Phi-4 Information Extraction Demo

Interactive CLI demo for extracting entities, relations, and knowledge graph triples from business text using a fine-tuned Phi-4 model (15B parameters).

Features

• Named Entity Recognition (NER): Extract entities from text based on custom schemas
• Relation Extraction (RE): Identify relationships between entities
• Triple Generation: Output structured knowledge graph triples
• Completely Local: No external API calls, runs 100% on your machine
• Schema-Driven IE: Flexible extraction using interchangeable JSON schemas
• Domain Switching: Same text, different semantic extractions by changing schemas

Prerequisites

• macOS (or Linux/Windows with Ollama support)
• Python 3.8+
• 16GB+ RAM recommended (model is 8.3GB quantized)

Installation

1. Install Ollama

# macOS
brew install ollama
brew services start ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download from https://ollama.com/download

2. Pull the Model

This downloads the quantized model (8.3GB):

ollama pull jckalo/phi4-ie

3. Install Python Dependency

pip install requests

4. Clone This Repository

git clone https://github.com/JanKalo/phi4-ie-demo.git
cd phi4-ie-demo

Usage

Run the interactive demo with a schema:

# Business domain extraction
python interactive_ie_demo.py --schema schema_business.json

# Academic domain extraction
python interactive_ie_demo.py --schema schema_academic.json

Example: Schema Switching Demo

The same text extracts different information depending on the schema.

Input Text:

Dr. Sarah Johnson at Stanford University published a paper on AI funded by the National Science Foundation.
###

With Business Schema:

python interactive_ie_demo.py --schema schema_business.json

Entities Extracted:

• Dr. Sarah Johnson → person
• Stanford University → institution
• Artificial intelligence → topic
• National Science Foundation → funding_source

Relations & Triples:

• (AI, funded_by, National Science Foundation)
• (Stanford University, has_member, Dr. Sarah Johnson)
• (AI, published_in, "Journal of Artificial Intelligence")

With Academic Schema:

python interactive_ie_demo.py --schema schema_academic.json

Entities Extracted:

• Sarah Johnson (scientist) → researcher
• Stanford University → institution
• National Science Foundation → funding_agency
• Artificial intelligence → research_field

Relations & Triples:

• (Dr. Sarah Johnson, affiliated_with, Stanford University)
• (AI, funded_by, National Science Foundation)
• (Dr. Sarah Johnson, published_in, "Artificial Intelligence")

Key Difference: Business schema sees organizational membership (has_member), while academic schema sees research affiliation (affiliated_with). Same text, different semantic interpretation!

Type ### on a new line when done, or quit to exit.

Performance Notes

⚠️ This is a 15B parameter model running on CPU - inference will be slow:

• First query: 30-60 seconds (model loading into RAM)
• Subsequent queries: 10-30 seconds per extraction
• RAM usage: ~10-12GB during inference
• Best performance: Machines with high-end CPUs or GPUs

Unloading the Model

The model stays loaded in RAM for faster subsequent queries. To free up memory:

# Stop Ollama completely
brew services stop ollama

# Or restart it
brew services restart ollama

Performance Tips

For production use, consider:

• Running on a machine with GPU support
• Using a smaller quantized version (contact maintainer)
• Deploying to a dedicated inference server

Included Schemas

Business Schema (schema_business.json)

Extracts corporate and commercial information:

• 40 entity types: company, product, service, industry, brand, financial metrics, people, locations, events, etc.
• 42 relations: acquired, founded_by, owns_brand, has_CEO, manufactures, had_revenue_of, etc.

Academic Schema (schema_academic.json)

Extracts research and scholarly information:

• 44 entity types: researcher, professor, university, paper, journal, conference, grant, funding_agency, etc.
• 46 relations: affiliated_with, authored, published_in, cited, funded_by, collaborated_with, etc.

Custom Schemas

You can create your own schema JSON files! Format:

{
  "name": "Your Schema Name",
  "description": "What this schema extracts",
  "entity_types": ["type1", "type2", ...],
  "relations": ["relation1", "relation2", ...]
}

Project Structure

phi4-ie-demo/
├── README.md                    # This file
├── interactive_ie_demo.py       # Main demo script
├── schema_business.json         # Business domain schema
├── schema_academic.json         # Academic domain schema
├── Modelfile                    # (Reference) Ollama model config
└── convert_and_quantize.sh      # (Advanced) Convert HF model to GGUF

Troubleshooting

"Model not found" error

Make sure you've pulled the model:

ollama pull jckalo/phi4-ie

"Cannot connect to Ollama" error

Start the Ollama service:

brew services start ollama

Laptop freezing or very slow

The 15B model is resource-intensive. Try:

• Closing other applications
• Running on a more powerful machine
• Using a smaller model variant

Advanced: Model Conversion

Want to convert a different HuggingFace model to GGUF format? See convert_and_quantize.sh for the conversion pipeline.

This repo includes the conversion script for reference, but you don't need it to run the demo.

Credits

• Base model: Microsoft Phi-4
• Fine-tuned model: FinaPolat/phi4_adaptable_IE
• Quantization: llama.cpp
• Local inference: Ollama
• Model hosting: Ollama Registry

License

Model usage subject to Microsoft Phi-4 license terms.

Citation

If you use this model in your research, please cite:

@misc{phi4-ie-demo,
  author = {Jan Kalo},
  title = {Phi-4 Information Extraction Demo},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/JanKalo/phi4-ie-demo}
}