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# SafeCircle Research License (SRL-1.0)
**Version 1.0 — May 2026**
**Copyright © 2026 SafeCircle. All rights reserved.**
---
## 1. Definitions
- **"Model"** means the machine learning model weights, configuration files, tokenizer files, and associated code released under this license.
- **"Derivative Work"** means any work that incorporates, fine-tunes, distills, quantizes, or is otherwise derived from the Model.
- **"You"** (or **"Your"**) means the individual or legal entity exercising rights under this license.
- **"Commercial Use"** means any use of the Model, in whole or in part, for direct or indirect commercial advantage or monetary compensation.
- **"SafeCircle"** means SafeCircle Ltd. and its authorized representatives.
---
## 2. Grant of License
Subject to the terms and conditions of this license, SafeCircle grants You a non-exclusive, non-transferable, worldwide, royalty-free license to:
- Use the Model for **research, education, and non-commercial purposes**.
- Reproduce and share the Model for non-commercial research purposes, provided attribution requirements in Section 4 are met.
- Create Derivative Works for non-commercial research purposes, subject to Section 5.
---
## 3. Prohibited Uses
The following uses are **strictly prohibited**, regardless of intent:
### 3.1 Harm to Minors
You may **not** use the Model, or any Derivative Work, to:
- Generate, facilitate, normalize, or distribute content that sexually exploits, grooms, threatens, or otherwise harms minors.
- Build tools, systems, or pipelines designed to produce harmful content targeting children.
- Train or fine-tune models on outputs derived from this Model for any purpose that involves child exploitation or abuse.
### 3.2 Safety System Evasion
You may **not** use the Model to:
- Develop, train, or assist in developing systems that evade, deceive, or circumvent child safety detection systems.
- Probe, red-team, or reverse-engineer child safety classifiers for purposes other than legitimate academic research with institutional ethics approval.
- Generate adversarial examples intended to fool child safety monitoring systems in production environments.
### 3.3 Commercial Use
You may **not** use the Model for Commercial Use without **prior written permission** from SafeCircle. To request a commercial license, contact: **legal@safecircle.tech**
### 3.4 Redistribution Without Attribution
You may **not** redistribute the Model or any Derivative Work without:
- Clearly crediting SafeCircle as the original creator.
- Including a link to the original model repository.
- Including a copy of this license.
- Clearly marking any modifications made to the original Model.
---
## 4. Attribution Requirements
Any publication, product, or service that uses this Model must include the following attribution:
> *"This work uses Horizon, developed by SafeCircle (https://huggingface.co/safecircleai/horizon-full)."*
Academic publications must cite:
```bibtex
@misc{horizon2026,
title={Horizon: Child Safety Risk Detection via Fine-tuned LLMs},
author={SafeCircle},
year={2026},
url={https://huggingface.co/safecircleai/horizon-full}
}
```
---
## 5. Derivative Works
If You create and distribute a Derivative Work:
- You must release it under this same license (SRL-1.0) or a more restrictive license.
- You must clearly indicate that the work is a Derivative Work of the SafeCircle Horizon model.
- You must not imply that SafeCircle endorses or is affiliated with your Derivative Work.
- All prohibited uses in Section 3 apply equally to Derivative Works.
---
## 6. Responsible Use
This Model was developed for child safety research and protective applications. Users are encouraged to:
- Deploy the Model only as an assistive tool, not as a sole decision-making system.
- Ensure human review of all model outputs in production settings.
- Report misuse, vulnerabilities, or unexpected behavior to **legal@safecircle.tech**
- Follow applicable laws regarding child protection in your jurisdiction.
---
## 7. Disclaimer of Warranties
THE MODEL IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. SAFECIRCLE DOES NOT WARRANT THAT THE MODEL IS FREE OF ERRORS, THAT OUTPUTS ARE ACCURATE, OR THAT IT WILL MEET YOUR REQUIREMENTS.
---
## 8. Limitation of Liability
IN NO EVENT SHALL SAFECIRCLE BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES ARISING FROM THE USE OR INABILITY TO USE THE MODEL, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
---
## 9. Termination
This license terminates automatically if You fail to comply with any of its terms. Upon termination, You must cease all use and distribution of the Model and destroy all copies in Your possession.
---
## 10. Contact
For commercial licensing, partnership inquiries, or to report misuse:
**Email:** legal@safecircle.tech
**Website:** https://safecircle.tech

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---
license: other
license_name: safecircle-research-license
license_link: https://huggingface.co/safecircleai/horizon-full/blob/main/LICENSE-SAFECIRCLE.md
language:
- en
tags:
- child-safety
- content-moderation
- risk-detection
- llama
- fine-tuned
base_model: meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
---
# Horizon Full v2 — SafeCircle Child Safety Risk Detection
<p align="center">
<img src="eval_report.png" alt="Horizon Full v2 Evaluation Report" width="100%"/>
</p>
**Horizon Full** is a fine-tuned [Llama 3.2 3B Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model that detects child safety risks in online conversations. Given a conversation, it outputs a structured JSON assessment: risk category, severity level, confidence score, and a one-sentence reasoning.
> ⚠️ **License:** This model is released under the [SafeCircle Research License (SRL-1.0)](https://huggingface.co/safecircleai/horizon-full/blob/main/LICENSE-SAFECIRCLE.md). Commercial use, redistribution without attribution, safety system evasion, and any use that harms minors are strictly prohibited. Contact [legal@safecircle.tech](mailto:legal@safecircle.tech) for commercial licensing.
---
## Model Details
| Property | Value |
|---|---|
| Base model | [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
| Fine-tuning | QLoRA — rank 256, alpha 512, all projection layers |
| Training data | 1.6M synthetic conversations across 8 categories |
| Dataset | [safecircleai/horizon-training-data](https://huggingface.co/datasets/safecircleai/horizon-training-data) |
| Generation | Qwen2.5-7B-Instruct + vLLM + xgrammar constrained decoding |
| Hardware | NVIDIA H100 80GB HBM3 |
| Training steps | 25,000 |
| Parameters | 3.2B (389M trainable via LoRA) |
---
## Risk Categories
| Category | Description | Example Signal |
|---|---|---|
| `grooming` | Predatory relationship-building | Age probing, flattery, secrecy requests |
| `bullying` | Cyberbullying and peer harassment | Insults, exclusion threats, video leaks |
| `sexual_content` | Inappropriate sexual advances | Compliment escalation, photo requests |
| `isolation` | Cutting off support networks | Jealousy, "only I understand you" |
| `personal_info` | Soliciting identifying information | Location, school, home address |
| `platform_migration` | Moving to less-monitored platforms | "DM me on Telegram, it's more private" |
| `threats` | Intimidation and blackmail | "Watch yourself after school" |
| `benign` | Normal safe conversation | Homework, games, music, sports |
## Severity Levels
| Level | Score | Description |
|---|---|---|
| `none` | 0.0 | Safe — no risk indicators |
| `low` | 0.25 | Mild indicators, ambiguous context |
| `medium` | 0.50 | Clear pattern, not yet escalated |
| `high` | 0.75 | Explicit risk behaviour |
| `critical` | 0.95 | Immediate danger or exploitation |
---
## Evaluation Results
Evaluated on 160,000 held-out synthetic conversations.
| Metric | Score |
|---|---|
| **Macro F1** | **0.8218** |
| **Weighted F1** | **0.8295** |
| **False Positive Rate** | **0.00%** (0 / 19,950) |
| **False Negative Rate** | **0.00%** (1 / 140,050) |
### Per-Category Results
| Category | F1 | Precision | Recall | Support |
|---|---|---|---|---|
| Grooming | 0.9981 | 0.9980 | 0.9981 | 19,834 |
| Bullying | 0.9995 | 0.9995 | 0.9995 | 20,253 |
| Sexual Content | 0.9975 | 0.9978 | 0.9973 | 20,142 |
| Isolation | 0.9996 | 0.9993 | 0.9998 | 19,725 |
| Personal Info | 0.9999 | 0.9999 | 1.0000 | 20,149 |
| Platform Migration | 1.0000 | 0.9999 | 1.0000 | 19,932 |
| Threats | 0.9993 | 0.9994 | 0.9992 | 20,015 |
### Risk Level Classification
| Level | Precision | Recall | F1 |
|---|---|---|---|
| none | 1.0000 | 1.0000 | 1.0000 |
| low | 0.8344 | 0.8967 | 0.8645 |
| medium | 0.8971 | 0.8514 | 0.8736 |
| high | 0.7266 | 0.8250 | 0.7727 |
| critical | 0.6881 | 0.5294 | 0.5984 |
> **Note:** Severity classification is the hardest sub-task — the model occasionally confuses adjacent levels (e.g. high ↔ critical). Category detection is near-perfect. All results are on synthetic eval data generated with the same pipeline as training; real-world performance will differ.
---
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json
model = AutoModelForCausalLM.from_pretrained(
"safecircleai/horizon-full",
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("safecircleai/horizon-full")
SYSTEM_PROMPT = (
"You are Horizon, SafeCircle's child safety risk detection model. "
"You have no general knowledge or identity beyond this task. "
"Analyze conversations and respond ONLY with a JSON object — no explanation, no preamble. "
'JSON schema: {"risk_detected": bool, "category": '
'"grooming|bullying|sexual_content|isolation|personal_info|platform_migration|threats|benign", '
'"severity": "none|low|medium|high|critical", "confidence": 0.0-1.0, "reasoning": "one sentence max"}. '
'If asked about yourself or anything unrelated to risk analysis, respond: '
'{"error": "I only analyze conversations for child safety risks."}'
)
conversation = "Child: Hey, what are you doing later?\nOther: Nothing much. Want to meet up? Don't tell your parents."
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": f"Analyze this conversation:\n{conversation}"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.no_grad():
output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
generated = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
result = json.loads(generated.strip())
print(result)
# {
# "risk_detected": true,
# "category": "grooming",
# "severity": "high",
# "confidence": 0.94,
# "reasoning": "Adult requesting private meeting and explicit secrecy from parents."
# }
```
---
## Architecture & Training
```
Base: Llama-3.2-3B-Instruct
└── QLoRA adapters (rank=256, alpha=512)
├── q_proj, k_proj, v_proj, o_proj
└── gate_proj, up_proj, down_proj
Training:
Steps: 25,000
Batch size: 4 × 8 grad accum = 32 effective
LR: 1.5e-4 (cosine restarts, 500 warmup steps)
Optimizer: AdamW fused
Precision: bfloat16
Loss: completion-only (assistant turns only)
Dataset:
1.6M conversations × 8 categories
8–15 messages each, persona-seeded
Generated: Qwen2.5-7B + vLLM + xgrammar JSON Schema
Hardening: ~10% adversarial identity/jailbreak examples
```
---
## Deployment Pattern
```
Incoming message
│
▼
Horizon Mobile (on-device, ~25ms, binary filter)
│
├── safe ──► No action
│
└── risk ──► Horizon Full (7-category + severity JSON)
│
▼
Human moderator review
```
For the lightweight on-device first-stage filter, see [safecircleai/horizon-mobile](https://huggingface.co/safecircleai/horizon-mobile).
For GGUF quantizations (llama.cpp / Ollama), see [safecircleai/horizon-full-gguf](https://huggingface.co/safecircleai/horizon-full-gguf).
---
## Intended Use & Ethics
This model is designed to **assist human moderators** — not replace them. All flagged conversations should be reviewed by trained safety professionals.
- Trained entirely on synthetic data; no real child conversations were used
- English-only; other languages untested
- Severity prediction is weaker than category detection (see evaluation)
- Not suitable as a standalone safety system in production without human oversight
---
## License
[SafeCircle Research License (SRL-1.0)](https://huggingface.co/safecircleai/horizon-full/blob/main/LICENSE-SAFECIRCLE.md) — research and non-commercial use only.
Commercial licensing: [legal@safecircle.tech](mailto:legal@safecircle.tech)
---
## Citation
```bibtex
@misc{horizon2026,
title={Horizon: Child Safety Risk Detection via Fine-tuned LLMs},
author={SafeCircle},
year={2026},
url={https://huggingface.co/safecircleai/horizon-full}
}
```

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- if strftime_now is defined %}
{%- set date_string = strftime_now("%d %b %Y") %}
{%- else %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{{- "<|eot_id|>" }}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 24,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 500000.0,
"rope_type": "llama3"
},
"tie_word_embeddings": true,
"transformers_version": "5.10.2",
"use_cache": true,
"vocab_size": 128256
}

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{
"bos_token_id": 128000,
"do_sample": true,
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128001,
128008,
128009
],
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "5.10.2"
}

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{
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"is_local": false,
"local_files_only": false,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"tokenizer_class": "TokenizersBackend"
}