diff --git a/.github/workflows/amd-ci.yml b/.github/workflows/amd-ci.yml
index 4a74521d2..857e5415f 100644
--- a/.github/workflows/amd-ci.yml
+++ b/.github/workflows/amd-ci.yml
@@ -1,4 +1,4 @@
-name: GitHub Actions CI (AMD)
+name: AMD GPU CI
 
 on:
   push:
@@ -10,9 +10,13 @@ on:
   pull_request:
     branches:
       - main
-    # paths:
-    #   - "src/**"
-    #   - "test/**"
+    paths:
+      - "src/**"
+      - "test/**"
+  schedule:
+    # Runs at 00:00 UTC daily
+    - cron: '0 0 * * *'
+  workflow_dispatch:  # Enables manual trigger
 
 concurrency:
   # This causes it to cancel previous in-progress actions on the same PR / branch,
diff --git a/.github/workflows/ci.yml b/.github/workflows/nvi-ci.yml
similarity index 99%
rename from .github/workflows/ci.yml
rename to .github/workflows/nvi-ci.yml
index a78f7c903..7efe5c05f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/nvi-ci.yml
@@ -1,4 +1,4 @@
-name: Modal GPU CI
+name: NVIDIA GPU CI
 
 on:
   push:
diff --git a/test/transformers/test_cross_entropy.py b/test/transformers/test_cross_entropy.py
index f2bf0d62f..c5e371654 100644
--- a/test/transformers/test_cross_entropy.py
+++ b/test/transformers/test_cross_entropy.py
@@ -9,10 +9,10 @@
     LigerCrossEntropyFunction,
     liger_cross_entropy_kernel,
 )
+from liger_kernel.ops.utils import is_hip
 from liger_kernel.transformers.cross_entropy import LigerCrossEntropyLoss
 from liger_kernel.transformers.functional import liger_cross_entropy
 from liger_kernel.utils import infer_device
-from liger_kernel.ops.utils import is_hip
 
 device = infer_device()
 set_seed(42)
diff --git a/test/utils.py b/test/utils.py
index 584b6b9d6..711c4f870 100644
--- a/test/utils.py
+++ b/test/utils.py
@@ -373,7 +373,6 @@ def get_batch_logps(
             labels: Labels for which to compute the log probabilities. Label tokens with a value of ignore_index are ignored. Shape: (batch_size, sequence_length)
             average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
             is_encoder_decoder: Whether the model is an encoder-decoder model.
-
         Returns:
             A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
         """