f

app.py

@@ -3,10 +3,10 @@ import pandas as pd
 from datasets import Dataset
 from transformers import AutoTokenizer, TrainingArguments, Trainer, AutoModelForCausalLM
 import torch
-import zerogpu
+import os
 
-# Initialize ZeroGPU
-zerogpu.initialize()
+# Force CPU
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
 def train_model(file, hf_token):
     try:
@@ -19,10 +19,9 @@ def train_model(file, hf_token):
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModelForCausalLM.from_pretrained(
             model_name,
-            device_map=None,  # Force simple device mapping
-            low_cpu_mem_usage=True
+            low_cpu_mem_usage=True,
+            torch_dtype=torch.float32
         )
-        model = model.to('cpu')  # Explicitly move to CPU
         
         # Basic dataset creation
         dataset = Dataset.from_pandas(df)
@@ -32,7 +31,8 @@ def train_model(file, hf_token):
             per_device_train_batch_size=1,
             num_train_epochs=1,
             no_cuda=True,
-            local_rank=-1
+            local_rank=-1,
+            use_cpu=True
         )
         
         trainer = Trainer(
@@ -58,4 +58,4 @@ demo = gr.Interface(
 )
 
 if __name__ == "__main__":
-    demo.launch(debug=True)  # Enable debug mode
+    demo.launch(debug=True)

requirements.txt

@@ -3,5 +3,4 @@ transformers==4.37.2
 torch==2.1.2
 datasets==2.16.1
 pandas==2.2.0
-huggingface-hub==0.27.0
-zerogpu
+huggingface-hub==0.27.0