LEK-1 LoRA Training Data
Format
Training data for MLX LoRA fine-tuning of Gemma 3 12B.
Files:
train.jsonl— Training pairs (Axioms-signed prompt → response)valid.jsonl— Validation set (10% holdout)lora-config.yaml— MLX LoRA hyperparameters
Data Generation Pipeline
- Hypnos (Gemini 3 Pro) generates 200 prompt-response pairs using Axioms kernel
- Format as JSONL:
{"text": "<bos>user\n{prompt}<eos>\n<bos>model\n{response}<eos>"} - Split 180/20 train/valid
- Run MLX LoRA on M3 Ultra
Training Command (M3 Ultra)
pip install mlx-lm
python -m mlx_lm.lora \
--model google/gemma-3-12b \
--train-data train.jsonl \
--valid-data valid.jsonl \
--num-layers 8 \
--batch-size 1 \
--num-iters 500 \
--learning-rate 1e-5 \
--adapter-path ./adapters
Merge & Test
python -m mlx_lm.fuse \
--model google/gemma-3-12b \
--adapter-path ./adapters \
--save-path ./gemma-3-12b-lek1
# Convert to GGUF for Ollama
python -m mlx_lm.convert --model ./gemma-3-12b-lek1 --to-gguf
License
EUPL-1.2 — All training data and derivative weights.