Run the MiniCPM5 2B Midtrain GGUF model locally with llama.cpp on Windows, macOS, or Linux, expose it through the llama-server OpenAI-compatible endpoint, and verify tool-calling by sending a model request that includes a tool definition and checking the returned tool call.
Run the MiniCPM5 2B Midtrain GGUF model locally with llama.cpp on Windows, macOS, or Linux, expose it through the llama-server OpenAI-compatible endpoint, and verify tool-calling by sending a model request that includes a tool definition and checking the returned tool call.
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.
You need the MiniCPM5 2B Midtrain GGUF file named MiniCPM5-2B-F16.gguf, a llama.cpp build with the llama-server binary, and at least 8 GB of memory or a supported GPU to host the model. Obtain the GGUF file so it is available at the path you will reference when starting the server.
Launch llama.cpp’s OpenAI-compatible server with the MiniCPM5 model and 8192-token context. The --jinja flag enables the chat template, and -ngl 99 offloads all layers to the GPU when available. Run this from the directory containing MiniCPM5-2B-F16.gguf, or replace the path with the full path to the GGUF file.
llama-server -m MiniCPM5-2B-F16.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 8192 --jinjaIn a second terminal, send a request to the server using the OpenAI-compatible chat completions endpoint. Keep the sampling settings from the model documentation to avoid repetitive output.
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
}'Send a message that asks the model to call a tool, and pass a definition for a simple tool so the response can be checked for a complete tool round-trip. The model's tool-calling support is part of its documented capabilities.
Read the JSON returned by the server. In the model response choices, look for an emitted tool call: a tool_calls array with the function name plus its arguments. A response that only contains plain text without a tool call means the round-trip did not complete.
Give the agent a small bounded task. Confirm the tool request, its result, and the final response all complete through the local endpoint.
This is a source-linked setup, not a YouRunAI hardware test. Confirm your exact runtime version, package, and output before relying on it.
The publisher documents that llama.cpp defaults to min_p=0.05; pass min_p=0.0 in the request or server settings to avoid filtering out tokens needed to break repetition loops.
Confirm the exact model files, runtime version, and platform shown in the source-linked steps, then retry the documented command.
Original instructions, model files, and compatibility notes behind this setup.
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