diff --git a/README.md b/README.md
index b0cf481d..b19be94d 100644
--- a/README.md
+++ b/README.md
@@ -126,7 +126,7 @@ linear_probe = spt.callbacks.OnlineProbe(
     input="embedding",  # Which output from forward to monitor
     target="label",      # Ground truth from batch
     probe=torch.nn.Linear(512, 10),
-    loss_fn=torch.nn.CrossEntropyLoss(),
+    loss=torch.nn.CrossEntropyLoss(),
     metrics={
         "top1": torchmetrics.classification.MulticlassAccuracy(10),
         "top5": torchmetrics.classification.MulticlassAccuracy(10, top_k=5),
@@ -143,6 +143,7 @@ knn_probe = spt.callbacks.OnlineKNN(
     input="embedding",
     target="label",
     queue_length=20000,
+    metrics={"accuracy": torchmetrics.classification.MulticlassAccuracy(10)},
     k=10,
 )
 ```
@@ -282,7 +283,7 @@ linear_probe = spt.callbacks.OnlineProbe(
     input="embedding",
     target="label",
     probe=torch.nn.Linear(512, 10),
-    loss_fn=torch.nn.CrossEntropyLoss(),
+    loss=torch.nn.CrossEntropyLoss(),
     metrics={
         "top1": torchmetrics.classification.MulticlassAccuracy(10),
         "top5": torchmetrics.classification.MulticlassAccuracy(10, top_k=5),
@@ -326,14 +327,14 @@ The `spt` command launches training from YAML configuration files using Hydra.
 
 ```bash
 # Run with a config file
-spt examples/simclr_cifar10_config.yaml
+spt run examples/simclr_cifar10_config.yaml
 
 # With parameter overrides
-spt examples/simclr_cifar10_config.yaml trainer.max_epochs=50 module.optim.lr=0.01
+spt run examples/simclr_cifar10_config.yaml trainer.max_epochs=50 module.optim.lr=0.01
 
 # Run from any directory - supports absolute and relative paths
-spt ../configs/my_config.yaml
-spt /path/to/config.yaml
+spt run ../configs/my_config.yaml
+spt run /path/to/config.yaml
 ```
 
 ### SLURM Cluster Training
@@ -342,10 +343,10 @@ For training on SLURM clusters, use the `-m` flag to enable multirun mode:
 
 ```bash
 # Use the provided SLURM template (customize partition/QOS in the file)
-spt examples/simclr_cifar10_slurm.yaml -m
+spt run examples/simclr_cifar10_slurm.yaml -m
 
 # Override SLURM parameters via command line
-spt examples/simclr_cifar10_slurm.yaml -m \
+spt run examples/simclr_cifar10_slurm.yaml -m \
     hydra.launcher.partition=gpu \
     hydra.launcher.qos=normal \
     hydra.launcher.timeout_min=720
@@ -378,12 +379,10 @@ The library is not yet available on PyPI. You can install it from the source cod
     uv pip install -e .  # Core dependencies only
     ```
 
-    For optional features (vision models, experiment tracking, cluster support, etc.):
+    For development (tests, linting, docs):
     ```bash
-    uv pip install -e ".[vision,tracking]"  # Example: add vision models and wandb
-    uv pip install -e ".[all]"  # Or install all optional dependencies
+    uv pip install -e ".[dev]"
     ```
-    See `pyproject.toml` for available dependency groups (`vision`, `tracking`, `cluster`, `visualization`, `datasets`, `extras`, `dev`, `doc`).
 
     If you do not want to use uv, simply remove it from the above commands.