Instructions to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile",
	filename="model.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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile 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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
# Run inference directly in the terminal:
./llama-cli -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

Use Docker

docker model run hf.co/dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

LM Studio
Jan

vLLM

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

Ollama
How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with Ollama:
```
ollama run hf.co/dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
```

Unsloth Studio

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile 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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile 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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile to start chatting

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

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": "dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

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 dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with Docker Model Runner:
```
docker model run hf.co/dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile
```

Lemonade

How to use dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile

Run and chat with the model

lemonade run user.Llama-3.2-1B-Instruct-Q4-mobile-{{QUANT_TAG}}

List all available models

lemonade list

Llama 3.2 1B Instruct - Q4 Mobile (GGUF)

Meta's Llama 3.2 1B Instruct, quantized to INT4 GGUF format for mobile deployment by Dispatch AI.

Property	Value
Base	meta-llama/Llama-3.2-1B-Instruct
Parameters	1.23 billion
Quantization	Q4_K_M (4-bit k-means)
Size	~767 MB
Format	GGUF (llama.cpp)
License	Llama 3.2 Community

Why This Model?

Mobile-optimized for deployment on Android phones (Snapdragon 865+), laptops, IoT devices, and any hardware with 4GB+ RAM. No GPU required.

Performance on Samsung S20 FE (Snapdragon 865)

Metric	This Version	Original FP16
Size	767 MB	~2.5 GB
Speed	~28 tok/s CPU	~8 tok/s
Memory	~1.2 GB	~3.8 GB
Quality	~95% of original	100% baseline

Use Cases

Chatbots & conversational AI on mobile devices
Instruction following in resource-constrained environments
Content summarization, text classification, RAG pipelines
Educational apps, tutoring systems

Quick Start

# Install llama.cpp
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && cmake -B build -DLLAMA_NATIVE=ON && cmake --build build --config Release

# Download this model
huggingface-cli download dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile ggml-model-Q4_K_M.gguf --local-dir ./models

# Run inference immediately
./build/bin/main -m ./models/ggml-model-Q4_K_M.gguf -p "Hello" -n 256 -t 4

Hardware Requirements

Requirement	Minimum	Recommended
RAM	4 GB	6 GB+
Storage	1 GB free	2 GB+
CPU	4-core ARM64/x86_64	8-core Snapdragon 865+
GPU	Not required	Any (faster)

Limitations

~5% quality degradation vs FP16 on complex reasoning tasks
Not suitable for high-precision numerical computation
Context window follows base model (~128K tokens)

About Dispatch AI

Re-engineering LLMs for mobile and edge deployment. HuggingFace - 40+ models, 13K+ downloads

Downloads last month: 991

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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