diff --git a/src/helm/proxy/clients/huggingface_client.py b/src/helm/proxy/clients/huggingface_client.py
index e9f93aa9f9..9bb6f24f57 100644
--- a/src/helm/proxy/clients/huggingface_client.py
+++ b/src/helm/proxy/clients/huggingface_client.py
@@ -2,6 +2,12 @@
 import torch
 from dataclasses import asdict
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.generation.stopping_criteria import (
+    StoppingCriteria,
+    StoppingCriteriaList,
+    STOPPING_CRITERIA_INPUTS_DOCSTRING,
+    add_start_docstrings,
+)
 from typing import Any, Dict, List, Optional
 
 from helm.common.cache import Cache, CacheConfig
@@ -35,6 +41,21 @@ def resolve_alias(model_name: str) -> str:
     return _MODEL_NAME_ALIASES.get(model_name, model_name)
 
 
+class StopAtSpecificTokenCriteria(StoppingCriteria):
+    def __init__(self, stop_sequence: List[int] = None):
+        super().__init__()
+        self.stop_sequence = stop_sequence
+
+    # @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        # Create a tensor from the stop_sequence
+        stop_sequence_tensor = torch.tensor(self.stop_sequence, device=input_ids.device, dtype=input_ids.dtype)
+
+        # Check if the current sequence ends with the stop_sequence
+        current_sequence = input_ids[:, -len(self.stop_sequence) :]
+        return torch.all(current_sequence == stop_sequence_tensor).item()
+
+
 class HuggingFaceServer:
     """A thin wrapper around a Hugging Face AutoModelForCausalLM for HuggingFaceClient to call."""
 
@@ -72,9 +93,10 @@ def serve_request(self, raw_request: Dict[str, Any]):
                 raw_request["stop_sequences"], return_token_type_ids=False, add_special_tokens=False
             )
             assert len(stop_sequence_ids.input_ids) == 1, "Total number of stop words should be 1."
-            assert len(stop_sequence_ids.input_ids[0]) == 1, "Total number of tokens in each stop word should be 1."
+            # assert len(stop_sequence_ids.input_ids[0]) == 1, "Total number of tokens in each stop word should be 1."
+            if len(stop_sequence_ids.input_ids[0]) == 1:
+                raw_request["eos_token_id"] = stop_sequence_ids.input_ids[0][0]
             del raw_request["stop_sequences"]
-            raw_request["eos_token_id"] = stop_sequence_ids.input_ids[0][0]
 
         # Strip out irrelevant parameters
         relevant_raw_request = {
@@ -83,8 +105,16 @@ def serve_request(self, raw_request: Dict[str, Any]):
             if key not in ["engine", "prompt", "echo_prompt", "stop_sequences"]
         }
 
+        stopping_criteria = StoppingCriteriaList()
+        if stop_sequence_ids != None:
+            stopping_criteria.append(StopAtSpecificTokenCriteria(stop_sequence=stop_sequence_ids.input_ids[0]))
+
         # Use HuggingFace's `generate` method.
-        output = self.model.generate(**encoded_input, **relevant_raw_request)
+        output = self.model.generate(
+            **encoded_input,
+            **relevant_raw_request,
+            stopping_criteria=stopping_criteria if len(stop_sequence_ids.input_ids[0]) > 1 else None,
+        )
         sequences = output.sequences
         scores = output.scores