Instructions to use dispatchAI/MiniCPM5-1B-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dispatchAI/MiniCPM5-1B-mobile with Transformers:

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

pipe = pipeline("text-generation", model="dispatchAI/MiniCPM5-1B-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dispatchAI/MiniCPM5-1B-mobile", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/MiniCPM5-1B-mobile

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dispatchAI/MiniCPM5-1B-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-mobile
# Run inference directly in the terminal:
./llama-cli -hf dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-mobile
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dispatchAI/MiniCPM5-1B-mobile

Use Docker

docker model run hf.co/dispatchAI/MiniCPM5-1B-mobile

LM Studio
Jan

vLLM

How to use dispatchAI/MiniCPM5-1B-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dispatchAI/MiniCPM5-1B-mobile

SGLang

How to use dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dispatchAI/MiniCPM5-1B-mobile with Ollama:
```
ollama run hf.co/dispatchAI/MiniCPM5-1B-mobile
```
Unsloth Desktop

How to use dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-mobile

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": "dispatchAI/MiniCPM5-1B-mobile"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use dispatchAI/MiniCPM5-1B-mobile with Docker Model Runner:
```
docker model run hf.co/dispatchAI/MiniCPM5-1B-mobile
```

Lemonade

How to use dispatchAI/MiniCPM5-1B-mobile with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dispatchAI/MiniCPM5-1B-mobile

Run and chat with the model

lemonade run user.MiniCPM5-1B-mobile-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-mobile

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dispatchAI/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-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"

MiniCPM5-1B-mobile

✅ Verified on real phone hardware — Snapdragon 865, June 2026.

Phone Benchmark (Samsung S20 FE, Snapdragon 865)

Metric	Value
Phone Speed	27.9 tokens/sec
CPU Speed	18.1 tokens/sec
File Size	656 MB
Chat Format	None
Test Output	"Paris" ✅ (correct)

Usage

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="model.gguf", chat_format="None", n_ctx=512, n_threads=4, verbose=False)
response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "What is the capital of France?"}],
    max_tokens=50,
)
print(response["choices"][0]["message"]["content"])

dispatchAI SDK

from dispatchai import load_model
model = load_model("MiniCPM5-1B-mobile", backend="gguf")
print(model.chat("What is the capital of France?"))

On Android (via ADB)

hf download dispatchAI/MiniCPM5-1B-mobile model.gguf
MSYS_NO_PATHCONV=1 adb push model.gguf /data/local/tmp/
MSYS_NO_PATHCONV=1 adb shell "cd /data/local/tmp && LD_LIBRARY_PATH=/data/local/tmp ./llama-cli -m model.gguf -p 'Hello' -n 30 -t 4 -st"