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---
library_name: transformers
tags:
- robotics
license: mit
datasets:
- ACIDE/user-vlm-pt
language:
- en
base_model:
- google/paligemma2-10b-ft-docci-448
pipeline_tag: image-text-to-text
---
# User-VLM 360°
## Overview
**User-VLM 360°** is a series of personalized Vision-Language Models (VLMs) designed for social human-robot interactions. The model introduces **User-aware tuning**, addressing the **semantic gap** that arises from the misalignment between user queries and the observed scene as captured by a robot's camera. Unlike traditional instruction tuning, which introduces latency and reduces performance, **User-VLM 360°** enables **real-time, robust adaptation** in dynamic robotic environments by inherently aligning cross-modal user representations.
This model allows for **customization of open-weight VLMs** to produce **personalized responses** based on demographic attributes such as age, gender, emotion, and ethnicity while maintaining ethical and safety considerations.
## Training Details
**Base Model:** User-VLM 360° is built on **PaliGemma 2**, which consists of a **SigLIP vision encoder** and **Gemma 2 as the language model**.
### Fine-tuning Process:
1. **Base Model Tuning:**
- Tuned the **MLP layer** to provide **user and scene descriptions** over **1 epoch**.
2. **Instruction Model Tuning:**
- Instruction-tuned the **base model** using **personalized, user-specific Q&A datasets**.
- Used **Sparse Mixture of LoRA Experts (MoLE)** (3 LoRA modules, rank=16, alpha=32, one chosen) and a standalone **LoRA (rank=16, alpha=32)** over **2 epochs**.
3. **Bias Mitigation:**
- Applied **Direct Preference Optimization (DPO)** over **1 epoch** using **LoRA (rank=16, alpha=32)**.
## Model Usage
### Example Code:
```python
# The base model is not instruction-tuned and therefore is not suitable for use in a conversational mode.
from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration
import torch
model_id = "ACIDE/User-VLM-10B-base"
processor = PaliGemmaProcessor.from_pretrained(model_id)
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)
def generate_description(image, model, processor):
prompt = "<image> "
model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(torch.bfloat16).to(model.device)
input_len = model_inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
return decoded
# Example usage
from transformers.image_utils import load_image
url = "https://media.istockphoto.com/id/1282695693/photo/little-boy-sitting-on-chair-at-the-table.jpg"
image = load_image(url)
description = generate_description(image, model, processor)
print(description)
```
## Ethical Considerations & Limitations
- **Research-Only Use:** This model is intended strictly for **research purposes** and should not be deployed in real-world applications without further ethical validation.
- **Demographic Personalization:** While the model can adapt responses based on user attributes, **care must be taken to prevent bias and discrimination**.
- **No Liability:** The authors **do not accept any liability** regarding the use of this model. Responsibility for ethical and appropriate use remains with the users.
## Citation
If you use this model in your research, please cite the following papers:
```bibtex
@article{rahimi2025user,
title={User-VLM: LLM Contextualization with Multimodal Pre-trained User Models},
author={Rahimi, Hamed and Abrini, Mouad and Khoramshahi, Mahdi and Chetouani, Mohamed},
year={2025}
}
@article{rahimi2025user,
title={User-VLM 360°: Personalized Vision Language Models with User-aware Tuning for Social Human Robot Interactions},
author={Rahimi, Hamed and Bhaj, Adil, Abrini, Mouad, Khoramshahi, Mahdi, Ghogho, Mounir, and Chetouani, Mohamed},
year={2025}
}
```
## License
This model is licensed under the **MIT License**.
## Contact
For any questions or issues regarding the model, please open an issue on the repository or contact the maintainers directly. |