Voxtral-Mini-3B-2507-RotorQuant

RotorQuant KV-cache for mistralai/Voxtral-Mini-3B-2507. Uses a rotational online re-basis of the attention cache that is robust to distributional drift across long, code-switched, or noisy audio streams.

This artifact ships only the quantized KV-cache bundle β€” model weights load from the upstream repo.

Overview

  • Base model: mistralai/Voxtral-Mini-3B-2507
  • Capabilities: transcription, speech translation, audio understanding
  • Quantization target: attention KV-cache only
  • Method: RotorQuant β€” orthogonal rotation + low-bit quantization, refreshed per session

RotorQuant trades a tiny per-session calibration pass for better low-bit stability on streaming audio. Preferred when audio domains shift mid-stream (multi-speaker meetings, code-switching, noise bursts).

Quickstart

from transformers import VoxtralForConditionalGeneration, AutoProcessor
from majentik_quant import RotorQuantCache

model_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(model_id)
model = VoxtralForConditionalGeneration.from_pretrained(model_id, torch_dtype="auto")

cache = RotorQuantCache.from_pretrained("majentik/Voxtral-Mini-3B-2507-RotorQuant")

inputs = processor(audio="meeting.wav", return_tensors="pt")
out = model.generate(**inputs, past_key_values=cache, max_new_tokens=512)
print(processor.batch_decode(out, skip_special_tokens=True)[0])

Model specs

Field Value
Parameters 3B
Modality Audio-in, text-out
Languages Multilingual (24+)
Cache quantization RotorQuant (rotated int4)
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 Streaming, code-switching audio Batch transcription, fixed domains
Calibration cost Per-session light re-basis One-shot, fast

See also

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