# Spec for the HF README of handy-computer/Voxtral-Mini-3B-2507-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: mistralai/Voxtral-Mini-3B-2507
target_repo: handy-computer/Voxtral-Mini-3B-2507-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/voxtral-mini-3b-2507.md

upstream_commit: "3060fe3"
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
  - de
  - es
  - it
  - pt
  - nl
  - hi
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - voxtral
  - audio-llm
  - multilingual

summary: |
  Offline audio-LLM speech-to-text and speech translation. A Whisper-large-v3
  bidirectional audio encoder feeds a 4-frame-group projector (375 audio tokens
  per 30 s chunk) into a Ministral-3B causal LM (30 layers, GQA 32/8, NEOX RoPE,
  SwiGLU) via audio-token injection. Takes a 16 kHz mono WAV and produces a
  transcript via greedy decoding; speech translation runs through the
  mistral-common instruct template. The smaller sibling of Voxtral Small 24B —
  same encoder, projector, log-mel frontend, and tekken tokenizer, with a 3B
  decoder in place of Mistral-Small-24B.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: false
  translate: true
  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: 14.9
    cpu: 2
  ryzen-4750u:
    vulkan: 1.2
    cpu: 0.5

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances) with
    the Whisper English text normalizer, batch size 8 on an NVIDIA L40S.
    Same-machine HuggingFace transformers reference
    (VoxtralForConditionalGeneration, BF16, attn_implementation=eager, greedy):
    1.87%; the BF16 GGUF matches within rounding. The BF16-vs-reference parity is
    the family's tensor-level numerical gate — 43 checkpointed tensors within
    tolerance, transcript byte-exact.

quants:
  - name: BF16
    filename: Voxtral-Mini-3B-2507-BF16.gguf
    size: 9.37 GB
    wer: 1.88%
  - name: F16
    filename: Voxtral-Mini-3B-2507-F16.gguf
    size: 9.38 GB
    wer: 1.89%
  - name: Q8_0
    filename: Voxtral-Mini-3B-2507-Q8_0.gguf
    size: 5.00 GB
    wer: 1.87%
  - name: Q6_K
    filename: Voxtral-Mini-3B-2507-Q6_K.gguf
    size: 3.87 GB
    wer: 1.87%
  - name: Q5_K_M
    filename: Voxtral-Mini-3B-2507-Q5_K_M.gguf
    size: 3.46 GB
    wer: 1.91%
  - name: Q4_K_M
    filename: Voxtral-Mini-3B-2507-Q4_K_M.gguf
    size: 2.98 GB
    wer: 1.94%
