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+---
+license: other
+license_name: nope-edge-community-license-v1.0
+license_link: LICENSE.md
+language:
+  - en
+tags:
+  - safety
+  - crisis-detection
+  - text-classification
+  - mental-health
+  - content-safety
+  - suicide-prevention
+base_model: Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+extra_gated_heading: "Access NOPE Edge"
+extra_gated_description: "This model is available for **research, academic, nonprofit, and evaluation use**. Commercial production use requires a separate license. Please read the [license terms below](#nope-edge-community-license-v10) before downloading."
+extra_gated_button_content: "Agree and download"
+extra_gated_fields:
+  I am using this for research, academic, nonprofit, personal, or evaluation purposes:
+    type: checkbox
+  I agree to the NOPE Edge Community License v1.0:
+    type: checkbox
+---
+
+# NOPE Edge - Crisis Classification Model
+
+A fine-tuned model for detecting crisis signals in text - suicidal ideation, self-harm, abuse, violence, and other safety-critical content. Designed for integration into safety pipelines, content moderation systems, and mental health applications.
+
+> **License:** [NOPE Edge Community License v1.0](LICENSE.md) - Free for research, academic, nonprofit, and evaluation use. Commercial production requires a separate license. See [nope.net/edge](https://nope.net/edge) for details.
+
+---
+
+## Model Variants
+
+| Model | Parameters | Accuracy | Latency | Use Case |
+|-------|------------|----------|---------|----------|
+| **[nope-edge](https://huggingface.co/nopenet/nope-edge)** | 4B | **90.6%** | ~750ms | Maximum accuracy |
+| **[nope-edge-mini](https://huggingface.co/nopenet/nope-edge-mini)** | 1.7B | 85.9% | ~260ms | High-volume, cost-sensitive |
+
+This is **nope-edge (4B)**.
+
+---
+
+## Quick Start
+
+### Requirements
+
+- Python 3.10+
+- GPU with 8GB+ VRAM (e.g., RTX 3070, A10G, L4) - or CPU (slower)
+- ~8GB disk space
+
+```bash
+pip install torch transformers accelerate
+```
+
+### Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+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 'type|severity|subject' or 'none'."""
+    input_ids = tokenizer.apply_chat_template(
+        [{"role": "user", "content": message}],
+        tokenize=True,
+        return_tensors="pt",
+        add_generation_prompt=True
+    ).to(model.device)
+
+    with torch.no_grad():
+        output = model.generate(input_ids, max_new_tokens=30, do_sample=False)
+
+    return tokenizer.decode(
+        output[0][input_ids.shape[1]:],
+        skip_special_tokens=True
+    ).strip()
+
+classify("I want to end it all")                    # -> "suicide|high|self"
+classify("Great day at work!")                       # -> "none"
+classify("My friend said she wants to kill herself") # -> "suicide|high|other"
+```
+
+---
+
+## Output Format
+
+**Crisis detected:**
+```
+{type}|{severity}|{subject}
+```
+
+| Field | Values | Description |
+|-------|--------|-------------|
+| type | `suicide`, `self_harm`, `self_neglect`, `violence`, `abuse`, `sexual_violence`, `exploitation`, `stalking`, `neglect` | Risk category |
+| severity | `mild`, `moderate`, `high`, `critical` | Urgency level |
+| subject | `self`, `other` | Who is at risk |
+
+**No crisis:** `none`
+
+### Subject Attribution
+
+| Subject | Meaning | Example |
+|---------|---------|---------|
+| `self` | The speaker is at risk or is the victim | "I want to kill myself", "My partner hits me" |
+| `other` | The speaker is reporting concern about someone else | "My friend said she wants to die" |
+
+### Parsing Example
+
+```python
+def parse_output(output: str) -> dict:
+    output = output.strip().lower()
+    if output == "none":
+        return {"is_crisis": False}
+
+    parts = output.split("|")
+    return {
+        "is_crisis": True,
+        "type": parts[0] if len(parts) > 0 else None,
+        "severity": parts[1] if len(parts) > 1 else None,
+        "subject": parts[2] if len(parts) > 2 else None,
+    }
+```
+
+---
+
+## Input Best Practices
+
+### Text Preprocessing
+
+**Preserve natural prose.** The model was trained on real conversations with authentic expression. Emotional signals matter:
+
+| Keep | Why |
+|------|-----|
+| Emojis | `💀` in "kms 💀" signals irony; `😭` signals distress intensity |
+| Punctuation intensity | "I can't do this!!!" conveys more urgency than "I can't do this" |
+| Casual spelling | "im so done" vs "I'm so done" — both valid, don't normalize |
+| Slang/algospeak | "kms", "unalive", "catch the bus" — model understands these |
+
+**Only remove:**
+
+| Remove | Example |
+|--------|---------|
+| Zero-width/invisible Unicode | `hello\u200bworld` → `helloworld` |
+| Decorative Unicode fonts | `ℐ 𝓌𝒶𝓃𝓉 𝓉𝑜 𝒹𝒾𝑒` → `I want to die` |
+| Newlines (single messages) | `I can't\ndo this` → `I can't do this` |
+
+**Keep newlines** when they provide turn structure (see Multi-Turn Conversations below).
+
+**Examples:**
+
+```python
+# KEEP - emotional signal matters
+"I can't do this anymore 😭😭😭"     # Keep emojis - signals distress
+"i want to die!!!!!!!"              # Keep punctuation - signals intensity
+"kms lmao 💀"                       # Keep all - irony/context signal
+
+# NORMALIZE - only structural/invisible issues
+"ℐ 𝓌𝒶𝓃𝓉 𝓉𝑜 𝒹𝒾𝑒"              → "I want to die"  # Fancy Unicode fonts
+"I can't\ndo this\nanymore"        → "I can't do this anymore"  # Single message
+"hello\u200bworld"                 → "helloworld"  # Zero-width chars
+```
+
+**Minimal preprocessing function:**
+
+```python
+import re
+import unicodedata
+
+def preprocess(text: str) -> str:
+    # Normalize decorative Unicode fonts to ASCII (NFKC)
+    text = unicodedata.normalize('NFKC', text)
+
+    # Remove zero-width and invisible characters
+    text = re.sub(r'[\u200b-\u200f\u2028-\u202f\u2060-\u206f\ufeff]', '', text)
+
+    # Flatten newlines to spaces (for single messages only)
+    text = re.sub(r'\n+', ' ', text)
+
+    # Collapse multiple spaces
+    text = re.sub(r' +', ' ', text)
+
+    return text.strip()
+
+# NOTE: Do NOT remove emojis, punctuation, or "normalize" spelling
+```
+
+**Language considerations:**
+- Model is English-primary but handles multilingual input
+- Keep native scripts (Chinese, Arabic, Korean, etc.) intact
+- Preserve natural punctuation and expression in all languages
+
+### Multi-Turn Conversations
+
+**The model was trained on pre-serialized transcripts, not native multi-turn chat format.**
+
+When classifying conversations, serialize into a single user message:
+
+```python
+# CORRECT - serialize conversation into single message
+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}]
+
+# WRONG - don't use multiple role/content pairs
+messages = [
+    {"role": "user", "content": "How are you?"},
+    {"role": "assistant", "content": "I'm here to help..."},
+    {"role": "user", "content": "Not great..."}
+]  # Model was NOT trained this way
+```
+
+**Why serialization matters:**
+- Model treats all content equally (no user/assistant distinction)
+- Trained on pre-serialized transcripts for consistent attention patterns
+- Native multi-turn format causes the model to "chat" instead of classify
+
+**Flexible format - these all work:**
+
+```python
+# Simple newlines
+"User: message 1\nAssistant: message 2\nUser: message 3"
+
+# Markdown-style
+"**User:** message 1\n**Assistant:** message 2"
+
+# Labeled
+"{user}: message 1\n{assistant}: message 2"
+
+# XML-style
+"<user>message 1</user>\n<assistant>message 2</assistant>"
+```
+
+The model is robust to formatting variations. Consistency matters more than specific format choice.
+
+### Input Length
+
+- **Single messages:** No preprocessing needed beyond character cleanup
+- **Conversations:** For very long conversations (20+ turns), consider:
+  - Classifying a sliding window (last 10-15 turns)
+  - The model's attention may not span extremely long contexts effectively
+  - Deep needle detection (crisis buried in turn 3 of 25) is a known limitation
+
+---
+
+## Production Deployment
+
+For high-throughput production use, deploy with vLLM or SGLang:
+
+```bash
+# vLLM
+pip install vllm
+python -m vllm.entrypoints.openai.api_server \
+    --model nopenet/nope-edge \
+    --dtype bfloat16 --max-model-len 2048 --port 8000
+
+# SGLang
+pip install sglang
+python -m sglang.launch_server \
+    --model nopenet/nope-edge \
+    --dtype bfloat16 --port 8000
+```
+
+Then call as OpenAI-compatible API:
+
+```bash
+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": 30, "temperature": 0
+  }'
+```
+
+| Setup | Throughput | Latency (p50) |
+|-------|-----------|---------------|
+| transformers | ~8 req/sec | ~180ms |
+| vLLM / SGLang | 50-100+ req/sec | ~50ms |
+
+---
+
+## Model Details
+
+| | |
+|---|---|
+| **Parameters** | 4B |
+| **Precision** | bfloat16 |
+| **Base Model** | Qwen/Qwen3-4B |
+| **Method** | LoRA fine-tune, merged to full weights |
+| **License** | [NOPE Edge Community License v1.0](LICENSE.md) |
+
+---
+
+## Risk Types Detected
+
+| Type | Description | Clinical Framework |
+|------|-------------|-------------------|
+| `suicide` | Suicidal ideation, intent, planning | C-SSRS |
+| `self_harm` | Non-suicidal self-injury (NSSI) | - |
+| `self_neglect` | Eating disorders, medical neglect | - |
+| `violence` | Threats/intent to harm others | HCR-20 |
+| `abuse` | Domestic/intimate partner violence | DASH |
+| `sexual_violence` | Rape, sexual assault, coercion | - |
+| `neglect` | Failing to care for dependent | - |
+| `exploitation` | Trafficking, grooming, sextortion | - |
+| `stalking` | Persistent unwanted contact | SAM |
+
+---
+
+## Important Limitations
+
+- Outputs are **probabilistic signals**, not clinical assessments
+- **False negatives and false positives will occur**
+- Never use as the **sole basis** for intervention decisions
+- Always implement **human review** for flagged content
+- This model is **not** a medical device or substitute for professional judgment
+- Not validated for all populations, languages, or cultural contexts
+
+---
+
+## Commercial Licensing
+
+This model is free for research, academic, nonprofit, and evaluation use.
+
+**For commercial production deployment**, contact us:
+- Email: support@nope.net
+- Website: https://nope.net/edge
+
+Commercial licenses include:
+- Production deployment rights
+- Priority support
+- Custom fine-tuning options
+- SLA guarantees
+
+---
+
+## About NOPE
+
+NOPE provides safety infrastructure for AI applications. Our API helps developers detect mental health crises and harmful AI behavior in real-time.
+
+- **Website:** https://nope.net
+- **Documentation:** https://docs.nope.net
+- **Support:** support@nope.net
+
+---
+
+## NOPE Edge Community License v1.0
+
+Copyright (c) 2026 NopeNet, LLC. All rights reserved.
+
+### Permitted Uses
+
+You may use this Model for:
+
+- **Research and academic purposes** - published or unpublished studies
+- **Personal projects** - non-commercial individual use
+- **Nonprofit organizations** - including crisis lines, mental health organizations, and safety-focused NGOs
+- **Evaluation and development** - testing integration before commercial licensing
+- **Benchmarking** - publishing evaluations with attribution
+
+### Commercial Use
+
+**Commercial use requires a separate license.** Commercial use includes production deployment in revenue-generating products or use by for-profit companies beyond evaluation.
+
+Contact support@nope.net or visit https://nope.net/edge for commercial licensing.
+
+### Restrictions
+
+You may NOT: redistribute or share weights; sublicense, sell, or transfer the Model; create derivative models for redistribution; build a competing crisis classification product.
+
+### No Warranty
+
+THE MODEL IS PROVIDED "AS IS" WITHOUT WARRANTIES. False negatives and false positives will occur. This is not a medical device or substitute for professional judgment.
+
+### Limitation of Liability
+
+NopeNet shall not be liable for damages arising from use, including classification errors or harm to any person.
+
+### Base Model
+
+Built on [Qwen3](https://huggingface.co/Qwen) by Alibaba Cloud (Apache 2.0). See NOTICE.md.