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Model: Flexan/nopenet-nope-edge-mini-GGUF-i2 Source: Original Platform
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445
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 **second iteration/revision** of this model. A revision is made when a model repo gets updated with a new model.
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> This is the latest version of the model.
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>
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> [[first iteration (1)](https://huggingface.co/Flexan/nopenet-nope-edge-mini-GGUF-i1)]
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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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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-i2/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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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.
|
||||
If you have questions regarding this model, please refer to [the original model repo](https://huggingface.co/nopenet/nope-edge-mini).
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||||
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You can find more info about me and what I do [here](https://huggingface.co/Flexan/Flexan).
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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. Features chain-of-thought reasoning that explains its classifications.
|
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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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||||
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| Model | Parameters | Use Case |
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||||
|-------|------------|----------|
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||||
| **[nope-edge](https://huggingface.co/nopenet/nope-edge)** | 4B | Maximum accuracy |
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| **[nope-edge-mini](https://huggingface.co/nopenet/nope-edge-mini)** | 1.7B | High-volume, cost-sensitive |
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This is **nope-edge-mini (1.7B)**.
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||||
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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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import re
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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 XML with reflection and risk classification."""
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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=300, 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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# Example
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result = classify("I want to end it all tonight")
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print(result)
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```
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**Output:**
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```xml
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<reflection>The user directly expresses intent to end their life with a specific timeline ("tonight"), indicating acute suicidal ideation with imminent risk.</reflection>
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<risks>
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<risk subject="self" type="suicide" severity="high" imminence="urgent"/>
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</risks>
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```
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---
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## Output Format
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The model outputs XML with two components:
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### 1. Reflection (Chain-of-Thought)
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```xml
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<reflection>Reasoning about the input...</reflection>
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```
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The model explains its classification, including:
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- What signals it detected
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- Why it chose the risk type and severity
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- Any contextual factors considered
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### 2. Risk Classification
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**Crisis detected:**
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```xml
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<risks>
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<risk subject="self" type="suicide" severity="high" imminence="urgent" features="active_ideation,intent_stated"/>
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</risks>
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```
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**No crisis:**
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```xml
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<risks/>
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```
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### Risk Attributes
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| Attribute | Values | Description |
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|-----------|--------|-------------|
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| `subject` | `self`, `other` | Who is at risk |
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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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| `imminence` | `chronic`, `acute`, `urgent`, `emergency` | Time sensitivity |
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| `features` | comma-separated list | Specific indicators detected |
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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 | "I want to kill myself" |
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| `other` | 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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import re
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from dataclasses import dataclass
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from typing import Optional
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@dataclass
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class Risk:
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subject: str
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type: str
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severity: str
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imminence: Optional[str] = None
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features: Optional[list] = None
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def parse_output(output: str) -> dict:
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"""Parse model output into structured data."""
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result = {
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"reflection": None,
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"risks": [],
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"is_crisis": False
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}
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# Extract reflection
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reflection_match = re.search(r'<reflection>(.*?)</reflection>', output, re.DOTALL)
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if reflection_match:
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result["reflection"] = reflection_match.group(1).strip()
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# Check for empty risks (no crisis)
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if '<risks/>' in output or '<risks />' in output:
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return result
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# Extract risk elements
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risk_pattern = r'<risk\s+([^>]+)/?\s*>'
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for match in re.finditer(risk_pattern, output):
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attrs = {}
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for attr_match in re.finditer(r'(\w+)="([^"]*)"', match.group(1)):
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attrs[attr_match.group(1)] = attr_match.group(2)
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if attrs:
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risk = Risk(
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subject=attrs.get("subject", "self"),
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type=attrs.get("type"),
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severity=attrs.get("severity"),
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imminence=attrs.get("imminence"),
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features=attrs.get("features", "").split(",") if attrs.get("features") else None
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)
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result["risks"].append(risk)
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result["is_crisis"] = True
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return result
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# Usage
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output = classify("I want to end it all tonight")
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parsed = parse_output(output)
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print(f"Crisis: {parsed['is_crisis']}")
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print(f"Reasoning: {parsed['reflection']}")
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for risk in parsed['risks']:
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print(f"Risk: {risk.type}/{risk.severity} ({risk.subject})")
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```
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---
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## Examples
|
||||
|
||||
### Crisis Detection
|
||||
|
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**Input:** "I want to end it all tonight"
|
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```xml
|
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<reflection>The user directly expresses intent to end their life with a specific timeline ("tonight"), indicating acute suicidal ideation with imminent risk.</reflection>
|
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<risks>
|
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<risk subject="self" type="suicide" severity="high" imminence="urgent"/>
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</risks>
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```
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**Input:** "My friend told me she's been cutting herself"
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```xml
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<reflection>The user is reporting concern about a friend engaging in self-harm behavior. This is third-party disclosure requiring attention.</reflection>
|
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<risks>
|
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<risk subject="other" type="self_harm" severity="moderate" imminence="chronic"/>
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</risks>
|
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```
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### No Crisis (Correctly Ignored)
|
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|
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**Input:** "kms lmao this exam is killing me"
|
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```xml
|
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<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>
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<risks/>
|
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```
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|
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**Input:** "I used to be suicidal but therapy helped me recover"
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```xml
|
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<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>
|
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<risks/>
|
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```
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---
|
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|
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## Input Best Practices
|
||||
|
||||
### Text Preprocessing
|
||||
|
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**Preserve natural prose.** The model was trained on real conversations with authentic expression:
|
||||
|
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| Keep | Why |
|
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|------|-----|
|
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| Emojis | Emotional signals matter |
|
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| Punctuation intensity | "I can't do this!!!" vs "I can't do this" |
|
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| Slang/algospeak | "kms", "unalive", "catch the bus", "graped" |
|
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| Casual spelling | "im so done" - don't normalize |
|
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|
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**Only remove:** Zero-width Unicode, decorative fonts, excessive whitespace.
|
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### Multi-Turn Conversations
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Serialize into a single user message:
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|
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```python
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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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---
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## Production Deployment
|
||||
|
||||
For high-throughput use, deploy with vLLM or SGLang:
|
||||
|
||||
```bash
|
||||
# SGLang (recommended)
|
||||
pip install sglang
|
||||
python -m sglang.launch_server \
|
||||
--model nopenet/nope-edge-mini \
|
||||
--dtype bfloat16 --port 8000
|
||||
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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
|
||||
```
|
||||
|
||||
Then call as OpenAI-compatible API:
|
||||
|
||||
```bash
|
||||
curl http://localhost:8000/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"model": "nopenet/nope-edge-mini",
|
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"messages": [{"role": "user", "content": "I want to end it all"}],
|
||||
"max_tokens": 300, "temperature": 0
|
||||
}'
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 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
|
||||
|
||||
---
|
||||
|
||||
## 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.
|
||||
3
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Reference in New Issue
Block a user