Disable spaces

app.py

@@ -3,7 +3,7 @@ from gradio_client import Client
 from gradio_client.exceptions import AppError
 import frontmatter
 import os
-import spaces
+#import spaces
 import torch
 import logging
 from transformers import AutoTokenizer, AutoModelForCausalLM
@@ -51,43 +51,18 @@ print(f"Model dtype: {next(vardecoder_model.parameters()).dtype}")
 print(f"Model is meta: {next(vardecoder_model.parameters()).is_meta}")
 print(f"Model parameters: {sum(p.numel() for p in vardecoder_model.parameters() if p.requires_grad):,}")
 
-# Check if parameters actually have data
-sample_param = next(vardecoder_model.parameters())
-print(f"Sample parameter shape: {sample_param.shape}")
-print(f"Sample parameter requires_grad: {sample_param.requires_grad}")
-print(f"Sample parameter data type: {type(sample_param.data)}")
-#print(f"Sample parameter storage: {sample_param.storage()}")
-
 # Check memory after first model
 print(f"GPU memory after vardecoder:")
 print(f"Allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
 print(f"Reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")
 
-# Try manual memory allocation test
-test_tensor = torch.randn(1000, 1000, dtype=torch.bfloat16, device='cuda')
-print(f"GPU memory after test tensor:")
-print(f"Allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
-print(f"Reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")
-del test_tensor
-torch.cuda.empty_cache()
-
-# Add more detailed debugging before loading the second model
-try:
-    logger.info("Loading fielddecoder model...")
-    print(f"CUDA available: {torch.cuda.is_available()}")
-    print(f"CUDA device count: {torch.cuda.device_count()}")
-    print(f"Current device: {torch.cuda.current_device()}")
-    print(f"Device name: {torch.cuda.get_device_name()}")
-    
-    fielddecoder_model = AutoModelForCausalLM.from_pretrained(
-        "ejschwartz/resym-fielddecoder", 
-        torch_dtype=torch.bfloat16,
-    )
-    logger.info("Successfully loaded fielddecoder model")
-except Exception as e:
-    logger.error(f"Error loading fielddecoder model: {e}")
-    import traceback
-    logger.error(traceback.format_exc())
+logger.info("Loading fielddecoder model...")
+
+fielddecoder_model = AutoModelForCausalLM.from_pretrained(
+    "ejschwartz/resym-fielddecoder", 
+    torch_dtype=torch.bfloat16,
+)
+logger.info("Successfully loaded fielddecoder model")
 
 make_gradio_client = lambda: Client("https://ejschwartz-resym-field-helper.hf.space/")