diff --git a/fastembed/late_interaction/colbert.py b/fastembed/late_interaction/colbert.py
index 686595d7..1a2fbcd2 100644
--- a/fastembed/late_interaction/colbert.py
+++ b/fastembed/late_interaction/colbert.py
@@ -12,6 +12,7 @@
 )
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
 
+
 supported_colbert_models = [
     {
         "model": "colbert-ir/colbertv2.0",
@@ -41,7 +42,7 @@
 class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[np.ndarray]):
     QUERY_MARKER_TOKEN_ID = 1
     DOCUMENT_MARKER_TOKEN_ID = 2
-    MIN_QUERY_LENGTH = 32
+    MIN_QUERY_LENGTH = 31  # it's 32, we add one additional special token in the beginning
     MASK_TOKEN = "[MASK]"
 
     def _post_process_onnx_output(
@@ -69,10 +70,9 @@ def _post_process_onnx_output(
     def _preprocess_onnx_input(
         self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
     ) -> Dict[str, np.ndarray]:
-        if is_doc:
-            onnx_input["input_ids"][:, 1] = self.DOCUMENT_MARKER_TOKEN_ID
-        else:
-            onnx_input["input_ids"][:, 1] = self.QUERY_MARKER_TOKEN_ID
+        marker_token = self.DOCUMENT_MARKER_TOKEN_ID if is_doc else self.QUERY_MARKER_TOKEN_ID
+        onnx_input["input_ids"] = np.insert(onnx_input["input_ids"], 1, marker_token, axis=1)
+        onnx_input["attention_mask"] = np.insert(onnx_input["attention_mask"], 1, 1, axis=1)
         return onnx_input
 
     def tokenize(self, documents: List[str], is_doc: bool = True) -> List[Encoding]:
@@ -83,9 +83,6 @@ def tokenize(self, documents: List[str], is_doc: bool = True) -> List[Encoding]:
         )
 
     def _tokenize_query(self, query: str) -> List[Encoding]:
-        # "@ " is added to a query to be replaced with a special query token
-        # make sure that "@ " is considered as a single token
-        query = f"@ {query}"
         encoded = self.tokenizer.encode_batch([query])
         # colbert authors recommend to pad queries with [MASK] tokens for query augmentation to improve performance
         if len(encoded[0].ids) < self.MIN_QUERY_LENGTH:
@@ -105,9 +102,6 @@ def _tokenize_query(self, query: str) -> List[Encoding]:
         return encoded
 
     def _tokenize_documents(self, documents: List[str]) -> List[Encoding]:
-        # "@ " is added to a document to be replaced with a special document token
-        # make sure that "@ " is considered as a single token
-        documents = ["@ " + doc for doc in documents]
         encoded = self.tokenizer.encode_batch(documents)
         return encoded
 
diff --git a/fastembed/late_interaction/jina_colbert.py b/fastembed/late_interaction/jina_colbert.py
index 42232aeb..9f7c4b32 100644
--- a/fastembed/late_interaction/jina_colbert.py
+++ b/fastembed/late_interaction/jina_colbert.py
@@ -5,6 +5,7 @@
 from fastembed.late_interaction.colbert import Colbert
 from fastembed.text.onnx_text_model import TextEmbeddingWorker
 
+
 supported_jina_colbert_models = [
     {
         "model": "jinaai/jina-colbert-v2",
@@ -24,7 +25,7 @@
 class JinaColbert(Colbert):
     QUERY_MARKER_TOKEN_ID = 250002
     DOCUMENT_MARKER_TOKEN_ID = 250003
-    MIN_QUERY_LENGTH = 32
+    MIN_QUERY_LENGTH = 31  # it's 32, we add one additional special token in the beginning
     MASK_TOKEN = "<mask>"
 
     @classmethod
@@ -43,11 +44,10 @@ def list_supported_models(cls) -> List[Dict[str, Any]]:
     def _preprocess_onnx_input(
         self, onnx_input: Dict[str, np.ndarray], is_doc: bool = True
     ) -> Dict[str, np.ndarray]:
-        if is_doc:
-            onnx_input["input_ids"][:, 1] = self.DOCUMENT_MARKER_TOKEN_ID
-        else:
-            onnx_input["input_ids"][:, 1] = self.QUERY_MARKER_TOKEN_ID
-            # the attention mask for jina-colbert-v2 is always 1 in queries
+        onnx_input = super()._preprocess_onnx_input(onnx_input, is_doc)
+
+        # the attention mask for jina-colbert-v2 is always 1 in queries
+        if not is_doc:
             onnx_input["attention_mask"][:] = 1
         return onnx_input