Instructions to use kistepAI/SPARK-Summarization-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kistepAI/SPARK-Summarization-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kistepAI/SPARK-Summarization-GGUF",
	filename="kistep-mistral-nemo-summarization-bf16.gguf",
)

llm.create_chat_completion(
	messages = "\"The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use kistepAI/SPARK-Summarization-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kistepAI/SPARK-Summarization-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf kistepAI/SPARK-Summarization-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kistepAI/SPARK-Summarization-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf kistepAI/SPARK-Summarization-GGUF:BF16

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 kistepAI/SPARK-Summarization-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf kistepAI/SPARK-Summarization-GGUF:BF16

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 kistepAI/SPARK-Summarization-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kistepAI/SPARK-Summarization-GGUF:BF16

Use Docker

docker model run hf.co/kistepAI/SPARK-Summarization-GGUF:BF16

LM Studio
Jan
Ollama
How to use kistepAI/SPARK-Summarization-GGUF with Ollama:
```
ollama run hf.co/kistepAI/SPARK-Summarization-GGUF:BF16
```

Unsloth Studio new

How to use kistepAI/SPARK-Summarization-GGUF 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 kistepAI/SPARK-Summarization-GGUF 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 kistepAI/SPARK-Summarization-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kistepAI/SPARK-Summarization-GGUF to start chatting

Pi new

How to use kistepAI/SPARK-Summarization-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kistepAI/SPARK-Summarization-GGUF:BF16

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": "kistepAI/SPARK-Summarization-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kistepAI/SPARK-Summarization-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kistepAI/SPARK-Summarization-GGUF:BF16

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 kistepAI/SPARK-Summarization-GGUF:BF16

Run Hermes

hermes

Docker Model Runner
How to use kistepAI/SPARK-Summarization-GGUF with Docker Model Runner:
```
docker model run hf.co/kistepAI/SPARK-Summarization-GGUF:BF16
```

Lemonade

How to use kistepAI/SPARK-Summarization-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kistepAI/SPARK-Summarization-GGUF:BF16

Run and chat with the model

lemonade run user.SPARK-Summarization-GGUF-BF16

List all available models

lemonade list

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