# Spec for the HF README of handy-computer/Voxtral-Mini-4B-Realtime-2602-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: mistralai/Voxtral-Mini-4B-Realtime-2602
target_repo: handy-computer/Voxtral-Mini-4B-Realtime-2602-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/voxtral-realtime.md

upstream_commit: "2769294"
pin_date: 2026-06-06

# Validation pin for the most recent upload. Updated on each release —
# older HF revisions carry whatever value was current at their upload time.
validation:
  reference: Transformers
  commit: 483c122
  date: 2026-06-06

license: apache-2.0
license_display: Apache-2.0
pipeline_tag: automatic-speech-recognition
languages:
  - en
  - fr
  - es
  - de
  - ru
  - zh
  - ja
  - it
  - pt
  - nl
  - ar
  - hi
  - ko
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - voxtral
  - audio-llm
  - streaming
  - multilingual

summary: |
  Streaming audio-LLM speech-to-text. A ~970M causal audio encoder (left-pad
  causal conv stem + 32-layer sliding-window RoPE transformer) feeds a
  4-frame-group projector whose audio embeddings are added onto a ~3.4B
  Ministral decoder (26 layers, GQA 32/8, NEOX RoPE) with delay-token latency
  conditioning, emitting one text token per 80 ms audio slot (12.5 Hz). Takes a
  16 kHz mono WAV and supports both incremental streaming (configurable
  latency/quality via --stream-chunk-ms and --stream-voxtral-delay) and offline
  transcription with a byte-equal final transcript. Architecturally distinct
  from the offline Voxtral 2507 family — own arch, streaming frontend, causal
  encoder, additive audio fusion.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: true
  translate: false
  lang_detect: true
  timestamps: none  # none | segment | word | token

# Speedup-over-realtime (×RT) per rig/backend, from docs/models; published
# raw as rtf_<machine> in the metadata block.
perf:
  m4-max:
    metal: 8.6
    cpu: 2.5
  ryzen-4750u:
    vulkan: 0.9
    cpu: 0.6

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances) with
    the Whisper English text normalizer, offline path, batch size 8 on an NVIDIA
    L40S. Same-machine HuggingFace transformers reference
    (VoxtralRealtimeForConditionalGeneration, BF16, greedy): 2.08%; the BF16 GGUF
    matches at 2.08%. Every shipped quant stays within bootstrap noise
    (2.07-2.09%), so the quantization ladder is WER-neutral down to Q4_K_M. The
    model is multilingual (13 languages, auto-detect); the published WER is
    English only.

quants:
  - name: BF16
    filename: Voxtral-Mini-4B-Realtime-2602-BF16.gguf
    size: 8.87 GB
    wer: 2.08%
  - name: F16
    filename: Voxtral-Mini-4B-Realtime-2602-F16.gguf
    size: 8.88 GB
    wer: 2.09%
  - name: Q8_0
    filename: Voxtral-Mini-4B-Realtime-2602-Q8_0.gguf
    size: 4.73 GB
    wer: 2.07%
  - name: Q6_K
    filename: Voxtral-Mini-4B-Realtime-2602-Q6_K.gguf
    size: 3.66 GB
    wer: 2.08%
  - name: Q5_K_M
    filename: Voxtral-Mini-4B-Realtime-2602-Q5_K_M.gguf
    size: 3.28 GB
    wer: 2.08%
  - name: Q4_K_M
    filename: Voxtral-Mini-4B-Realtime-2602-Q4_K_M.gguf
    size: 2.83 GB
    wer: 2.08%
