Document context_model using SumContextEncoder

README.md

@@ -51,3 +51,16 @@ 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.