Instructions to use aufklarer/CosyVoice3-0.5B-MLX-8bit-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use aufklarer/CosyVoice3-0.5B-MLX-8bit-full with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir CosyVoice3-0.5B-MLX-8bit-full aufklarer/CosyVoice3-0.5B-MLX-8bit-full
- CosyVoice
How to use aufklarer/CosyVoice3-0.5B-MLX-8bit-full with CosyVoice:
# 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
- Local Apps Settings
- LM Studio
- Atomic Chat
CosyVoice3 0.5B — MLX 8-bit (LLM + Flow)
MLX port of FunAudioLLM/Fun-CosyVoice3-0.5B-2512 for Apple Silicon, with 8-bit quantization on both the LLM and the flow-matching DiT (the "full 8-bit" bundle). HiFi-GAN stays float32 — vocoder quality degrades noticeably under quantization.
Sibling bundles in the aufklarer/ org:
CosyVoice3-0.5B-MLX-4bit— smallest (~600 MB), 4-bit everywhereCosyVoice3-0.5B-MLX-8bit— LLM 8-bit, DiT 4-bit (~900 MB)CosyVoice3-0.5B-MLX-8bit-full(this repo) — LLM 8-bit, DiT 8-bit (~1.1 GB)
Bundle contents
| file | size | purpose |
|---|---|---|
llm.safetensors |
640 MB | Qwen2-derived text→speech-token LLM |
flow.safetensors |
342 MB | DiT flow-matching speech-token→mel |
hifigan.safetensors |
79 MB | mel→24 kHz waveform vocoder |
speech_tokenizer.safetensors |
484 MB | s3tokenizer-v3 (reference audio → FSQ codes; required for zero-shot voice cloning) |
config.json |
— | model + quantization config |
Total: ~1.5 GB.
Usage with speech-swift
import CosyVoiceTTS
let model = try await CosyVoiceTTSModel.fromPretrained(
modelId: "aufklarer/CosyVoice3-0.5B-MLX-8bit-full"
)
let tokenizer = try SpeechTokenizerModel.fromSafetensors(
at: cacheDir.appendingPathComponent("speech_tokenizer.safetensors"))
// Zero-shot voice cloning: prompt_token + prompt_feat carry the voice.
let refSamples = try AudioFileLoader.load(url: refURL, targetSampleRate: 16000)
let profile = try model.extractVoiceProfile(
audio: refSamples, sampleRate: 16000,
speechTokenizer: tokenizer,
referenceTranscript: "Exact transcript of the reference clip.")
let audio = model.synthesize(
text: "Hello world.",
promptToken: profile.promptToken,
promptFeat: profile.promptFeat,
promptText: profile.promptText)
Conversion
Converted from the PyTorch original via speech-models' models/cosyvoice-tts/export/convert.py:
python convert.py --llm-bits 8 --quantize-dit --flow-bits 8 \
--output-dir cosyvoice3-mlx-8bit-full
Group size 64 for both LLM and Flow quantization. HiFi-GAN kept at float32. See the upstream README for the underlying model architecture and license.
License
Apache 2.0, inherited from the upstream FunAudioLLM/Fun-CosyVoice3-0.5B-2512.
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Quantized
Model tree for aufklarer/CosyVoice3-0.5B-MLX-8bit-full
Base model
FunAudioLLM/Fun-CosyVoice3-0.5B-2512