Update app.py

app.py

@@ -4,7 +4,6 @@ import transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 from PIL import Image
 import warnings
-import os
 
 # disable some warnings
 transformers.logging.set_verbosity_error()
@@ -16,7 +15,6 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"Using device: {device}")
 
 model_name = 'cognitivecomputations/dolphin-vision-72b'
-model_path = '/data/dolphin-vision-72b'
 
 # Configure 8-bit quantization
 quantization_config = BitsAndBytesConfig(
@@ -25,32 +23,18 @@ quantization_config = BitsAndBytesConfig(
     llm_int8_has_fp16_weight=False
 )
 
-# Check if the model is already downloaded
-if not os.path.exists(model_path):
-    print(f"Downloading model to {model_path}")
-    # create model and save it to the specified path
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        quantization_config=quantization_config,
-        device_map="auto",
-        trust_remote_code=True
-    )
-    model.save_pretrained(model_path)
-    
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-    tokenizer.save_pretrained(model_path)
-else:
-    print(f"Loading model from {model_path}")
-
-# Load the model from the saved path
+# create model and load it to the specified device with 8-bit quantization
 model = AutoModelForCausalLM.from_pretrained(
-    model_path,
+    model_name,
     quantization_config=quantization_config,
-    device_map="auto",
+    device_map="auto",  # This will automatically use the GPU if available
     trust_remote_code=True
 )
 
-tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
 
 def inference(prompt, image):
     messages = [