Update README.md

README.md

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