tweak log_artifact language

content/docs/dvclive/live/log_artifact.md

@@ -36,17 +36,21 @@ with Live() as live:
 
 ## Description
 
-Uses `dvc add` to [track] `path` with DVC, saving it to the DVC
-<abbr>cache</abbr> and generating a `{path}.dvc` file that acts as a pointer to
-the cached data.
+Log `path`, saving its contents to DVC storage. Also annotate with any included
+metadata fields (for example, to be consumed in [Studio model registry] or
+automation scenarios).
+
+If `cache=True` (which is the default), uses `dvc add` to [track] `path` with
+DVC, saving it to the DVC <abbr>cache</abbr> and generating a `{path}.dvc` file
+that acts as a pointer to the cached data.
 
 If `Live` was initialized with `dvcyaml=True` (which is the default) and you
 include any of the optional metadata fields (`type`, `name`, `desc`, `labels`,
 `meta`), it will add an
 [artifact](/doc/user-guide/project-structure/dvcyaml-files#artifacts) and all
 the metadata passed as arguments to the corresponding `dvc.yaml`. Passing
 `type="model"` will mark it as a `model` for DVC and will make it appear in
-[Studio Model Registry](/doc/studio).
+[Studio model registry].
 
 ## Parameters
 
@@ -81,3 +85,5 @@ the metadata passed as arguments to the corresponding `dvc.yaml`. Passing
   have a supported type.
 
 [track]: /doc/dvclive/how-it-works#track-large-artifacts-with-dvc
+[Studio model registry]:
+  /doc/studio/user-guide/model-registry/what-is-a-model-registry)

content/docs/dvclive/ml-frameworks/pytorch-lightning.md

@@ -84,12 +84,10 @@ checkpointing at all as described in the
 
 ### Log model checkpoints
 
-Use `log_model` to save the checkpoints. DVCLive will first delete checkpoints
-from previous <abbr>experiments</abbr> (since DVC tracks checkpoints per
-experiment) and then <abbr>cache</abbr> the entire checkpoints directory using
-`Live.log_artifact()`. At the end of training, DVCLive will annotate the
-[`best_model_path`][`ModelCheckpoint`] with `type=model` and `name=best` for use
-in [Studio model registry].
+Use `log_model` to save the checkpoints (it will use `Live.log_artifact()`
+internally to save those). At the end of training, DVCLive will annotate the
+[`best_model_path`][`ModelCheckpoint`] with name `best` (for example, to be
+consumed in [Studio model registry] or automation scenarios).
 
 - Save updates to the checkpoints directory at the end of training: