diff --git a/lm_eval/models/vllm_vlms.py b/lm_eval/models/vllm_vlms.py
index 15813b8aa9a..59b25dd2da7 100644
--- a/lm_eval/models/vllm_vlms.py
+++ b/lm_eval/models/vllm_vlms.py
@@ -1,6 +1,5 @@
 import copy
 import logging
-from typing import Dict, List, Optional
 
 import transformers
 from more_itertools import distribute
@@ -40,14 +39,14 @@ class VLLM_VLM(VLLM):
     def __init__(
         self,
         pretrained: str,
-        trust_remote_code: Optional[bool] = False,
-        revision: Optional[str] = None,
+        trust_remote_code: bool | None = False,
+        revision: str | None = None,
         interleave: bool = True,
         # TODO<baber>: handle max_images and limit_mm_per_prompt better
         max_images: int = 999,
-        image_width: Optional[int] = None,
-        image_height: Optional[int] = None,
-        image_max_side: Optional[int] = None,
+        image_width: int | None = None,
+        image_height: int | None = None,
+        image_max_side: int | None = None,
         **kwargs,
     ):
         self.image_width = image_width
@@ -79,9 +78,9 @@ def __init__(
 
     def tok_batch_multimodal_encode(
         self,
-        strings: List[str],  # note that input signature of this fn is different
+        strings: list[str],  # note that input signature of this fn is different
         images,  # TODO: typehint on this
-        left_truncate_len: int = None,
+        left_truncate_len: int | None = None,
         truncation: bool = False,
     ):
         images = [img[: self.max_images] for img in images]
@@ -98,7 +97,7 @@ def tok_batch_multimodal_encode(
             ]
 
         outputs = []
-        for x, i in zip(strings, images):
+        for x, i in zip(strings, images, strict=True):
             inputs = {
                 "prompt": x,
                 "multi_modal_data": {"image": i},
@@ -108,14 +107,14 @@ def tok_batch_multimodal_encode(
 
     def _multimodal_model_generate(
         self,
-        requests: List[List[dict]] = None,
+        requests: list[list[dict]] = None,
         generate: bool = False,
         max_tokens: int = None,
-        stop: Optional[List[str]] = None,
+        stop: list[str] | None = None,
         **kwargs,
     ):
         if generate:
-            kwargs = self.modify_gen_kwargs(kwargs)
+            kwargs, _, _ = self.modify_gen_kwargs(kwargs)
             sampling_params = SamplingParams(max_tokens=max_tokens, stop=stop, **kwargs)
         else:
             sampling_params = SamplingParams(
@@ -127,7 +126,7 @@ def _multimodal_model_generate(
             # see https://github.com/vllm-project/vllm/issues/973
             @ray.remote
             def run_inference_one_model(
-                model_args: dict, sampling_params, requests: List[List[dict]]
+                model_args: dict, sampling_params, requests: list[list[dict]]
             ):
                 llm = LLM(**model_args)
                 return llm.generate(requests, sampling_params=sampling_params)
@@ -147,19 +146,19 @@ def run_inference_one_model(
             outputs = self.model.generate(
                 requests,
                 sampling_params=sampling_params,
-                use_tqdm=True if self.batch_size == "auto" else False,
+                use_tqdm=self.batch_size == "auto",
                 lora_request=self.lora_request,
             )
         else:
             outputs = self.model.generate(
                 requests,
                 sampling_params=sampling_params,
-                use_tqdm=True if self.batch_size == "auto" else False,
+                use_tqdm=self.batch_size == "auto",
             )
         return outputs
 
     def apply_chat_template(
-        self, chat_history: List[Dict[str, str]], add_generation_prompt=True
+        self, chat_history: list[dict[str, str]], add_generation_prompt=True
     ) -> str:
         self.chat_applied = True
         if not self.interleave:
@@ -216,8 +215,8 @@ def apply_chat_template(
         )
 
     def generate_until(
-        self, requests: List[Instance], disable_tqdm: bool = False
-    ) -> List[str]:
+        self, requests: list[Instance], disable_tqdm: bool = False
+    ) -> list[str]:
         if requests and len(requests[0].args) < 3:
             # Fall back to non-multimodal generation.
             return super().generate_until(requests=requests, disable_tqdm=disable_tqdm)
@@ -253,7 +252,7 @@ def _collate(x):
         chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
         eos = self.tokenizer.decode(self.eot_token_id)
         for chunk in chunks:
-            contexts, all_gen_kwargs, aux_arguments = zip(*chunk)
+            contexts, all_gen_kwargs, aux_arguments = zip(*chunk, strict=True)
 
             visuals = [
                 [
@@ -300,7 +299,7 @@ def _collate(x):
                 inputs, stop=until, generate=True, max_tokens=max_gen_toks, **kwargs
             )
 
-            for output, context in zip(cont, contexts):
+            for output, context in zip(cont, contexts, strict=True):
                 generated_text = output.outputs[0].text
                 res.append(generated_text)
                 self.cache_hook.add_partial(
@@ -313,7 +312,7 @@ def _collate(x):
         pbar.close()
         return res
 
-    def loglikelihood_rolling(self, requests: List[Instance]) -> List[float]:
+    def loglikelihood_rolling(self, requests: list[Instance]) -> list[float]:
         if requests and len(requests[0].args) < 3:
             # Fall back to non-multimodal generation.
             return super().loglikelihood_rolling(requests=requests)