Voxtral-4B-TTS-2603-RotorQuant-MLX-8bit

8-bit MLX weight-quantized build of mistralai/Voxtral-4B-TTS-2603 with a RotorQuant KV-cache profile. Highest-fidelity MLX TTS variant — preferred when batches mix voices or languages.

Overview

Base: mistralai/Voxtral-4B-TTS-2603 — 4B multilingual TTS with zero-shot voice cloning
Weight precision: 8-bit (group-wise)
KV-cache profile: RotorQuant (rotational online re-basis)
Approx. on-disk size: ~4 GB
Runtime: MLX on Apple Silicon

Quickstart

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("majentik/Voxtral-4B-TTS-2603-RotorQuant-MLX-8bit")

for text, voice in utterances:
    prompt = tokenizer.apply_chat_template(
        [{"role": "user", "content": [
            {"type": "audio", "path": voice},
            {"type": "text", "text": text},
        ]}],
        add_generation_prompt=True,
    )
    audio_tokens = generate(model, tokenizer, prompt=prompt, max_tokens=2048)

Model specs

Field	Value
Parameters	4B
Weight bits	8
Group size	64
Cache profile	RotorQuant
Languages	9
Voice cloning	Zero-shot
Size on disk	~4 GB
Target hardware	Apple Silicon (M1/M2/M3/M4)
License	Apache 2.0

RotorQuant vs TurboQuant

	RotorQuant	TurboQuant
Strategy	Rotational online re-basis	Per-head static calibration
Memory reduction	~4x on KV-cache	~3.5x on KV-cache
Best for	Multi-voice / multi-language batches	Single-voice sessions

Model tree for majentik/Voxtral-4B-TTS-2603-RotorQuant-MLX-8bit

Base model

mistralai/Ministral-3-3B-Base-2512

Finetuned

mistralai/Voxtral-4B-TTS-2603

Quantized

(10)

this model