dev

app.py

@@ -13,15 +13,7 @@ from peft import get_peft_model, LoraConfig
 
 os.environ['HF_HOME'] = '/data/.huggingface'
 
-lora_config = LoraConfig(
-    r=16,  # Rank of the low-rank adaptation
-    lora_alpha=32,  # Scaling factor
-    lora_dropout=0.1,  # Dropout for LoRA layers
-    bias="none"  # Bias handling
-)
-model = AutoModelForSeq2SeqLM.from_pretrained('google/t5-efficient-tiny', num_labels=2, force_download=True)
-model = get_peft_model(model, lora_config)
-model.gradient_checkpointing_enable()   
+  
 
 
 def fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):
@@ -143,6 +135,15 @@ def predict(text):
 
 @spaces.GPU(duration=120)
 def run_train(dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad):
+    lora_config = LoraConfig(
+        r=16,  # Rank of the low-rank adaptation
+        lora_alpha=32,  # Scaling factor
+        lora_dropout=0.1,  # Dropout for LoRA layers
+        bias="none"  # Bias handling
+    )
+    model = AutoModelForSeq2SeqLM.from_pretrained('google/t5-efficient-tiny', num_labels=2, force_download=True)
+    model = get_peft_model(model, lora_config)
+    model.gradient_checkpointing_enable()     
     result = fine_tune_model(model, dataset_name, hub_id, api_key, num_epochs, batch_size, lr, grad)
     return result
 # Create Gradio interface