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

Libraries

How to use openbmb/MiniCPM-S-1B-sft with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM-S-1B-sft", 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/MiniCPM-S-1B-sft", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use openbmb/MiniCPM-S-1B-sft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/MiniCPM-S-1B-sft

SGLang

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

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

Add pipeline tag, library name and link to code

by nielsr HF Staff - opened Jun 10, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-2

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -2,19 +2,23 @@
 language:
 - en
 - zh
 tags:
 - MiniCPM
 - ModelBest
 - THUNLP
-license: apache-2.0
 ---
 # MiniCPM-S-1B-sft
 - Original model: [MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)
 - Model creator and fine-tuned by: [ModelBest](https://modelbest.cn/), [OpenBMB](https://huggingface.co/openbmb), and [THUNLP](https://nlp.csai.tsinghua.edu.cn/)
 - Paper: [link](https://arxiv.org/pdf/2402.13516.pdf) (Note: `MiniCPM-S-1B` is denoted as `ProSparse-1B` in the paper.)
 - Adapted LLaMA version: [MiniCPM-S-1B-sft-llama-format](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-llama-format/)
 - Adapted PowerInfer version: [MiniCPM-S-1B-sft-gguf](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf)

 language:
 - en
 - zh
+license: apache-2.0
 tags:
 - MiniCPM
 - ModelBest
 - THUNLP
+pipeline_tag: text-generation
+library_name: transformers
 ---
 # MiniCPM-S-1B-sft
+This repository contains the model of the paper [MiniCPM4: Ultra-Efficient LLMs on End Devices](https://huggingface.co/papers/2506.07900).
 - Original model: [MiniCPM-1B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-1B-sft-bf16)
 - Model creator and fine-tuned by: [ModelBest](https://modelbest.cn/), [OpenBMB](https://huggingface.co/openbmb), and [THUNLP](https://nlp.csai.tsinghua.edu.cn/)
 - Paper: [link](https://arxiv.org/pdf/2402.13516.pdf) (Note: `MiniCPM-S-1B` is denoted as `ProSparse-1B` in the paper.)
+- Code: https://github.com/OpenBMB/MiniCPM
 - Adapted LLaMA version: [MiniCPM-S-1B-sft-llama-format](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-llama-format/)
 - Adapted PowerInfer version: [MiniCPM-S-1B-sft-gguf](https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf)