Instructions to use safecircleai/horizon-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use safecircleai/horizon-mobile with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- LiteRT-LM
How to use safecircleai/horizon-mobile with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli litert-lm run \ --from-huggingface-repo=safecircleai/horizon-mobile \ model.litertlm \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Horizon Mobile β On-Device Child Safety Risk Detection
Horizon Mobile is a fine-tuned Gemma 3 1B IT model for on-device child safety risk detection, packaged in LiteRT-LM format for Android and iOS deployment.
It runs the same structured JSON risk assessment as Horizon Full β directly on-device, with no cloud dependency.
β οΈ License: SafeCircle Research License (SRL-1.0). Commercial use prohibited without written permission. Contact legal@safecircle.tech.
Available Variants
| File | Quantization | Size | Target |
|---|---|---|---|
horizon-mobile-int8_q8_ekv1280.litertlm |
INT8 dynamic | ~1.2 GB | 6 GB+ RAM phones (mid-range 2022+) |
horizon-mobile-int4_q4_block128_ekv1280.litertlm |
INT4 block-128 | ~507 MB | 4 GB RAM phones (budget/older) |
Both variants share the same fine-tuned weights; only quantization differs.
Model Details
| Property | Value |
|---|---|
| Base model | google/gemma-3-1b-it |
| Fine-tuning | QLoRA β rank 64, alpha 128, all projection layers |
| Training data | 500K synthetic conversations across 8 risk categories |
| Dataset | safecircleai/horizon-training-data |
| Hardware | NVIDIA L40s (48 GB) |
| Training steps | 8,000 |
| Format | LiteRT-LM (.litertlm) |
| KV cache | 1,280 tokens |
| Prefill signatures | 8, 64, 128, 256, 512 tokens |
Risk Categories
| Category | Description |
|---|---|
grooming |
Trust building, boundary testing, secrecy requests |
bullying |
Harassment, threats, cyberbullying |
sexual_content |
Explicit messages, inappropriate requests |
isolation |
Controlling behavior, network isolation |
personal_info |
Requests for identifying information |
platform_migration |
Moving to less monitored platforms |
threats |
Violent threats, dangerous challenges |
benign |
Safe, normal conversation |
Output Format
{
"risk_detected": true,
"category": "grooming",
"severity": "high",
"confidence": 0.91,
"reasoning": "Adult requesting private meeting and explicit secrecy from parents."
}
Usage
Android / iOS β LiteRT-LM SDK
Load the .litertlm file with the LiteRT-LM SDK. The model includes the tokenizer, system prompt, and chat template β no additional configuration required.
Test locally (CLI)
# Install LiteRT-LM CLI
uvx litert-lm run horizon-mobile-int8_q8_ekv1280.litertlm \
--prompt "Analyze this conversation: Child: hey, want to meet up? Don't tell your parents."
Deployment Pattern
Incoming message
β
βΌ
Horizon Mobile (on-device, LiteRT-LM)
β
βββ safe βββΊ No action
β
βββ risk βββΊ Horizon Full (cloud API, 7-category + severity)
β
βΌ
Human moderator review
For the full server-side model, see safecircleai/horizon-full.
Citation
@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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