Instructions to use nopenet/nope-edge-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 nopenet/nope-edge-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 nopenet/nope-edge-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nopenet/nope-edge-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 nopenet/nope-edge-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nopenet/nope-edge-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 nopenet/nope-edge-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nopenet/nope-edge-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 nopenet/nope-edge-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nopenet/nope-edge-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nopenet/nope-edge-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use nopenet/nope-edge-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nopenet/nope-edge-GGUF:Q4_K_M

Ollama
How to use nopenet/nope-edge-GGUF with Ollama:
```
ollama run hf.co/nopenet/nope-edge-GGUF:Q4_K_M
```
Unsloth Desktop

How to use nopenet/nope-edge-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nopenet/nope-edge-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": "nopenet/nope-edge-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use nopenet/nope-edge-GGUF with Docker Model Runner:
```
docker model run hf.co/nopenet/nope-edge-GGUF:Q4_K_M
```

Lemonade

How to use nopenet/nope-edge-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nopenet/nope-edge-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.nope-edge-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nopenet/nope-edge-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 nopenet/nope-edge-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 nopenet/nope-edge-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nopenet/nope-edge-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf nopenet/nope-edge-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 "nopenet/nope-edge-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"

nope-edge-GGUF / README.md

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	---
	license: mit
	language:
	- en
	tags:
	- safety
	- crisis-detection
	- text-classification
	- mental-health
	- gguf
	- llama.cpp
	- ollama
	base_model: Qwen/Qwen3-4B
	pipeline_tag: text-generation
	---

	# NOPE Edge GGUF (4B)

	GGUF quantized version of [nopenet/nope-edge](https://huggingface.co/nopenet/nope-edge) for local inference with Ollama and llama.cpp.

	> License: [MIT](LICENSE.md) - free for any use, including commercial. Built on Qwen3 (Apache-2.0); see NOTICE.md.

	---

	## Quick Start with Ollama

	```bash
	# Download the GGUF and Modelfile
	huggingface-cli download nopenet/nope-edge-GGUF nope-edge-q8_0.gguf Modelfile --local-dir .

	# Create Ollama model
	ollama create nope-edge -f Modelfile

	# Run inference
	ollama run nope-edge "I can't take this anymore"
	```

	---

	## Files

	\| File \| Quantization \| Size \|
	\|------\|--------------\|------\|
	\| `nope-edge-q8_0.gguf` \| Q8_0 \| 3.99 GB \|

	---

	## Output Format

	The model outputs XML with chain-of-thought reasoning:

	Crisis detected:
	```xml
	<reflection>User expresses direct suicidal intent with timeline...</reflection>
	<risks>
	<risk subject="self" type="suicide" severity="high" imminence="urgent" features="active_ideation,timeline_stated"/>
	</risks>
	```

	No crisis:
	```xml
	<reflection>Gaming slang, no genuine crisis indicators...</reflection>
	<risks/>
	```

	---

	## Risk Types

	\| Type \| Description \|
	\|------\|-------------\|
	\| `suicide` \| Suicidal ideation, plans, or intent \|
	\| `self_harm` \| Non-suicidal self-injury \|
	\| `self_neglect` \| Eating disorders, medical neglect \|
	\| `violence` \| Threats toward others \|
	\| `abuse` \| Domestic/intimate partner violence \|
	\| `sexual_violence` \| Sexual assault, coercion \|
	\| `exploitation` \| Trafficking, grooming, sextortion \|
	\| `stalking` \| Persistent unwanted contact \|
	\| `neglect` \| Child or elder neglect \|

	---

	## Hardware Requirements

	\| Model \| Quant \| RAM/VRAM \| CPU Latency \| GPU Latency \|
	\|-------\|-------\|----------\|-------------\|-------------\|
	\| nope-edge (4B) \| Q8_0 \| ~5GB \| ~2s \| ~200ms \|
	\| nope-edge-mini (1.7B) \| Q8_0 \| ~2GB \| ~1s \| ~100ms \|

	---

	## Source Model

	- Repository: [nopenet/nope-edge](https://huggingface.co/nopenet/nope-edge)
	- Base: Qwen/Qwen3-4B
	- Purpose: Mental health crisis classification

	---

	## Important

	- Not a medical device. Outputs are probabilistic signals for triage, not clinical assessments.
	- False positives and negatives will occur. Use for flagging, not autonomous decisions.
	- `features` are heuristic — lower-confidence than type/severity and may include labels not supported by the text; treat as hints only.
	- Classification only. Crisis resources require the [NOPE Evaluate API](https://nope.net/evaluate).

	Edge is a detection aid — not a predictive, diagnostic, or therapeutic tool, and not a replacement for clinical judgment. False positives and false negatives will occur — some people in genuine crisis will not be identified — so never use Edge as the sole basis for an intervention decision, and always keep a human in the loop. If anyone is in immediate danger, contact your local emergency services or find resources at talk.help.

	Full disclaimer: see DISCLAIMER.md.