Instructions to use openbmb/MiniCPM5-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM5-2B with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM5-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-2B")
model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-2B", device_map="auto")
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/MiniCPM5-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM5-2B"
# 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/MiniCPM5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM5-2B

SGLang

How to use openbmb/MiniCPM5-2B 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/MiniCPM5-2B" \
    --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/MiniCPM5-2B",
		"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/MiniCPM5-2B" \
        --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/MiniCPM5-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM5-2B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM5-2B
```

mlboydaisuke commited on 3 days ago

Commit

71c6883

verified ·

1 Parent(s): cd199ce

Add a LiteRT-LM row to the Deployment cookbook table

Browse files

Thanks for listing the LiteRT-LM variant in this card. This adds the matching row to the Deployment table, in the table's own format, pointing at the LiteRT-LM cookbook and Agent Skill proposed in OpenBMB/MiniCPM#375: the litert-lm CLI on a desktop, the Kotlin API and the AI Edge Gallery app on Android, one bundle for CPU and GPU. The two links resolve once that PR merges. If the wording should differ, I'll change it. Thanks again.

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -383,6 +383,7 @@ MiniCPM5-2B uses the **standard `LlamaForCausalLM` architecture**, so mainstream
 | MLX          | MLX / 4bit local inference on Apple Silicon             | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/mlx.md)                   | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-mlx/SKILL.md)                   |
 | ArcLight     | GGUF local on-device, CPU, Desktop & Server             | [arclight.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/arclight.md)         | [minicpm5-deploy-arclight](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-arclight/SKILL.md)         |
 | vLLM Ascend  | BF16 / FP16 OpenAI server                               | [vllm_ascend.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/vllm_ascend.md)   | [minicpm5-deploy-vllm-ascend](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-vllm-ascend/SKILL.md)   |
 ### Fine-tuning

 | MLX          | MLX / 4bit local inference on Apple Silicon             | [mlx.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/mlx.md)                   | [minicpm5-deploy-mlx](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-mlx/SKILL.md)                   |
 | ArcLight     | GGUF local on-device, CPU, Desktop & Server             | [arclight.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/arclight.md)         | [minicpm5-deploy-arclight](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-arclight/SKILL.md)         |
 | vLLM Ascend  | BF16 / FP16 OpenAI server                               | [vllm_ascend.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/vllm_ascend.md)   | [minicpm5-deploy-vllm-ascend](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-vllm-ascend/SKILL.md)   |
+| LiteRT-LM    | `.litertlm` on-device runtime: Android / iOS / desktop, CPU + GPU (community conversion) | [litert.md](https://github.com/OpenBMB/MiniCPM/blob/main/docs/deployment/litert.md)             | [minicpm5-deploy-litert](https://github.com/OpenBMB/MiniCPM/blob/main/skills/minicpm5-deploy-litert/SKILL.md)             |
 ### Fine-tuning