---
license: mit
tags:
- graph
- few-shot
- meta-learning
- graph-foundation-model
- node-classification
- link-prediction
- graph-classification
library_name: mochi
---

# Mochi / Mochi++

Pretrained checkpoints for **Mochi** and **Mochi++** — a meta-learned few-shot
graph foundation model that unifies node classification, link prediction, and
graph classification under a single differentiable-ridge readout.

Source code: https://github.com/joaopedromattos/mochi

## Contents

| File                                 | Variant   | Seed |
|--------------------------------------|-----------|------|
| `checkpoints/mochi++_s0.pt`          | Mochi++   | 0    |
| `checkpoints/mochi++_s1.pt`          | Mochi++   | 1    |
| `checkpoints/mochi++_s2.pt`          | Mochi++   | 2    |

All checkpoints use the paper-default configuration (latdim=512, gnn_layer=3,
niter=2, ridge_lambda=10.0), trained on the 15-dataset `link1` LP group plus
NC={citeseer, pubmed, physics, computers} and GC={DD, ENZYMES, REDDIT-MULTI-5K}
for 12 991 steps.

## Quickstart

```python
from mochi import Mochi, default_params, load_pretrained

model = Mochi(**default_params)
load_pretrained(model, seed=2)   # downloads from this repo and loads weights
```

Or via ``huggingface_hub`` directly:

```python
from huggingface_hub import hf_hub_download
import torch
from mochi import Mochi, default_params

path = hf_hub_download(repo_id="jrm28/mochi",
                       filename="checkpoints/mochi++_s2.pt")
model = Mochi(**default_params)
model.load_state_dict(torch.load(path, map_location="cpu"))
```

## Citation

If you use these weights, please cite the Mochi paper.