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

OpenClaw new

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

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 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 "dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile" \
  --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 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 / README.md

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	---
	language:
	- en
	license: llama3.2
	tags:
	- mobile
	- edge-ai
	- quantized
	- gguf
	- on-device
	- small-language-model
	pipeline_tag: text-generation
	---

	# 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

	```bash
	# 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](https://huggingface.co/dispatchAI) - 40+ models, 13K+ downloads