f

app.py

@@ -5,8 +5,6 @@ from transformers import AutoTokenizer, TrainingArguments, Trainer, AutoModelFor
 import torch
 import os
 
-# Force CPU-only execution
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
 def train_model(file, hf_token):
     try:
@@ -19,7 +17,7 @@ def train_model(file, hf_token):
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForCausalLM.from_pretrained(
             model_name,
-            low_cpu_mem_usage=True,
+            low_cpu_mem_usage=True,  # Lower memory usage
             torch_dtype=torch.float32  # Ensure compatibility with CPU
         )
         
@@ -30,9 +28,7 @@ def train_model(file, hf_token):
             output_dir="./results",
             per_device_train_batch_size=1,
             num_train_epochs=1,
-            no_cuda=True,  # Disable GPU
-            use_cpu=True,  # Ensure CPU usage
-            fp16=False     # Disable mixed precision
+            no_cuda=True,  # Explicitly disable GPU
         )
         
         trainer = Trainer(
@@ -59,4 +55,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch(debug=True)  # Removed ssr argument
+    demo.launch(debug=True, share=True)  # Enable public link for easier testing