Instructions to use NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/MiniCPM5-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/MiniCPM5-1B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M
```

Unsloth Studio

How to use NANI-Nithin/MiniCPM5-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NANI-Nithin/MiniCPM5-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for NANI-Nithin/MiniCPM5-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NANI-Nithin/MiniCPM5-1B-GGUF to start chatting

How to use NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/MiniCPM5-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/MiniCPM5-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/MiniCPM5-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/MiniCPM5-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/MiniCPM5-1B-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/MiniCPM5-1B-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/MiniCPM5-1B-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"

MiniCPM5-1B-GGUF

GGUF quantizations of openbmb/MiniCPM5-1B for use with llama.cpp, Ollama, LM Studio, Jan, OpenWebUI, llama-cpp-python, and other GGUF-compatible runtimes.

Base Model

Base Model: openbmb/MiniCPM5-1B

MiniCPM5-1B is a compact 1B-parameter language model from OpenBMB focused on:

General chat
Reasoning
Coding assistance
Tool calling
Long-context workloads
Edge and local deployment

This repository contains community-generated GGUF conversions and quantizations derived from the original BF16 checkpoint.

Available Files

Full Precision

File	Description
MiniCPM5-1B-BF16.gguf	BF16 GGUF conversion of the original checkpoint

Standard Quantizations

Quant	Recommended Usage
Q2_K	Smallest footprint
Q3_K_S	Extreme memory savings
Q3_K_M	Low memory systems
Q3_K_L	Better quality than Q3_K_M
Q4_0	Legacy quant
Q4_1	Legacy quant
Q4_K_S	Balanced
Q4_K_M	Recommended general-purpose quant
Q5_K_S	Higher quality
Q5_K_M	Near-lossless experience
Q6_K	Very high quality
Q8_0	Maximum quality quantized version

Importance Matrix (IQ) Quantizations

Generated using llama.cpp importance matrix quantization.

Quant	Recommended Usage
IQ2_M	Smallest IQ variant
IQ3_XXS	Ultra compact
IQ3_XS	Compact
IQ3_M	Strong quality-to-size ratio
IQ4_XS	High quality compact quant
IQ4_NL	Best IQ variant in this repository

Recommended Quant

If you are unsure which file to choose:

Most Users

MiniCPM5-1B-Q4_K_M.gguf

Best balance between:

Quality
Speed
Memory usage
Compatibility

Higher Quality

MiniCPM5-1B-Q5_K_M.gguf

MiniCPM5-1B-Q6_K.gguf

Maximum Quality

MiniCPM5-1B-Q8_0.gguf

Best IQ Quant

MiniCPM5-1B-IQ4_NL.gguf

Example Usage

llama.cpp

llama-cli \
    -m MiniCPM5-1B-Q4_K_M.gguf \
    -ngl 99 \
    -c 8192

llama-server

llama-server \
    -m MiniCPM5-1B-Q4_K_M.gguf \
    -c 8192 \
    -ngl 99

Ollama

Create a Modelfile:

FROM MiniCPM5-1B-Q4_K_M.gguf

Then:

ollama create minicpm5-1b -f Modelfile
ollama run minicpm5-1b

Python

from llama_cpp import Llama

llm = Llama(
    model_path="MiniCPM5-1B-Q4_K_M.gguf",
    n_ctx=8192,
    n_gpu_layers=-1,
)

print(
    llm(
        "Explain quantum computing in simple terms.",
        max_tokens=256
    )
)

Conversion Details

Conversion pipeline:

Download original Hugging Face model.
Convert to BF16 GGUF using llama.cpp.
Generate importance matrix for IQ quantization.
Generate standard GGUF quantizations.
Generate IQ quantizations.
Upload all artifacts to Hugging Face.

Tools used:

llama.cpp
convert_hf_to_gguf.py
llama-quantize
llama-imatrix

Hardware Guidance

Quant	Approximate Use Case
Q2_K	Very constrained devices
Q3_*	Raspberry Pi / older CPUs
Q4_K_M	Most laptops and desktops
Q5_K_M	Modern systems
Q6_K	High-end CPUs and GPUs
Q8_0	Largest RAM footprint

Disclaimer

This repository is a community conversion of the original model and is not an official OpenBMB release.

All model weights, architecture, training methodology, and evaluation results belong to the original authors of MiniCPM5-1B.

If you use this model in research or production environments, please cite and credit the original MiniCPM team.

Credits

OpenBMB for creating MiniCPM5-1B
ggml-org for llama.cpp
Hugging Face for model hosting
Community contributors supporting GGUF deployment

Original model:

https://huggingface.co/openbmb/MiniCPM5-1B

Downloads last month: 962

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/MiniCPM5-1B-GGUF

Base model

openbmb/MiniCPM5-1B

Quantized

(83)

this model