removed low_cpu_memory_usage

interfaces/cap.py

@@ -86,7 +86,7 @@ def build_huggingface_path(language: str, domain: str):
 #@spaces.GPU
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN).to(device)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN).to(device)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/cap_media_demo.py

@@ -35,7 +35,7 @@ def build_huggingface_path(language: str, domain: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/cap_minor.py

@@ -67,7 +67,7 @@ def build_huggingface_path(language: str, domain: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/cap_minor_media.py

@@ -150,7 +150,7 @@ def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
 
 def predict_flat(text, model_id, tokenizer_id, HF_TOKEN=None):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN).to(device)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN).to(device)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/emotion.py

@@ -27,7 +27,7 @@ def build_huggingface_path(language: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
     model.to(device)
 

interfaces/emotion9.py

@@ -26,7 +26,7 @@ def build_huggingface_path(language: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/illframes.py

@@ -59,7 +59,7 @@ def build_huggingface_path(domain: str):
 def predict(text, model_id, tokenizer_id, label_names):
     device = torch.device("cpu")
     try:
-        model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+        model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     except:
         disk_space = get_disk_space('/data/')
         print("Disk Space Error:")
@@ -67,7 +67,7 @@ def predict(text, model_id, tokenizer_id, label_names):
             print(f"{key}: {value}")
 
         shutil.rmtree("/data")
-        model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN, force_download=True)
+        model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN, force_download=True)
         
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 

interfaces/manifesto.py

@@ -26,7 +26,7 @@ def build_huggingface_path(language: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     inputs = tokenizer(text,

interfaces/ontolisst.py

@@ -44,7 +44,7 @@ def build_huggingface_path(language: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
 
     # --- DEBUG ---

interfaces/sentiment.py

@@ -30,7 +30,7 @@ def build_huggingface_path(language: str):
 
 def predict(text, model_id, tokenizer_id):
     device = torch.device("cpu")
-    model = AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+    model = AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
     model.to(device)
 

utils.py

@@ -54,7 +54,7 @@ tokenizers = ["xlm-roberta-large"]
 
 def download_hf_models():
     for model_id in models:
-        AutoModelForSequenceClassification.from_pretrained(model_id, low_cpu_mem_usage=True, device_map="auto", token=HF_TOKEN)
+        AutoModelForSequenceClassification.from_pretrained(model_id, device_map="auto", token=HF_TOKEN)
     for tokenizer_id in tokenizers:
         AutoTokenizer.from_pretrained(tokenizer_id)