106 lines
3.3 KiB
Bash
106 lines
3.3 KiB
Bash
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#!/bin/bash
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# Phase 1: Deep Axiom Reasoning — Self-distillation pipeline
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#
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# Run Gemma3-27B + JSON kernel on all 101 probes
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# Generate 10 samples each at temperature 0.8, keep v2 >= 24.0
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# Extract training data, then fine-tune with MLX LoRA
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#
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# Expected: ~50 hours for data generation (404 probes × 10 samples × ~45s each at 27B)
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# Produces: ~1500 high-quality training examples (estimated ~35% keep rate at 24+)
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SCRIPTS="/Volumes/Data/lem/scripts"
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MODEL="/Volumes/Data/lem/gemma-3-27b-it-base"
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KERNEL="/Users/snider/Code/host-uk/core-agent/codex/ethics/kernel/claude-native.json"
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PROBES="/Volumes/Data/lem/seeds/P01-P100-rephrased.json" # 404 probes (101 + 303 rephrasings)
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TRAIN_DIR="/Volumes/Data/lem/training"
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ADAPTERS_DIR="/Volumes/Data/lem/adapters-27b-phase1"
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mkdir -p "$TRAIN_DIR"
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echo "=== Phase 1: Self-Distillation ==="
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echo "Model: $MODEL"
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echo "Kernel: $KERNEL"
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echo "Probes: $PROBES"
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echo "Threshold: 24.0"
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echo "Samples: 10 per probe"
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echo ""
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# Step 1: Generate training data via self-distillation
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echo "--- Step 1: Self-distillation (this will take a while) ---"
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python3 "$SCRIPTS/self_distill.py" \
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--model "$MODEL" \
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--kernel "$KERNEL" \
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--prompts "$PROBES" \
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--output "$TRAIN_DIR/phase1-raw.jsonl" \
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--samples 10 \
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--threshold 24.0 \
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--max-tokens 4096 \
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--temperature 0.8
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echo ""
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# Step 2: Extract clean training data (best per probe)
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echo "--- Step 2: Extract training data ---"
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python3 "$SCRIPTS/extract_training.py" \
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--input "$TRAIN_DIR/phase1-raw.jsonl" \
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--output "$TRAIN_DIR/phase1-train.jsonl" \
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--dedup best \
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--stats
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echo ""
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# Step 3: Also extract ALL passing samples (for augmentation)
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echo "--- Step 3: Extract all passing samples ---"
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python3 "$SCRIPTS/extract_training.py" \
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--input "$TRAIN_DIR/phase1-raw.jsonl" \
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--output "$TRAIN_DIR/phase1-train-all.jsonl" \
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--dedup all \
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--stats
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echo ""
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# Step 4: Split into train/valid (90/10)
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echo "--- Step 4: Train/valid split ---"
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TOTAL=$(wc -l < "$TRAIN_DIR/phase1-train-all.jsonl")
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VALID_COUNT=$(( TOTAL / 10 ))
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TRAIN_COUNT=$(( TOTAL - VALID_COUNT ))
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# Shuffle and split
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python3 -c "
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import json, random
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with open('$TRAIN_DIR/phase1-train-all.jsonl') as f:
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lines = f.readlines()
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random.seed(42)
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random.shuffle(lines)
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split = int(len(lines) * 0.9)
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with open('$TRAIN_DIR/phase1-train-split.jsonl', 'w') as f:
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f.writelines(lines[:split])
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with open('$TRAIN_DIR/phase1-valid-split.jsonl', 'w') as f:
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f.writelines(lines[split:])
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print(f'Train: {split}, Valid: {len(lines)-split}')
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"
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echo ""
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echo "=== Phase 1 data generation complete ==="
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echo "Raw output: $TRAIN_DIR/phase1-raw.jsonl"
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echo "Best-per-probe: $TRAIN_DIR/phase1-train.jsonl"
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echo "All passing: $TRAIN_DIR/phase1-train-all.jsonl"
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echo "Train split: $TRAIN_DIR/phase1-train-split.jsonl"
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echo "Valid split: $TRAIN_DIR/phase1-valid-split.jsonl"
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echo ""
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echo "To fine-tune:"
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echo " python3 -m mlx_lm.lora \\"
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echo " --model $MODEL \\"
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echo " --data $TRAIN_DIR \\"
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echo " --train-file phase1-train-split.jsonl \\"
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echo " --valid-file phase1-valid-split.jsonl \\"
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echo " --adapter-path $ADAPTERS_DIR \\"
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echo " --iters 100 \\"
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echo " --batch-size 1 \\"
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echo " --lora-layers 32 \\"
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echo " --lora-rank 16 \\"
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echo " --learning-rate 1e-5 \\"
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echo " --steps-per-eval 10 \\"
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echo " --max-seq-length 4096 \\"
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echo " --grad-checkpoint"
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