Publisher GGUF repository with F16, Q8_0, and Q4_K_M files for MiniCPM5 2B. Q4_K_M is about 1.56 GB; F16 is about 5.04 GB.
EstimatedSource checked 9/28/2026
LOCALRENTED GPU
Freshness 86 out of 100. Repository age: 40 days old, Sep 5, 2026. Measures age, not quality.86/ 100Freshness40 days oldRepository age · Sep 5, 2026
Popularity 71 out of 100. 209,975 downloads in 30 days; 372 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.71/ 100Popularity210K downloads / 30d372 lifetime likes · Checked Oct 15, 2026
What do these scores mean?+
Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.
Freshness · release age
A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.
Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.
8192 tokens (runtime); publisher max 131072; 8 GB system RAM leaves headroom.
8192 tokens (runtime); publisher max 131072; 8 GB system RAM leaves headroom.
Local chat setupSOURCE-LINKED SETUP
Run MiniCPM5 2B GGUF locally for private chat
This workflow sets up private local chat with MiniCPM5 2B GGUF using llama.cpp. It covers the publisher Q4_K_M quant for a balanced size and an uncensored community GGUF alternative, with a brief hardware summary and checks for a first prompt and follow-up.
Choose a package Run its commands Check the result
01
BEFORE YOU BEGIN
Choose your package
Select the weights you want. The instructions, downloads and settings change with your choice.
The publisher has not provided a complete memory target for this task. Check its hardware guidance before starting.
CURRENT SETUP
MiniCPM5 2B GGUF Q4_K_M (publisher) · official
Exact memory requirements depend on context length, backend and runtime overhead. Leave comfortable RAM/VRAM headroom.
Freshness 86 out of 100. Repository age: 40 days old, Sep 5, 2026. Measures age, not quality.86/ 100Freshness40 days oldRepository age · Sep 5, 2026
Popularity 71 out of 100. 209,975 downloads in 30 days; 372 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.71/ 100Popularity210K downloads / 30d372 lifetime likes · Checked Oct 15, 2026
What do these scores mean?+
Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.
Freshness · release age
A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.
Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.
Set up MiniCPM5 2B GGUF Q4_K_M (publisher) on your machine
Choose where you want to run it. The instructions below update to match your selection.
CHOOSE YOUR SETUPMac
Use the documented llama.cpp binary or build for Apple Silicon or Intel Macs.
01
Install llama.cpp for macOS
Open Terminal. Download the prebuilt macOS binary for your Mac (Apple Silicon arm64 or Intel x86_64) from the official llama.cpp releases page, unpack the archive, and make llama-server available on your PATH. If you prefer to build locally instead, follow the official llama.cpp build guide below from a cloned repository. Confirm the binary is callable in this Terminal before continuing. No model file is fetched in this step.
Create the working folder and download the exact Q4_K_M GGUF
In the same Terminal, create a dedicated working folder and download the publisher Q4_K_M quant (MiniCPM5-2B-Q4_K_M.gguf, about 1.56 GB) from the exact revision pinned in the package metadata. Everything for this path stays inside ~/YouRunAI-MiniCPM5.
mkdir -p ~/YouRunAI-MiniCPM5
cd ~/YouRunAI-MiniCPM5
curl -L -o MiniCPM5-2B-Q4_K_M.gguf "https://huggingface.co/openbmb/MiniCPM5-2B-GGUF/resolve/2079a22f3beaa4e306449978533478fe0522f4b3/MiniCPM5-2B-Q4_K_M.gguf"
ls -lh MiniCPM5-2B-Q4_K_M.gguf
03
Launch the local chat server (first Terminal, stays open)
Start llama-server with the exact publisher flags: -m names the downloaded Q4_K_M file, -a sets the served alias MiniCPM5-2B, --port 8080 exposes the OpenAI-compatible API and web UI, -ngl 99 offloads all layers to Metal on Apple Silicon, -c 8192 sets an 8k active runtime context, and --jinja applies the model chat template. Leave this Terminal running; it is the inference process for the rest of the guide.
cd ~/YouRunAI-MiniCPM5
llama-server -m MiniCPM5-2B-Q4_K_M.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 8192 --jinja
04
Verify the served model ID (second Terminal)
Open a second Terminal window and leave the first one running. Query the models endpoint and confirm the response lists the MiniCPM5-2B alias that you set with -a. Do this before connecting any client, because clients must request the served ID rather than the repository name.
curl http://localhost:8080/v1/models
05
Send the first local chat response
Call the OpenAI-compatible chat completions route on the local server. The publisher recommends temperature=1.0, top_p=0.95, min_p=0.0 for generation, so those values are used here. The reply should be a short self-introduction produced entirely by the local model.
Run a deterministic arithmetic prompt and confirm the reply contains 144. This is a smoke test for the local endpoint, not the chat interface itself.
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{"model":"MiniCPM5-2B","messages":[{"role":"user","content":"What is 12 multiplied by 12? Reply with only the number."}],"max_tokens":16,"temperature":1.0,"top_p":0.95,"min_p":0.0}'
07
Switch to the reusable browser chat UI
llama-server ships a built-in web UI on the same port. In a browser on this Mac, open http://localhost:8080, type a message into the input box, and click Send. Read the reply, then type a follow-up in the same page and send it to continue the conversation in the same session without restarting the server. Keep the first Terminal running the whole time.
08
Adjust the active runtime context when memory is tight
MiniCPM5 2B advertises up to 131072 token positions, but that maximum is a model ceiling, not the context this path runs with. The active context is controlled by the -c flag on llama-server, and this path starts at 8192 tokens, which is a comfortable starting point for an Apple machine with 8 GB unified memory. If the process is short on memory, stop it with Ctrl+C in the first Terminal and restart with a smaller -c value such as 4096. Raising -c above 8192 increases KV cache memory and should only be done after confirming free unified memory.
cd ~/YouRunAI-MiniCPM5
llama-server -m MiniCPM5-2B-Q4_K_M.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 4096 --jinja
09
Inspect the finished result
Confirm the first Terminal shows the server listening on port 8080, that /v1/models returned the MiniCPM5-2B alias, that the first chat request returned a relevant self-introduction, and that the arithmetic check returned 144. Then confirm the browser page at http://localhost:8080 shows your typed message, the model reply, and a second reply from your follow-up in the same conversation. Chat is working locally when those replies arrive without any external API key.
Start a local chat runtime for one selected MiniCPM5 2B GGUF package. Send an initial chat prompt and a follow-up in the same session, and confirm the model returns relevant text. Verify the served model ID matches the selected package and quant before connecting a client.
This workflow focuses on chat. Tool calling, coding, agents and media workflows are separate concerns.
The runtime starts but the chat client cannot reach it.
Check the client endpoint and port against the llama.cpp server output, and verify the served model ID with the runtime's model-list endpoint.
Generation is very slow or the process runs out of memory.
Choose a smaller GGUF quant such as Q3_K_M for the uncensored variant, or reduce context size; also confirm the llama.cpp build matches your CPU/GPU backend.
The model answers the first prompt but not a follow-up.
Keep the same session open and check that the chat template and context are preserved; the llama.cpp server should maintain conversation state when the client sends the full message history.
REFERENCE LIBRARY
Sources and files
Original instructions, model files, and compatibility notes behind this setup.