Instructions to use sdfprotocol/sdf-extract with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use sdfprotocol/sdf-extract with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="sdfprotocol/sdf-extract", filename="sdf-extract-SmolLM3-3B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use sdfprotocol/sdf-extract with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sdfprotocol/sdf-extract:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sdfprotocol/sdf-extract:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf sdfprotocol/sdf-extract:Q4_K_M # Run inference directly in the terminal: llama-cli -hf sdfprotocol/sdf-extract:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf sdfprotocol/sdf-extract:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sdfprotocol/sdf-extract:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf sdfprotocol/sdf-extract:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sdfprotocol/sdf-extract:Q4_K_M
Use Docker
docker model run hf.co/sdfprotocol/sdf-extract:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use sdfprotocol/sdf-extract with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sdfprotocol/sdf-extract" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sdfprotocol/sdf-extract", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sdfprotocol/sdf-extract:Q4_K_M
- Ollama
How to use sdfprotocol/sdf-extract with Ollama:
ollama run hf.co/sdfprotocol/sdf-extract:Q4_K_M
- Unsloth Studio new
How to use sdfprotocol/sdf-extract with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sdfprotocol/sdf-extract to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for sdfprotocol/sdf-extract to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for sdfprotocol/sdf-extract to start chatting
- Pi new
How to use sdfprotocol/sdf-extract with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sdfprotocol/sdf-extract:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "sdfprotocol/sdf-extract:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use sdfprotocol/sdf-extract with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf sdfprotocol/sdf-extract:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default sdfprotocol/sdf-extract:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use sdfprotocol/sdf-extract with Docker Model Runner:
docker model run hf.co/sdfprotocol/sdf-extract:Q4_K_M
- Lemonade
How to use sdfprotocol/sdf-extract with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sdfprotocol/sdf-extract:Q4_K_M
Run and chat with the model
lemonade run user.sdf-extract-Q4_K_M
List all available models
lemonade list
File size: 1,843 Bytes
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language: en
license: mit
tags:
- sdf
- extraction
- smollm3
- gguf
- structured-data
- web-content
base_model: HuggingFaceTB/SmolLM3-3B
pipeline_tag: text-generation
---
# SDF Extract
Structured data extractor for the [SDF Protocol](https://sdfprotocol.org). Fine-tuned from SmolLM3-3B using QLoRA.
## Purpose
Extracts structured semantic data from web content: entities, claims, relationships, summaries, and type-specific fields. Takes the type classification from [sdf-classify](https://huggingface.co/pranab2050/sdf-classify) as input to condition extraction on the content type.
## Training
- **Base model**: HuggingFaceTB/SmolLM3-3B
- **Method**: QLoRA (rank 32, alpha 64, dropout 0.05)
- **Training data**: 2,335 extracted web documents
- **Accuracy**: 90% exact extraction match across all field types
## Files
| File | Size | Description |
|------|------|-------------|
| `sdf-extract-SmolLM3-3B-Q4_K_M.gguf` | 1.8 GB | Quantized (Q4_K_M) β recommended for deployment |
| `sdf-extract-SmolLM3-3B-f16.gguf` | 5.8 GB | Full precision (f16) |
| `Modelfile` | β | Ollama import configuration |
## Usage with Ollama
```bash
# Download the Q4_K_M file, then:
ollama create sdf-extract -f Modelfile
```
## Part of SDF Protocol
- **Protocol**: [sdfprotocol.org](https://sdfprotocol.org)
- **Specification**: [github.com/sdfprotocol/sdf](https://github.com/sdfprotocol/sdf)
- **Whitepaper**: [DOI 10.5281/zenodo.18559223](https://doi.org/10.5281/zenodo.18559223)
- **Classifier model**: [pranab2050/sdf-classify](https://huggingface.co/pranab2050/sdf-classify)
## Citation
```bibtex
@article{sarkar2026sdf,
title={Convert Once, Consume Many: SDF for Cacheable, Typed Semantic Extraction from Web Pages},
author={Sarkar, Pranab},
year={2026},
doi={10.5281/zenodo.18559223},
publisher={Zenodo}
}
```
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