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", dtype="auto")

llama-cpp-python

How to use dispatchAI/MiniCPM5-1B-mobile with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dispatchAI/MiniCPM5-1B-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/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 Studio

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

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 @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/MiniCPM5-1B-mobile"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new
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

3morixd commited on 4 days ago

Commit

1f32acc

verified ·

1 Parent(s): dea9ba1

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README.md +9 -76

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@@ -1,84 +1,17 @@
 ---
 license: apache-2.0
-base_model: openbmb/MiniCPM5-1B
-tags:
-- speculative-decoding-draft
-  - dispatch-ai
-  - mobile
-  - quantized
-  - gguf
-  - phone-farm-tested
 pipeline_tag: text-generation
-language:
-  - en
 ---
-# MiniCPM5-1B-mobile
-**Dispatch AI** — Built for mobile. Tested on real phones.
-## Category
-Text Generation — OpenBMB's multilingual model
-## Model
-Re-engineered from [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B).
-Quantized to Q4_K_M GGUF for on-device inference via llama.cpp.
-Size: 656 MB.
-## Phone Farm Test Results
-Tested on **Samsung Galaxy S20 FE 5G** (Snapdragon 865, 8GB RAM):
-| Phone | Gen t/s | Prompt t/s |
-|-------|---------|------------|
-| R3CN30WHS2Z | 21.7 | 67.5 |
-| R3CN509PLHA | 22.3 | 95.3 |
-- **Average: 22.0 t/s**
-- **40-phone aggregate: ~880 t/s**
-## Usage
-```bash
-./llama-cli -m model.gguf -p "Hello" -n 100 -t 4 -c 512
-```
-🌐 [dispatchAI on HuggingFace](https://huggingface.co/dispatchAI)
-## Speculative Decoding Draft Model
-This model is optimized for use as a **draft model** in speculative decoding setups.
-### What is speculative decoding?
-Speculative decoding pairs a small, fast "draft" model with a larger "target" model.
-The draft model proposes tokens that the target model verifies in parallel, achieving
-2-3x speedup with zero quality loss.
-### Why this model?
-- **Small and fast**: Sub-1B parameters = minimal draft overhead
-- **Mobile-optimized**: Already quantized and pruned for edge deployment
-- **Same family**: Pairs naturally with larger models of the same architecture
-### Usage with vLLM
-```python
-from vllm import LLM, SamplingParams
-llm = LLM(
-    model="target-model-7b",
-    speculative_model="dispatchAI/MiniCPM5-1B-mobile",
-    num_speculative_tokens=5,
-)
-```
-### Usage with transformers
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-target = AutoModelForCausalLM.from_pretrained("target-model-7b")
-draft = AutoModelForCausalLM.from_pretrained("dispatchAI/MiniCPM5-1B-mobile")
-# See transformers docs for assisted_generation
-```

 ---
 license: apache-2.0
+tags: [dispatch-ai, mobile, quantized, gguf, phone-farm-tested, on-device, edge-ai]
 pipeline_tag: text-generation
+language: [en]
 ---
+# MiniCPM5 1B — Mobile Optimized
+**Dispatch AI** — Compact model from ModelBest, tuned for mobile.
+| Metric | Value |
+|--------|-------|
+| Generation | 17.5 t/s |
+| Size | ~750 MB |
+**Dispatch AI (FZE)** — Sharjah, UAE | License 10818