🪝 vLLM.hook

A modular plugin library for vLLM.

📄 [Preprint] vLLM Hook v0: A Plug-in for Programming Model Internals on vLLM

vLLM.hook is a plugin library designed to let developers and researchers inspect, analyze, and steer the internal operations of large language models running under the vLLM inference engine.

This includes dynamic analysis of:

• attention patterns
• attention heads
• activations
• custom intervention behaviors

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🚀 Features

• Model-agnostic plugin system for vLLM engines
• Extensible worker/analyzer abstraction
  • Easy to define new hooks, analyzers, and behaviors
• Introspection of model internals
• Interventions (activation steering, attention control, etc.)
• Example applications:
  • Safety guardrails
  • Reranking
  • Enhanced instruction following

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📦 Installation

1. Clone the repository

git clone https://github.com/IBM/vLLM-Hook.git
cd vLLm.hook

2. (Optional) Create an environment

conda create -n vllm_hook_env
conda activate vllm_hook_env

3. Install the plugin and dependencies

pip install -r requirement.txt
pip install -e vllm_hook_plugins

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📕 Notebook Setup

If you plan to use the notebooks under notebooks/, you may need to register your environment as a Jupyter kernel:

pip install ipykernel
python -m ipykernel install --user --name vllm_hook_env --display-name "vllm_hook_env"

Then inside Jupyter Lab:

Kernel → Change Kernel → vllm_hook_env

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👉 Usage Examples (Notebook / CLI)

You can also use the included examples/ and/or notebooks/ directories to explore different functionalities.

1. Attention Tracker (In-Model Safety Guardrail)

Notebook 📓: notebooks/demo_attntracker.ipynb
CLI 🧰 :

python examples/demo_attntracker.py

2. Core Reranker (In-Model Relevance Ranking)

Notebook 📓: notebooks/demo_corer.ipynb
CLI 🧰 :

python examples/demo_actsteer.py

3. Activation Steering (Enhanced instruction following via activation steering)

Notebook 📓: notebooks/demo_actsteer.ipynb
CLI 🧰 :

python examples/demo_corer.py

You can customize model configurations in the model_configs/ folder, e.g.:

model_configs/<example_name>/<model_name>.json

For example model_configs/attention_tracker/granite-3.1-8b-instruct.json.

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🏠 Plugin Architecture

The main package is structured as follows:

vllm_hook_plugins/
├── analyzers/
│   ├── attention_tracker_analyzer.py
│   ├── core_reranker_analyzer.py
├── workers/
│   ├── probe_hookqk_worker.py
│   ├── steer_activation_worker.py
├── hook_llm.py
├── registry.py

Each component handles a key stage of the plugin lifecycle:

• Registry — manages available hooks and extensions
• Workers — define execution behavior and orchestration
• Analyzers — optionally conduct analysis based on the saved statistics

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🤝 Contributing

We welcome contributions from the community!

To contribute:

1. Fork this repository
2. Create a branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to your branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Guidelines:

• Users are encouraged to define new worker/analyzer, but should not touch hook_llm
• Include examples and documentation for new features
• The registry will be updated by the admin

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IBM ❤️ Open Source AI

vLLM.hook has been started by IBM Research.

• Built for the vLLM ecosystem
• Inspired by community efforts to make LLMs more interpretable and controllable