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Releases: thom899g/quantizy

Quantizy V1.0.1

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@thom899g thom899g released this 14 Jul 13:22

Quantizy V1.0.1

This hotfix rebuilds the signed Apple Silicon app from the latest tested core.

Included

  • DeepSeek method audit covering MLA, DSA, Engram, Memory Grafting, and dynamic
    3-bit baseline evidence.
  • Fixed adaptive latent cold-tail options being dropped by MLX export
    preflight/retry paths.
  • Receipt-gated sparse/latent runtime planning remains unchanged: unsupported
    model integrations are not silently enabled.
  • Full regression suite: 939 passed, 2 warnings.

Download

V1.0.1 keeps the same honest product scope: current-RAM fit advice,
memory guardrails, and validated artifact handoff. It does not claim a
universal quality win over uniform, GGUF dynamic, or Unsloth-style quants.

Quantizy V1.0.0

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@thom899g thom899g released this 14 Jul 12:48

Quantizy V1.0.0

Quantizy V1 is a signed and notarized Apple Silicon Mac fit-advisor release.

Included

  • Current-RAM fit checks for models already on the Mac
  • RAM reserve and pressure-ladder guardrails for busy desktops
  • Safer context, quantization, KV, paging, and prefill recipes
  • Offline license verification and buyer start guidance
  • Developer ID signing, Apple notarization, stapled tickets, and SHA-256 manifest

Honest Scope

V1 helps normal local-AI users decide what can run with their available memory.
It does not claim that every huge model fits on every Mac, or that Quantizy
beats Unsloth, GGUF dynamic quants, or the best uniform bit-width on every
quality benchmark. That research gate remains open.

Install

  1. Download Quantizy-macos-arm64.dmg.

  2. Verify SHA-256:

    51b5b2f1bd1923d5d427c859f407840e1020031d39d312d85dedec71e7474622

  3. Open the DMG, drag Quantizy to Applications, launch it, and enter the
    offline license key from the buyer handoff.

  4. Keep Use current RAM enabled and run Fit Matrix before export.

Payment

V1 personal access is $49 through GitHub Sponsors with manual license delivery
after payment confirmation. Automatic Stripe checkout is closed until the
external fulfillment worker passes its readiness gate.

Quantizy 0.1.9 paid-ready beta

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@thom899g thom899g released this 17 Jun 23:56

Quantizy 0.1.9 is the first paid-ready beta build.

Price:

  • Early paid beta: $19 through GitHub Sponsors.
  • Planned V1 personal license after beta: $49.

Buy / request access:

  1. Sponsor thom899g for $19 or more: https://github.com/sponsors/thom899g
  2. Download the signed DMG from this release.
  3. Open a license request: https://github.com/thom899g/quantizy/issues/new/choose
  4. Manual offline license delivery follows after sponsor/payment verification.

What changed:

  • Signed, notarized, stapled macOS Apple Silicon DMG.
  • App bundle embeds the Quantizy public license key.
  • Squeeze/export path requires a valid offline license by default.
  • Manual license fulfillment is possible from the Keychain-stored signing seed.
  • Includes the 0.1.8 score-aware route-group prefetch improvement for crowded-memory MoE runs.

Verification:

  • GitHub release DMG SHA-256: bfc39f8a38893ebe0b75b64c49eb7930c523619fa87ae6f6a3d9d55c9c8baa1e
  • Apple notary submission: a98eea9a-e985-4506-88a2-06199f9a17e7
  • Local test suite: 649 passed, 2 warnings

Commercial status:

  • GitHub Sponsors is the near-term paid beta intake path.
  • Paid activation currently uses manual license delivery after sponsorship/payment.
  • Automatic Stripe fulfillment remains gated until https://worker.ontarioprotocol.com/ready resolves and the Ontarioprotocol DMG mirror passes checksum verification.

Quantizy 0.1.8

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@thom899g thom899g released this 17 Jun 23:39

Quantizy 0.1.8 is the signed/notarized macOS paid-beta candidate with score-aware route-group prefetch for tighter RAM/cache budgets on crowded local Macs.

Download the notarized Apple Silicon DMG from Ontario Protocol:
https://ontarioprotocol.com/Quantizy-macos-arm64.dmg

Expected SHA-256:
d8b50895970676602fef31875cd6f99b79d8026f1ecead19e38e6744754acd19

Also available:

What changed:

  • Saved route-group scores now flow into the lazy expert runtime.
  • If a predicted offloaded MoE companion group is too large for the current cache, Quantizy can warm the highest-value subset instead of skipping the full group.
  • Release gate passed locally for paid-beta fit-advisor scope with worker readiness skipped pending production DNS/readiness.

Honest scope: V1 is a local Mac fit advisor, memory guardrail, license/activation shell, and validated-artifact handoff. It is not a universal claim that every giant model runs on every small PC.

Payment note: GitHub Sponsors/Stripe payout approval is separate from this artifact release. Do not claim automated paid fulfillment is live until the worker readiness gate passes.

Quantizy 0.1.7

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@thom899g thom899g released this 17 Jun 23:15

Quantizy 0.1.7 is the signed/notarized macOS paid-beta candidate with runtime pressure-ladder guidance for crowded local Macs.

Download the notarized Apple Silicon DMG from Ontario Protocol:
https://ontarioprotocol.com/Quantizy-macos-arm64.dmg

Expected SHA-256:
6d046444e53366b237e8e1f17d8675e6b2c14128470230c2dcab687f80af9e6e

Also available:

Honest scope: V1 is a local Mac fit advisor, memory guardrail, license/activation shell, and validated-artifact handoff. It is not a universal claim that every giant model runs on every small PC.

Payment note: GitHub Sponsors/Stripe payout approval is separate from this artifact release. Do not claim automated paid fulfillment is live until the worker readiness gate passes.

Quantizy 0.1.6

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@thom899g thom899g released this 17 Jun 21:15

Quantizy 0.1.6 is the signed/notarized macOS paid-beta candidate.

Download the notarized Apple Silicon DMG from Ontario Protocol:
https://ontarioprotocol.com/Quantizy-macos-arm64.dmg

Expected SHA-256:
3c434903611312db20f681576e0a7b456ab05b30c7862e036cb4b49b1eb9a492

Also available:

Honest scope: V1 is a local Mac fit advisor, memory guardrail, license/activation shell, and validated-artifact handoff. It is not a universal claim that every giant model runs on every small PC.