diff --git a/snekmer/rules/model.smk b/snekmer/rules/model.smk
index 317d4ce..3edbf40 100644
--- a/snekmer/rules/model.smk
+++ b/snekmer/rules/model.smk
@@ -52,8 +52,10 @@ unzipped = [
     fa.rstrip(".gz")
     for fa, ext in product(input_files, config["input_file_exts"])
     if fa.rstrip(".gz").endswith(f".{ext}")
+    and skm.utils.check_n_seqs(fa, config["model"]["cv"], show_warning=False)
 ]
 
+
 # map extensions to basename (basename.ext.gz -> {basename: ext})
 UZ_MAP = {
     skm.utils.split_file_ext(f)[0]: skm.utils.split_file_ext(f)[1] for f in zipped
@@ -62,6 +64,11 @@ FA_MAP = {
     skm.utils.split_file_ext(f)[0]: skm.utils.split_file_ext(f)[1] for f in unzipped
 }
 
+# final file map: checks that files are large enough for model building
+# FA_MAP = {
+#     k: v for k, v in f_map.items() if skm.utils.check_n_seqs(k, config["model"]["cv"])
+# }
+
 # get unzipped filenames
 UZS = [f"{f}.{ext}" for f, ext in UZ_MAP.items()]
 
@@ -82,6 +89,11 @@ out_dir = skm.io.define_output_dir(
     config["alphabet"], config["k"], nested=config["nested_output"]
 )
 
+# show warnings if files excluded
+onstart:
+    [
+        skm.utils.check_n_seqs(fa, config["model"]["cv"], show_warning=True) for fa in input_files
+    ]
 
 # define output files to be created by snekmer
 rule all:
diff --git a/snekmer/scripts/model_model.py b/snekmer/scripts/model_model.py
index 0e15ac8..0d8886e 100644
--- a/snekmer/scripts/model_model.py
+++ b/snekmer/scripts/model_model.py
@@ -40,9 +40,7 @@
 #    )
 # print(lookup)
 with open(snakemake.input.matrix, "rb") as f:
-    matrix = pickle.load(f)
-
-data = matrix
+    data = pickle.load(f)
 
 # load all input data and encode rule-wide variables
 # data = pd.read_csv(input.data)
diff --git a/snekmer/utils.py b/snekmer/utils.py
index 42e9f40..6f3a333 100644
--- a/snekmer/utils.py
+++ b/snekmer/utils.py
@@ -7,6 +7,7 @@
 import collections.abc
 import datetime
 import re
+
 from os.path import basename, splitext
 from typing import Any, List, Optional, Tuple, Union
 
@@ -200,3 +201,53 @@ def to_feature_matrix(
         length_array = np.ones(len(array))
     array = [np.array(a) / length for a, length in zip(array, length_array)]
     return np.asarray(array)
+
+
+def count_n_seqs(filename: str) -> int:
+    """Count number of sequences in a file.
+
+    Parameters
+    ----------
+    filename : str
+        /path/to/sequence/file.fasta
+        Other text formatted sequence files (.faa, etc.) also work.
+
+    Returns
+    -------
+    int
+        Number of sequences contained within the input file.
+
+    """
+    return len([1 for line in open(filename) if line.startswith(">")])
+
+
+def check_n_seqs(filename: str, k: int, show_warning: bool = True) -> bool:
+    """Check that a file contains at least k sequences.
+
+    Parameters
+    ----------
+    filename : str
+        /path/to/sequence/file.fasta
+        Other text formatted sequence files (.faa, etc.) also work.
+    k : int
+        Minimum threshold.
+    show_warning : bool, optional
+        When True, if len(file) < k, a warning is displayed;
+        by default True.
+
+    Returns
+    -------
+    bool
+        True if len(file) < k; False otherwise.
+
+    """
+    n_seqs = len([1 for line in open(filename) if line.startswith(">")])
+    if (n_seqs < k) and (show_warning):
+        print(
+            f"\nWARNING: {filename} contains an insufficient"
+            " number of sequences for model cross-validation"
+            " and will thus be excluded from Snekmer modeling."
+            f" ({k} folds specified in config; {n_seqs}"
+            " sequence(s) detected.)\n"
+        )
+    return n_seqs >= k