diff --git a/gensim/models/doc2vec.py b/gensim/models/doc2vec.py
index 1a55ad9b5f..ff93dbfcb5 100644
--- a/gensim/models/doc2vec.py
+++ b/gensim/models/doc2vec.py
@@ -790,7 +790,7 @@ def load(cls, *args, **kwargs):
         except AttributeError as ae:
             logger.error(
                 "Model load error. Was model saved using code from an older Gensim Version? "
-                "Try loading older model using gensim-3.8.1, then re-saving, to restore "
+                "Try loading older model using gensim-3.8.3, then re-saving, to restore "
                 "compatibility with current code.")
             raise ae
 
diff --git a/gensim/models/fasttext.py b/gensim/models/fasttext.py
index 5c07a0b540..928142580f 100644
--- a/gensim/models/fasttext.py
+++ b/gensim/models/fasttext.py
@@ -279,6 +279,7 @@
 import os
 
 import numpy as np
+import itertools as it
 from numpy import ones, vstack, float32 as REAL
 import six
 from collections.abc import Iterable
@@ -822,7 +823,6 @@ def save(self, *args, **kwargs):
             Load :class:`~gensim.models.fasttext.FastText` model.
 
         """
-        kwargs['ignore'] = kwargs.get('ignore', []) + ['buckets_word', ]
         super(FastText, self).save(*args, **kwargs)
 
     @classmethod
@@ -845,25 +845,15 @@ def load(cls, *args, **kwargs):
             Save :class:`~gensim.models.fasttext.FastText` model.
 
         """
-        model = super(FastText, cls).load(*args, rethrow=True, **kwargs)
-
-        if not hasattr(model.wv, 'vectors_vocab_lockf') and hasattr(model.wv, 'vectors_vocab'):
-            # TODO: try trainables-location
-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)
-        if not hasattr(model, 'vectors_ngrams_lockf') and hasattr(model.wv, 'vectors_ngrams'):
-            # TODO: try trainables-location
-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)
-        # fixup mistakenly overdimensioned gensim-3.x lockf arrays
-        if len(model.wv.vectors_vocab_lockf.shape) > 1:
-            model.wv.vectors_vocab_lockf = ones(1, dtype=REAL)
-        if len(model.wv.vectors_ngrams_lockf.shape) > 1:
-            model.wv.vectors_ngrams_lockf = ones(1, dtype=REAL)
-        if hasattr(model, 'bucket'):
-            del model.bucket  # should only exist in one place: the wv subcomponent
-        if not hasattr(model.wv, 'buckets_word') or not model.wv.buckets_word:
-            model.wv.recalc_char_ngram_buckets()
+        return super(FastText, cls).load(*args, rethrow=True, **kwargs)
 
-        return model
+    def _load_specials(self, *args, **kwargs):
+        """Handle special requirements of `.load()` protocol, usually up-converting older versions."""
+        super(FastText, self)._load_specials(*args, **kwargs)
+        if hasattr(self, 'bucket'):
+            # should only exist in one place: the wv subcomponent
+            self.wv.bucket = self.bucket
+            del self.bucket
 
 
 class FastTextVocab(utils.SaveLoad):
@@ -1197,12 +1187,47 @@ def __init__(self, vector_size, min_n, max_n, bucket):
 
     @classmethod
     def load(cls, fname_or_handle, **kwargs):
-        model = super(FastTextKeyedVectors, cls).load(fname_or_handle, **kwargs)
-        if isinstance(model, FastTextKeyedVectors):
-            if not hasattr(model, 'compatible_hash') or model.compatible_hash is False:
-                raise TypeError("Pre-gensim-3.8.x Fasttext models with nonstandard hashing are no longer compatible."
-                                "Loading into gensim-3.8.3 & re-saving may create a compatible model.")
-        return model
+        """Load a previously saved `FastTextKeyedVectors` model.
+
+        Parameters
+        ----------
+        fname : str
+            Path to the saved file.
+
+        Returns
+        -------
+        :class:`~gensim.models.fasttext.FastTextKeyedVectors`
+            Loaded model.
+
+        See Also
+        --------
+        :meth:`~gensim.models.fasttext.FastTextKeyedVectors.save`
+            Save :class:`~gensim.models.fasttext.FastTextKeyedVectors` model.
+
+        """
+        return super(FastTextKeyedVectors, cls).load(fname_or_handle, **kwargs)
+
+    def _load_specials(self, *args, **kwargs):
+        """Handle special requirements of `.load()` protocol, usually up-converting older versions."""
+        super(FastTextKeyedVectors, self)._load_specials(*args, **kwargs)
+        if not isinstance(self, FastTextKeyedVectors):
+            raise TypeError("Loaded object of type %s, not expected FastTextKeyedVectors" % type(self))
+        if not hasattr(self, 'compatible_hash') or self.compatible_hash is False:
+            raise TypeError("Pre-gensim-3.8.x Fasttext models with nonstandard hashing are no longer compatible."
+                            "Loading into gensim-3.8.3 & re-saving may create a compatible model.")
+        if not hasattr(self, 'vectors_vocab_lockf') and hasattr(self, 'vectors_vocab'):
+            self.vectors_vocab_lockf = ones(1, dtype=REAL)
+        if not hasattr(self, 'vectors_ngrams_lockf') and hasattr(self, 'vectors_ngrams'):
+            self.vectors_ngrams_lockf = ones(1, dtype=REAL)
+        # fixup mistakenly overdimensioned gensim-3.x lockf arrays
+        if len(self.vectors_vocab_lockf.shape) > 1:
+            self.vectors_vocab_lockf = ones(1, dtype=REAL)
+        if len(self.vectors_ngrams_lockf.shape) > 1:
+            self.vectors_ngrams_lockf = ones(1, dtype=REAL)
+        if not hasattr(self, 'buckets_word') or not self.buckets_word:
+            self.recalc_char_ngram_buckets()
+        if not hasattr(self, 'vectors') or self.vectors is None:
+            self.adjust_vectors()  # recompose full-word vectors
 
     def __contains__(self, word):
         """Check if `word` or any character ngrams in `word` are present in the vocabulary.
@@ -1250,14 +1275,15 @@ def save(self, *args, **kwargs):
             Load object.
 
         """
-        # don't bother storing the cached normalized vectors
-        ignore_attrs = [
-            'buckets_word',
-            'hash2index',
-        ]
-        kwargs['ignore'] = kwargs.get('ignore', ignore_attrs)
         super(FastTextKeyedVectors, self).save(*args, **kwargs)
 
+    def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol, compress, subname):
+        """Arrange any special handling for the gensim.utils.SaveLoad protocol"""
+        # don't save properties that are merely calculated from others
+        ignore = set(it.chain(ignore, ('buckets_word', 'vectors')))
+        return super(FastTextKeyedVectors, self)._save_specials(
+            fname, separately, sep_limit, ignore, pickle_protocol, compress, subname)
+
     def get_vector(self, word, use_norm=False):
         """Get `word` representations in vector space, as a 1D numpy array.
 
@@ -1401,6 +1427,7 @@ def adjust_vectors(self):
 
         """
         if self.bucket == 0:
+            self.vectors = self.vectors_vocab  # no ngrams influence
             return
 
         self.vectors = self.vectors_vocab[:].copy()
diff --git a/gensim/models/word2vec.py b/gensim/models/word2vec.py
index a6523babdf..6306a038c4 100755
--- a/gensim/models/word2vec.py
+++ b/gensim/models/word2vec.py
@@ -128,7 +128,7 @@
 from collections import defaultdict, namedtuple
 from types import GeneratorType
 import threading
-import itertools
+import itertools as it
 import copy
 
 from gensim.utils import keep_vocab_item, call_on_class_only, deprecated
@@ -779,6 +779,7 @@ def reset_weights(self):
             self.syn1neg = np.zeros((len(self.wv), self.layer1_size), dtype=REAL)
 
         self.wv.vectors_lockf = np.ones(1, dtype=REAL)  # 0.0 values suppress word-backprop-updates; 1.0 allows
+        logger.info("weights initialized")
 
     def update_weights(self):
         """Copy all the existing weights, and reset the weights for the newly added vocabulary."""
@@ -1788,20 +1789,14 @@ def save(self, *args, **kwargs):
             Path to the file.
 
         """
-        # don't bother storing recalculable table
-        kwargs['ignore'] = kwargs.get('ignore', []) + ['cum_table', ]
         super(Word2Vec, self).save(*args, **kwargs)
 
-    def get_latest_training_loss(self):
-        """Get current value of the training loss.
-
-        Returns
-        -------
-        float
-            Current training loss.
-
-        """
-        return self.running_training_loss
+    def _save_specials(self, fname, separately, sep_limit, ignore, pickle_protocol, compress, subname):
+        """Arrange any special handling for the gensim.utils.SaveLoad protocol"""
+        # don't save properties that are merely calculated from others
+        ignore = set(it.chain(ignore, ('cum_table',)))
+        return super(Word2Vec, self)._save_specials(
+            fname, separately, sep_limit, ignore, pickle_protocol, compress, subname)
 
     @classmethod
     def load(cls, *args, rethrow=False, **kwargs):
@@ -1828,49 +1823,65 @@ def load(cls, *args, rethrow=False, **kwargs):
             if not isinstance(model, Word2Vec):
                 rethrow = True
                 raise AttributeError("Model of type %s can't be loaded by %s" % (type(model), str(cls)))
-            # for backward compatibility
-            if not hasattr(model, 'ns_exponent'):
-                model.ns_exponent = 0.75
-            if model.negative and hasattr(model.wv, 'index2word'):
-                model.make_cum_table()  # rebuild cum_table from vocabulary  ## TODO: ???
-            if not hasattr(model, 'corpus_count'):
-                model.corpus_count = None
-            if not hasattr(model, 'corpus_total_words'):
-                model.corpus_total_words = None
-            if not hasattr(model.wv, 'vectors_lockf') and hasattr(model.wv, 'vectors'):
-                model.wv.vectors_lockf = getattr(model, 'vectors_lockf', np.ones(1, dtype=REAL))
-            if not hasattr(model, 'random'):
-                model.random = np.random.RandomState(model.seed)
-            if not hasattr(model, 'train_count'):
-                model.train_count = 0
-                model.total_train_time = 0
-            if not hasattr(model, 'epochs'):
-                model.epochs = model.iter
-                del model.iter
-            if not hasattr(model, 'max_final_vocab'):
-                model.max_final_vocab = None
-            if hasattr(model, 'vocabulary'):  # re-integrate state that had been moved
-                for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):
-                    setattr(model, a, getattr(model.vocabulary, a))
-                del model.vocabulary
-            if hasattr(model, 'trainables'):  # re-integrate state that had been moved
-                for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):
-                    if hasattr(model.trainables, a):
-                        setattr(model, a, getattr(model.trainables, a))
-                if hasattr(model, 'syn1'):
-                    model.syn1 = model.syn1
-                    del model.syn1
-                del model.trainables
             return model
         except AttributeError as ae:
             if rethrow:
                 raise ae
             logger.error(
                 "Model load error. Was model saved using code from an older Gensim Version? "
-                "Try loading older model using gensim-3.8.1, then re-saving, to restore "
+                "Try loading older model using gensim-3.8.3, then re-saving, to restore "
                 "compatibility with current code.")
             raise ae
 
+    def _load_specials(self, *args, **kwargs):
+        """Handle special requirements of `.load()` protocol, usually up-converting older versions."""
+        super(Word2Vec, self)._load_specials(*args, **kwargs)
+        # for backward compatibility, add/rearrange properties from prior versions
+        if not hasattr(self, 'ns_exponent'):
+            self.ns_exponent = 0.75
+        if self.negative and hasattr(self.wv, 'index_to_key'):
+            self.make_cum_table()  # rebuild cum_table from vocabulary
+        if not hasattr(self, 'corpus_count'):
+            self.corpus_count = None
+        if not hasattr(self, 'corpus_total_words'):
+            self.corpus_total_words = None
+        if not hasattr(self.wv, 'vectors_lockf') and hasattr(self.wv, 'vectors'):
+            self.wv.vectors_lockf = getattr(self, 'vectors_lockf', np.ones(1, dtype=REAL))
+        if not hasattr(self, 'random'):
+            # use new instance of numpy's recommended generator/algorithm
+            self.random = np.random.default_rng(seed=self.seed)
+        if not hasattr(self, 'train_count'):
+            self.train_count = 0
+            self.total_train_time = 0
+        if not hasattr(self, 'epochs'):
+            self.epochs = self.iter
+            del self.iter
+        if not hasattr(self, 'max_final_vocab'):
+            self.max_final_vocab = None
+        if hasattr(self, 'vocabulary'):  # re-integrate state that had been moved
+            for a in ('max_vocab_size', 'min_count', 'sample', 'sorted_vocab', 'null_word', 'raw_vocab'):
+                setattr(self, a, getattr(self.vocabulary, a))
+            del self.vocabulary
+        if hasattr(self, 'trainables'):  # re-integrate state that had been moved
+            for a in ('hashfxn', 'layer1_size', 'seed', 'syn1neg', 'syn1'):
+                if hasattr(self.trainables, a):
+                    setattr(self, a, getattr(self.trainables, a))
+            if hasattr(self, 'syn1'):
+                self.syn1 = self.syn1
+                del self.syn1
+            del self.trainables
+
+    def get_latest_training_loss(self):
+        """Get current value of the training loss.
+
+        Returns
+        -------
+        float
+            Current training loss.
+
+        """
+        return self.running_training_loss
+
 
 class BrownCorpus(object):
     def __init__(self, dirname):
@@ -1958,7 +1969,7 @@ def __iter__(self):
             # Assume it is a file-like object and try treating it as such
             # Things that don't have seek will trigger an exception
             self.source.seek(0)
-            for line in itertools.islice(self.source, self.limit):
+            for line in it.islice(self.source, self.limit):
                 line = utils.to_unicode(line).split()
                 i = 0
                 while i < len(line):
@@ -1967,7 +1978,7 @@ def __iter__(self):
         except AttributeError:
             # If it didn't work like a file, use it as a string filename
             with utils.open(self.source, 'rb') as fin:
-                for line in itertools.islice(fin, self.limit):
+                for line in it.islice(fin, self.limit):
                     line = utils.to_unicode(line).split()
                     i = 0
                     while i < len(line):
@@ -2021,7 +2032,7 @@ def __iter__(self):
         for file_name in self.input_files:
             logger.info('reading file %s', file_name)
             with utils.open(file_name, 'rb') as fin:
-                for line in itertools.islice(fin, self.limit):
+                for line in it.islice(fin, self.limit):
                     line = utils.to_unicode(line).split()
                     i = 0
                     while i < len(line):
diff --git a/gensim/test/test_fasttext.py b/gensim/test/test_fasttext.py
index c8c9b0582c..94885be3d7 100644
--- a/gensim/test/test_fasttext.py
+++ b/gensim/test/test_fasttext.py
@@ -18,7 +18,8 @@
 from gensim.models.word2vec import LineSentence
 from gensim.models.fasttext import FastText as FT_gensim, FastTextKeyedVectors, _unpack
 from gensim.models.keyedvectors import KeyedVectors
-from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences
+from gensim.test.utils import datapath, get_tmpfile, temporary_file, common_texts as sentences, lee_corpus_list
+from gensim.test.test_word2vec import TestWord2VecModel
 import gensim.models._fasttext_bin
 from gensim.models.fasttext_inner import compute_ngrams, compute_ngrams_bytes, ft_hash_bytes
 
@@ -43,14 +44,7 @@
 FT_CMD = os.path.join(FT_HOME, "fasttext") if FT_HOME else None
 
 
-class LeeCorpus(object):
-    def __iter__(self):
-        with open(datapath('lee_background.cor')) as f:
-            for line in f:
-                yield utils.simple_preprocess(line)
-
-
-list_corpus = list(LeeCorpus())
+list_corpus = lee_corpus_list
 
 new_sentences = [
     ['computer', 'artificial', 'intelligence'],
@@ -1371,6 +1365,7 @@ def _read_fb(fin):
 
 
 class ZeroBucketTest(unittest.TestCase):
+    """Tests FastText with no buckets/no-ngrams (essentially FastText-as-Word2Vec"""
     def test_in_vocab(self):
         model = train_gensim(bucket=0)
         self.assertIsNotNone(model.wv['anarchist'])
@@ -1379,6 +1374,15 @@ def test_out_of_vocab(self):
         model = train_gensim(bucket=0)
         self.assertRaises(KeyError, model.wv.word_vec, 'streamtrain')
 
+    def test_cbow_neg(self):
+        """See gensim.test.test_word2vec.TestWord2VecModel.test_cbow_neg"""
+        model = FT_gensim(
+            sg=0, cbow_mean=1, alpha=0.05, window=5, hs=0, negative=15,
+            min_count=5, epochs=10, workers=2, sample=0,
+            max_n=0  # force no char-ngram buckets
+        )
+        TestWord2VecModel.model_sanity(self, model)
+
 
 class UnicodeVocabTest(unittest.TestCase):
     def test_ascii(self):