Add model card for HAC

This PR adds a comprehensive model card for the HAC model. It includes:
- A link to the paper: [HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA](https://huggingface.co/papers/2604.23665).
- A link to the official GitHub repository.
- Relevant metadata including the `image-text-to-text` pipeline tag.
- Instructions for environment setup and evaluation as found in the repository.

README.md

@@ -0,0 +1,51 @@
+---
+pipeline_tag: image-text-to-text
+---
+
+# HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA
+
+HAC (Hyperbolic Adaptation of CLIP) is a parameter-efficient framework that enables pretrained CLIP models to transition into hyperbolic space via lightweight fine-tuning. This approach captures hierarchical structures more effectively than traditional Euclidean embeddings, specifically for tasks like zero-shot Visual Question Answering (VQA).
+
+This repository contains the weights for **HAC-B w/ LoRA**.
+
+- **Paper:** [HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA](https://huggingface.co/papers/2604.23665)
+- **GitHub Repository:** [https://github.com/fdibiton/HAC](https://github.com/fdibiton/HAC)
+
+## Environment Setup
+
+Create and configure the environment using Conda:
+
+```bash
+git clone https://github.com/fdibiton/HAC.git
+cd HAC
+conda create -n hac python=3.9 --yes
+conda activate hac
+
+# Install dependencies
+python -m pip install --pre timm
+python -m pip install -r requirements.txt
+```
+
+## Evaluation
+
+To run zero-shot VQA evaluation with the HAC-B w/ LoRA model, place the `hac_vit_b_lora.pth` file in the `./checkpoints` directory and run:
+
+```bash
+python scripts/evaluate.py \
+    --config configs/eval_vqa_all_categories.py \
+    --train-config configs/train_hac_vit_b_lora.py \
+    --checkpoint-path checkpoints/hac_vit_b_lora.pth
+```
+
+> **Note:** The VQA evaluation datasets need to be downloaded and arranged beforehand. Please refer to the instructions in the [GitHub repository](https://github.com/fdibiton/HAC) for details.
+
+## Citation
+
+```bibtex
+@inproceedings{dibiton2026hac,
+    title={HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA},
+    author={Dibitonto, Francesco and Beyan, Cigdem and Murino, Vittorio},
+    booktitle={International Conference on Pattern Recognition (ICPR)},
+    year={2026}
+}
+```