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 / config.json

FrankC0st1e

Add `model_type` to config (#17)

6fcf8b4 verified over 1 year ago

raw

history blame

1.93 kB

	{
	"_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
	}