diff --git a/mteb/models/model_implementations/nvidia_models.py b/mteb/models/model_implementations/nvidia_models.py
index b7c232e791..9df229958d 100644
--- a/mteb/models/model_implementations/nvidia_models.py
+++ b/mteb/models/model_implementations/nvidia_models.py
@@ -1,4 +1,5 @@
 import logging
+from collections.abc import Callable
 from typing import Any
 
 import torch
@@ -29,7 +30,7 @@
 }"""
 
 
-def instruction_template(
+def _instruction_template(
     instruction: str, prompt_type: PromptType | None = None
 ) -> str:
     return f"Instruct: {instruction}\nQuery: " if instruction else ""
@@ -100,10 +101,77 @@ def instruction_template(
     "MrTidyRetrieval",
 }
 
+
+class _NVEmbedWrapper(InstructSentenceTransformerModel):
+    """Inherited, because nvembed requires `sbert==2`, but it doesn't have tokenizers kwargs"""
+
+    def __init__(
+        self,
+        model_name: str,
+        revision: str,
+        instruction_template: str
+        | Callable[[str, PromptType | None], str]
+        | None = None,
+        max_seq_length: int | None = None,
+        apply_instruction_to_passages: bool = True,
+        padding_side: str | None = None,
+        add_eos_token: bool = False,
+        prompts_dict: dict[str, str] | None = None,
+        **kwargs: Any,
+    ):
+        from sentence_transformers import __version__ as sbert_version
+
+        required_transformers_version = "4.42.4"
+        required_sbert_version = "2.7.0"
+
+        if Version(transformers_version) != Version(required_transformers_version):
+            raise RuntimeError(
+                f"transformers version {transformers_version} is not match with required "
+                f"install version {required_transformers_version} to run `nvidia/NV-Embed-v2`"
+            )
+
+        if Version(sbert_version) != Version(required_sbert_version):
+            raise RuntimeError(
+                f"sbert version {sbert_version} is not match with required "
+                f"install version {required_sbert_version} to run `nvidia/NV-Embed-v2`"
+            )
+
+        requires_package(
+            self, "flash_attn", model_name, "pip install 'mteb[flash_attention]'"
+        )
+
+        from sentence_transformers import SentenceTransformer
+
+        if (
+            isinstance(instruction_template, str)
+            and "{instruction}" not in instruction_template
+        ):
+            raise ValueError(
+                "Instruction template must contain the string '{instruction}'."
+            )
+        if instruction_template is None:
+            logger.warning(
+                "No instruction template provided. Instructions will be used as-is."
+            )
+
+        self.instruction_template = instruction_template
+
+        self.model_name = model_name
+        self.model = SentenceTransformer(model_name, revision=revision, **kwargs)
+        self.model.tokenizer.padding_side = padding_side
+        self.model.tokenizer.add_eos_token = add_eos_token
+
+        if max_seq_length:
+            # https://github.com/huggingface/sentence-transformers/issues/3575
+            self.model.max_seq_length = max_seq_length
+        self.apply_instruction_to_passages = apply_instruction_to_passages
+        self.prompts_dict = prompts_dict
+
+
 NV_embed_v2 = ModelMeta(
-    loader=InstructSentenceTransformerModel,
+    loader=_NVEmbedWrapper,
     loader_kwargs=dict(
-        instruction_template=instruction_template,
+        instruction_template=_instruction_template,
         trust_remote_code=True,
         max_seq_length=32768,
         padding_side="right",
@@ -131,9 +199,9 @@ def instruction_template(
 )
 
 NV_embed_v1 = ModelMeta(
-    loader=InstructSentenceTransformerModel,
+    loader=_NVEmbedWrapper,
     loader_kwargs=dict(
-        instruction_template=instruction_template,
+        instruction_template=_instruction_template,
         trust_remote_code=True,
         max_seq_length=32768,
         padding_side="right",