Instructions to use desert-ant-labs/emo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use desert-ant-labs/emo 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
- Notebooks
- Google Colab
- Kaggle
Emo
Suggest emoji faster than you can type.
Multilingual on-device emoji suggestion.
- SDKs, install and examples: https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/emo.md
- Website: https://desertant.com/models/emo/
Type a word or a sentence and get the emoji that fits. Tuned for to-dos, calendar entries, notes, and message drafts across 22 languages (including CJK, Arabic, Thai, Hindi, and more). The whole thing, model and tokenizer, is small (5MB on Apple via Core ML, 11MB via LiteRT elsewhere) and runs in well under 2ms on device.
"Dentist appointment"→ 🦷 ·"réserver un vol pour Tokyo"→ ✈️ ·"犬の散歩"→ 🐕 ·"จองโรงแรม"→ 🏨
Try it
- Live demo: desert-ant-labs/emo-demo: type a phrase, get emojis, fully in your browser.
| Platforms | iOS, macOS, tvOS, visionOS, Android, Linux, Windows, Browser, Node |
| Languages | 22 |
| Weights | v0.7.0 |
Install
Swift (requirements)
.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "3.1.0")
Then add the Emo product to your target.
Kotlin (requirements)
implementation("ai.desertant:emo:3.1.0")
JavaScript (requirements)
npm i @desert-ant-labs/emo @litertjs/core # browser
npm i @desert-ant-labs/emo # Node, prebuilt native core
Files
| File | Format | Size | Contents |
|---|---|---|---|
emo.tflite |
LiteRT / TFLite (int8) | 10.2MB | Runs on Android, Linux, Node, and the web (bundled by default in the Kotlin SDK; downloaded on demand by the JavaScript SDK) |
emo.mlmodelc |
Compiled Core ML | 4.6MB | Ready to load on Apple platforms (used by the Swift SDK) |
emo_tokenizer.bin |
Unigram tokenizer | 750KB | Tokenizer the runtime needs |
emo_meta.json |
JSON | tiny | Emoji labels and runtime config |
Older revisions (tags v0.6.0 and earlier) carry Emo.mlmodelc and emo.safetensors for SDK versions that predate the unified cross-platform migration.
Inputs and outputs
- Input: a plain text string. Best on short, intent-oriented text.
- Output: a probability distribution over the 812-emoji vocabulary; take the top-1 (or top-k). Optimized for top-1 relevance.
Languages
English, Spanish, Portuguese, French, German, Italian, Dutch, Russian, Polish, Turkish, Arabic, Chinese (Simplified & Traditional), Japanese, Korean, Hindi, Indonesian, Thai, Vietnamese, Ukrainian, Swedish, Danish, Czech.
Limitations
- Tuned for short, intent-oriented text; long-form text produces noisier suggestions.
- Emoji semantics are imprecise; near-ties at the top of the ranking are expected.
- Per-language quality varies; lower-resource languages in the set are somewhat weaker.
License
Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: licensing@desertant.com.
Citation
@software{emo_2026,
title = {Emo: Multilingual on-device emoji suggestion},
author = {Desert Ant Labs},
year = {2026},
url = {https://huggingface.co/desert-ant-labs/emo},
}
© 2026 Desert Ant Labs · https://desertant.com
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