Instructions to use openbmb/MiniCPM4-Survey with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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
Commit History
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Enhance model card: Add project page, abstract, and transformers sample usage
Verified signatureThis PR improves the model card by: - Adding an explicit link to the Hugging Face collection project page. - Including the full paper abstract for better context. - Adding a `transformers` specific sample usage section, directly taken from the GitHub repository, to help users quickly get started with the model. - Adding relevant tags: `llm-agent` and `summarization`. - Renaming the existing 'Quick Start' section for clarity to 'Quick Start (MiniCPM4-Survey Agent)'.bb12f6e
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fix typo (#2)
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Add link to paper and mention it in the description (#1)
Verified signature- Add link to paper and mention it in the description (cc94bbc72311db38d213667116911954c6fd9681) - Remove file information section (9af7f6ee114cecf8fbbb7f7ff5508827db5e3588) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>08c3956
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