horizon-full

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 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). Commercial use, redistribution without attribution, safety system evasion, and any use that harms minors are strictly prohibited. Contact [email protected] for commercial licensing.

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Model Details

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Risk Categories

Severity Levels

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Evaluation Results

Evaluated on 160,000 held-out synthetic conversations.

Per-Category Results

Risk Level Classification

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.

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Usage

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."
# }

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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

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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. For GGUF quantizations (llama.cpp / Ollama), see safecircleai/horizon-full-gguf.

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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

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License

SafeCircle Research License (SRL-1.0) — research and non-commercial use only. Commercial licensing: [email protected]

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Citation

@misc{horizon2026,
  title={Horizon: Child Safety Risk Detection via Fine-tuned LLMs},
  author={SafeCircle},
  year={2026},
  url={https://huggingface.co/safecircleai/horizon-full}
}