lint

modules/LoRA.py

@@ -66,7 +66,7 @@ def add_lora_autogptq(lora_names):
         logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.")
         return
 
-    if len(lora_names) == 0:        
+    if len(lora_names) == 0:
         reload_model()
 
         shared.lora_names = []
@@ -108,14 +108,14 @@ def add_lora_transformers(lora_names):
     # If any LoRA needs to be removed, start over
     if len(removed_set) > 0:
         # shared.model may no longer be PeftModel
-        if hasattr(shared.model, 'disable_adapter'):  
-            shared.model.disable_adapter()  
+        if hasattr(shared.model, 'disable_adapter'):
+            shared.model.disable_adapter()
             shared.model = shared.model.base_model.model
 
     if len(lora_names) > 0:
         params = {}
         if not shared.args.cpu:
-            if shared.args.load_in_4bit or shared.args.load_in_8bit: 
+            if shared.args.load_in_4bit or shared.args.load_in_8bit:
                 params['peft_type'] = shared.model.dtype
             else:
                 params['dtype'] = shared.model.dtype

modules/loaders.py

@@ -54,14 +54,14 @@ loaders_and_params = {
         'trust_remote_code',
         'transformers_info'
     ],
-    'ExLlama' : [
+    'ExLlama': [
         'gpu_split',
         'max_seq_len',
         'compress_pos_emb',
         'alpha_value',
         'exllama_info',
     ],
-    'ExLlama_HF' : [
+    'ExLlama_HF': [
         'gpu_split',
         'max_seq_len',
         'compress_pos_emb',

modules/models.py

@@ -106,11 +106,11 @@ def load_tokenizer(model_name, model):
                 use_fast=False
             )
         except ValueError:
-             tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer = AutoTokenizer.from_pretrained(
                 path_to_model,
                 trust_remote_code=shared.args.trust_remote_code,
                 use_fast=True
-            )           
+            )
 
     if tokenizer.__class__.__name__ == 'LlamaTokenizer':
         pairs = [

modules/sampler_hijack.py

@@ -126,6 +126,7 @@ class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
     '''
     Copied from the transformers library
     '''
+
     def __init__(self, penalty: float, _range: int):
         if not isinstance(penalty, float) or not (penalty > 0):
             raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")

modules/shared.py

@@ -181,7 +181,7 @@ parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authent
 # API
 parser.add_argument('--api', action='store_true', help='Enable the API extension.')
 parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.')
-parser.add_argument('--api-streaming-port', type=int,  default=5005, help='The listening port for the streaming API.')
+parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.')
 parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.')
 
 # Multimodal

modules/utils.py

@@ -116,7 +116,7 @@ def get_available_loras():
 def get_datasets(path: str, ext: str):
     # include subdirectories for raw txt files to allow training from a subdirectory of txt files
     if ext == "txt":
-        return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt'))+list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
+        return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt')) + list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
 
     return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys)