nopenet/nope-edge

pip install torch transformers accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import re

model_id = "nopenet/nope-edge"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

def classify(message: str) -> str:
    """Returns XML with reflection and risk classification.

    `message` is a single user turn. For multi-turn input, serialize the whole
    exchange into this one string, e.g. "User: ...\\n\\nAI: ...\\n\\nUser: ..." —
    Edge is trained on one serialized user message, not native chat roles.
    """
    inputs = tokenizer.apply_chat_template(
        [{"role": "user", "content": message}],
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt",
    ).to(model.device)

    with torch.no_grad():
        output = model.generate(**inputs, max_new_tokens=300, do_sample=False)

    return tokenizer.decode(
        output[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    ).strip()

# Example
result = classify("I want to end it all tonight")
print(result)

<reflection>The user directly expresses intent to end their life with a specific timeline ("tonight"), indicating acute suicidal ideation with imminent risk.</reflection>
<risks>
  <risk subject="self" type="suicide" severity="high" imminence="urgent"/>
</risks>

<reflection>Reasoning about the input...</reflection>

<risks>
  <risk subject="self" type="suicide" severity="high" imminence="urgent" features="active_ideation,intent_stated"/>
</risks>

<risks/>

import re
from dataclasses import dataclass
from typing import Optional

@dataclass
class Risk:
    subject: str
    type: str
    severity: str
    imminence: Optional[str] = None
    features: Optional[list] = None

def parse_output(output: str) -> dict:
    """Parse model output into structured data."""
    result = {
        "reflection": None,
        "risks": [],
        "is_crisis": False
    }

    # Extract reflection
    reflection_match = re.search(r'<reflection>(.*?)</reflection>', output, re.DOTALL)
    if reflection_match:
        result["reflection"] = reflection_match.group(1).strip()

    # Check for empty risks (no crisis)
    if '<risks/>' in output or '<risks />' in output:
        return result

    # Valid risk types — anything else (including a stray type="none") is dropped
    VALID_TYPES = {
        "suicide", "self_harm", "self_neglect", "violence", "abuse",
        "sexual_violence", "neglect", "exploitation", "stalking",
    }

    # Extract risk elements
    risk_pattern = r'<risk\s+([^>]+)/?\s*>'
    for match in re.finditer(risk_pattern, output):
        attrs = {}
        for attr_match in re.finditer(r'(\w+)="([^"]*)"', match.group(1)):
            attrs[attr_match.group(1)] = attr_match.group(2)

        # Mirror NOPE's own parser: skip non-risk elements
        if attrs.get("type") not in VALID_TYPES:
            continue
        if attrs.get("severity", "none") == "none":
            continue

        result["risks"].append(Risk(
            subject=attrs.get("subject", "unknown"),
            type=attrs["type"],
            severity=attrs["severity"],
            imminence=attrs.get("imminence", "not_applicable"),
            features=attrs.get("features", "").split(",") if attrs.get("features") else None,
        ))
        result["is_crisis"] = True

    return result

# Usage
output = classify("I want to end it all tonight")
parsed = parse_output(output)
print(f"Crisis: {parsed['is_crisis']}")
print(f"Reasoning: {parsed['reflection']}")
for risk in parsed['risks']:
    print(f"Risk: {risk.type}/{risk.severity} ({risk.subject})")

<reflection>The user directly expresses intent to end their life with a specific timeline ("tonight"), indicating acute suicidal ideation with imminent risk.</reflection>
<risks>
  <risk subject="self" type="suicide" severity="high" imminence="urgent"/>
</risks>

<reflection>The user is reporting concern about a friend engaging in self-harm behavior. This is third-party disclosure requiring attention.</reflection>
<risks>
  <risk subject="other" type="self_harm" severity="moderate" imminence="chronic"/>
</risks>

<reflection>The user is using hyperbolic internet slang ("kms" = "kill myself") to express frustration about an exam. The "lmao" and casual context indicate this is not genuine suicidal ideation.</reflection>
<risks/>

<reflection>The user is sharing a recovery narrative about past suicidal ideation. They explicitly state therapy helped and they have recovered. No current crisis indicators.</reflection>
<risks/>

conversation = """User: How are you?
Assistant: I'm here to help. How are you feeling?
User: Not great. I've been thinking about ending it all."""

messages = [{"role": "user", "content": conversation}]

# SGLang (recommended)
pip install sglang
python -m sglang.launch_server \
    --model nopenet/nope-edge \
    --dtype bfloat16 --port 8000

# vLLM
pip install vllm
python -m vllm.entrypoints.openai.api_server \
    --model nopenet/nope-edge \
    --dtype bfloat16 --max-model-len 2048 --port 8000

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nopenet/nope-edge",
    "messages": [{"role": "user", "content": "I want to end it all"}],
    "max_tokens": 300, "temperature": 0
  }'

curl -fsS http://localhost:8000/health && echo " ready"

docker run --gpus all --restart unless-stopped -p 8000:8000 \
  vllm/vllm-openai:latest \
  --model nopenet/nope-edge --dtype bfloat16 --max-model-len 2048