Update README.md

README.md

@@ -20,3 +20,66 @@ pipeline_tag: image-text-to-text
 - **Developed by:** saishshinde15
 - **License:** apache-2.0
 - **Finetuned from model :**llama-3.2-11b-vision-instruct
+
+## How to use this model
+- use unsolth for faster model download and faster inference speed . Users can also use transformers module from hugging face
+
+```python
+from unsloth import FastVisionModel
+from PIL import Image
+import requests
+from transformers import TextStreamer
+
+# Load the model and tokenizer
+model, tokenizer = FastVisionModel.from_pretrained(
+    model_name="saishshinde15/VisionAI",  # YOUR MODEL YOU USED FOR TRAINING
+    load_in_4bit=False  # Set to False for 16bit LoRA
+)
+
+# Enable the model for inference
+FastVisionModel.for_inference(model)
+
+# Load the image from URL
+url = 'your image url'
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Define the instruction and user query
+instruction = (
+    "You are an expert in answering questions related to the image provided: "
+    "Answer to the questions given by the user accurately by referring to the image."
+)
+query = "What is this image about?"
+
+# Create the chat message structure
+messages = [
+    {"role": "user", "content": [
+        {"type": "image"},
+        {"type": "text", "text": instruction},
+        {"type": "text", "text": query}
+    ]}
+]
+
+# Generate input text using the tokenizer's chat template
+input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+
+# Tokenize the inputs
+inputs = tokenizer(
+    image,
+    input_text,
+    add_special_tokens=False,
+    return_tensors="pt",
+).to("cuda")
+
+# Initialize the text streamer
+text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+
+# Generate the response
+_ = model.generate(
+    **inputs,
+    streamer=text_streamer,
+    max_new_tokens=128,
+    use_cache=True,
+    temperature=1.5,
+    min_p=0.1
+)
+