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

Libraries

How to use openbmb/MiniCPM5-1B-Base with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B-Base")
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM5-1B-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/MiniCPM5-1B-Base

SGLang

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

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

OpenBMB-LLM commited on May 24

Commit

be33d68

verified ·

1 Parent(s): 78b1099

Update Chinese model card (README-cn.md)

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README-cn.md +3 -1

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@@ -47,7 +47,9 @@ datasets:
 🐱 **桌宠**：我们也提供了由 MiniCPM5-1B 本地驱动的桌宠应用。点击下方封面可打开演示视频。
-[<img src="https://raw.githubusercontent.com/OpenBMB/MiniCPM/minicpm5/assets/minicpm5/minicpm5_desktop_pet_cover.png" alt="MiniCPM Desk Pet" width="720">](https://github.com/OpenBMB/MiniCPM/raw/minicpm5/assets/minicpm5/minicpm5_desktop_pet_demo.mp4)
 ## 模型列表

 🐱 **桌宠**：我们也提供了由 MiniCPM5-1B 本地驱动的桌宠应用。点击下方封面可打开演示视频。
+<a href="https://youtu.be/UXtUccouXGY"><img src="https://img.youtube.com/vi/UXtUccouXGY/0.jpg" alt="MiniCPM Desk Pet video demo" width="720"></a>
+**项目仓库**: [OpenBMB/MiniCPM-Desk-Pet](https://github.com/OpenBMB/MiniCPM-Desk-Pet)
 ## 模型列表