| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - on-device-ai |
| - privacy-first |
| - mlx |
| - asr |
| - speech-recognition |
| - local-llm |
| --- |
| |
| # Yooz Labs |
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| **Sovereign Intelligence. Built for the skeptical.** |
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| Privacy-first AI that runs entirely on your devices. No cloud, no tracking, no compromises. |
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| --- |
|
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| ## What we're building |
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| We're the privacy infrastructure for the AI decade. Every Yooz product is designed for the 70% of consumers who don't trust cloud AI but lack consumer-grade alternatives. |
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| - **Yooz Engine** — unified local AI service for macOS (STT, LLM, grammar, VAD, TTS). |
| - **Yooz Whisper** — voice keyboard for macOS. |
| - **Yooz Notes** — note-taking with private AI memory. |
| - **Remi** — Claude Code's distant friend. Secure peer-to-peer remote sessions for Claude Code (and soon Codex), with an iPhone app and local auto-approve. |
| - **Yooz Vault** — privacy hardware (home server). |
| - **Universal AI Platform Layer** — one API across Apple Core ML, Android ML Kit, Windows DirectML. |
|
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| ## What lives on this Hugging Face org |
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| The **model weights** — Apache 2.0, fully open source. The Yooz product code is source-available on GitHub under PolyForm Shield, but the weights stay open so the research community can build on them, audit them, and remix them. |
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| ### Model categories |
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| | Category | What it is | |
| |---|---| |
| | **ASR** | Speech-to-text checkpoints (Qwen3-ASR Swift port, Parakeet derivatives) | |
| | **LLMs (Touchup)** | Fine-tuned small LLMs that fix/clean speech-to-text transcripts | |
| | **Distillations** | Small students distilled from larger teachers for on-device inference | |
| | **Adapters** | LoRA / DoRA adapters published alongside their fused checkpoints | |
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| All checkpoints document their **lineage** (base model + Hugging Face link), **eval numbers** (real benchmarks, not vibes), and **Swift / Python usage snippets**. |
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| ## Why open weights? |
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| The competitive moat in privacy-first AI lives in the **product**, not the weights: |
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| - Multi-device orchestration (phone → PC → Vault) over WireGuard mesh. |
| - Universal platform abstraction across Apple, Android, Windows AI APIs. |
| - Private AI memory: encrypted, local, with permissioned cross-app context. |
| - Beautiful, consumer-grade UX. |
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| The weights themselves should be open so the research community can audit privacy claims, reproduce evals, and build on top. Releases follow the standard "ship the artifact + the recipe to reproduce it." |
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| ## Provenance |
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| We never train on user data without opt-in. All training corpora and synthetic data sources are documented in the model card for each checkpoint. Where we fine-tune from a base model (Qwen, Gemma, etc.), the lineage is preserved and the upstream license is respected. |
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| ## Source-available, not closed |
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| The product code lives at [github.com/yooz-labs](https://github.com/yooz-labs) under PolyForm Shield. You can read it, fork it, and build on it for non-competing use cases. We chose this path because we want to stay community-aligned without enabling AWS-style "managed Yooz" competing services. |
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| ## Get in touch |
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| - **Engineering & research**: dev@yooz.info |
| - **Bugs and feature requests**: file on the relevant GitHub repo under [yooz-labs](https://github.com/yooz-labs) |
| - **Mailing list / news**: coming soon |
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| --- |
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| *We're building the privacy infrastructure for the AI decade. Every decision prioritizes user sovereignty, data privacy, and beautiful simplicity.* |
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