diff --git a/tests/testthat/test-tunable.R b/tests/testthat/test-tunable.R
index fdaf946..4a87dd2 100644
--- a/tests/testthat/test-tunable.R
+++ b/tests/testthat/test-tunable.R
@@ -151,109 +151,3 @@ test_that("workflow with tunable recipe and model", {
     c(rep("model_spec", 9), rep("recipe", 4))
   )
 })
-
-# ------------------------------------------------------------------------------
-# test specific values
-
-
-test_that('test tunable parameter values', {
-  # depends on whether tune >= 0.1.6.9001 is installed
-  skip_if(inherits(try(tunable(), silent = TRUE), "try-error"))
-
-  print_parameters <- function(x) {
-    params <- tunable(x)
-    info <- params$call_info
-    names(info) <- params$names
-    print(info)
-    invisible(NULL)
-  }
-
-  expect_snapshot(
-    boost_tree(trees = tune(), min_n = tune(), sample_size = tune()) %>%
-      set_engine('C5.0') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    rules::C5_rules(trees = tune(), min_n = tune()) %>%
-      set_engine('C5.0') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    decision_tree(min_n = tune()) %>%
-      set_engine('C5.0') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    logistic_reg(penalty = tune()) %>%
-      set_engine('brulee') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    mars(prod_degree = tune()) %>%
-      set_engine('earth') %>%
-      set_mode('classification') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    multinom_reg(penalty = tune()) %>%
-      set_engine('brulee') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    rand_forest(mtry = tune(), min_n = tune()) %>%
-      set_engine('randomForest') %>%
-      set_mode('classification') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    rand_forest(mtry = tune(), min_n = tune()) %>%
-      set_engine('ranger') %>%
-      set_mode('classification') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    linear_reg(penalty = tune()) %>%
-      set_engine('brulee') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    boost_tree(
-      tree_depth = tune(),
-      trees = tune(),
-      learn_rate = tune(),
-      min_n = tune(),
-      loss_reduction = tune(),
-      sample_size = tune(),
-      stop_iter = tune()
-    ) %>%
-      set_engine('xgboost') %>%
-      set_mode('classification') %>%
-      print_parameters()
-  )
-
-  expect_snapshot(
-    mlp(
-      hidden_units = tune(),
-      penalty = tune(),
-      dropout = tune(),
-      epochs = tune(),
-      activation = tune()
-    ) %>%
-      set_engine('brulee') %>%
-      set_mode('classification') %>%
-      print_parameters()
-  )
-
-})
-
-
-