Instructions to use openbmb/MiniCPM4-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM4-8B with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM4-8B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM4-8B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM4-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM4-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openbmb/MiniCPM4-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM4-8B

SGLang

How to use openbmb/MiniCPM4-8B 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 "openbmb/MiniCPM4-8B" \
    --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": "openbmb/MiniCPM4-8B",
		"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 "openbmb/MiniCPM4-8B" \
        --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": "openbmb/MiniCPM4-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM4-8B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM4-8B
```

BigDong commited on Jun 5, 2025

Commit

e3388ea

1 Parent(s): e6931f2

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@@ -1,3 +1,45 @@
 ---
 license: apache-2.0
 ---

 ---
 license: apache-2.0
+language:
+- zh
+- en
+pipeline_tag: text-generation
+library_name: transformers
 ---
+<div align="center">
+<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
+</div>
+<p align="center">
+<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
+<a href="" target="_blank">Technical Report</a>
+</p>
+<p align="center">
+👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
+</p>
+## What's New
+- [2025.06.06] **MiniCPM4** series are released! You can find technical report on [arXiv]().🔥🔥🔥
+## MiniCPM4 Series
+- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): TODO
+- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): TODO **(<-- you are here)**
+- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): TODO
+- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): TODO
+- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): TODO
+- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): TODO
+## Introduction
+## Usage
+### Inference with Transformers
+### Inference with [vLLM](https://github.com/vllm-project/vllm)
+## Evaluation Results
+## Statement
+## LICENSE
+## Citation