feat: deploy training data to zoe root

scripts/deploy_training_data.sh

@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+cd "$(dirname "$0")/.."
+
+export COPYFILE_DISABLE=1
+
+CUSTOM_LOCAL_LLM_ROOT="${CUSTOM_LOCAL_LLM_ROOT:-/Volumes/Zoe/custom-local-llm}"
+TRAINING_DATA_DIR="${TRAINING_DATA_DIR:-$CUSTOM_LOCAL_LLM_ROOT/training-data}"
+TRAINING_DATA_MANIFEST="${TRAINING_DATA_MANIFEST:-$CUSTOM_LOCAL_LLM_ROOT/manifest.llm.json}"
+
+if [[ ! -d "$CUSTOM_LOCAL_LLM_ROOT" ]]; then
+  echo "missing custom local LLM root: $CUSTOM_LOCAL_LLM_ROOT" >&2
+  exit 1
+fi
+
+mkdir -p "$TRAINING_DATA_DIR" "$CUSTOM_LOCAL_LLM_ROOT/logs" "$CUSTOM_LOCAL_LLM_ROOT/models" "$CUSTOM_LOCAL_LLM_ROOT/benchmarks"
+
+if [[ ! -s "$TRAINING_DATA_MANIFEST" ]]; then
+  echo "missing training data manifest: $TRAINING_DATA_MANIFEST" >&2
+  exit 1
+fi
+
+cargo run -- dictionary generate
+python3 scripts/generate_training_data.py \
+  --dictionary-dir dictionary/ \
+  --output-dir "$TRAINING_DATA_DIR" \
+  --manifest "$TRAINING_DATA_MANIFEST"
+
+python3 - <<'PY'
+import os
+from pathlib import Path
+
+root = Path(os.environ.get("CUSTOM_LOCAL_LLM_ROOT", "/Volumes/Zoe/custom-local-llm"))
+for path in root.rglob("._*"):
+    if path.is_file():
+        path.unlink()
+PY