Instructions to use openbmb/MiniCPM4-Survey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openbmb/MiniCPM4-Survey with Transformers:

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

pipe = pipeline("text-generation", model="openbmb/MiniCPM4-Survey", 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/MiniCPM4-Survey", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openbmb/MiniCPM4-Survey with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openbmb/MiniCPM4-Survey"
# 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/MiniCPM4-Survey",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openbmb/MiniCPM4-Survey

SGLang

How to use openbmb/MiniCPM4-Survey 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/MiniCPM4-Survey" \
    --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/MiniCPM4-Survey",
		"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/MiniCPM4-Survey" \
        --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/MiniCPM4-Survey",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openbmb/MiniCPM4-Survey with Docker Model Runner:
```
docker model run hf.co/openbmb/MiniCPM4-Survey
```

Kaguya-19 commited on Jun 10, 2025

Commit

f24b538

1 Parent(s): f3e7ca3

update readme

Browse files

Files changed (2) hide show

README.md +4 -4
code/requirements.txt +8 -0

README.md CHANGED Viewed

@@ -58,7 +58,7 @@ Download [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey) from
 We recommend using [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light) as the embedding model, which can be downloaded from Hugging Face and placed in `model/MiniCPM-Embedding-Light`.
 ### Perpare the environment
-You can download the [paper data](https://www.kaggle.com/datasets/Cornell-University/arxiv) from Kaggle, then extract it. You can run `python dataset_process.py` to process the data and generate the retrieval database. Then you can run `python build_index.py` to build the retrieval database.
 ```
 cd ./code
@@ -66,7 +66,7 @@ curl -L -o ~/Downloads/arxiv.zip\
    https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
 unzip ~/Downloads/arxiv.zip -d .
 mkdir data
-python ./src/preprocess/dataset_process.py
 mkdir index
 python ./src/preprocess/build_index.py
 ```
@@ -151,14 +151,14 @@ MiniCPM4-Survey是由[THUNLP](https://nlp.csai.tsinghua.edu.cn)、中国人民
 我们建议使用[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)作为表征模型，放在model/MiniCPM-Embedding-Light中。
 ### 准备环境
-从 Kaggle 下载论文数据，然后解压。运行`python dataset_process.py`，处理数据并生成检索数据库。然后运行`python build_index.py`，构建检索数据库。
 ``` bash
 cd ./code
 curl -L -o ~/Downloads/arxiv.zip\
    https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
 unzip ~/Downloads/arxiv.zip -d .
 mkdir data
-python ./src/preprocess/dataset_process.py
 mkdir index
 python ./src/preprocess/build_index.py
 ```

 We recommend using [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light) as the embedding model, which can be downloaded from Hugging Face and placed in `model/MiniCPM-Embedding-Light`.
 ### Perpare the environment
+You can download the [paper data](https://www.kaggle.com/datasets/Cornell-University/arxiv) from Kaggle, then extract it. You can run `python data_process.py` to process the data and generate the retrieval database. Then you can run `python build_index.py` to build the retrieval database.
 ```
 cd ./code
    https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
 unzip ~/Downloads/arxiv.zip -d .
 mkdir data
+python ./src/preprocess/data_process.py
 mkdir index
 python ./src/preprocess/build_index.py
 ```
 我们建议使用[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)作为表征模型，放在model/MiniCPM-Embedding-Light中。
 ### 准备环境
+从 Kaggle 下载论文数据，然后解压。运行`python data_process.py`，处理数据并生成检索数据库。然后运行`python build_index.py`，构建检索数据库。
 ``` bash
 cd ./code
 curl -L -o ~/Downloads/arxiv.zip\
    https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv
 unzip ~/Downloads/arxiv.zip -d .
 mkdir data
+python ./src/preprocess/data_process.py
 mkdir index
 python ./src/preprocess/build_index.py
 ```

code/requirements.txt ADDED Viewed

	@@ -0,0 +1,8 @@

+openai
+vllm
+jsonlines
+faiss-cpu
+# faiss-gpu
+fastapi
+uvicorn
+yarl