Update README.md

README.md

@@ -28,7 +28,56 @@ More information needed
 
 ## How to use
 
-More information needed
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+import torch
+from PIL import Image
+import requests
+
+# Check device availability
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Load model configurations
+config = PeftConfig.from_pretrained("firqaaa/vsft-llava-1.5-7b-hf-liveness-trl")
+processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+base_model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf",
+                                                               torch_dtype=torch.float16,
+                                                               low_cpu_mem_usage=True,
+                                                               device_map="auto",
+                                                               load_in_4bit=True,
+                                                               attn_implementation="flash_attention_2")
+model = PeftModel.from_pretrained(base_model, "firqaaa/vsft-llava-1.5-7b-hf-liveness-trl")
+model.to(device)
+
+# url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+# image = Image.open(requests.get(url, stream=True).raw)
+
+image_path = "/home/firqaaa/Python/Retired-Yann-LeCun/silicone_frames/5/frame7.jpg"
+image = Image.open(image_path)
+
+prompt = """USER: <image>\nI ask you to be an liveness image annotator expert to determine if an image "Real" or "Spoof". 
+If an image is a "Spoof" define what kind of attack, is it spoofing attack that used Print(flat), Replay(monitor, laptop), or Mask(paper, crop-paper, silicone)?
+If an image is a "Real" or "Normal" return "No Attack". 
+Whether if an image is "Real" or "Spoof" give an explanation to this.
+Return your response using following format :
+
+Real/Spoof : 
+Attack Type :
+Explanation :\nASSISTANT:"""
+
+# Prepare inputs and move to device
+inputs = processor(prompt, images=image, return_tensors="pt").to(device)
+
+# Generate output
+output = model.generate(**inputs, max_new_tokens=300)
+
+print("Response :")
+# Decode and print the output
+decoded_output = processor.decode(output[0], skip_special_tokens=True).split("ASSISTANT:")[-1].strip()
+print(decoded_output)
+```
 
 ## Intended uses & limitations