HQQ¶
Quantize eligible linear weights without calibration data; this is not yet a VoiceHub public pass.
Use¶
python -m pip install \
"hqq @ git+https://github.com/dropbox/hqq.git@d88a488ec8aa2d58362ef2038a52bca862db2e74"
Support¶
| Property | Value |
|---|---|
| Availability | Optional library; no registered VoiceHub pass |
| Fidelity | Quantized; measure task quality and generated audio |
| Runtime | Backend-dependent |
| Registry name | Not registered; do not report this backend as an applied VoiceHub pass |
Unsupported explicit configurations must fail before mutation. A pass that
does not match a model reports not-applicable; it is not an acceleration.
Paper and GitHub¶
- Paper: Half-Quadratic Quantization of Large Machine Learning Models
- Upstream GitHub: HQQ
- VoiceHub source: No VoiceHub pass implementation; use the external source project directly.
Verify¶
Compare the eager and optimized paths with the same checkpoint, input, seed, warm-up, device, and dtype. Record latency, memory, output quality, the exact source revision, and the optimization manifest.
See the related workflow and optimization API.