diff --git a/modeling/inference_models/basic_hf/class.py b/modeling/inference_models/basic_hf/class.py
index 5cb64c30..ecbc55cc 100644
--- a/modeling/inference_models/basic_hf/class.py
+++ b/modeling/inference_models/basic_hf/class.py
@@ -25,6 +25,9 @@ model_backend_type = "Huggingface"
 
 
 class model_backend(HFInferenceModel):
+    # Model backends must inherit from InferenceModel. We inherit from HFInferenceModel here,
+    # as it provides some helpers for handling Huggingface configs.
+
     def __init__(self) -> None:
         super().__init__()
         self.model_name = "Basic Huggingface"
@@ -60,16 +63,10 @@ class model_backend(HFInferenceModel):
 
         self.tokenizer = self._get_tokenizer(self.get_local_model_path())
 
-        # Patch sampler to use KAI samplers
-        def _patched_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
-            processors = _patched_get_logits_processor.original(*args, **kwargs)
-            return processors
-
-        use_core_manipulations.get_logits_processor = _patched_get_logits_processor
-        _patched_get_logits_processor.original = (
-            transformers.GenerationMixin._get_logits_processor
-        )
+        self.model.kai_model = self
+        utils.koboldai_vars.modeldim = self.model.get_input_embeddings().embedding_dim
 
+        # Patch Huggingface stuff to use our samplers
         class KoboldLogitsWarperList(LogitsProcessorList):
             def __call__(
                 _self,  # Unused
@@ -78,8 +75,10 @@ class model_backend(HFInferenceModel):
                 *args,
                 **kwargs,
             ):
+                # Kobold sampling is done here.
                 scores = self._apply_warpers(scores=scores, input_ids=input_ids)
 
+                # Things like Lua integration, phrase bias, and probability visualization are done here.
                 for processor in self.logits_processors:
                     scores = processor(self, scores=scores, input_ids=input_ids)
                     assert (
@@ -89,7 +88,7 @@ class model_backend(HFInferenceModel):
 
         def new_sample(self, *args, **kwargs):
             assert kwargs.pop("logits_warper", None) is not None
-            kwargs["logits_warper"] = lambda: KoboldLogitsWarperList()
+            kwargs["logits_warper"] = KoboldLogitsWarperList()
 
             if utils.koboldai_vars.newlinemode in ["s", "ns"]:
                 kwargs["eos_token_id"] = -1
@@ -100,12 +99,18 @@ class model_backend(HFInferenceModel):
         new_sample.old_sample = transformers.GenerationMixin.sample
         use_core_manipulations.sample = new_sample
 
-        self.model.kai_model = self
-        utils.koboldai_vars.modeldim = self.model.get_input_embeddings().embedding_dim
-
     def _apply_warpers(
         self, scores: torch.Tensor, input_ids: torch.Tensor
     ) -> torch.Tensor:
+        """Applies samplers/warpers to the given scores, returning the altered scores.
+
+        Args:
+            scores (torch.Tensor): The original scores.
+            input_ids (torch.Tensor): The input token sequence.
+
+        Returns:
+            torch.Tensor: The altered scores.
+        """
         warpers.update_settings()
 
         for sid in utils.koboldai_vars.sampler_order:
@@ -115,7 +120,7 @@ class model_backend(HFInferenceModel):
                 continue
 
             if warper == warpers.RepetitionPenalty:
-                # Rep pen needs more data than other samplers
+                # Rep pen needs access to input tokens to decide what to penalize
                 scores = warper.torch(scores, input_ids=input_ids)
             else:
                 scores = warper.torch(scores)
@@ -137,6 +142,7 @@ class model_backend(HFInferenceModel):
             gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
         else:
             gen_in = prompt_tokens
+
         if not self.usegpu:
             gen_in = gen_in.to("cpu")
         else:
@@ -161,6 +167,7 @@ class model_backend(HFInferenceModel):
                 use_cache=True,
                 num_return_sequences=batch_count,
             )
+
         logger.debug(
             "torch_raw_generate: run generator {}s".format(time.time() - start_time)
         )