diff --git a/lm_eval/models/vllm_causallms.py b/lm_eval/models/vllm_causallms.py
index 390a14a7e3b..e35cac2a079 100644
--- a/lm_eval/models/vllm_causallms.py
+++ b/lm_eval/models/vllm_causallms.py
@@ -1,6 +1,5 @@
 import copy
 import gc
-import inspect
 import logging
 import os
 from importlib.metadata import version
@@ -33,7 +32,7 @@
 
 try:
     import ray
-    from vllm import LLM, SamplingParams
+    from vllm import LLM, SamplingParams, TokensPrompt
     from vllm.lora.request import LoRARequest
     from vllm.transformers_utils.tokenizer import get_tokenizer
     from vllm.utils import get_open_port
@@ -79,7 +78,7 @@ def _vllm_mp_worker(
     try:
         llm = LLM(**model_args)
         res = llm.generate(
-            prompt_token_ids=requests,
+            [TokensPrompt(prompt_token_ids=request) for request in requests],
             sampling_params=sampling_params,
             lora_request=lora_request,
         )
@@ -239,13 +238,6 @@ def __init__(
                     model_config = engine_args.create_model_config()
 
                     kwargs_resolve_hf_chat_template["model_config"] = model_config
-
-            # https://github.com/vllm-project/vllm/pull/18259
-            if (
-                "trsut_remote_code"
-                in inspect.signature(resolve_hf_chat_template).parameters
-            ):
-                kwargs_resolve_hf_chat_template["trsut_remote_code"] = trust_remote_code
             else:
                 kwargs_resolve_hf_chat_template["trust_remote_code"] = trust_remote_code
 
@@ -395,7 +387,7 @@ def run_inference_one_model(
             ):
                 llm = LLM(**model_args)
                 return llm.generate(
-                    prompt_token_ids=requests,
+                    [TokensPrompt(prompt_token_ids=request) for request in requests],
                     sampling_params=sampling_params,
                     lora_request=lora_request,
                 )
@@ -484,7 +476,7 @@ def run_inference_one_model(
 
         else:
             outputs = self.model.generate(
-                prompt_token_ids=requests,
+                [TokensPrompt(prompt_token_ids=request) for request in requests],
                 sampling_params=sampling_params,
                 use_tqdm=True if self.batch_size == "auto" else False,
                 lora_request=self.lora_request,