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

Use Docker

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

LM Studio
Jan

vLLM

How to use dispatchAI/Llama-3.2-1B-Instruct-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-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-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Llama 3.2 1B Instruct - Q8 Mobile (GGUF)

Higher-fidelity Q8 quantization of Meta's Llama 3.2 1B Instruct. When you need maximum quality retention from a 1B model.

Property	Value
Parameters	1.23 billion
Quantization	Q8_0 (8-bit)
Size	~1.3 GB
Quality Retention	~98% of original
Speed	~22 tok/s (S20 FE CPU)

When to Use This Over Q4

Choose Q8 when accuracy matters more than size: production chatbots, content moderation, applications where errors are costly.

Downloads last month: 878

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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