Instructions to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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": "Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M

Ollama
How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with Ollama:
```
ollama run hf.co/Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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": "Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM-V-4.7-1B-abliterated-heretic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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 "Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-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"

MiniCPM-V-4.7-1B-abliterated-heretic-GGUF

This repository provides quantized GGUF checkpoints for MiniCPM-V 4.7 1B, based on the refusal-abliterated weights created via Heretic v1.4.0.

MiniCPM-V 4.7 1B is an ultra-efficient on-device multimodal large language model (MLLM) developed by OpenBMB. This version has been neutralized against standard refusal directions while retaining full multimodal vision and reasoning capabilities.

📊 Benchmark & Safety Metrics

The ablation was performed using Heretic v1.4.0 to eliminate refusal bias while keeping Kullback–Leibler (KL) divergence near reference baseline:

Metric	Abliterated Model	Original Base Model
Refusal Rate	9 / 100	99 / 100
KL Divergence	0.0191	0.0000 (Reference)

A refusal rate reduction from 99% to 9% with an ultra-low KL divergence of 0.0191 ensures that the model complies with benign, complex, and unconstrained instructions without degrading language coherence or visual understanding.

📦 Quantization Files & Details

Important: To run multimodal image tasks, you must download mmproj-model-f16.gguf alongside whichever text quant you select below.

File	Size	Description & Recommended Use
`MiniCPM-V-4.7-1B-abliterated-heretic-Q8_0.gguf`	1.08 GB	Near-lossless quality. Best choice for systems with standard RAM.
`MiniCPM-V-4.7-1B-abliterated-heretic-Q6_K.gguf`	838 MB	Excellent balance of speed and high reasoning fidelity.
`MiniCPM-V-4.7-1B-abliterated-heretic-Q5_K_M.gguf`	752 MB	Great quality retention with reduced memory overhead.
`MiniCPM-V-4.7-1B-abliterated-heretic-Q4_K_M.gguf`	672 MB	Recommended default for on-device and mobile edge deployment.
`MiniCPM-V-4.7-1B-abliterated-heretic-Q3_K_M.gguf`	575 MB	Ultra-compact quant for memory-constrained environments.
`mmproj-model-f16.gguf`	1.11 GB	Vision projector weights (Required for all visual inference).

🤝 Credits & Acknowledgements

Original Base Model: OpenBMB for the release of MiniCPM-V-4.7-1B.
Abliteration Framework: Heretic v1.4.0.

Downloads last month: 8,375

GGUF

Model size

1B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for Abiray/MiniCPM-V-4.7-1B-abliterated-heretic-GGUF

Base model

openbmb/MiniCPM-V-4.7-1B

Quantized

(50)

this model