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

Libraries

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

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM4-8B-marlin-vLLM", 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-marlin-vLLM", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM4-8B-marlin-vLLM"
# 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-marlin-vLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Improve model card: Add paper/project links, abstract, and code-generation tag

by nielsr HF Staff - opened Sep 5, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-3

nielsr

Sep 5, 2025

This PR significantly improves the model card for openbmb/MiniCPM4-8B by:

Adding a direct link to the official Hugging Face paper page.
Including a link to the Hugging Face collection acting as the project page.
Adding a dedicated "Paper Abstract" section with the model's abstract.
Adding the code-generation tag to the metadata, reflecting the model's capabilities as evidenced by its evaluations and features (e.g., "代码解释器").

These additions enhance the model's discoverability and provide more comprehensive and structured information for users.

Improve model card: Add paper/project links, abstract, and code-generation tag7d0f0576

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