InstructGLM provides a natural language interface for graph machine learning by leveraging instruction-finetuned Graph Language Models. It allows users to describe graph structures and node features using natural language to solve graph-related problems, targeting researchers and practitioners in graph ML.

How It Works

InstructGLM utilizes a generative large language model (LLM) finetuned with instructions related to graph data. This approach enables the LLM to understand and process natural language descriptions of graph properties and tasks, translating them into actionable graph machine learning operations. The advantage lies in offering a more intuitive and accessible interface for complex graph analysis.

Quick Start & Requirements

• Install via git clone https://github.com/agiresearch/InstructGLM.git.
• Requires preprocessed data (Arxiv, Cora, PubMed) or data preprocessing scripts.
• Needs Llama-7b pretrained checkpoint (provided via Google Drive link).
• Training/Inference command example: bash scripts/train_llama_arxiv.sh 8 (requires 8 GPUs for DDP).
• Official documentation and demo links are not explicitly provided in the README.

Highlighted Details

• Implements InstructGLM, an instruction-finetuned Graph Language Model.
• Enables natural language interface for graph structure and node feature description.
• Supports multi-task, multi-prompt instruction tuning.
• Provides scripts for training and inference.

Maintenance & Community

• The project is associated with agiresearch.
• Acknowledgements list TAPE, GIANT, OGB, P5, OpenP5, and Planetoid.
• No specific community channels (Discord/Slack) or roadmap are mentioned.

Licensing & Compatibility

• The README does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project requires specific pretrained LLM checkpoints and preprocessed datasets, with setup potentially involving significant data handling and GPU resources. Community support and detailed documentation appear limited based on the provided README.