---
base_model: google/paligemma-3b-pt-224
library_name: peft
license: mit
language:
- en
tags:
- vision-language
- multimodal
- fine-tuning
- generative-modeling
---

# Model Card for PaliGemma Fine-Tuned Model

This model is a **fine-tuned version of Google’s PaliGemma-3B**, designed for **Vision-Language tasks**, particularly **image-based question answering** and **multimodal reasoning**. The model has been optimized using **Parameter-Efficient Fine-Tuning (PEFT)** methods, such as **LoRA and QLoRA**, to reduce computational costs while maintaining high performance.

## Model Details

### Model Description

- **Developed by:** [Taha Majlesi]
- **Funded by:** [More Information Needed]
- **Model Type:** Vision-Language Model (VLM)
- **Language(s):** English
- **License:** MIT
- **Finetuned from model:** google/paligemma-3b-pt-224

### Model Sources

- **Repository:** [More Information Needed]
- **Paper (if available):** [More Information Needed]
- **Demo:** [More Information Needed]

## Uses

### Direct Use
- **Visual Question Answering (VQA)**  
- **Multimodal reasoning on image-text pairs**  
- **Image captioning with contextual understanding**  

### Downstream Use
- Custom **fine-tuning** for **domain-specific multimodal datasets**  
- Integration into **AI assistants for visual understanding**  
- Enhancements in **image-text search systems**  

### Out-of-Scope Use
- This model is **not designed** for **pure NLP tasks** without visual inputs.
- The model may **not perform well** on **low-resource languages**.
- **Not intended for real-time inference on edge devices** due to model size constraints.

## Bias, Risks, and Limitations

- **Bias:** The model may reflect biases present in the training data, especially in image-text relationships.
- **Limitations:** Performance may degrade on **unseen, highly abstract, or domain-specific images**.
- **Risks:** Misinterpretation of **ambiguous images** and **hallucination of non-existent details**.

### Recommendations
- Use **dataset-specific fine-tuning** to mitigate biases.
- Evaluate performance on **diverse benchmarks** before deployment.
- Implement **human-in-the-loop validation** in sensitive applications.

## How to Get Started with the Model

To use the fine-tuned model, install the required libraries:

```sh
pip install transformers peft accelerate bitsandbytes