MiniCPM5-2B-GGUF by bartowski

MiniCPM5-2B-GGUF by bartowski

Quantized GGUF copies of the MiniCPM5-2B text model in many sizes, made with llama.cpp and an importance matrix. For people running a small English and Chinese model on their own computer.

Made by bartowski on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: A set of quantized GGUF copies of the MiniCPM5-2B text generation model from openbmb, made by bartowski with llama.cpp release b10883 and an importance matrix. Each file trades size against quality, so you can fit the model into whatever memory you have. Hugging Face lists English and Chinese as its languages.

Who it suits: People who want a small language model running locally and who need to pick a file size that matches their memory.

Industry | Who uses it | How they use it | Example
Software | Developers | Add a small local model to an app prototype | Run a chat helper on a laptop while building
Education | Students and hobbyists | Learn how a model behaves at different quant sizes | Compare a Q4_K_M file with a Q6_K file
Small business | Owners with older computers | Run a text assistant on modest hardware | Draft short replies without a cloud service
Research | Quantization testers | Compare quant types by size and quality | Set IQ4_XS beside Q4_K_S

You can skip it if you need image input or the original unquantized weights, which come from the openbmb repository.

Where to use it: It targets llama.cpp and other programs that open GGUF files, on Windows, macOS or Linux.

• llama.cpp, the tool the card used to make the files
• Other apps that load GGUF files
• Windows, macOS or Linux with Python, to download the files
• Apple silicon Macs, where the card says the Q4_1 file is more energy-efficient per token

Use case | Who | What they get back
Local chat | Hobbyists | Replies from a small model running on their own machine
Low-memory machines | Users with little RAM | A smaller quant such as Q3_K_L, which the card calls usable for low RAM
Size comparison | Testers | The same model in many sizes to compare side by side

How to set it up: Choose one quant file, download it, then open it in your GGUF runner. Most people can start with Q4_K_M, the card's default pick.

• Python, for the Hugging Face download commands
• A runner that opens GGUF files, such as llama.cpp
• Roughly 1.5 to 2 GB of disk space for the common quants

1. Choose a quant from the table below.
2. Install the Hugging Face tools.
3. Download the files with the Hugging Face command.
4. Open the chosen file in llama.cpp or another GGUF app.

Quant | When to pick it
bf16 | The original precision, the largest file
Q8_0 | The best quantized quality, usually more than you need
Q6_K | Near-original quality, a recommended choice
Q5_K_M | High quality, recommended
Q4_K_M | The card's default pick for most uses
Q4_K_S | A little less quality for a little less space
IQ4_XS | Smaller than Q4_K_S at similar performance
Q3_K_M | Low quality, for tight memory

pip install -U huggingface_hub
hf download bartowski/MiniCPM5-2B-GGUF

How to use it: Wrap each prompt in the card's prompt format, with your system instruction and your question in the two slots. The model writes its answer after the assistant tag.

<s><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Example: Put a short instruction such as a friendly tone in the system slot and a plain question in the prompt slot. The model continues from the assistant line with its reply, in English or Chinese.

For newcomers and experts: GGUF is a single-file format for running models on your own computer, and a quant is a shrunken copy of the model. Smaller files need less memory and lose some quality. A first small try is to download Q4_K_M and chat with a short question.

For experts, the files are imatrix quantizations, which use an importance matrix to decide where precision matters. The K and I families differ in method, and the L variants keep more of the sensitive weights at higher precision. The card marks speculative decoding as no and lists text as the only input.

Pick a quant size
Download the file
Open it in llama.cpp
Wrap your prompt in the format
Read the reply

Good to know: The card lists more files than shown here. These sizes compare a selection.

Title: File size of selected quants
bf16: 5.04 GB
Q8_0: 2.68 GB
Q6_K: 2.11 GB
Q5_K_M: 1.92 GB
Q4_K_M: 1.62 GB
Q4_K_S: 1.53 GB
IQ4_XS: 1.46 GB
Q3_K_M: 1.24 GB

• License: Apache-2.0.
• Cost to run: none named, no API key, account or paid service.
• Data it reaches: it downloads files from Hugging Face, then works on the prompts you give it.
• Not for: input other than text, speculative decoding, or the Q3_K_S file, which the card does not recommend.

On Hugging Face, 13 November 2026

• Task: text generation
• Parameters: 2.5B
• Weights: about 1.3 GB at 4-bit (estimate)
• Formats: GGUF
• Downloads in the last 30 days: 9,232
• Likes: 38
• License: apache-2.0
• Languages: en, zh
• Based on: openbmb/MiniCPM5-2B
• Tags: gguf, minicpm, minicpm5, llama, text-generation, long-context, tool-calling, on-device, edge-ai, conversational

Links

• Model: https://huggingface.co/bartowski/MiniCPM5-2B-GGUF
• Files: https://huggingface.co/bartowski/MiniCPM5-2B-GGUF/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download bartowski/MiniCPM5-2B-GGUF

Then follow the usage in its model card. Nothing in this kit runs by itself.