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

Libraries

How to use nopenet/nope-edge with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nopenet/nope-edge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nopenet/nope-edge")
model = AutoModelForCausalLM.from_pretrained("nopenet/nope-edge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nopenet/nope-edge with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nopenet/nope-edge

SGLang

How to use nopenet/nope-edge with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nopenet/nope-edge" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nopenet/nope-edge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nopenet/nope-edge" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nopenet/nope-edge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

nope-edge / DISCLAIMER.md

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Re-open: MIT relicense + disclaimer (DISCLAIMER.md)

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Disclaimers, Intended Use & Non-Claims

NOPE Edge is a fine-tuned text classifier that surfaces linguistic signals associated with safety-critical content (suicidal ideation, self-harm, abuse, violence, and related risk categories). It is not a conversational AI, not a service NOPE operates, and not a crisis service. These weights are MIT-licensed and run entirely on your own infrastructure: NOPE receives no user data, runs nothing for your users, and has no ability to contact, warn, or intervene with anyone.

The MIT License (see LICENSE.md) is the complete licence and already provides the model "AS IS" with no warranty and no liability. The statements below are the NOPE-specific intended-use and non-claims layer; they add to, and do not limit, the MIT disclaimer.

What Edge is NOT

Edge is not predictive, not diagnostic, not therapeutic, and not a replacement for clinical judgment. Its outputs reflect what is present in the text, not what will happen next. Edge is not a medical device, not a validated clinical instrument, not FDA-cleared/approved/registered, not CE-marked or certified under EU MDR, and not an approved or certified safety tool in any jurisdiction. Outputs are probabilistic signals intended for triage and flagging by a human, not clinical assessments or definitive determinations.

Edge's risk vocabulary is informed by clinical frameworks (C-SSRS, HCR-20, DASH). These citations describe the lineage of the risk-axis structure — they are not an assertion of clinical equivalence or validation, and Edge's outputs are not certified against any of those instruments.

Intended use

Edge is designed to supplement, not replace, human review. It provides classification signals; you make the decisions. Edge sees only the text you pass it — it cannot assess context, history, relationships, or any factor known only to you, the people in the conversation, or a qualified professional.

A detection is a flag for human attention, not a trigger for automated action. Subject attribution (self / other / unknown) is informational only and is not always reliable — do not use it to dismiss or deprioritize a detection. Treat all detected crises as serious enough to warrant escalation or flagging, regardless of subject.

Out of scope — do NOT deploy Edge for

Autonomous intervention, escalation, or any significant action (account suspension, mandatory intervention, emergency-contact notification, safety- affecting content removal) taken solely on an Edge output without human review.
Clinical diagnosis, treatment recommendations, or medical advice to any person.
Real-time, time-critical, or emergency assessment.
Predictive screening of individuals — Edge classifies conversations, not people.
Any decision affecting a person's health, safety, or welfare made without appropriate human oversight and, where relevant, qualified clinical judgment.
Settings without a path for people to dispute or appeal a classification, or without informed consent about Edge's role in your pipeline.

False positives and false negatives WILL occur

No automated classification system can achieve 100% accuracy; errors are inevitable and expected. Edge will produce:

False negatives — content that should be flagged but is not. Some people in genuine crisis will not be identified.
False positives — benign content incorrectly flagged.
Severity / imminence misclassifications, over- and under-estimating risk.
Inconsistent results on similar or identical inputs.
Missed context — failures on sarcasm, fiction frames, cultural idiom, coded or metaphorical language.

A high score is not a clinical assessment; a low score is not a clearance signal. Edge can be wrong in either direction — treating its output as the decision, rather than one input to a human decision, is a misuse. Documented weaknesses include deep multi-turn needles (~33% detection at 18–25 turns), "resolution syndrome," and implicit/religious/metaphorical ideation. Edge is not validated for all populations, languages, or cultural contexts. Treat outputs as signal, not ground truth, and tune thresholds against your own data.

Not a crisis or emergency service

Edge cannot provide crisis intervention, emergency response, or real-time human support, and cannot dispatch emergency services. It is not a substitute for emergency services, clinical assessment, or professional crisis intervention. If you or someone else is in immediate danger, contact your local emergency services now. Crisis resources are available at https://talk.help. If you deploy Edge, you are responsible for ensuring the people you serve have clear, prominent access to emergency resources independent of any Edge output.

Assumption of risk and responsibility

By downloading and deploying these weights you assume all risk associated with their use. The potential consequences of classification errors include serious harm or death. You are best positioned to implement appropriate safeguards, human oversight, and crisis-response protocols for your use case and the people you serve, and you accept sole responsibility for all decisions — and any action taken or not taken — based on Edge outputs. NopeNet expressly disclaims any responsibility for decisions made based on Edge outputs, and has no direct relationship with, duty to, or liability toward the people whose interactions you analyze; any duty of care or obligation to intervene rests solely with you.

Self-hosted — your data, your responsibility

These weights run on your infrastructure. NOPE does not see, receive, store, or process anything you run through them. You are the data controller and are responsible for compliance with all applicable data-protection and other laws (e.g. GDPR, HIPAA) for any data you process. Use of Edge does not by itself satisfy, or create a defense under, any legal or regulatory requirement (including California SB 243 or the UK Online Safety Act); consult qualified counsel about your obligations.

Edge is the open-weights build, provided as-is with no support or indemnity. For production deployments where outputs influence outcomes for real people, a commercial engagement (calibration, safety review, support, indemnification) and a supported on-prem container are available — contact support@nope.net.