Instructions to use KitKatL/Hojo-ASR-Multi-V1-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use KitKatL/Hojo-ASR-Multi-V1-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Hojo-ASR-Multi-V1-MLX-4bit KitKatL/Hojo-ASR-Multi-V1-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Hojo-ASR-Multi-V1 MLX mobile 4-bit
This is a private MLX conversion of
HojoAI/Hojo-ASR-Multi-V1
for Apple silicon and the Live Transcriber native Swift runtime.
It is not a drop-in replacement for the upstream hojo-asr Python package.
The native Swift/MLX model implementation is maintained separately in Live
Transcriber.
Conversion
- Source repository:
HojoAI/Hojo-ASR-Multi-V1 - Source revision:
aa1d0dfc369deb3b3235572fa151acd7b498481f - Source checkpoint SHA-256:
c046a5a26c142f5aded7331d36892293a025f579e342a27861e80509afaca88d - Quantization: affine 4-bit, group size 64
- Quantized scope: text decoder and frontend linear layers
- Remaining frontend dtype: float16
- Weight size: 3,089,867,955 bytes across six safetensors shards
- Tensor count: 1,942
The exact architecture and generation metadata are recorded in config.json.
.complete.json is the manifest validated by Live Transcriber before loading.
Compatibility
- macOS on Apple silicon with MLX/Metal
- Live Transcriber Hojo-ASR native runtime
- Automatic multilingual recognition
- Post-recording transcription; no word-level alignment
The public upstream inference interface does not expose forced-language, prompt, hotword, custom-dictionary, or speaker-adaptation controls. The Live Transcriber runtime therefore uses deterministic decoding with repetition penalty rather than exposing unvalidated generation controls.
Validation
The bundle was loaded directly from external storage with the native Swift/MLX runtime on Apple silicon. A 5.293-second German fixture produced:
guten morgen dies ist ein kurzer test für die lokale spracherkennung auf einem mac
Measured native test runtime was approximately 6.01 seconds. MLX reported an approximately 3.42 GB peak allocation during that test. These figures describe one local validation run and are not general performance guarantees.
The same bundle was also loaded on an iPhone 16 Pro Max in an isolated device probe, but distribution and production iOS integration remain separate gates.
License
The source model is published under the Apache License 2.0. See LICENSE. This repository contains a converted derivative and retains the upstream license notices.
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