Instructions to use tiny-random/minicpm4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/minicpm4 with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/minicpm4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/minicpm4", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiny-random/minicpm4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/minicpm4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/minicpm4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/minicpm4

SGLang

How to use tiny-random/minicpm4 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 "tiny-random/minicpm4" \
    --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": "tiny-random/minicpm4",
		"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 "tiny-random/minicpm4" \
        --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": "tiny-random/minicpm4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/minicpm4 with Docker Model Runner:
```
docker model run hf.co/tiny-random/minicpm4
```

yujiepan commited on Jun 13, 2025

Commit

d7557ce

verified ·

1 Parent(s): f1885fe

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

README.md +0 -2

README.md CHANGED Viewed

@@ -38,7 +38,6 @@ prompt_text = tokenizer.apply_chat_template(
     add_generation_prompt=True,
 )
 model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)
 model_outputs = model.generate(
     **model_inputs,
     max_new_tokens=32,
@@ -48,7 +47,6 @@ model_outputs = model.generate(
 output_token_ids = [
     model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
 ]
 responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
 print(responses)
 ```

     add_generation_prompt=True,
 )
 model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)
 model_outputs = model.generate(
     **model_inputs,
     max_new_tokens=32,
 output_token_ids = [
     model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
 ]
 responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
 print(responses)
 ```