---
base_model:
- meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
datasets:
- unsloth/RLAIF-V-Dataset
pipeline_tag: image-text-to-text
---

# 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

```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
)