Update README.md

README.md

@@ -1,13 +1,13 @@
----
-library_name: peft
-license: apache-2.0
-base_model: HuggingFaceTB/SmolVLM-Base
-tags:
-- generated_from_trainer
-model-index:
-- name: SmolVLM-Base-vqav2
-  results: []
----
+---
+library_name: peft
+license: apache-2.0
+base_model: HuggingFaceTB/SmolVLM-Base
+tags:
+- generated_from_trainer
+model-index:
+- name: SmolVLM-Base-vqav2
+  results: []
+---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
@@ -20,6 +20,87 @@ This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Base](https://huggi
 
 More information needed
 
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+from peft import PeftModel
+import torch
+from PIL import Image
+
+
+DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu" ### DEVICE = "cuda:0" instead of  DEVICE = "cuda"  it fixes flash attention warning!!
+
+
+model_id = "HuggingFaceTB/SmolVLM-Instruct"  # Base Model
+
+base_model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2" if DEVICE == "cuda" else "eager"
+).to(DEVICE)
+
+print(f"Model is on device: {base_model.device}")
+
+
+# QLoRA adapter
+adapter_path = r"C:\Users\.....\SmolVLM-Base-vqav2\checkpoint-670"
+model = PeftModel.from_pretrained(base_model, adapter_path)
+
+model = model.to(DEVICE)  # Check the model device #####################################
+
+# Load the processor
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Functıon for load images from local
+def load_image_from_file(file_path):
+    try:
+        image = Image.open(file_path)
+        return image
+    except Exception as e:
+        print(f"Error loading image: {e}")
+        return None
+
+
+image1_path = "C:/Users/.../IMG_4.jpg"
+image2_path = "C:/Users/.../IMG_35.jpg"
+
+# Load images
+image1 = load_image_from_file(image1_path)
+image2 = load_image_from_file(image2_path)
+
+# Check the images
+if image1 and image2:
+
+    # Create message type
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image"},
+                {"type": "image"},
+                {"type": "text", "text": "Can you describe and compare the two images?"}
+            ]
+        },
+    ]
+    
+    # Prepare the Prompt
+
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=[image1, image2], return_tensors="pt")
+    inputs = inputs.to(DEVICE)
+    
+    # Run the model
+    generated_ids = model.generate(**inputs, max_new_tokens=500)
+    generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+    # Print the result
+    print(generated_texts[0])  # Çıktı
+
+else:
+    print("Images can not be loaded")
+
+
+```
+
 ## Intended uses & limitations
 
 More information needed