Add model card for GILT (#1)

- Add model card for GILT (a24636c6bf8429f824b1353bdb4c0f4c12e3e4bc)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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+---
+pipeline_tag: graph-ml
+---
+
+# GILT: Graph In-context Learning Transformer
+
+GILT is a novel Graph Foundational Model (GFM) that achieves **LLM-free** and **tuning-free** in-context learning on graphs. Unlike existing approaches that rely on Large Language Models or require costly per-graph tuning, GILT reframes few-shot graph learning as a token-based reasoning problem, enabling direct inference on new tasks without any parameter updates.
+
+- **Paper**: [GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning](https://huggingface.co/papers/2510.04567)
+- **Repository**: [https://github.com/yiming421/inductnode/](https://github.com/yiming421/inductnode/)
+
+## Key Features
+
+- **LLM-Free**: Works directly with numerical features without text dependency.
+- **Tuning-Free**: Adapts to new tasks via in-context learning without gradient updates.
+- **Multi-Task**: Unified framework for node, link, and graph classification.
+- **Efficient**: Orders of magnitude faster than tuning-based or LLM-based methods.
+- **Strong Performance**: State-of-the-art few-shot results across diverse benchmarks.
+
+## Installation
+
+```bash
+# Create conda environment
+conda env create -f env.yml
+conda activate gnn
+```
+
+## Quick Start
+
+### Training
+
+To train GILT from scratch with the default configuration:
+
+```bash
+python train.py
+```
+
+### Few-Shot Evaluation
+
+GILT requires no tuning for new tasks. You can simply provide a few labeled examples at inference time. To evaluate using a pre-trained model:
+
+```bash
+# Download checkpoint (replace with local path if downloaded manually)
+# Multi-task evaluation
+python train.py --use_pretrained_model true --load_checkpoint checkpoints/gilt_model.pt
+```
+
+The model performs in-context learning without any parameter updates, directly inferring from few-shot examples.
+
+## Architecture
+
+GILT consists of two main components:
+1. **Graph-Native Tokenization**: Converts heterogeneous graphs into unified token representations using PCA-based feature alignment and a deep linear GCN encoder.
+2. **In-Context Reasoning**: A Transformer-based module that performs reasoning over contextual tokens using a two-stage attention mechanism and a prototypical prediction head.