Document the conditioning-tutorial checkpoints

README.md

@@ -13,7 +13,7 @@ tags:
 Small checkpoints used by the [`stip`](https://github.com/instadeepai/stip) tutorial
 notebooks, so that a tutorial can demonstrate sampling without spending ten
 minutes training first. They are toy models (a two-layer MLP, ~50k parameters,
-trained for 1500 steps on a 4-component 2D Gaussian mixture) and have no
+trained for 3000 steps on a 4-component 2D Gaussian mixture) and have no
 use outside the notebooks.
 
 Checkpoints are [Orbax](https://orbax.readthedocs.io) directories written by
@@ -52,3 +52,15 @@ checkpointer = Checkpointer(
 gen_model = nnx.merge(graphdef, checkpointer.restore_ema(params))
 ```
 
+`restore_ema` reads only `ema_params` and `extra`, and applies the same bias
+correction the training loop uses for evaluation.
+
+## Reproducing
+
+```bash
+uv run python tutorials/scripts/train_conditioning_checkpoints.py
+```
+
+The script mirrors the notebook's model definitions and PRNG chain, so it
+reproduces these exact weights. A checkpoint pins the parameter structure: if a
+notebook's network changes, re-run the script and re-upload.