diff --git a/tests/models/embedding/language/test_gritlm.py b/tests/models/embedding/language/test_gritlm.py
index b947265be9e9d..55c2e5d4ed412 100644
--- a/tests/models/embedding/language/test_gritlm.py
+++ b/tests/models/embedding/language/test_gritlm.py
@@ -35,7 +35,7 @@ def test_find_array(monkeypatch):
     from vllm.model_executor.models.gritlm import GritLMPooler
 
     # Create an LLM object to get the model config.
-    llm = vllm.LLM(MODEL_NAME, task="embedding", max_model_len=MAX_MODEL_LEN)
+    llm = vllm.LLM(MODEL_NAME, task="embed", max_model_len=MAX_MODEL_LEN)
     pooler = GritLMPooler(model_config=llm.llm_engine.model_config)
 
     arr = _arr([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
@@ -55,7 +55,7 @@ def server_embedding():
     with pytest.MonkeyPatch.context() as mp:
         mp.setenv("VLLM_ATTENTION_BACKEND", "XFORMERS")
 
-        args = ["--task", "embedding", "--max_model_len", str(MAX_MODEL_LEN)]
+        args = ["--task", "embed", "--max_model_len", str(MAX_MODEL_LEN)]
         with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
             yield remote_server
 
@@ -141,7 +141,7 @@ def test_gritlm_offline_embedding(monkeypatch):
 
     queries, q_instruction, documents, d_instruction = get_test_data()
 
-    llm = vllm.LLM(MODEL_NAME, task="embedding", max_model_len=MAX_MODEL_LEN)
+    llm = vllm.LLM(MODEL_NAME, task="embed", max_model_len=MAX_MODEL_LEN)
 
     d_rep = run_llm_encode(
         llm,
diff --git a/vllm/model_executor/models/gritlm.py b/vllm/model_executor/models/gritlm.py
index ec01a07c16a62..34c1332ac4a66 100644
--- a/vllm/model_executor/models/gritlm.py
+++ b/vllm/model_executor/models/gritlm.py
@@ -203,12 +203,12 @@ def __init__(
     ) -> None:
         super().__init__(vllm_config=vllm_config, prefix=prefix, **kwargs)
 
-        self.task = vllm_config.model_config.task
+        self.runner_type = vllm_config.model_config.runner_type
 
         self._pooler = GritLMPooler(vllm_config.model_config)
 
         for layer in self.model.layers:
-            if self.task == "embedding" and hasattr(layer, "self_attn"):
+            if self.runner_type == "pooling" and hasattr(layer, "self_attn"):
                 assert isinstance(layer.self_attn.attn.impl, XFormersImpl), (
                     "GritLM embedding is only supported by XFormers backend, "
                     "which can be forced by VLLM_ATTENTION_BACKEND=XFORMERS")
@@ -222,8 +222,8 @@ def forward(
         **kwargs,
     ) -> Union[torch.Tensor, IntermediateTensors]:
 
-        # Change attention to non-causal for embedding task.
-        if self.task == "embedding":
+        # Change attention to non-causal for pooling tasks.
+        if self.runner_type == "pooling":
             assert attn_metadata.prefill_metadata.attn_bias is None
             attn_metadata.prefill_metadata.attn_bias = [
                 BlockDiagonalMask.from_seqlens(attn_metadata.seq_lens)