diff --git a/cmdstanpy/cmdstan_args.py b/cmdstanpy/cmdstan_args.py
index 2fc7526..49b96a2 100644
--- a/cmdstanpy/cmdstan_args.py
+++ b/cmdstanpy/cmdstan_args.py
@@ -81,7 +81,7 @@ class SamplerArgs:
         * if file(s) for metric are supplied, check contents.
         * length of per-chain lists equals specified # of chains
         """
-        if not isinstance(chains, int) or chains < 1:
+        if not isinstance(chains, (int, np.integer)) or chains < 1:
             raise ValueError(
                 'Sampler expects number of chains to be greater than 0.'
             )
@@ -110,7 +110,9 @@ class SamplerArgs:
                 raise ValueError(msg)
 
         if self.iter_warmup is not None:
-            if self.iter_warmup < 0 or not isinstance(self.iter_warmup, int):
+            if self.iter_warmup < 0 or not isinstance(
+                self.iter_warmup, (int, np.integer)
+            ):
                 raise ValueError(
                     'Value for iter_warmup must be a non-negative integer,'
                     ' found {}.'.format(self.iter_warmup)
@@ -122,28 +124,30 @@ class SamplerArgs:
                 )
         if self.iter_sampling is not None:
             if self.iter_sampling < 0 or not isinstance(
-                self.iter_sampling, int
+                self.iter_sampling, (int, np.integer)
             ):
                 raise ValueError(
                     'Argument "iter_sampling" must be a non-negative integer,'
                     ' found {}.'.format(self.iter_sampling)
                 )
         if self.thin is not None:
-            if self.thin < 1 or not isinstance(self.thin, int):
+            if self.thin < 1 or not isinstance(self.thin, (int, np.integer)):
                 raise ValueError(
                     'Argument "thin" must be a positive integer,'
                     'found {}.'.format(self.thin)
                 )
         if self.max_treedepth is not None:
             if self.max_treedepth < 1 or not isinstance(
-                self.max_treedepth, int
+                self.max_treedepth, (int, np.integer)
             ):
                 raise ValueError(
                     'Argument "max_treedepth" must be a positive integer,'
                     ' found {}.'.format(self.max_treedepth)
                 )
         if self.step_size is not None:
-            if isinstance(self.step_size, (float, int)):
+            if isinstance(
+                self.step_size, (float, int, np.integer, np.floating)
+            ):
                 if self.step_size <= 0:
                     raise ValueError(
                         'Argument "step_size" must be > 0, '
@@ -178,7 +182,7 @@ class SamplerArgs:
                     else:
                         self.metric_type = 'dense_e'
                     self.metric_file = self.metric
-            elif isinstance(self.metric, Dict):
+            elif isinstance(self.metric, dict):
                 if 'inv_metric' not in self.metric:
                     raise ValueError(
                         'Entry "inv_metric" not found in metric dict.'
@@ -289,7 +293,7 @@ class SamplerArgs:
                 )
         if self.adapt_init_phase is not None:
             if self.adapt_init_phase < 0 or not isinstance(
-                self.adapt_init_phase, int
+                self.adapt_init_phase, (int, np.integer)
             ):
                 raise ValueError(
                     'Argument "adapt_init_phase" must be a non-negative '
@@ -297,7 +301,7 @@ class SamplerArgs:
                 )
         if self.adapt_metric_window is not None:
             if self.adapt_metric_window < 0 or not isinstance(
-                self.adapt_metric_window, int
+                self.adapt_metric_window, (int, np.integer)
             ):
                 raise ValueError(
                     'Argument "adapt_metric_window" must be a non-negative '
@@ -305,7 +309,7 @@ class SamplerArgs:
                 )
         if self.adapt_step_size is not None:
             if self.adapt_step_size < 0 or not isinstance(
-                self.adapt_step_size, int
+                self.adapt_step_size, (int, np.integer)
             ):
                 raise ValueError(
                     'Argument "adapt_step_size" must be a non-negative integer,'
@@ -426,14 +430,14 @@ class OptimizeArgs:
                 raise ValueError(
                     'init_alpha requires that algorithm be set to bfgs or lbfgs'
                 )
-            if isinstance(self.init_alpha, float):
+            if isinstance(self.init_alpha, (float, np.floating)):
                 if self.init_alpha <= 0:
                     raise ValueError('init_alpha must be greater than 0')
             else:
                 raise ValueError('init_alpha must be type of float')
 
         if self.iter is not None:
-            if isinstance(self.iter, int):
+            if isinstance(self.iter, (int, np.integer)):
                 if self.iter < 0:
                     raise ValueError('iter must be greater than 0')
             else:
@@ -444,7 +448,7 @@ class OptimizeArgs:
                 raise ValueError(
                     'tol_obj requires that algorithm be set to bfgs or lbfgs'
                 )
-            if isinstance(self.tol_obj, float):
+            if isinstance(self.tol_obj, (float, np.floating)):
                 if self.tol_obj <= 0:
                     raise ValueError('tol_obj must be greater than 0')
             else:
@@ -456,7 +460,7 @@ class OptimizeArgs:
                     'tol_rel_obj requires that algorithm be set to bfgs'
                     ' or lbfgs'
                 )
-            if isinstance(self.tol_rel_obj, float):
+            if isinstance(self.tol_rel_obj, (float, np.floating)):
                 if self.tol_rel_obj <= 0:
                     raise ValueError('tol_rel_obj must be greater than 0')
             else:
@@ -467,7 +471,7 @@ class OptimizeArgs:
                 raise ValueError(
                     'tol_grad requires that algorithm be set to bfgs or lbfgs'
                 )
-            if isinstance(self.tol_grad, float):
+            if isinstance(self.tol_grad, (float, np.floating)):
                 if self.tol_grad <= 0:
                     raise ValueError('tol_grad must be greater than 0')
             else:
@@ -479,7 +483,7 @@ class OptimizeArgs:
                     'tol_rel_grad requires that algorithm be set to bfgs'
                     ' or lbfgs'
                 )
-            if isinstance(self.tol_rel_grad, float):
+            if isinstance(self.tol_rel_grad, (float, np.floating)):
                 if self.tol_rel_grad <= 0:
                     raise ValueError('tol_rel_grad must be greater than 0')
             else:
@@ -490,7 +494,7 @@ class OptimizeArgs:
                 raise ValueError(
                     'tol_param requires that algorithm be set to bfgs or lbfgs'
                 )
-            if isinstance(self.tol_param, float):
+            if isinstance(self.tol_param, (float, np.floating)):
                 if self.tol_param <= 0:
                     raise ValueError('tol_param must be greater than 0')
             else:
@@ -501,7 +505,7 @@ class OptimizeArgs:
                 raise ValueError(
                     'history_size requires that algorithm be set to lbfgs'
                 )
-            if isinstance(self.history_size, int):
+            if isinstance(self.history_size, (int, np.integer)):
                 if self.history_size < 0:
                     raise ValueError('history_size must be greater than 0')
             else:
@@ -610,52 +614,62 @@ class VariationalArgs:
                 )
             )
         if self.iter is not None:
-            if self.iter < 1 or not isinstance(self.iter, int):
+            if self.iter < 1 or not isinstance(self.iter, (int, np.integer)):
                 raise ValueError(
                     'iter must be a positive integer,'
                     ' found {}'.format(self.iter)
                 )
         if self.grad_samples is not None:
-            if self.grad_samples < 1 or not isinstance(self.grad_samples, int):
+            if self.grad_samples < 1 or not isinstance(
+                self.grad_samples, (int, np.integer)
+            ):
                 raise ValueError(
                     'grad_samples must be a positive integer,'
                     ' found {}'.format(self.grad_samples)
                 )
         if self.elbo_samples is not None:
-            if self.elbo_samples < 1 or not isinstance(self.elbo_samples, int):
+            if self.elbo_samples < 1 or not isinstance(
+                self.elbo_samples, (int, np.integer)
+            ):
                 raise ValueError(
                     'elbo_samples must be a positive integer,'
                     ' found {}'.format(self.elbo_samples)
                 )
         if self.eta is not None:
-            if self.eta < 0 or not isinstance(self.eta, (int, float)):
+            if self.eta < 0 or not isinstance(
+                self.eta, (int, float, np.integer, np.floating)
+            ):
                 raise ValueError(
                     'eta must be a non-negative number,'
                     ' found {}'.format(self.eta)
                 )
         if self.adapt_iter is not None:
-            if self.adapt_iter < 1 or not isinstance(self.adapt_iter, int):
+            if self.adapt_iter < 1 or not isinstance(
+                self.adapt_iter, (int, np.integer)
+            ):
                 raise ValueError(
                     'adapt_iter must be a positive integer,'
                     ' found {}'.format(self.adapt_iter)
                 )
         if self.tol_rel_obj is not None:
             if self.tol_rel_obj <= 0 or not isinstance(
-                self.tol_rel_obj, (int, float)
+                self.tol_rel_obj, (int, float, np.integer, np.floating)
             ):
                 raise ValueError(
                     'tol_rel_obj must be a positive number,'
                     ' found {}'.format(self.tol_rel_obj)
                 )
         if self.eval_elbo is not None:
-            if self.eval_elbo < 1 or not isinstance(self.eval_elbo, int):
+            if self.eval_elbo < 1 or not isinstance(
+                self.eval_elbo, (int, np.integer)
+            ):
                 raise ValueError(
                     'eval_elbo must be a positive integer,'
                     ' found {}'.format(self.eval_elbo)
                 )
         if self.output_samples is not None:
             if self.output_samples < 1 or not isinstance(
-                self.output_samples, int
+                self.output_samples, (int, np.integer)
             ):
                 raise ValueError(
                     'output_samples must be a positive integer,'
@@ -792,7 +806,10 @@ class CmdStanArgs:
                     ' cannot write to dir: {}.'.format(self.output_dir)
                 ) from exc
         if self.refresh is not None:
-            if not isinstance(self.refresh, int) or self.refresh < 1:
+            if (
+                not isinstance(self.refresh, (int, np.integer))
+                or self.refresh < 1
+            ):
                 raise ValueError(
                     'Argument "refresh" must be a positive integer value, '
                     'found {}.'.format(self.refresh)
@@ -800,7 +817,7 @@ class CmdStanArgs:
 
         if self.sig_figs is not None:
             if (
-                not isinstance(self.sig_figs, int)
+                not isinstance(self.sig_figs, (int, np.integer))
                 or self.sig_figs < 1
                 or self.sig_figs > 18
             ):
@@ -822,13 +839,13 @@ class CmdStanArgs:
             rng = RandomState()
             self.seed = rng.randint(1, 99999 + 1)
         else:
-            if not isinstance(self.seed, (int, list)):
+            if not isinstance(self.seed, (int, list, np.integer)):
                 raise ValueError(
                     'Argument "seed" must be an integer between '
                     '0 and 2**32-1, found {}.'.format(self.seed)
                 )
-            if isinstance(self.seed, int):
-                if self.seed < 0 or self.seed > 2 ** 32 - 1:
+            if isinstance(self.seed, (int, np.integer)):
+                if self.seed < 0 or self.seed > 2**32 - 1:
                     raise ValueError(
                         'Argument "seed" must be an integer between '
                         '0 and 2**32-1, found {}.'.format(self.seed)
@@ -847,7 +864,7 @@ class CmdStanArgs:
                         )
                     )
                 for seed in self.seed:
-                    if seed < 0 or seed > 2 ** 32 - 1:
+                    if seed < 0 or seed > 2**32 - 1:
                         raise ValueError(
                             'Argument "seed" must be an integer value'
                             ' between 0 and 2**32-1,'
@@ -861,7 +878,7 @@ class CmdStanArgs:
             raise ValueError('Argument "data" must be string or dict')
 
         if self.inits is not None:
-            if isinstance(self.inits, (float, int)):
+            if isinstance(self.inits, (float, int, np.floating, np.integer)):
                 if self.inits < 0:
                     raise ValueError(
                         'Argument "inits" must be > 0, found {}'.format(
diff --git a/cmdstanpy/compiler_opts.py b/cmdstanpy/compiler_opts.py
index 4729827..cab5d49 100644
--- a/cmdstanpy/compiler_opts.py
+++ b/cmdstanpy/compiler_opts.py
@@ -297,7 +297,10 @@ class CompilerOptions:
 
     def compose(self) -> List[str]:
         """Format makefile options as list of strings."""
-        opts = ['STANCFLAGS+=' + flag for flag in self.compose_stanc()]
+        opts = [
+            'STANCFLAGS+=' + flag.replace(" ", "\\ ")
+            for flag in self.compose_stanc()
+        ]
         if self._cpp_options is not None and len(self._cpp_options) > 0:
             for key, val in self._cpp_options.items():
                 opts.append(f'{key}={val}')

diff --git a/test/test_cmdstan_args.py b/test/test_cmdstan_args.py
index 8c6364d..049c592 100644
--- a/test/test_cmdstan_args.py
+++ b/test/test_cmdstan_args.py
@@ -6,6 +6,7 @@ import platform
 from test import check_present
 from time import time
 
+import numpy as np
 import pytest
 
 from cmdstanpy import _TMPDIR, cmdstan_path
@@ -349,6 +350,23 @@ def test_args_good() -> None:
     cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
     assert 'id=7 random seed=' in ' '.join(cmd)
 
+    # integer type
+    rng = np.random.default_rng(42)
+    seed = rng.integers(low=0, high=int(1e7))
+    assert not isinstance(seed, int)
+    assert isinstance(seed, np.integer)
+
+    cmdstan_args = CmdStanArgs(
+        model_name='bernoulli',
+        model_exe=exe,
+        chain_ids=[7, 11, 18, 29],
+        data=jdata,
+        seed=seed,
+        method_args=sampler_args,
+    )
+    cmd = cmdstan_args.compose_command(idx=0, csv_file='bern-output-1.csv')
+    assert f'id=7 random seed={seed}' in ' '.join(cmd)
+
     dirname = 'tmp' + str(time())
     if os.path.exists(dirname):
         os.rmdir(dirname)
diff --git a/test/test_model.py b/test/test_model.py
index 700e6ed..7127a8e 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -451,6 +451,35 @@ def test_model_compile_space() -> None:
         assert exe_time == os.path.getmtime(model2.exe_file)
 
 
+def test_model_includes_space() -> None:
+    """Test model with include file in path with spaces."""
+    stan = os.path.join(DATAFILES_PATH, 'bernoulli_include.stan')
+    stan_divide = os.path.join(DATAFILES_PATH, 'divide_real_by_two.stan')
+
+    with tempfile.TemporaryDirectory(
+        prefix="cmdstanpy_testfolder_"
+    ) as tmp_path:
+        path_with_space = os.path.join(tmp_path, "space in path")
+        os.makedirs(path_with_space, exist_ok=True)
+        bern_stan_new = os.path.join(path_with_space, os.path.split(stan)[1])
+        stan_divide_new = os.path.join(
+            path_with_space, os.path.split(stan_divide)[1]
+        )
+        shutil.copyfile(stan, bern_stan_new)
+        shutil.copyfile(stan_divide, stan_divide_new)
+
+        model = CmdStanModel(
+            stan_file=bern_stan_new,
+            stanc_options={'include-paths': path_with_space},
+        )
+        assert "space in path" in str(model.exe_file)
+
+        assert "space in path" in model.src_info()['included_files'][0]
+        assert (
+            "divide_real_by_two.stan" in model.src_info()['included_files'][0]
+        )
+
+
 def test_model_includes_explicit() -> None:
     if os.path.exists(BERN_EXE):
         os.remove(BERN_EXE)

Seed argument doesn't accept np.int types
#### Summary:

ValueError: Argument "seed" must be an integer between 0 and 2**32-1, found 76113.

I believe that  76113 < 2**32-1 (= 4294967295)

Any help?

#### Current Version:
cmdstan                   2.29.0              
cmdstanpy                 1.0.8
test/test_cmdstan_args.py::test_args_good
test/test_cmdstan_args.py::test_args_algorithm test/test_cmdstan_args.py::test_args_algorithm_init_alpha test/test_cmdstan_args.py::test_args_algorithm_iter test/test_cmdstan_args.py::test_args_min test/test_cmdstan_args.py::test_args_chains test/test_cmdstan_args.py::test_good test/test_cmdstan_args.py::test_bad test/test_cmdstan_args.py::test_adapt test/test_cmdstan_args.py::test_metric test/test_cmdstan_args.py::test_fixed_param test/test_cmdstan_args.py::test_compose test/test_cmdstan_args.py::test_no_chains test/test_cmdstan_args.py::test_args_inits test/test_cmdstan_args.py::test_args_fitted_params test/test_cmdstan_args.py::test_args_variational test/test_cmdstan_args.py::test_variational_args_bad test/test_model.py::test_stanc_options test/test_model.py::test_cpp_options test/test_model.py::test_model_none test/test_model.py::test_model_file_does_not_exist test/test_model.py::test_model_syntax_error_without_compile