porting-5-quants

---

name: porting-5-quants description: Produces the shipped quant matrix from the reference-dtype GGUF, smoke-tests each, publishes the matrix to a private HF repo, and takes a tentative WER read (Modal or local) for human review. Use after porting-4-cpp has finalized tolerances and passed validate.py + the full ref-dtype WER gate. Input: models//-.gguf. Output: F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M alongside the reference-dtype GGUF; a CLI smoke pass per file; quants pushed to a private HF repo; tentative per-quant WER. Authoritative quant WER is Stage 7. No tensor-level numerical comparison is required for quant acceptance — that is intentional.

porting-5-quants

Stage 5 of the porting pipeline. Builds the quantizer, runs scripts/quantize-all.py, smoke-tests each GGUF, publishes the matrix to a private HF repo, and takes a tentative WER read for human review. Authoritative quant WER is Stage 7.

Preconditions

• porting-4-cpp complete: validate.py all green at ref dtype, the family-doc Capability Validation table is fully resolved (no TODO rows), and the full ref-dtype C++ WER is no more than the Oracle reference WER + 0.01.
• models/<variant>/<variant>-<REFDTYPE>.gguf exists, where REFDTYPE is the intake's dtype.expected mapped to the GGUF preset suffix.
• build/bin/transcribe-cli and build/bin/transcribe-quantize are buildable.
• hf authenticated for the target org (private upload). Modal optional for the tentative WER sweep.

Workflow

Quants progress:
- [ ] Step 1: Build transcribe-quantize
- [ ] Step 2: Run quantize-all
- [ ] Step 3: CLI output-validity smoke per produced GGUF
- [ ] Step 4: Publish quants to a private HF repo
- [ ] Step 5: Tentative WER sweep (Modal or local)
- [ ] Step 6: Sign-off review

Step 1: Build the quantizer (execute)

cmake --build build --target transcribe-quantize

Step 2: Run quantize-all (execute)

uv run scripts/quantize-all.py models/<variant>/<variant>-<REFDTYPE>.gguf

Produces <variant>-F16.gguf, <variant>-Q8_0.gguf, <variant>-Q6_K.gguf, <variant>-Q5_K_M.gguf, <variant>-Q4_K_M.gguf alongside the reference- dtype GGUF. The script skips the tier suffix that duplicates the source.

Step 3: CLI output-validity smoke (execute)

For every produced GGUF, confirm the C++ runtime can load it and produce a valid transcript on the primary sample for the family. Do not run tensor/numeric comparisons on quantized GGUFs — quantization is intentionally lossy, and Stage 7 WER is the user-facing quant quality report.

SAMPLE=samples/jfk.wav  # primary sample for the family
for gguf in models/<variant>/<variant>-*.gguf; do
  out="$(build/bin/transcribe-cli -m "$gguf" "$SAMPLE")" && \
    printf '%s\n' "$out" | grep -q '^text: .\+' && echo "OK $gguf" \
    || echo "FAIL $gguf"
done

Any FAIL is a real bug; investigate before sign-off.

Step 4: Publish quants to a private HF repo (execute)

Push the full matrix to a private repo (convention <org>/<variant>-gguf). Everything stays private for now; flipping it public is a future Stage 8 action, not done here.

hf repo create <org>/<variant>-gguf --repo-type model --private  # if absent
hf upload <org>/<variant>-gguf models/<variant> . --repo-type model

Step 5: Tentative WER sweep (execute)

Per-quant WER for human review on the full acceptance manifest, not a subset. "Tentative" here means "not the published number" (Stage 7 re-runs and confirms), NOT "small N". Use Modal if credentials are available; otherwise run locally. Do not pass --n-utts unless you have a specific debugging reason and call it out in the sign-off.

# Modal: sweeps the private repo from Step 4 on GPU (full dataset)
modal run scripts/wer/remote/modal_sweep.py::sweep \
  --models <org>/<variant>-gguf --quants ""
# local
for q in F16 Q8_0 Q6_K Q5_K_M Q4_K_M; do
  uv run scripts/wer/run.py --model models/<variant>/<variant>-$q.gguf \
    --manifest "$MANIFEST" --out reports/wer/<variant>-$q.<dataset>.jsonl
  uv run scripts/wer/score.py reports/wer/<variant>-$q.<dataset>.jsonl
done

Report the per-quant WER table for user review before Stage 6.

Step 6: Sign-off

Report:

• Every produced GGUF with file size.
• Any GGUF that failed the CLI smoke (with the failing output).
• The private HF repo the matrix was pushed to.
• Tentative per-quant WER table (preliminary; Stage 7 authoritative).

Do not commit.

Postconditions

• All five derived presets (F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M) exist under models/<variant>/, with the source tier suffix skipped where it duplicates one of them.
• Every produced GGUF loads via build/bin/transcribe-cli and emits a non-empty text: line on the primary sample.
• No tensor-level numerical comparison is required (or expected) for quant acceptance.
• Quant matrix pushed to a private HF repo (<org>/<variant>-gguf).
• Tentative per-quant WER produced and reviewed; authoritative WER is Stage 7.

Pointers (read, not execute)

• docs/tools/quantization.md — transcribe-quantize invocation
• scripts/quantize-all.py — quant matrix driver
• scripts/lib/quant_policy.py — per-family tier policy if a family needs to override the default DERIVED_PRESETS