Serve MiniCPM5 2B Base locally with llama.cpp on Windows, macOS, or Linux, then send a short chat-completion request to inspect generated text in the model's response.
Serve MiniCPM5 2B Base locally with llama.cpp on Windows, macOS, or Linux, then send a short chat-completion request to inspect generated text in the model's response.
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 llama.cpp. 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.
Install or build the llama.cpp runtime so that the llama-server executable is available on your PATH. The model runs on Windows, macOS, and Linux. If you build from source, use your llama.cpp build workflow; the published documented invocation requires only llama-server and the model weights.
Download MiniCPM5-2B-F16.gguf from openbmb/MiniCPM5-2B-Base, or use the F16 GGUF file already provided in your local release, and place it in a directory from which you can run the server. The command below expects the file name MiniCPM5-2B-F16.gguf to be resolvable from the current working directory.
This launches the server on port 8080, with alias MiniCPM5-2B, loading MiniCPM5-2B-F16.gguf, offloading 99 layers to the GPU if present, using an 8192-token context, and enabling the Jinja chat template. Run it from the directory containing the model file.
llama-server -m MiniCPM5-2B-F16.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 8192 --jinjaWith the server listening on localhost:8080, send an OpenAI-compatible chat-completion request for MiniCPM5-2B. The request uses the recommended sampling settings temperature=1.0, top_p=0.95, min_p=0.0 and a 256-token generation limit.
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "MiniCPM5-2B",
"messages": [{"role": "user", "content": "1+1=?"}],
"temperature": 1.0, "top_p": 0.95, "min_p": 0.0, "max_tokens": 256
}'In the JSON response returned by the server, examine choices[0].message.content. For the prompt "1+1=?" you should see a short generated answer, for example text stating that 2 is the result of the addition. This confirms the model loaded and completed the prompt; a service-only check that does not inspect choices[0].message.content does not verify model output.
Give the base model a short prompt and inspect the generated continuation. A base checkpoint is not assumed to follow chat instructions.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
Keep min_p at 0.0 as shown, or set repetition_penalty to 1.05, following the documented sampling guidance. The published recommendation is temperature=1.0, top_p=0.95, min_p=0.0, and for repetitive outputs add repetition_penalty=1.05.
Ensure MiniCPM5-2B-F16.gguf is in the directory from which you run llama-server, or run the command from the directory where the file was downloaded, and confirm the file name matches exactly.
Increase the context value. The documented command uses -c 8192 and states you can adjust this value as needed.
Original instructions, model files, and compatibility notes behind this setup.
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