Instructions to use openbmb/MiniCPM3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM3-4B with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM3-4B", 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/MiniCPM3-4B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use openbmb/MiniCPM3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM3-4B"
# 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/MiniCPM3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM3-4B

SGLang

How to use openbmb/MiniCPM3-4B 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/MiniCPM3-4B" \
    --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/MiniCPM3-4B",
		"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/MiniCPM3-4B" \
        --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/MiniCPM3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM3-4B with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM3-4B
```

MiniCPM3-4B

File size: 1,929 Bytes

{
    "_name_or_path": "openbmb/MiniCPM3-4B",
    "architectures": [
        "MiniCPM3ForCausalLM"
    ],
    "auto_map": {
        "AutoConfig": "configuration_minicpm.MiniCPM3Config",
        "AutoModel": "modeling_minicpm.MiniCPM3Model",
        "AutoModelForCausalLM": "modeling_minicpm.MiniCPM3ForCausalLM",
        "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPM3ForCausalLM",
        "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPM3ForSequenceClassification"
    },
    "bos_token_id": 1,
    "eos_token_id": [2, 73440],
    "hidden_act": "silu",
    "initializer_range": 0.1,
    "hidden_size": 2560,
    "num_hidden_layers": 62,
    "intermediate_size": 6400,
    "max_position_embeddings": 32768,
    "model_type": "minicpm3",
    "num_attention_heads": 40,
    "num_key_value_heads": 40,
    "qk_nope_head_dim": 64,
    "qk_rope_head_dim": 32,
    "q_lora_rank": 768,
    "kv_lora_rank": 256,
    "rms_norm_eps": 1e-05,
    "rope_scaling": {
        "type": "longrope", 
        "long_factor": [1.0591234137867171, 1.1241891283591912, 1.2596935748670968, 1.5380380402321725, 2.093982484148734, 3.1446935121267696, 4.937952647693647, 7.524541999994549, 10.475458000005451, 13.062047352306353, 14.85530648787323, 15.906017515851266, 16.461961959767827, 16.740306425132907, 16.87581087164081, 16.940876586213285],
        "short_factor": [1.0591234137867171, 1.1241891283591912, 1.2596935748670968, 1.5380380402321725, 2.093982484148734, 3.1446935121267696, 4.937952647693647, 7.524541999994549, 10.475458000005451, 13.062047352306353, 14.85530648787323, 15.906017515851266, 16.461961959767827, 16.740306425132907, 16.87581087164081, 16.940876586213285],
        "original_max_position_embeddings": 32768
    },
    "torch_dtype": "bfloat16",
    "transformers_version": "4.41.0",
    "use_cache": true,
    "vocab_size": 73448,
    "scale_emb": 12,
    "dim_model_base": 256,
    "scale_depth": 1.4
}