Update README.md

README.md

@@ -36,15 +36,18 @@ This model allows for **customization of open-weight VLMs** to produce **persona
 ## Model Usage
 ### Example Code:
 ```python
+
+# The base model is not instruction-tuned and therefore is not suitable for use in a conversational mode.
+
 from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration
 import torch
 
-model_id = "ACIDE/User-VLM-10B-Instruct"
+model_id = "ACIDE/User-VLM-10B-base"
 processor = PaliGemmaProcessor.from_pretrained(model_id)
 model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)
 
-def generate_response(question, image, model, processor):
-    prompt = f"<image> <|im_start|>USER: {question}<|im_end|> ASSISTANT:"
+def generate_description(image, model, processor):
+    prompt = "<image> "
     model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(model.device)
     input_len = model_inputs["input_ids"].shape[-1]
     
@@ -58,9 +61,9 @@ def generate_response(question, image, model, processor):
 from transformers.image_utils import load_image
 url = "https://media.istockphoto.com/id/1282695693/photo/little-boy-sitting-on-chair-at-the-table.jpg"
 image = load_image(url)
-question = "Does Santa Claus exist?"
-answer = generate_response(question, image, model, processor)
-print(answer)
+
+description = generate_description(image, model, processor)
+print(description)
 ```
 
 ## Ethical Considerations & Limitations