Document the conditioning-tutorial checkpoints

README.md

@@ -12,7 +12,7 @@ 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,
+minutes training first. They are toy models (a two-layer MLP, 18k-25k parameters,
 trained for 3000 steps on a 4-component 2D Gaussian mixture) and have no
 use outside the notebooks.
 
@@ -23,13 +23,13 @@ Checkpoints are [Orbax](https://orbax.readthedocs.io) directories written by
 ## `conditioning_and_guidance/`
 
 Used by `tutorials/notebooks/4.conditioning_and_guidance.ipynb`. Both models are
-`VelocityGenerativeModel`s with a `FlowMatchingOneSidedInterpolant`, but over
+`VelocityOneSidedGenerativeModel`s with a `FlowMatchingOneSidedInterpolant`, but over
 different modalities:
 
 | Path | Model | Modalities | Role in the notebook |
 |---|---|---|---|
 | `conditioning_and_guidance/joint_model` | Unconditional cross-modal MLP | `coordinates` (continuous, 2D) and `index` (discrete, 4 categories) | Intrinsic guidance (Section 3): conditioning a model that was never trained to be conditional |
-| `conditioning_and_guidance/context_model` | The same MLP plus a label context path, trained with 50% context dropout | `coordinates` only; the corner label is passed as `context_data` instead of as a modality | Context conditioning and classifier-free guidance (Sections 4) |
+| `conditioning_and_guidance/context_model` | The same MLP plus a label context path, trained with 50% context dropout | `coordinates` only; the corner label is passed as `context_data` instead of as a modality | Context conditioning and classifier-free guidance (Sections 4-5) |
 
 ### Loading
 
@@ -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.