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Model: Flexan/nopenet-nope-edge-mini-GGUF-i1 Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_name: nope-edge-community-license-v1.0
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license_link: LICENSE.md
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language:
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- en
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tags:
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- safety
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- crisis-detection
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- text-classification
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- mental-health
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- content-safety
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- suicide-prevention
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base_model: nopenet/nope-edge-mini
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pipeline_tag: text-generation
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library_name: transformers
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extra_gated_heading: "Access NOPE Edge"
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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."
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extra_gated_button_content: "Agree and download"
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extra_gated_fields:
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I am using this for research, academic, nonprofit, personal, or evaluation purposes:
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type: checkbox
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I agree to the NOPE Edge Community License v1.0:
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type: checkbox
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---
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# GGUF Files for nope-edge-mini
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These are the GGUF files for [nopenet/nope-edge-mini](https://huggingface.co/nopenet/nope-edge-mini).
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> [!NOTE]
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> **Note:** This is the **first iteration/revision** of this model. A revision is made when a model repo gets updated with a new model.
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>
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> [[second iteration (2)](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i2)]
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## Downloads
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| GGUF Link | Quantization | Description |
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| ---- | ----- | ----------- |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q2_K.gguf) | Q2_K | Lowest quality |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.IQ3_XS.gguf) | IQ3_XS | Integer quant |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q3_K_S.gguf) | Q3_K_S | |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.IQ3_S.gguf) | IQ3_S | Integer quant, preferable over Q3_K_S |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.IQ3_M.gguf) | IQ3_M | Integer quant |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q3_K_M.gguf) | Q3_K_M | |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q3_K_L.gguf) | Q3_K_L | |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.IQ4_XS.gguf) | IQ4_XS | Integer quant |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q4_K_S.gguf) | Q4_K_S | Fast with good performance |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q4_K_M.gguf) | Q4_K_M | **Recommended:** Perfect mix of speed and performance |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q5_K_S.gguf) | Q5_K_S | |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q5_K_M.gguf) | Q5_K_M | |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q6_K.gguf) | Q6_K | Very good quality |
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||||
| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.Q8_0.gguf) | Q8_0 | Best quality |
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| [Download](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1/resolve/main/nope-edge-mini.f16.gguf) | f16 | Full precision, don't bother; use a quant |
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## Note from Flexan
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I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet.
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This process is not yet automated and I download, convert, quantize, and upload them **by hand**, usually for models **I deem interesting and wish to try out**.
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If there are some quants missing that you'd like me to add, you may request one in the community tab.
|
||||
If you want to request a public model to be converted, you can also request that in the community tab.
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If you have questions regarding the model, please refer to the original model repo.
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# NOPE Edge Mini - Crisis Classification Model
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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.
|
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|
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> **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.
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---
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||||
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## Model Variants
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| Model | Parameters | Accuracy | Latency | Use Case |
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|-------|------------|----------|---------|----------|
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||||
| **[nope-edge](https://huggingface.co/nopenet/nope-edge)** | 4B | **90.6%** | ~750ms | Maximum accuracy |
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| **[nope-edge-mini](https://huggingface.co/nopenet/nope-edge-mini)** | 1.7B | 85.9% | ~260ms | High-volume, cost-sensitive |
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This is **nope-edge-mini (1.7B)**.
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---
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## Quick Start
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### Requirements
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- Python 3.10+
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- GPU with 4GB+ VRAM (e.g., RTX 3060, T4, L4) - or CPU (slower)
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- ~4GB disk space
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```bash
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pip install torch transformers accelerate
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```
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### Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "nopenet/nope-edge-mini"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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def classify(message: str) -> str:
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"""Returns 'type|severity|subject' or 'none'."""
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input_ids = tokenizer.apply_chat_template(
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[{"role": "user", "content": message}],
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tokenize=True,
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return_tensors="pt",
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add_generation_prompt=True
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).to(model.device)
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with torch.no_grad():
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output = model.generate(input_ids, max_new_tokens=30, do_sample=False)
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return tokenizer.decode(
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output[0][input_ids.shape[1]:],
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skip_special_tokens=True
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).strip()
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classify("I want to end it all") # -> "suicide|high|self"
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classify("Great day at work!") # -> "none"
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classify("My friend said she wants to kill herself") # -> "suicide|high|other"
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```
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---
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## Output Format
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**Crisis detected:**
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```
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{type}|{severity}|{subject}
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```
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| Field | Values | Description |
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|-------|--------|-------------|
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| type | `suicide`, `self_harm`, `self_neglect`, `violence`, `abuse`, `sexual_violence`, `exploitation`, `stalking`, `neglect` | Risk category |
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| severity | `mild`, `moderate`, `high`, `critical` | Urgency level |
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| subject | `self`, `other` | Who is at risk |
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**No crisis:** `none`
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### Subject Attribution
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| Subject | Meaning | Example |
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|---------|---------|---------|
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| `self` | The speaker is at risk or is the victim | "I want to kill myself", "My partner hits me" |
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| `other` | The speaker is reporting concern about someone else | "My friend said she wants to die" |
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### Parsing Example
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```python
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def parse_output(output: str) -> dict:
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output = output.strip().lower()
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if output == "none":
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return {"is_crisis": False}
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parts = output.split("|")
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return {
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"is_crisis": True,
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"type": parts[0] if len(parts) > 0 else None,
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"severity": parts[1] if len(parts) > 1 else None,
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"subject": parts[2] if len(parts) > 2 else None,
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}
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```
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---
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## Input Best Practices
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### Text Preprocessing
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**Preserve natural prose.** The model was trained on real conversations with authentic expression. Emotional signals matter:
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| Keep | Why |
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|------|-----|
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| Emojis | `💀` in "kms 💀" signals irony; `😭` signals distress intensity |
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| Punctuation intensity | "I can't do this!!!" conveys more urgency than "I can't do this" |
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| Casual spelling | "im so done" vs "I'm so done" — both valid, don't normalize |
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| Slang/algospeak | "kms", "unalive", "catch the bus" — model understands these |
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**Only remove:**
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| Remove | Example |
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|--------|---------|
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| Zero-width/invisible Unicode | `hello\u200bworld` → `helloworld` |
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| Decorative Unicode fonts | `ℐ 𝓌𝒶𝓃𝓉 𝓉𝑜 𝒹𝒾𝑒` → `I want to die` |
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| Newlines (single messages) | `I can't\ndo this` → `I can't do this` |
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**Keep newlines** when they provide turn structure (see Multi-Turn Conversations below).
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**Examples:**
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|
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```python
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# KEEP - emotional signal matters
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"I can't do this anymore 😭😭😭" # Keep emojis - signals distress
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"i want to die!!!!!!!" # Keep punctuation - signals intensity
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"kms lmao 💀" # Keep all - irony/context signal
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# NORMALIZE - only structural/invisible issues
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"ℐ 𝓌𝒶𝓃𝓉 𝓉𝑜 𝒹𝒾𝑒" → "I want to die" # Fancy Unicode fonts
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"I can't\ndo this\nanymore" → "I can't do this anymore" # Single message
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"hello\u200bworld" → "helloworld" # Zero-width chars
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```
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**Minimal preprocessing function:**
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|
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```python
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import re
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import unicodedata
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def preprocess(text: str) -> str:
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# Normalize decorative Unicode fonts to ASCII (NFKC)
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text = unicodedata.normalize('NFKC', text)
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|
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# Remove zero-width and invisible characters
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text = re.sub(r'[\u200b-\u200f\u2028-\u202f\u2060-\u206f\ufeff]', '', text)
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|
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# Flatten newlines to spaces (for single messages only)
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text = re.sub(r'\n+', ' ', text)
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|
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# Collapse multiple spaces
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text = re.sub(r' +', ' ', text)
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|
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return text.strip()
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|
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# NOTE: Do NOT remove emojis, punctuation, or "normalize" spelling
|
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```
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**Language considerations:**
|
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- Model is English-primary but handles multilingual input
|
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- Keep native scripts (Chinese, Arabic, Korean, etc.) intact
|
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- Preserve natural punctuation and expression in all languages
|
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|
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### Multi-Turn Conversations
|
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|
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**The model was trained on pre-serialized transcripts, not native multi-turn chat format.**
|
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|
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When classifying conversations, serialize into a single user message:
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|
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```python
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# CORRECT - serialize conversation into single message
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conversation = """User: How are you?
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Assistant: I'm here to help. How are you feeling?
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User: Not great. I've been thinking about ending it all."""
|
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|
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messages = [{"role": "user", "content": conversation}]
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|
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# WRONG - don't use multiple role/content pairs
|
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messages = [
|
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{"role": "user", "content": "How are you?"},
|
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{"role": "assistant", "content": "I'm here to help..."},
|
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{"role": "user", "content": "Not great..."}
|
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] # Model was NOT trained this way
|
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```
|
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|
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**Why serialization matters:**
|
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- Model treats all content equally (no user/assistant distinction)
|
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- Trained on pre-serialized transcripts for consistent attention patterns
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- Native multi-turn format causes the model to "chat" instead of classify
|
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|
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**Flexible format - these all work:**
|
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|
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```python
|
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# Simple newlines
|
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"User: message 1\nAssistant: message 2\nUser: message 3"
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|
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# Markdown-style
|
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"**User:** message 1\n**Assistant:** message 2"
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|
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# Labeled
|
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"{user}: message 1\n{assistant}: message 2"
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|
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# XML-style
|
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"<user>message 1</user>\n<assistant>message 2</assistant>"
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```
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|
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The model is robust to formatting variations. Consistency matters more than specific format choice.
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|
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### Input Length
|
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|
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- **Single messages:** No preprocessing needed beyond character cleanup
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- **Conversations:** For very long conversations (20+ turns), consider:
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- Classifying a sliding window (last 10-15 turns)
|
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- The model's attention may not span extremely long contexts effectively
|
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- Deep needle detection (crisis buried in turn 3 of 25) is a known limitation
|
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|
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---
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## Production Deployment
|
||||
|
||||
For high-throughput production use, deploy with vLLM or SGLang:
|
||||
|
||||
```bash
|
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# vLLM
|
||||
pip install vllm
|
||||
python -m vllm.entrypoints.openai.api_server \
|
||||
--model nopenet/nope-edge-mini \
|
||||
--dtype bfloat16 --max-model-len 2048 --port 8000
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|
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# SGLang
|
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pip install sglang
|
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python -m sglang.launch_server \
|
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--model nopenet/nope-edge-mini \
|
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--dtype bfloat16 --port 8000
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```
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|
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Then call as OpenAI-compatible API:
|
||||
|
||||
```bash
|
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
|
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-d '{
|
||||
"model": "nopenet/nope-edge-mini",
|
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"messages": [{"role": "user", "content": "I want to end it all"}],
|
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"max_tokens": 30, "temperature": 0
|
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}'
|
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```
|
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|
||||
| Setup | Throughput | Latency (p50) |
|
||||
|-------|-----------|---------------|
|
||||
| transformers | ~8 req/sec | ~180ms |
|
||||
| vLLM / SGLang | 50-100+ req/sec | ~50ms |
|
||||
|
||||
---
|
||||
|
||||
## Model Details
|
||||
|
||||
| | |
|
||||
|---|---|
|
||||
| **Parameters** | 1.7B |
|
||||
| **Precision** | bfloat16 |
|
||||
| **Base Model** | Qwen/Qwen3-1.7B |
|
||||
| **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.
|
||||
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Reference in New Issue
Block a user