diff --git a/spacy/cli/init_model.py b/spacy/cli/init_model.py
index e685377a9..0a9879213 100644
--- a/spacy/cli/init_model.py
+++ b/spacy/cli/init_model.py
@@ -66,14 +66,14 @@ def init_model(lang, output_dir, freqs_loc=None, clusters_loc=None, jsonl_loc=No
         if freqs_loc is not None and not freqs_loc.exists():
             prints(freqs_loc, title=Messages.M037, exits=1)
         lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
-    vectors_loc = ensure_path(vectors_loc)
-    if vectors_loc and vectors_loc.parts[-1].endswith('.npz'):
-        vectors_data = numpy.load(vectors_loc.open('rb'))
-        vector_keys = [lex['orth'] for lex in lex_attrs
-                       if 'id' in lex and lex['id'] < vectors_data.shape[0]]
-    else:
-        vectors_data, vector_keys = read_vectors(vectors_loc) if vectors_loc else (None, None)
-    nlp = create_model(lang, lex_attrs, vectors_data, vector_keys, prune_vectors)
+
+    nlp = create_model(lang, lex_attrs)
+    if vectors_loc is not None:
+        add_vectors(nlp, vectors_loc, prune_vectors)
+    vec_added = len(nlp.vocab.vectors)
+    lex_added = len(nlp.vocab)
+    prints(Messages.M039.format(entries=lex_added, vectors=vec_added),
+           title=Messages.M038)
     if not output_dir.exists():
         output_dir.mkdir()
     nlp.to_disk(output_dir)
@@ -112,7 +112,7 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
     return lex_attrs
 
 
-def create_model(lang, lex_attrs, vectors_data, vector_keys, prune_vectors):
+def create_model(lang, lex_attrs):
     print("Creating model...")
     lang_class = get_lang_class(lang)
     nlp = lang_class()
@@ -120,28 +120,38 @@ def create_model(lang, lex_attrs, vectors_data, vector_keys, prune_vectors):
         lexeme.rank = 0
     lex_added = 0
     for attrs in lex_attrs:
+        if 'settings' in attrs:
+            continue
         lexeme = nlp.vocab[attrs['orth']]
-        lexeme.set_attrs(**intify_attrs(attrs))
+        lexeme.set_attrs(**attrs)
         lexeme.is_oov = False
         lex_added += 1
         lex_added += 1
     oov_prob = min(lex.prob for lex in nlp.vocab)
     nlp.vocab.cfg.update({'oov_prob': oov_prob-1})
-    if vector_keys is not None:
-        for word in vector_keys:
-            if word not in nlp.vocab:
-                lexeme = nlp.vocab[word]
-                lexeme.is_oov = False
-                lex_added += 1
-    if vectors_data is not None:
-        nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
-    if prune_vectors >= 1:
-        nlp.vocab.prune_vectors(prune_vectors)
-    vec_added = len(nlp.vocab.vectors)
-    prints(Messages.M039.format(entries=lex_added, vectors=vec_added),
-           title=Messages.M038)
     return nlp
 
+def add_vectors(nlp, vectors_loc, prune_vectors):
+    vectors_loc = ensure_path(vectors_loc)
+    if vectors_loc and vectors_loc.parts[-1].endswith('.npz'):
+        nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open('rb')))
+        for lex in nlp.vocab:
+            if lex.rank:
+                nlp.vocab.vectors.add(lex.orth, row=lex.rank)
+    else:
+        vectors_data, vector_keys = read_vectors(vectors_loc) if vectors_loc else (None, None)
+        if vector_keys is not None:
+            for word in vector_keys:
+                if word not in nlp.vocab:
+                    lexeme = nlp.vocab[word]
+                    lexeme.is_oov = False
+                    lex_added += 1
+        if vectors_data is not None:
+            nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
+    nlp.vocab.vectors.name = '%s_model.vectors' % nlp.meta['lang']
+    nlp.meta['vectors']['name'] = nlp.vocab.vectors.name
+    if prune_vectors >= 1:
+        nlp.vocab.prune_vectors(prune_vectors)
 
 def read_vectors(vectors_loc):
     print("Reading vectors from %s" % vectors_loc)