---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
datasets:
- unsloth/Radiology_mini
library_name: transformers
---

# Uploaded finetuned  model

- **Developed by:** Haq Nawaz Malik
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit

# Documentation: Hnm_Llama3.2_(11B)-Vision_lora_model

## Overview
The **Hnm_Llama3.2_(11B)-Vision_lora_model** is a fine-tuned version of **Llama 3.2 (11B) Vision** with **LoRA-based parameter-efficient fine-tuning (PEFT)**. It specializes in **vision-language tasks**, particularly for **medical image captioning and understanding**.

This model was fine-tuned on a **Tesla T4 (Google Colab)** using **Unsloth**, a framework designed for efficient fine-tuning of large models.

---

## Features
- **Fine-tuned on Radiology Images**: Trained using the **Radiology_mini** dataset.
- **Supports Image Captioning**: Can describe medical images.
- **4-bit Quantization (QLoRA)**: Memory efficient, runs on consumer GPUs.
- **LoRA-based PEFT**: Trains only **1% of parameters**, significantly reducing computational cost.
- **Multi-modal Capabilities**: Works with both **text and image** inputs.
- **Supports both Vision and Language fine-tuning**.

---

## Model Details
- **Base Model**: `unsloth/Llama-3.2-11B-Vision-Instruct`
- **Fine-tuning Method**: LoRA + 4-bit Quantization (QLoRA)
- **Dataset**: `unsloth/Radiology_mini`
- **Framework**: Unsloth + Hugging Face Transformers
- **Training Environment**: Google Colab (Tesla T4 GPU)

---



### 2. Load the Model
```python
from unsloth import FastVisionModel

model, tokenizer = FastVisionModel.from_pretrained(
    "Hnm_Llama3.2_(11B)-Vision_lora_model",
    load_in_4bit=True  # Set to False for full precision
)
```

---

## Usage
### **1. Image Captioning Example**
```python
import torch
from transformers import TextStreamer

FastVisionModel.for_inference(model)  # Enable inference mode

# Load an image from dataset
dataset = load_dataset("unsloth/Radiology_mini", split="train")
image = dataset[0]["image"]
instruction = "Describe this medical image accurately."

messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": instruction}
    ]}
]

input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
inputs = tokenizer(
    image,
    input_text,
    add_special_tokens=False,
    return_tensors="pt"
).to("cuda")

text_streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=128,
                   use_cache=True, temperature=1.5, min_p=0.1)
```



## Notes
- This model is optimized for vision-language tasks in the medical field but can be adapted for other applications.
- Uses **LoRA adapters**, meaning you can fine-tune it efficiently with very few GPU resources.
- Supports **Hugging Face Model Hub** for deployment and sharing.

---

## Citation
If you use this model, please cite:
```
@misc{Hnm_Llama3.2_11B_Vision,
  author = {Haq Nawaz Malik},
  title = {Fine-tuned Llama 3.2 (11B) Vision Model},
  year = {2025},
  url = {https://huggingface.co/Omarrran/Hnm_Llama3_2_Vision_lora_model}
}
```

---

## Contact
For any questions or support, reach out via:
- **GitHub**: [view](https://github.com/Haq-Nawaz-Malik)
- **Hugging Face**: [view](https://huggingface.co/Omarrran)