Instructions to use NANI-Nithin/MiniCPM5-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use NANI-Nithin/MiniCPM5-2B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/MiniCPM5-2B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/MiniCPM5-2B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/MiniCPM5-2B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/MiniCPM5-2B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/MiniCPM5-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/MiniCPM5-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/MiniCPM5-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/MiniCPM5-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/MiniCPM5-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniCPM5-2B — GGUF quants

Full llama.cpp quant sweep of openbmb/MiniCPM5-2B, OpenBMB's dense 2B model for on-device and local deployment. 30 files — the BF16 source plus 29 quants from Q8_0 down to Q1_0 — all cut from the official BF16 release, with the importance matrix computed on the BF16 weights.

The source model uses the standard LlamaForCausalLM architecture — no custom kernels and no model-code fork are needed; these files load directly in llama.cpp, Ollama and LM Studio. (OpenBMB also publishes their own MiniCPM5-2B-GGUF; this repo complements it with the complete quant sweep including the IQ and extreme quants. For vLLM, SGLang or transformers, use the source repo directly.)

Which file should I get?

File	Size	Use
MiniCPM5-2B-BF16.gguf	4.69 GB	Full precision source. Every quant below is cut from this file.
MiniCPM5-2B-Q8_0.gguf	2.50 GB	Effectively lossless. Use when disk and RAM are not the constraint.
MiniCPM5-2B-Q6_K.gguf	1.93 GB	Near-lossless; the last stop before quality becomes measurable.
MiniCPM5-2B-Q5_1.gguf	1.78 GB	Legacy. Prefer Q5_K_M.
MiniCPM5-2B-Q5_K_M.gguf	1.68 GB	Very good quality, noticeably smaller than Q6_K.
MiniCPM5-2B-Q5_K_S.gguf	1.65 GB	Slightly smaller than Q5_K_M for a slight quality cost.
MiniCPM5-2B-Q5_0.gguf	1.65 GB	Legacy. Prefer Q5_K_M.
MiniCPM5-2B-Q4_1.gguf	1.52 GB	Legacy. Prefer Q4_K_M.
MiniCPM5-2B-Q4_K_M.gguf	1.45 GB	The usual default. Best quality-per-byte for most people.
MiniCPM5-2B-Q4_K_S.gguf	1.40 GB	A little smaller than Q4_K_M, a little worse.
MiniCPM5-2B-IQ4_NL.gguf	1.39 GB	Non-linear 4-bit; good on hardware without fast K-quant kernels.
MiniCPM5-2B-Q4_0.gguf	1.39 GB	Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this.
MiniCPM5-2B-IQ4_XS.gguf	1.33 GB	Best value at this size; usually beats Q4_K_S while smaller.
MiniCPM5-2B-Q3_K_L.gguf	1.28 GB	Small, with real quality loss. Usable when RAM is tight.
MiniCPM5-2B-Q3_K_M.gguf	1.20 GB	Smaller again; noticeable degradation.
MiniCPM5-2B-IQ3_M.gguf	1.14 GB	Strong 3-bit choice, clearly better than Q3_K_M.
MiniCPM5-2B-IQ3_S.gguf	1.11 GB	Slightly smaller than IQ3_M.
MiniCPM5-2B-Q3_K_S.gguf	1.11 GB	Aggressive. Prefer IQ3_M at a similar size.
MiniCPM5-2B-IQ3_XS.gguf	1.07 GB	Aggressive but coherent.
MiniCPM5-2B-IQ3_XXS.gguf	0.99 GB	Very aggressive; the last coherent step down.
MiniCPM5-2B-Q2_K.gguf	0.97 GB	Very small, heavily degraded. For experimentation.
MiniCPM5-2B-Q2_K_S.gguf	0.92 GB	Smaller than Q2_K, at a further quality cost.
MiniCPM5-2B-Q2_0.gguf	0.87 GB	Extreme, group-64. Included for completeness.
MiniCPM5-2B-IQ2_M.gguf	0.90 GB	The smallest size most people find usable.
MiniCPM5-2B-IQ2_S.gguf	0.85 GB	Below the usual usability line.
MiniCPM5-2B-IQ2_XS.gguf	0.80 GB	Experimental.
MiniCPM5-2B-IQ2_XXS.gguf	0.74 GB	Experimental.
MiniCPM5-2B-IQ1_M.gguf	0.68 GB	Extreme. Expect substantial degradation.
MiniCPM5-2B-IQ1_S.gguf	0.64 GB	Extreme. Expect substantial degradation.
MiniCPM5-2B-Q1_0.gguf	0.50 GB	Extreme. Included for completeness.

All 30 files combined: 40.15 GB.

Shortcuts: default Q4_K_M (1.45 GB) · lossless Q8_0 (2.50 GB) · smallest usable IQ2_M (0.90 GB) · 3-bit sweet spot IQ3_M (1.14 GB).

Usage

Run straight from the Hub:

llama-cli -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M -p "Hello"

Serve an OpenAI-compatible endpoint:

llama-server -hf NANI-Nithin/MiniCPM5-2B-GGUF:Q4_K_M

Or download a file first:

huggingface-cli download NANI-Nithin/MiniCPM5-2B-GGUF MiniCPM5-2B-Q4_K_M.gguf --local-dir .
llama-cli -m MiniCPM5-2B-Q4_K_M.gguf -p "Hello"

These are llama.cpp GGUF files. They are not loaded by vLLM, SGLang or transformers — for those backends use the source repo.

About MiniCPM5-2B

MiniCPM5-2B is the second model in the MiniCPM5 series from OpenBMB, following MiniCPM5-1B. It is a dense 2B Transformer built for local assistants, coding agents, tool-use workflows and reasoning scenarios where a compact footprint matters. Per the source model card:

2,516,756,480 parameters (1,981,982,720 non-embedding), 42 layers, GQA with 16 query heads and 2 KV heads
131,072-token context with native long-context support
English and Chinese
Trained with the UltraData tiered data management recipe (arXiv:2602.09003): base + mid-training on the released web datasets, then 400B tokens of deep-thinking SFT, RL, and On-Policy Distillation (OPD) of 16 RL teacher models into the release checkpoint
OpenBMB's own evaluation reports 2B-class open-source SOTA with an average score of 53.9 in their comparison set, competitive with 4B-class models, with particular strengths in code reasoning, math, long-context understanding, tool use and agentic tasks

Quantization notes

Quantized from the official BF16 release on 2026-09-07
Importance matrix computed on the BF16 weights, so the IQ and sub-Q4 quants benefit from calibration
All 29 quant types of the standard sweep are present; nothing was skipped for size

Citation

If you use this model, please cite the source work:

@article{minicpm4,
  title={Minicpm4: Ultra-efficient llms on end devices},
  author={MiniCPM, Team},
  journal={arXiv preprint arXiv:2506.07900},
  year={2025}
}

Disclaimer

Per the source model card, outputs are generated text and may be inaccurate, biased, or offensive; responses on sensitive topics are not professional advice. The model is provided AS IS, without warranty of any kind; users must use it only for lawful, compliant and ethical purposes.

License

Apache-2.0, matching the source model.

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

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Model tree for NANI-Nithin/MiniCPM5-2B-GGUF

Base model

openbmb/MiniCPM5-2B

Quantized

(19)

this model

Papers for NANI-Nithin/MiniCPM5-2B-GGUF

Data Science and Technology Towards AGI Part I: Tiered Data Management

Paper • 2602.09003 • Published Feb 9 • 10

MiniCPM4: Ultra-Efficient LLMs on End Devices

Paper • 2506.07900 • Published Jun 9, 2025 • 102