Instructions to use yarenty/llama32-datafusion-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yarenty/llama32-datafusion-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yarenty/llama32-datafusion-instruct-gguf",
	filename="llama32_datafusion.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use yarenty/llama32-datafusion-instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yarenty/llama32-datafusion-instruct-gguf
# Run inference directly in the terminal:
llama-cli -hf yarenty/llama32-datafusion-instruct-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yarenty/llama32-datafusion-instruct-gguf
# Run inference directly in the terminal:
llama-cli -hf yarenty/llama32-datafusion-instruct-gguf

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 yarenty/llama32-datafusion-instruct-gguf
# Run inference directly in the terminal:
./llama-cli -hf yarenty/llama32-datafusion-instruct-gguf

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 yarenty/llama32-datafusion-instruct-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yarenty/llama32-datafusion-instruct-gguf

Use Docker

docker model run hf.co/yarenty/llama32-datafusion-instruct-gguf

LM Studio
Jan

vLLM

How to use yarenty/llama32-datafusion-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yarenty/llama32-datafusion-instruct-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yarenty/llama32-datafusion-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yarenty/llama32-datafusion-instruct-gguf

Ollama
How to use yarenty/llama32-datafusion-instruct-gguf with Ollama:
```
ollama run hf.co/yarenty/llama32-datafusion-instruct-gguf
```

Unsloth Studio

How to use yarenty/llama32-datafusion-instruct-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 yarenty/llama32-datafusion-instruct-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 yarenty/llama32-datafusion-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yarenty/llama32-datafusion-instruct-gguf to start chatting

How to use yarenty/llama32-datafusion-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf yarenty/llama32-datafusion-instruct-gguf

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": "yarenty/llama32-datafusion-instruct-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yarenty/llama32-datafusion-instruct-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 yarenty/llama32-datafusion-instruct-gguf

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 yarenty/llama32-datafusion-instruct-gguf

Run Hermes

hermes

Docker Model Runner
How to use yarenty/llama32-datafusion-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/yarenty/llama32-datafusion-instruct-gguf
```

Lemonade

How to use yarenty/llama32-datafusion-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yarenty/llama32-datafusion-instruct-gguf

Run and chat with the model

lemonade run user.llama32-datafusion-instruct-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Llama 3.2 DataFusion Instruct (GGUF)

This repository contains the GGUF version of the yarenty/llama32-datafusion-instruct model, quantized for efficient inference on CPU and other compatible hardware.

For full details on the model, including its training procedure, data, intended use, and limitations, please see the full model card.

Model Details

Base model: yarenty/llama32-datafusion-instruct
Format: GGUF
Quantization: Q4_K_M

Prompt Template

This model follows the same instruction prompt template as the base model:

### Instruction:
{Your question or instruction here}

### Response:

Usage

These files are compatible with tools like llama.cpp and Ollama.

With Ollama

ollama pull jaro/llama32-datafusion-instruct
ollama run jaro/llama32-datafusion-instruct "How do I use the Ballista scheduler?"

With llama.cpp

./main -m llama32_datafusion.gguf --color -p "### Instruction:\nHow do I use the Ballista scheduler?\n\n### Response:" -n 256 --stop "### Instruction:" --stop "### Response:" --stop "### End"

Citation

If you use this model, please cite the original base model:

@misc{yarenty_2025_llama32_datafusion_instruct,
  author = {yarenty},
  title = {Llama 3.2 DataFusion Instruct},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://huggingface.co/yarenty/llama32-datafusion-instruct}}
}

Contact

For questions or feedback, please open an issue on the Hugging Face repository or the source GitHub repository.

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Base model

yarenty/llama32-datafusion-instruct

Quantized

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