diff --git a/text-generation-inference/server/text_generation_server/generator.py b/text-generation-inference/server/text_generation_server/generator.py
index e394726bb..3ddee690c 100644
--- a/text-generation-inference/server/text_generation_server/generator.py
+++ b/text-generation-inference/server/text_generation_server/generator.py
@@ -303,6 +303,12 @@ def attention_mask(self) -> torch.LongTensor:
     def max_token(self) -> int:
         return self._generation_config.max_length
 
+    @property
+    def max_new_tokens(self) -> int:
+        # The current value of max_new_tokens: might be different of the target max_new_tokens
+        # if the slot has been paused and resumed.
+        return self._generation_config.max_new_tokens
+
     @property
     def truncate(self) -> int:
         return self._truncate
@@ -384,7 +390,9 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
             slot = empty_slots.pop()
             slot.assign(self.batch_id, request, self.model.generation_config)
             new_slots.append(slot)
-            logger.debug(f"Request {slot.request_id} assigned to slot {slot.id}")
+            logger.debug(
+                f"Request {slot.request_id} assigned to slot {slot.id} with and max_new_tokens {slot.max_new_tokens}"
+            )
         if self.rebuild_cache_on_prefill:
             # We will clear pending slots and prefill all slots
             prefill_slots = self.slots
@@ -453,6 +461,8 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
                 # Append back the next token
                 slot.append(next_tokens[i])
         logger.debug("Model ready for decoding")
+        if next_batch is not None:
+            logger.debug(f"Next batch is {next_batch.id} with requests: {next_batch.request_ids}")
         return generation, next_batch
 
     def decode(self, batches: List[CachedBatch]) -> Tuple[List[Generation], CachedBatch]:
@@ -525,14 +535,16 @@ def _generate_token(
             if next_token == self.tokenizer.eos_token_id:
                 finish_reason = FinishReason.FINISH_REASON_EOS_TOKEN
             elif slot.stopped:
-                # For now we only support the length stopping criteria
-                finish_reason = FinishReason.FINISH_REASON_LENGTH
+                if slot.generated_tokens == slot.max_new_tokens:
+                    finish_reason = FinishReason.FINISH_REASON_LENGTH
+                else:
+                    finish_reason = FinishReason.FINISH_REASON_STOP_SEQUENCE
             if finish_reason is not None:
                 # We must include the generated text for each finished sequence in the response
                 generated_text = GeneratedText(
                     text=slot.generated_text, generated_tokens=slot.generated_tokens, finish_reason=finish_reason
                 )
-                logger.debug(f"Finished generating tokens for request {request_id}")
+                logger.debug(f"Decode complete for request {request_id} with {slot.generated_tokens} tokens")
                 # mark the slot as available
                 slot.clear()
             else:
@@ -590,7 +602,7 @@ def clear(self, batch_id: Optional[int] = None):
     def _clear(self, keep_slot_ids: List):
         for slot in self.slots:
             if slot.state != Slot.State.EMPTY and slot.id not in keep_slot_ids:
-                logger.info(f"Removing slot {slot.id} with request {slot.request_id}")
+                logger.debug(f"Removing slot {slot.id} with request {slot.request_id}")
                 slot.clear()
 
     @classmethod