Instructions to use FiditeNemini/CallMe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FiditeNemini/CallMe with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download FiditeNemini/CallMe --local-dir CallMe
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,165 Bytes
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license: other
license_name: mit-and-apache-2.0
tags: [callme, mlx, moonshine, ming, on-device]
---
# CallMe
The on-device models the **CallMe AI** iPhone app downloads with **Download All Three**
(Settings → Models). Each folder is one component of the voice stack. The app fetches only
the files it needs, checks each large file against its SHA-256, and resumes interrupted
downloads.
| Folder | Component | Model | Licence |
|---|---|---|---|
| `stt/` | Speech recognition | [Moonshine](https://github.com/moonshine-ai/moonshine) Small Streaming (English), ONNX Runtime | MIT |
| `llm/` | Conversation | [Gemma-4-E2B](https://huggingface.co/google/gemma-4-E2B-it), MLX 4-bit | [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) |
| `tts/` | Voice | [Ming-3.0-tts-0.5B](https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B), MLX Swift port, 8-bit (backbone, aggregator and DiT Linears; VAE decoder bf16) | MIT |
All three are permissively licensed (MIT / Apache-2.0); see each upstream model card
(Gemma 4: [model card](https://ai.google.dev/gemma/docs/core/model_card_4)).
Everything runs on the phone; nothing is sent to a server during calls.
|