python-gfm 0.1.13

An AI copilot for graph data and models (Under active development).

Installation · Supported Baselines · Configs (HF download) · Experiment workflow · Documentation

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pygfm is a unified Python toolkit for Graph Foundation Model (GFM) research. It integrates 17 state-of-the-art baseline methods under a single, pip-installable package with shared utilities, standardized interfaces, and fully reproducible experiment pipelines.

Developed by Beihang University · School of Computer Science and Engineering · ACT Lab · MAGIC GROUP.

Framework Overview

PyGFM is organized into four stacked layers — Graph Data Abstraction → Alignment & Fusion Bridge → Representation Backbones → Task Heads & Orchestration — with a unified CLI, model recipes, and an auto-experiment tracker sitting on top.

Highlights

• One package, 17 baselines — prompt-based GFMs, structure-aware models, LLM-integrated approaches, and retrieval-augmented methods all available via a single pip install.
• Reproducible pipelines — every baseline ships with YAML-driven experiment configs, training scripts, and evaluation helpers.
• Shared backbone library — common GNN encoders, loss functions, and data utilities are factored out and reused across all baselines, reducing code duplication.
• CLI-first design — launch pre-training, fine-tuning, and evaluation jobs directly from the command line without writing any boilerplate.
• LLM-ready — first-class support for LLM-integrated GFMs (GraphGPT, GraphText, LLaGA, OneForAll) with HuggingFace-compatible YAML configs.

Installation

CUDA (recommended)

Default (fresh env): torch + light together — PyTorch wheel index + PyPI + PyG find-links:

pip install "python-gfm[torch,light]" --index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://pypi.org/simple -f https://data.pyg.org/whl/torch-2.8.0+cu128.html

If CUDA PyTorch / PyG is already in the env — install [light] from PyPI only:

pip install "python-gfm[light]"

LLM-integrated GFMs — after [torch] and [light] are in place:

pip install "python-gfm[llm]"

CPU: --index-url https://download.pytorch.org/whl/cpu and -f https://data.pyg.org/whl/torch-2.8.0+cpu.html.

Extras overview

Extra	Contents (short)
torch	PyTorch Geometric stack, graph libs, sklearn helpers
light	NumPy/Pandas stack, Transformers, Hydra, APIs, Gradio, W&B, SwanLab
llm	PEFT, bitsandbytes, datasets, fschat, Ray, Vertex, DeepSpeed

Optional dev extra

pip install "python-gfm[dev]" adds pytest and ruff for testing and linting.

Package layout (installed wheel)

pygfm/
├── baseline_models/   # GFM baseline implementations
├── public/            # Shared utilities, losses, backbone encoders
├── private/           # Core encoders and internal helpers
└── cli/               # Console entry points

Supported Baselines

Category	Methods
Prompt-based GFM	MDGPT, SAMGPT, MDGFM, GraphPrompt, HGPrompt, MultiGPrompt, GCoT
Structure-aware GFM	SA2GFM, Bridge, GraphKeeper, GraphMore, Graver
LLM-integrated GFM	GraphGPT, GraphText, LLaGA, OneForAll
Retrieval-augmented GFM	RAG-GFM

Reproducing baselines (config download)

Published YAML configs and toolbox assets live in a Hugging Face dataset. With python-gfm installed (stdlib only; no extra deps for this step), run:

python -m pygfm.cli.download --repo aboutime233/gtb --path gfmtoolbox_docs

Outputs go under --outdir (default: downloads/). Command-line options for the downloader (repo, revision, path, output directory, etc.) are described in the official documentation on the project homepage.

Experiment workflow

Typical end-to-end flow (YAML names and paths are examples — point -c at the configs you downloaded or arranged for your baseline):

# Download config files, or manually fetch them from the Hugging Face dataset:
# https://huggingface.co/datasets/aboutime233/gtb
python -m pygfm.cli.download

# Configure datasets and other settings following each baseline’s official documentation on the project site.

# Step 1: Generate few-shot downstream splits
python -m pygfm.cli.run_yaml -c configs/mdgpt/01_split_cora_1shot.yaml
# -> downstream_data/mdgpt/splits.pt

# Step 2: Leave-one-domain pre-training
python -m pygfm.cli.run_yaml -c configs/mdgpt/02_pretrain_cora.yaml
# -> ckpts/mdgpt/preprompt.pth

# Step 3: Downstream fine-tuning & evaluation
python -m pygfm.cli.run_yaml -c configs/mdgpt/03_finetune_cora_1shot.yaml
# -> Cora 1-shot node classification accuracy (and other logged outputs)

The same YAML driver is available as pygfm / gfm (see Console Commands): pygfm -c configs/mdgpt/02_pretrain_cora.yaml.

Console Commands

Command	Description
python -m pygfm.cli.download	Fetch baseline / toolbox YAML and assets from Hugging Face (above)
python -m pygfm.cli.run_yaml	Same as pygfm / gfm: run a stage from YAML (-c /path/to/config.yaml) — see Experiment workflow

Configuration

After downloading configs, drive stages with pygfm / gfm or python -m pygfm.cli.run_yaml and -c (see Experiment workflow). For each baseline, read the official documentation on the project homepage (hyperparameters, data roots, optional API keys, etc.); do not commit secrets.

Baseline Documentation

Each baseline’s setup, data layout, and evaluation notes are published in the official documentation on the project homepage. Index of per-method guides:

Baseline	Docs
MDGPT	MDGPT README
SA2GFM	SA2GFM README
SAMGPT	SAMGPT README
MDGFM	MDGFM README
GraphPrompt	GraphPrompt README
HGPrompt	HGPrompt README
MultiGPrompt	MultiGPrompt README
GCoT	GCoT README
Graver	Graver README
GraphMore	GraphMore README
Bridge	Bridge README
GraphKeeper	GraphKeeper README
GraphGPT	GraphGPT README
GraphText	GraphText README
LLaGA	LLaGA README
OneForAll	OneForAll README
RAG-GFM	RAG-GFM README

Requirements

Dependency	Version
Python	≥ 3.12
PyTorch	2.8.0 (CUDA 12.8 recommended)
PyTorch Geometric	≥ 2.3.0
Transformers	≥ 4.36.0
Accelerate	≥ 0.26.0

See pyproject.toml on GitHub for the full dependency specification.

License

This project is licensed under the Apache License 2.0.

Team

MAGIC GROUP — Beihang University, School of Computer Science and Engineering, ACT Lab.

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