Uploaded model

• 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

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
)

Base model