Create model_3_of_10.safetensors

model_3_of_10.safetensors

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+import torch
+from safetensors.torch import load_file, save_file
+import logging
+import asyncio
+
+# Logging setup
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+
+# Define model checkpoint path
+MODEL_CHECKPOINT = "model-3-of-10.safetensors"
+
+# Detect GPU availability
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model with efficient memory management
+async def load_model(filepath: str) -> dict:
+    """Asynchronously loads a model from a safetensors file."""
+    try:
+        logging.info(f"Loading model from {filepath} on {DEVICE}...")
+        model_data = load_file(filepath, device=DEVICE)
+        logging.info(f"Model {filepath} successfully loaded.")
+        return model_data
+    except Exception as e:
+        logging.error(f"Error loading model: {str(e)}")
+        raise RuntimeError(f"Error loading model: {str(e)}")
+
+# Save model with optimized storage format
+async def save_model(filepath: str, model_tensors: dict):
+    """Asynchronously saves a model to a safetensors file."""
+    try:
+        logging.info(f"Saving model to {filepath}...")
+        save_file(model_tensors, filepath)
+        logging.info(f"Model saved at {filepath}")
+    except Exception as e:
+        logging.error(f"Error saving model: {str(e)}")
+        raise RuntimeError(f"Error saving model: {str(e)}")
+
+# Dynamically generate layers for efficient scaling
+def initialize_model(layers: list = [4096, 8192, 16384], dtype: torch.dtype = torch.float16) -> dict:
+    """Initializes a model with random tensors for each layer."""
+    model_tensors = {}
+    for i, size in enumerate(layers):
+        layer_name = f"layer_{i+1}"
+        logging.info(f"Initializing {layer_name} with size {size}x{size} on {DEVICE}...")
+        model_tensors[layer_name] = torch.randn(size, size, dtype=dtype, device=DEVICE)
+    
+    torch.cuda.empty_cache()  # Free unused memory
+    logging.info("Model initialization completed.")
+    return model_tensors
+
+# Main execution
+async def main():
+    model_data = initialize_model()
+
+    # Save the model for deployment
+    await save_model(MODEL_CHECKPOINT, model_data)
+
+    # Load the model for verification
+    loaded_model_data = await load_model(MODEL_CHECKPOINT)
+
+    # Verify loaded tensors match saved tensors
+    for key in model_data:
+        if not torch.allclose(model_data[key], loaded_model_data[key], atol=1e-5):
+            logging.warning(f"Tensor mismatch in {key}!")
+        else:
+            logging.info(f"Tensor {key} verified successfully.")
+
+# Run asynchronously
+asyncio.run(main())