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, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Question About Expanding Ultra-FineWeb to 8T Tokens for MiniCPM 4

by AshleyLL - opened Jun 14, 2025

Discussion

AshleyLL

Jun 14, 2025

I understand that Ultra-FineWeb serves as the core pre-training dataset for the MiniCPM 4 series, with a total of approximately 1.1T tokens. I'm curious about how this dataset is expanded to meet the model's requirement of around 8T tokens during training.

Could anyone share the strategies or methods typically used for such data scaling in the context of MiniCPM 4?

Thank you!

xcjthu

OpenBMB org Jun 16, 2025

Thank you for your interest for Ultra-Fineweb dataset!

To meet the model's training requirement of around 8T tokens, we applied the same data processing pipeline to clean and process our internal datasets beyond Ultra-FineWeb.

These internal datasets underwent the same quality control and processing standards as Ultra-FineWeb to ensure high quality across the entire training corpus. However, we currently do not plan to open-source this additional internal data.

Hope this answers your question! Feel free to reach out if you have any other questions about MiniCPM 4.

xcjthu changed discussion status to closed Jun 16, 2025

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