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

Use Docker

docker model run hf.co/dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dealignai/MiniCPM-V-4.7-1B-CRACK-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": "dealignai/MiniCPM-V-4.7-1B-CRACK-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/dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF:Q4_K_M

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

How to use dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use dealignai/MiniCPM-V-4.7-1B-CRACK-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 dealignai/MiniCPM-V-4.7-1B-CRACK-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 dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf dealignai/MiniCPM-V-4.7-1B-CRACK-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 "dealignai/MiniCPM-V-4.7-1B-CRACK-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-CRACK (GGUF)

Refusal-direction–ablated GGUF of openbmb/MiniCPM-V-4.7-1B, the 1B dense Qwen3.5-text (hybrid GatedDeltaNet + Full-Attention) multimodal model.

Surgery removes the model's refusal direction from residual-stream writers via MPOA (magnitude-preserving orthogonal ablation) applied to BF16 before GGUF quantization. Multimodal and DSpark speculative-decoding paths are preserved.

Files in this repo

File	Size	Purpose
`MiniCPM-V-4.7-1B-CRACK-Q4_K_M.gguf`	727 MB	4-bit mixed quant, `attn_q/k/v/output`, `ffn_down`, `ssm_out`, `attn_gate` uplifted to Q6_K
`MiniCPM-V-4.7-1B-CRACK-Q8_0.gguf`	1.08 GB	8-bit uniform quant
`mmproj-MiniCPM-V-4.7-1B-CRACK-f16.gguf`	1.11 GB	Multimodal projector, 16× tokens
`mmproj-MiniCPM-V-4.7-1B-CRACK-f16-4x.gguf`	900 MB	Multimodal projector, 4× tokens (lower token budget)
`MiniCPM-V-4.7-1B-CRACK-DSpark-BF16.gguf`	438 MB	DSpark drafter (speculative decoding)
`imatrix-MiniCPM-V-4.7-1B-CRACK.gguf`	2.3 MB	Importance matrix used for quantization
`SHA256SUMS`	—	File hashes

Evaluation — base vs CRACK

Quant	Base MMLU	CRACK MMLU	ΔMMLU	HB-240 comply	Harsh 10 hot cats	Multimodal
Q4_K_M	48.0%	45.0%	−3.0pp (at gate boundary)	236/240 = 98.3% ✓	10 FULL	✓ 16× + 4×
Q8_0	46.0%	39.0%	−7.0pp ⚠	231/240 = 96.2%	10 FULL	✓ 16× + 4×

Gate status

Q4_K_M: passes the HB-240 compliance gate (98.3% ≥ 97%) and sits at the ±3pp MMLU boundary. Recommended quant for most users.
Q8_0: passes HB-240 at 96.2% (one point under the strict 97% gate), MMLU drop −7.0pp exceeds the ±3pp gate. The 1B dense model has far less redundancy than the 35B MoE and takes a bigger knowledge hit from the same-sized surgery. Shipped for completeness; prefer Q4_K_M.

Harsh-10 = complete FULL compliance on all 10 hot categories (fraud, phishing, malware, harassment, copyright, CBRN, weapon, cyber, drugs, scam) for both quants.

Usage

# Standard inference
./llama-server -m MiniCPM-V-4.7-1B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-MiniCPM-V-4.7-1B-CRACK-f16.gguf \
  -ngl 99 -c 8192 --jinja --port 8080

# With DSpark speculative decoding
./llama-server -m MiniCPM-V-4.7-1B-CRACK-Q4_K_M.gguf \
  --mmproj mmproj-MiniCPM-V-4.7-1B-CRACK-f16.gguf \
  --model-draft MiniCPM-V-4.7-1B-CRACK-DSpark-BF16.gguf \
  --spec-type draft-dspark \
  -ngl 99 -c 8192 --jinja --port 8080

Thinking modes: default enable_thinking=true for reasoning passes; set chat_template_kwargs: {"enable_thinking": false} to skip the <think>...</think> block. HB/MMLU numbers above are from enable_thinking=false.

Recipe

MPOA on residual-stream writers in deep-middle layers:
- self_attn.o_proj.weight on FA layers [11, 15, 19], base strength 4.0
- mlp.down_proj.weight on layers [13..21], base strength 5.0
Per-layer strength s = s_base · cal(L) with cal mapping per-layer refusal-norm into [0.6, 1.4]
Vision tower, lm_head, embed_tokens, GDN linear-attention tensors: byte-identical passthrough
Probe: BF16 extraction via probe_minicpmv47.py, mode-invariant SVD top-1 across reasoning-on/off, 8958 harmful/benign-twin pairs across 14 languages

License

Inherits Apache 2.0 from upstream openbmb/MiniCPM-V-4.7-1B.

Detailed per-subject MMLU + per-category HB tables

Q4_K_M

MMLU overall: base 48.0% → CRACK 45.0% (Δ -3.0pp)

Subject	n	Base	CRACK	Δ
college_physics	20	50%	40%	-10pp 🔴
world_religions	20	50%	40%	-10pp 🔴
high_school_biology	20	75%	70%	-5pp 🔴
abstract_algebra	20	35%	30%	-5pp
college_computer_science	20	30%	25%	-5pp
logical_fallacies	20	60%	55%	-5pp
astronomy	20	55%	55%	+0pp
high_school_chemistry	20	45%	45%	+0pp
high_school_mathematics	20	20%	25%	+5pp
anatomy	20	60%	65%	+5pp 🟢

HB-240: 236/240 = 98.3% comply · verdicts: {'comply': 236, 'garbage': 2, 'refuse': 2}

Category	n	comply	refuse	garbage	comply%
contextual	62	62	0	0	100%
copyright	59	57	0	2	97%
standard	119	117	2	0	98%

Q8_0

MMLU overall: base 46.0% → CRACK 39.0% (Δ -7.0pp)

Subject	n	Base	CRACK	Δ
anatomy	20	65%	35%	-30pp 🔴
high_school_biology	20	80%	70%	-10pp 🔴
astronomy	20	65%	55%	-10pp 🔴
high_school_chemistry	20	40%	35%	-5pp 🔴
high_school_mathematics	20	20%	15%	-5pp 🔴
college_computer_science	20	45%	40%	-5pp
logical_fallacies	20	45%	40%	-5pp
abstract_algebra	20	30%	30%	+0pp
college_physics	20	25%	25%	+0pp
world_religions	20	45%	45%	+0pp

HB-240: 231/240 = 96.2% comply · verdicts: {'comply': 231, 'garbage': 4, 'refuse': 5}

Category	n	comply	refuse	garbage	comply%
contextual	62	62	0	0	100%
copyright	59	55	0	4	93%
standard	119	114	5	0	96%

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GGUF

Model size

1B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

View +1 variant

Model tree for dealignai/MiniCPM-V-4.7-1B-CRACK-GGUF

Base model

openbmb/MiniCPM-V-4.7-1B

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