diff --git a/.github/workflows/metal_plugin_ci.yml b/.github/workflows/metal_plugin_ci.yml
index a62b0a1b6eb2..6e67841460e9 100644
--- a/.github/workflows/metal_plugin_ci.yml
+++ b/.github/workflows/metal_plugin_ci.yml
@@ -17,7 +17,7 @@ jobs:
     strategy:
       fail-fast: false # don't cancel all jobs on failure
       matrix:
-        jaxlib-version: ["plugin_latest"]
+        jaxlib-version: ["pypi_latest", "nightly"]
     name: "Jax-Metal plugin test (jaxlib=${{ matrix.jaxlib-version }})"
     runs-on: [self-hosted, macOS, ARM64]
 
@@ -32,13 +32,14 @@ jobs:
           python3 -m venv ${GITHUB_WORKSPACE}/jax-metal-venv
           source  ${GITHUB_WORKSPACE}/jax-metal-venv/bin/activate
           pip install -U pip numpy wheel
-          pip install jax-metal absl-py pytest
+          pip install absl-py pytest
           if [[ "${{ matrix.jaxlib-version }}" == "nightly" ]]; then
             pip install --pre jaxlib \
               -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
           fi;
           cd jax
           pip install .
+          pip install jax-metal
       - name: Run test
         run: |
           source  ${GITHUB_WORKSPACE}/jax-metal-venv/bin/activate
diff --git a/tests/lax_metal_test.py b/tests/lax_metal_test.py
index 87ca6eaee322..5069187d2334 100644
--- a/tests/lax_metal_test.py
+++ b/tests/lax_metal_test.py
@@ -302,13 +302,11 @@ def testCountNonzero(self, shape, dtype, axis):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
     self._CompileAndCheck(jnp_fun, args_maker)
 
-  @jtu.sample_product(shape=all_shapes, dtype=all_dtypes)
+  @jtu.sample_product(shape=nonzerodim_shapes, dtype=all_dtypes)
   def testNonzero(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.nonzero, jnp.nonzero, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.nonzero, jnp.nonzero, args_maker, check_dtypes=False)
 
   @jtu.sample_product(
     [dict(shape=shape, fill_value=fill_value)
@@ -370,20 +368,16 @@ def np_fun(x):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
     self._CompileAndCheck(jnp_fun, args_maker)
 
-  @jtu.sample_product(shape=all_shapes, dtype=all_dtypes)
+  @jtu.sample_product(shape=nonzerodim_shapes, dtype=all_dtypes)
   def testArgWhere(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.argwhere, jnp.argwhere, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.argwhere, jnp.argwhere, args_maker, check_dtypes=False)
 
     # JIT compilation requires specifying a size statically. Full test of this
     # behavior is in testNonzeroSize().
     jnp_fun = lambda x: jnp.argwhere(x, size=np.size(x) // 2)
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CompileAndCheck(jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
     [dict(shape=shape, fill_value=fill_value)
@@ -2055,7 +2049,6 @@ def attempt_sideeffect(x):
     self.assertAllClose(np_input, expected_np_input_after_call)
     self.assertAllClose(jnp_input, expected_jnp_input_after_call)
 
-  @unittest.skip("Jax-metal fail to convert 1D convolution op.")
   @jtu.sample_product(
     mode=['full', 'same', 'valid'],
     op=['convolve', 'correlate'],
@@ -2077,7 +2070,6 @@ def np_fun(x, y):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=True, tol=tol)
     self._CompileAndCheck(jnp_fun, args_maker)
 
-  @unittest.skip("Jax-metal fail to convert 1D convolution op.")
   @jtu.sample_product(
     mode=['full', 'same', 'valid'],
     op=['convolve', 'correlate'],
@@ -4431,15 +4423,12 @@ def args_maker(): return []
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-    shape=all_shapes, dtype=all_dtypes,
+    shape=nonzerodim_shapes, dtype=all_dtypes,
   )
   def testWhereOneArgument(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.where, jnp.where, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.where, jnp.where, args_maker, check_dtypes=False)
 
     # JIT compilation requires specifying a size statically. Full test of
     # this behavior is in testNonzeroSize().
@@ -5724,7 +5713,7 @@ def test_gather_ir(self):
 #loc = loc(unknown)
 module @jit_gather attributes {mhlo.num_partitions = 1 : i32, mhlo.num_replicas = 1 : i32} {
   func.func public @main(%arg0: tensor<3x2x3xf32> {mhlo.sharding = "{replicated}"} loc(unknown), %arg1: tensor<3x2xi32> {mhlo.sharding = "{replicated}"} loc(unknown)) -> tensor<3x2xf32> {
-    %0 = "stablehlo.gather"(%arg0, %arg1) {dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0, 2], start_index_map = [0, 2], index_vector_dim = 1>, indices_are_sorted = false, slice_sizes = dense<[1, 2, 1]> : tensor<3xi64>} : (tensor<3x2x3xf32>, tensor<3x2xi32>) -> tensor<3x2xf32> loc(#loc2)
+    %0 = "stablehlo.gather"(%arg0, %arg1) {dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0, 2], start_index_map = [0, 2], index_vector_dim = 1>, indices_are_sorted = false, slice_sizes = array<i64: 1, 2, 1>} : (tensor<3x2x3xf32>, tensor<3x2xi32>) -> tensor<3x2xf32> loc(#loc2)
     return %0 : tensor<3x2xf32> loc(#loc)
   } loc(#loc)
 } loc(#loc)