diff --git a/tests/v1/engine/test_llm_engine.py b/tests/v1/engine/test_llm_engine.py
index f37686317fd14..2848420c22085 100644
--- a/tests/v1/engine/test_llm_engine.py
+++ b/tests/v1/engine/test_llm_engine.py
@@ -213,3 +213,29 @@ def test_engine_metrics(vllm_runner, monkeypatch, example_prompts):
         assert len(num_accepted_tokens_per_pos) == 1
         assert isinstance(num_accepted_tokens_per_pos[0], Vector)
         assert len(num_accepted_tokens_per_pos[0].values) == 5
+
+
+@pytest.mark.parametrize("model", ["meta-llama/Llama-3.2-1B-Instruct"])
+def test_skip_tokenizer_initialization(model: str,
+                                       monkeypatch: pytest.MonkeyPatch):
+    monkeypatch.setenv("VLLM_USE_V1", "1")
+    # This test checks if the flag skip_tokenizer_init skips the initialization
+    # of tokenizer and detokenizer. The generated output is expected to contain
+    # token ids.
+    llm = LLM(
+        model=model,
+        skip_tokenizer_init=True,
+        enforce_eager=True,
+    )
+    sampling_params = SamplingParams(prompt_logprobs=True, detokenize=True)
+
+    with pytest.raises(ValueError, match="cannot pass text prompts when"):
+        llm.generate("abc", sampling_params)
+
+    outputs = llm.generate({"prompt_token_ids": [1, 2, 3]},
+                           sampling_params=sampling_params)
+    assert len(outputs) > 0
+    completions = outputs[0].outputs
+    assert len(completions) > 0
+    assert completions[0].text == ""
+    assert completions[0].token_ids
diff --git a/vllm/v1/engine/processor.py b/vllm/v1/engine/processor.py
index 0f2f404a130ef..224acc47feb27 100644
--- a/vllm/v1/engine/processor.py
+++ b/vllm/v1/engine/processor.py
@@ -89,6 +89,10 @@ class Processor:
             return
         if not params.allowed_token_ids:
             raise ValueError("allowed_token_ids is not None and empty!")
+        if self.tokenizer is None:
+            # When skip_tokenizer_init=True, we can't validate token IDs
+            # Skip validation and let the model handle invalid tokens
+            return
         tokenizer = self.tokenizer.get_lora_tokenizer(lora_request)
         vocab_size = len(tokenizer)
         if not all(0 <= tid < vocab_size for tid in params.allowed_token_ids):
@@ -283,8 +287,9 @@ class Processor:
                     len(decoder_inputs["prompt_token_ids"]))
             sampling_params.update_from_generation_config(
                 self.generation_config_fields, eos_token_id)
-            sampling_params.update_from_tokenizer(
-                self.tokenizer.get_lora_tokenizer(lora_request))
+            if self.tokenizer is not None:
+                sampling_params.update_from_tokenizer(
+                    self.tokenizer.get_lora_tokenizer(lora_request))
         else:
             pooling_params = params.clone()