Install SGLang on Linux, launch the openbmb/MiniCPM5-1B model as a local OpenAI-compatible server, send your first chat request over localhost, and verify that the model replies. Everything runs on your own machine, so the conversation stays private.
Install SGLang on Linux, launch the openbmb/MiniCPM5-1B model as a local OpenAI-compatible server, send your first chat request over localhost, and verify that the model replies. Everything runs on your own machine, so the conversation stays private.
This setup uses the package documented for this task. Review its source and supported platforms before starting.
The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.
This setup uses SGLang. The exact checkpoint format, context, and runtime overhead determine its memory need; a weight-file size alone is not a GPU recommendation.
Pick your operating system. Every command below is for the selected package and runtime.
You need a Linux machine with Python and pip available. SGLang installs from PyPI, and the model weights download automatically from the Hugging Face repository openbmb/MiniCPM5-1B when the server starts. No custom kernels or model-code forks are required because MiniCPM5-1B uses the standard LlamaForCausalLM architecture.
Install the SGLang server runtime and its dependencies into your Python environment.
pip install "sglang[srt]>=0.5.12"Start the SGLang OpenAI-compatible server and point it at the openbmb/MiniCPM5-1B repository. The server listens on port 30000 and loads the BF16/FP16 model. This is the publisher-recommended backend for MiniCPM5, including tool calling.
python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000Keep the server running and open another terminal. This chat completion request asks MiniCPM5-1B to introduce itself. The model field must match the loaded model identifier exactly, and the call goes only to localhost, so the request stays on your machine.
curl http://localhost:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openbmb/MiniCPM5-1B",
"messages": [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}],
"max_tokens": 128,
"temperature": 0.7
}'Inspect the JSON returned by the curl command. A correct response contains a choices array whose first message has role "assistant" and a non-empty content string describing MiniCPM5-1B. If you see a self-introduction in the content field, the local chat is working and the model is responding.
Add your own turns to the messages array and resend the request to keep chatting privately with the model. You can raise max_tokens for longer answers or adjust temperature and top_p; the publisher recommends temperature=0.7, top_p=0.95 for the no-think chat template. If you see repetitive output, try temperature=1.0, top_p=0.95, min_p=0.0, repetition_penalty=1.05.
Ask a short question with a known answer. Confirm the selected local model responds and verify the answer yourself before using it for private work.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
The server pulls openbmb/MiniCPM5-1B from Hugging Face on first launch. Make sure the machine can reach Hugging Face and has room to cache the weights, then start the launch command again.
Stop the process bound to that port or relaunch the server with a different --port value, then update the curl URL to the same port.
The SGLang server may still be loading the model or may have exited. Wait for the startup log to report readiness, then rerun the curl request against http://localhost:30000/v1/chat/completions.
Adjust the sampling settings: try temperature=1.0, top_p=0.95, min_p=0.0, repetition_penalty=1.05 in the request body.
Original instructions, model files, and compatibility notes behind this setup.
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