ACIDE
/

User-VLM-3B-Instruct

@@ -1,199 +1,91 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+- robotics
+license: mit
+datasets:
+- ACIDE/user-vlm-instruct
+language:
+- en
+base_model:
+- ACIDE/User-VLM-3B-base
+pipeline_tag: visual-question-answering
 ---
+# User-VLM 360°
+![Architecture](result-final.pdf)
+## 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**.
+![Deployment on Pepper](pepper2.pdf)
+### 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
+from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration
+import torch
+model_id = "ACIDE/User-VLM-10B-Instruct"
+processor = PaliGemmaProcessor.from_pretrained(model_id)
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to(device)
+def generate_response(question, image, model, processor):
+    prompt = f"<image> <|im_start|>USER: {question}<|im_end|> ASSISTANT:"
+    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)
+question = "Does Santa Claus exist?"
+answer = generate_response(question, image, model, processor)
+print(answer)
+```
+## 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.