Instructions to use sdfprotocol/sdf-classify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sdfprotocol/sdf-classify with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sdfprotocol/sdf-classify",
	filename="sdf-classify-Qwen2.5-1.5B-Instruct-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-classify 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-classify:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sdfprotocol/sdf-classify: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-classify:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sdfprotocol/sdf-classify: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-classify:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sdfprotocol/sdf-classify: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-classify:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sdfprotocol/sdf-classify:Q4_K_M

Use Docker

docker model run hf.co/sdfprotocol/sdf-classify:Q4_K_M

LM Studio
Jan

vLLM

How to use sdfprotocol/sdf-classify with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sdfprotocol/sdf-classify"
# 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-classify",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sdfprotocol/sdf-classify:Q4_K_M

Ollama
How to use sdfprotocol/sdf-classify with Ollama:
```
ollama run hf.co/sdfprotocol/sdf-classify:Q4_K_M
```

Unsloth Studio new

How to use sdfprotocol/sdf-classify 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-classify 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-classify 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-classify to start chatting

Pi new

How to use sdfprotocol/sdf-classify 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-classify: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": "sdf-classify"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sdfprotocol/sdf-classify with Docker Model Runner:
```
docker model run hf.co/sdfprotocol/sdf-classify:Q4_K_M
```

Lemonade

How to use sdfprotocol/sdf-classify with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sdfprotocol/sdf-classify:Q4_K_M

Run and chat with the model

lemonade run user.sdf-classify-Q4_K_M

List all available models

lemonade list

SDF Classify

Content type classifier for the SDF Protocol. Fine-tuned from Qwen2.5-1.5B-Instruct using QLoRA.

Purpose

Classifies web content into SDF's hierarchical type system: 10 parent types and 50+ subtypes (e.g., article.news, commerce.product, documentation.api_docs).

Training

Base model: Qwen2.5-1.5B-Instruct
Method: QLoRA (rank 32, alpha 64, dropout 0.05)
Training data: 2,335 classified web documents
Accuracy: 95.2% exact type match

Files

File	Size	Description
`sdf-classify-Qwen2.5-1.5B-Instruct-Q4_K_M.gguf`	941 MB	Quantized (Q4_K_M) — recommended for deployment
`sdf-classify-Qwen2.5-1.5B-Instruct-f16.gguf`	2.9 GB	Full precision (f16)
`Modelfile`	—	Ollama import configuration

Usage with Ollama

# Download the Q4_K_M file, then:
ollama create sdf-classify -f Modelfile

Part of SDF Protocol

Protocol: sdfprotocol.org
Specification: github.com/sdfprotocol/sdf
Whitepaper: DOI 10.5281/zenodo.18559223
Extractor model: pranab2050/sdf-extract

Citation

@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}
}

Downloads last month: 8

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

16-bit

Model tree for sdfprotocol/sdf-classify

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(178)

this model