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 Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dispatchAI/Llama-3.2-1B-Instruct-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dispatchAI/Llama-3.2-1B-Instruct-mobile", dtype="auto")

llama-cpp-python

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dispatchAI/Llama-3.2-1B-Instruct-mobile" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dispatchAI/Llama-3.2-1B-Instruct-mobile" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/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?"
			}
		]
	}'

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-mobile

File size: 967 Bytes

4faa5e7

# Llama-3.2-1B-Instruct-mobile — Verified Usage Examples
# Chat format: llama-3
# CPU speed: 4.9 t/s
# Verified: June 2026

# === Using dispatchai SDK ===
from dispatchai import load_model
model = load_model("Llama-3.2-1B-Instruct-mobile", backend="gguf")

# Chat
response = model.chat("What is the capital of France?")
print(f"Capital: {response}")

# With system prompt
response = model.chat("Summarize this: The meeting is at 3pm.", system="You are a concise assistant.")
print(f"Summary: {response}")

# === Using llama-cpp-python directly ===
from llama_cpp import Llama
llm = Llama(model_path="model.gguf", chat_format="llama-3", n_ctx=512, n_threads=4, verbose=False)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "What is 2+2?"}],
    max_tokens=30,
)
print(f"Math: {response['choices'][0]['message']['content']}")

# === Using llama.cpp CLI ===
# llama-cli -m model.gguf -p "Hello!" -n 30 -t 4 -st --chat-format llama-3