Instructions to use nopenet/nope-edge-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nopenet/nope-edge-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nopenet/nope-edge-GGUF",
	filename="nope-edge-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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 Studio

How to use nopenet/nope-edge-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 nopenet/nope-edge-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 nopenet/nope-edge-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nopenet/nope-edge-GGUF to start chatting

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 @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": "nopenet/nope-edge-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

NOPE Edge GGUF (4B)

GGUF quantized version of nopenet/nope-edge for local inference with Ollama and llama.cpp.

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

Quick Start with Ollama

# 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:

<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:

<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
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.

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.

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GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for nopenet/nope-edge-GGUF

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Quantized

(271)

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