Update app.py

app.py

@@ -3,10 +3,6 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
 import gradio as gr
 import os
-from accelerate import Accelerator
-
-# Initialize the Accelerator to manage device placement and offloading
-accelerator = Accelerator()
 
 # Use GPU if available
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -15,23 +11,13 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 base_model_name = "microsoft/phi-2"  # Pull from HF Hub directly
 adapter_path = "Shriti09/Microsoft-Phi-QLora"  # Update with your Hugging Face repo path
 
-# Create an offload directory to store the model parts
-offload_dir = "./offload_dir"  # Set offload directory here
-
-# Ensure the offload directory exists
-os.makedirs(offload_dir, exist_ok=True)
-
 print("🔧 Loading base model...")
 # Using the Accelerator to load the model and dispatch to the correct devices
 base_model = AutoModelForCausalLM.from_pretrained(
     base_model_name,
-    device_map="auto",  # Automatically map the model to available devices
     torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
 )
 
-# Ensure the model is offloaded when necessary
-base_model = accelerator.prepare(base_model)
-
 print("🔧 Loading LoRA adapter...")
 adapter_model = PeftModel.from_pretrained(base_model, adapter_path)