Instructions to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF 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 CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Use Docker

docker model run hf.co/CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Llama new
LM Studio
Jan
Ollama
How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with Ollama:
```
ollama run hf.co/CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0
```
Unsloth Desktop

How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0
```

Lemonade

How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

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 CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CelesteImperia/Llama-3.2-1B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0

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 "CelesteImperia/Llama-3.2-1B-Instruct-GGUF:Q8_0" \
  --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"

Celeste Imperia | Llama-3.2-1B-Instruct (Optimized GGUF)

This repository hosts a hardware-aware quantization of Meta's Llama-3.2-1B-Instruct, specifically optimized for high-speed local execution on consumer edge hardware.

🛠️ Optimization Forge

Quantization: Q4_K_M (K-Quants)
Validation Rig: Intel i5-11400 / 40GB RAM / RTX A4000
Primary Target: Snapdragon X Elite (ARM64), mobile devices, and low-spec Intel/AMD laptops.

🚀 Performance

At just 763 MB, this model is designed to operate within a sub-1GB RAM footprint, making it ideal for background agentic tasks, text summarization, and local tool-calling.

⚖️ Attribution & License

Llama 3.2 is licensed under the Llama 3.2 Community License. Built with Llama.

Downloads last month: 87

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

View +1 variant

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