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TText generation▧Image generation▷Video generation⌘Code generation◖Speech synthesis♫Music generation⬡3D modeling▤Document analysis
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A target-specific classic DFlash draft for `ornith-ai/Ornith-1.5-9B`, initialized from `z-lab/Qwen3.5-9B-DFlash` and distilled against Ornith 1.5 target features. This is **not a standalone language model**. Pair it with the Ornith 1.5 9B target in a runtime that supports classic DFlash speculative decoding. The six-layer Qwen3.5 DFlash backbone was retained. Projection-specific distillation unfroze `fc` and `hidden_norm`, while the remaining draft layers stayed frozen.

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realistic

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End-to-end CoreML export of hexgrad/Kokoro-82M at FP16, optimized for Apple Neural Engine. Requires iOS 18+ / macOS 15+. A single `kokoro_5s.mlmodelc` runs the full pipeline (BERT → duration prediction → fixed-shape alignment → prosody → decoder) in one CoreML call. G2P (grapheme-to-phoneme) is a separate pair of CoreML models. Looking for a smaller variant? See `aufklarer/Kokoro-82M-CoreML-INT8` — INT8 k-means palettized, 83 MB vs 325 MB here, with log-spec distance 0.42 vs this FP16 reference on a validation utterance.

Speech synthesis
Input Text→ Output Audio
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Streaming speech recognition with end-of-utterance detection, converted to CoreML for Apple Neural Engine inference. Part of speech-swift — on-device speech AI for Apple Silicon. Based on nvidia/parakeet_realtime_eou_120m-v1 (FastConformer-RNNT, cache-aware streaming). Converted from nvidia/parakeet_realtime_eou_120m-v1 using coremltools 8.3 with INT8 palettization.

Speech recognition
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MLX-compatible weights for pyannote/segmentation-3.0 (PyanNet), converted from the official PyTorch Lightning checkpoint with pre-computed SincNet filters. PyanNet is a speaker segmentation model (~1.5M params) that processes 10-second audio windows and outputs 7-class powerset probabilities for up to 3 simultaneous speakers. Used for both voice activity detection (binary) and speaker diarization (per-speaker). Converts the gated pyannote/segmentation-3.0 checkpoint using a custom unpickler (no pyannote.audio dependency required). Key transformations:

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