Instructions to use RunSLM-AI/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 RunSLM-AI/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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RunSLM-AI/Llama-3.2-1B-Instruct-GGUF: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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RunSLM-AI/Llama-3.2-1B-Instruct-GGUF: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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use RunSLM-AI/Llama-3.2-1B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RunSLM-AI/Llama-3.2-1B-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": "RunSLM-AI/Llama-3.2-1B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Ollama
How to use RunSLM-AI/Llama-3.2-1B-Instruct-GGUF with Ollama:
```
ollama run hf.co/RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use RunSLM-AI/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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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": "RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RunSLM-AI/Llama-3.2-1B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use RunSLM-AI/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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RunSLM-AI/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 RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M

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 "RunSLM-AI/Llama-3.2-1B-Instruct-GGUF:Q4_K_M" \
  --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"

Llama-3.2-1B-Instruct (GGUF Q4_K_M for RunSLM AI)

This repository provides the official 4-bit medium quantized (Q4_K_M) single-file GGUF distribution of Meta Llama 3.2 1B Instruct (Llama-3.2-1B-Instruct-Q4_K_M.gguf) engineered for on-device execution in RunSLM AI and llama.cpp runtimes.

Model Summary

Architecture: Llama 3.2 (Autoregressive Transformer with Grouped-Query Attention)
Base Model: meta-llama/Llama-3.2-1B-Instruct
Parameters: ~1.23 Billion
Quantization: Q4_K_M (4-bit medium quantization)
File Format: GGUF (.gguf)
File Size: ~808 MB
Context Length: Up to 128,000 tokens
License: Llama 3.2 Community License
Target Deployment: Local mobile & tablet hardware (Apple Silicon Metal UMA & Android Arm64 KleidiAI)

Features

Built with Meta Llama 3.2: Retains high reasoning fidelity, structured formatting, and multi-turn instruction following.
100% Offline & Private: Runs entirely on physical device hardware with zero network transmission.
Low Memory Overhead: Consumes under 1.1 GB of RAM at 2,048 tokens context, running safely within standard 4GB–6GB mobile operating system budgets.
Single-File Deployment: Self-contained GGUF tensor format with integrated vocabulary and chat template metadata.

Direct Download

The raw model binary can be downloaded directly from: https://huggingface.co/RunSLM-AI/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_K_M.gguf

Prompt Template

This model uses the standard Llama 3 chat template:

<|start_header_id|>system<|end_header_id|>

You are RunSLM, an on-device AI assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Downloads last month: -

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for RunSLM-AI/Llama-3.2-1B-Instruct-GGUF

Base model

meta-llama/Llama-3.2-1B-Instruct

Quantized

(427)

this model