feat: deploy training data to zoe root

README.md

@@ -26,6 +26,7 @@ cargo run -- run to_slug --payload '{"text":"Hello, World!"}'
 cargo run -- run write_note --payload '{"filename":"hello.txt","content":"hello"}'
 scripts/precommit-check.sh
 python3 scripts/generate_training_data.py --dry-run
+scripts/deploy_training_data.sh
 ```
 
 `serve` starts:
@@ -116,7 +117,7 @@ git config core.hooksPath .githooks
 
 ## SDG Bridge
 
-`scripts/generate_training_data.py` generates instruction-format seed data from current dictionary artifacts.
+`scripts/generate_training_data.py` generates instruction-format seed data from current dictionary artifacts. The default export root is `/Volumes/Zoe/custom-local-llm`.
 
 Dry run:
 
@@ -129,10 +130,16 @@ Local dataset generation:
 ```sh
 python3 scripts/generate_training_data.py \
   --dictionary-dir dictionary/ \
-  --output-dir ../workspace_Data/data/ \
-  --manifest ../workspace_Data/manifest.llm.json
+  --output-dir /Volumes/Zoe/custom-local-llm/training-data/ \
+  --manifest /Volumes/Zoe/custom-local-llm/manifest.llm.json
 ```
 
 Optional Mimir/Data Designer expansion is enabled only when `--sdg-host`, `--sdg-user`, and `--sdg-key` are supplied. The bridge does not start or restart Mimir inference services.
 
 If remote expansion fails, the timestamped local seed dataset remains in place, `SDG_ERROR.txt` is written in that dataset directory, and the manifest entry is marked `generated_local_remote_failed`.
+
+Woodpecker deploys a new training-data export to Zoe on `main` pushes by running:
+
+```sh
+scripts/deploy_training_data.sh
+```