---
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
---

![imae](./image.webp)

# Finetuned Vision Model: unsloth/llama-3.2-11b-vision-instruct

## Overview

This model is a finetuned version of `unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit`, optimized for vision-based instruction tasks.  
It was trained 2x faster using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library, enabling efficient large model adaptation while maintaining precision and accuracy.

![Unsloth Logo](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)

## Key Features
- **Model Type**: Multimodal LLama-based Vision Instruction Model  
- **License**: Apache-2.0  
- **Base Model**: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit  
- **Developed by**: Daemontatox  
- **Language**: English  

## Training Details
- **Framework**: Hugging Face Transformers + TRL  
- **Optimization**: Unsloth methodology for accelerated finetuning  
- **Quantization**: 4-bit model, enabling deployment on resource-constrained devices  
- **Dataset**: Vision-specific instruction tasks (details to be added by user if public)  

## Performance Metrics
- **Inference Speed**: Optimized for low-latency environments  
- **Accuracy**: Improved on vision-related benchmarks (details TBD based on evaluation)  
- **Model Size**: Lightweight due to quantization  

## Applications
- Vision-based interactive AI  
- Instruction-following tasks with multimodal input  
- Resource-constrained deployment (e.g., edge devices)  

## How to Use
To load and use the model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your_model_repository_name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)

# Example usage
input_text = "Describe the image in detail:"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```