aufklarer/PersonaPlex-7B-MLX-4bit

Note: For full-duplex (real-time) inference, use the 8-bit variant instead. 4-bit quantization degrades PersonaPlex response quality significantly — INT8 is both 30% faster (112ms vs 158ms/step) and produces coherent responses where INT4 generates garbled output.

PersonaPlex-7B MLX 4-bit

PersonaPlex 7B full-duplex speech-to-speech model converted to MLX safetensors with 4-bit quantization for Apple Silicon.

Converted from nvidia/personaplex-7b-v1 (based on Kyutai Moshi architecture).

Swift inference: soniqo/speech-swift

Library Docs: soniqo.audio

Blog: PersonaPlex on Apple Silicon — Full-Duplex Speech-to-Speech in Native Swift with MLX

Model Details

Component	Architecture	Size
Temporal Transformer	32-layer, 4096d, 32 heads (7B params)	~3.5 GB (4-bit)
Depformer	6-layer, 1024d, 16 heads, per-codebook weights	~50 MB (fp16)
Mimi Codec	SEANet encoder/decoder + 8L transformer + 16 RVQ codebooks	~370 MB (fp16)
Embeddings	Text + 16 audio embeddings + output heads	~940 MB (fp16)
Total		~4.9 GB

Architecture

[User Audio 24kHz] → [Mimi Encoder] → 16 codebook tokens @ 12.5Hz
                                              ↓
              [Temporal Transformer: 32L, dim=4096, 7B params]
                  17 streams: text + 8 user audio + 8 agent audio
                                              ↓
              [Depformer: 6L, dim=1024, per-codebook weights]
                  16 sequential steps → agent audio codebook tokens
                                              ↓
[Agent Audio 24kHz] ← [Mimi Decoder] ← codebook tokens @ 12.5Hz

Voices

18 voice presets available:

Category	Voices
Natural Female	NATF0, NATF1, NATF2, NATF3
Natural Male	NATM0, NATM1, NATM2, NATM3
Variety Female	VARF0, VARF1, VARF2, VARF3, VARF4
Variety Male	VARM0, VARM1, VARM2, VARM3, VARM4

Files

• temporal.safetensors — Temporal transformer (4-bit quantized, group_size=64)
• depformer.safetensors — Depformer layers + input projections (fp16)
• embeddings.safetensors — Text/audio embeddings + output heads (fp16)
• mimi.safetensors — Mimi neural audio codec (fp16)
• voices/*.safetensors — Voice preset embeddings
• tokenizer_spm_32k_3.model — SentencePiece tokenizer
• config.json — Model configuration

Quantization

• Temporal transformer attention (Q/K/V output projections) and FFN: 4-bit with group_size=64
• Attention input projection (in_proj): kept fp16 (packed Q+K+V format)
• Depformer: kept fp16 (~50 MB, not worth quantizing)
• Mimi codec: kept fp16 (audio quality sensitive)

Usage

import PersonaPlex

let model = try await PersonaPlexModel.fromPretrained()
let response = model.respond(
    userAudio: audioSamples,  // [Float] 24kHz mono
    voice: .NATM0,
    maxSteps: 500
)

CLI

swift run personaplex-cli --input question.wav --output response.wav --voice NATM0

See soniqo/speech-swift for build instructions.

License

CC-BY-NC-4.0 (same as upstream PersonaPlex)

Citation

@article{nguyen2025personaplex,
  title={PersonaPlex: Enhancing Human-Centric AI Through Full-Duplex Multi-Turn Conversations With Persona-Conditioned Voice Responses},
  author={Nguyen, Tu Anh and others},
  journal={arXiv preprint arXiv:2504.07966},
  year={2025}
}