Model: Reformat and clean up

modeling/inference_models/hf_torch.py

@@ -53,6 +53,7 @@ except ModuleNotFoundError as e:
 # scores for each token.
 LOG_SAMPLER_NO_EFFECT = False
 
+
 class HFTorchInferenceModel(HFInferenceModel):
     def __init__(
         self,
@@ -249,9 +250,7 @@ class HFTorchInferenceModel(HFInferenceModel):
                 ret = []
 
                 for alt_phrase in [phrase, f" {phrase}"]:
-                    ret.append(
-                        model_self.tokenizer.encode(alt_phrase)
-                    )
+                    ret.append(model_self.tokenizer.encode(alt_phrase))
 
                 return ret
 
@@ -433,13 +432,6 @@ class HFTorchInferenceModel(HFInferenceModel):
                 *args,
                 **kwargs,
             ):
-                # sampler_order = utils.koboldai_vars.sampler_order[:]
-                # if (
-                #     len(sampler_order) < 7
-                # ):  # Add repetition penalty at beginning if it's not present
-                #     sampler_order = [6] + sampler_order
-                # for k in sampler_order:
-                #     scores = self.__warper_list[k](input_ids, scores, *args, **kwargs)
                 scores = model_self._apply_warpers(scores=scores, input_ids=input_ids)
                 visualize_probabilities(model_self, scores)
                 return scores
@@ -469,7 +461,7 @@ class HFTorchInferenceModel(HFInferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ) -> GenerationResult:
         if not isinstance(prompt_tokens, torch.Tensor):
             gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]