Instructions to use distil-labs/Llama-3_2-gitara-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use distil-labs/Llama-3_2-gitara-3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="distil-labs/Llama-3_2-gitara-3B",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use distil-labs/Llama-3_2-gitara-3B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf distil-labs/Llama-3_2-gitara-3B
# Run inference directly in the terminal:
llama cli -hf distil-labs/Llama-3_2-gitara-3B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf distil-labs/Llama-3_2-gitara-3B
# Run inference directly in the terminal:
llama cli -hf distil-labs/Llama-3_2-gitara-3B

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 distil-labs/Llama-3_2-gitara-3B
# Run inference directly in the terminal:
./llama-cli -hf distil-labs/Llama-3_2-gitara-3B

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 distil-labs/Llama-3_2-gitara-3B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf distil-labs/Llama-3_2-gitara-3B

Use Docker

docker model run hf.co/distil-labs/Llama-3_2-gitara-3B

LM Studio
Jan
Ollama
How to use distil-labs/Llama-3_2-gitara-3B with Ollama:
```
ollama run hf.co/distil-labs/Llama-3_2-gitara-3B
```

Unsloth Studio

How to use distil-labs/Llama-3_2-gitara-3B 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 distil-labs/Llama-3_2-gitara-3B 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 distil-labs/Llama-3_2-gitara-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for distil-labs/Llama-3_2-gitara-3B to start chatting

How to use distil-labs/Llama-3_2-gitara-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/Llama-3_2-gitara-3B

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": "distil-labs/Llama-3_2-gitara-3B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use distil-labs/Llama-3_2-gitara-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/Llama-3_2-gitara-3B

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 distil-labs/Llama-3_2-gitara-3B

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use distil-labs/Llama-3_2-gitara-3B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf distil-labs/Llama-3_2-gitara-3B

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "distil-labs/Llama-3_2-gitara-3B" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use distil-labs/Llama-3_2-gitara-3B with Docker Model Runner:
```
docker model run hf.co/distil-labs/Llama-3_2-gitara-3B
```

Lemonade

How to use distil-labs/Llama-3_2-gitara-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull distil-labs/Llama-3_2-gitara-3B

Run and chat with the model

lemonade run user.Llama-3_2-gitara-3B-{{QUANT_TAG}}

List all available models

lemonade list

distil-gitara

A Small Language Model illustrating how to use distillabs.ai to fine-tuned a small tool-calling model.

Model Details

Model Description

We fine-tuned a small, tool-calling language model to turn plain-English language questions into git commands with the accuracy of a cloud LLM. You can play with it by checking out this GitHub repo or getting the model directly from here. Because it's small, you can run it locally on your own machine.