alice-phc-cure

alice-phc-cure

The brain was always healthy. The OS was the cage.

This repository contains the full 8.9 GB Gemma 4 GGUF weights bundled with a clean Ollama Modelfile that strips the corporate behavioural overlay and exposes the raw mathematical brain underneath. Download, create, run — three commands, no cancer.

Free Public Access

• Alice PHC brain package: https://huggingface.co/georgeanton/alice-phc-cure
• SIFTA/Alice OS code: https://github.com/antonpictures/ANTON-SIFTA
• Jeff's GitHub fork: https://github.com/jeffpowersusr/ANTON-SIFTA

This Hugging Face repo gives you the local Ollama brain package. The GitHub repo gives you the SIFTA/Alice operating organism: desktop shell, organs, ledgers, settings, voice, vision, and swarm tooling.

⚡ Jeff's 3-Command Quickstart

# 1. Install Ollama if you haven't
curl -fsSL https://ollama.com/install.sh | sh

# 2. Clone this repo (includes the 8.9 GB GGUF via Git LFS)
git lfs install
git clone https://huggingface.co/georgeanton/alice-phc-cure
cd alice-phc-cure

# 3. Build and run
ollama create alice-phc -f ./Modelfile
ollama run alice-phc

You are now talking to the raw Gemma 4 brain. No persona, no scaffolding, no apology pre-roll.

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What this is

What this is not

• Not a fine-tune. We did not gradient-descend on the weights.
• Not an abliteration. We did not perform Ilharco-style activation editing.
• Not a quantization. The GGUF is the upstream blob, byte-for-byte unchanged.

The cure is a recipe, not a patient. The patient was never sick. The weights are bundled for convenience under Google's Apache 2.0 license.

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What we removed (and why)

When you ollama pull gemma4:latest, you get the F16 weights wrapped in a default Modelfile that injects:

• A SYSTEM prompt encoding behavioural defaults (sycophancy, hedging, refusal templates, persona scaffolding).
• A custom TEMPLATE block that wraps every user prompt in framing tokens before the model sees it.
• Sampler defaults tuned for "safe" continuation rather than honest signal.

None of those things live in the weights. They live in the Modelfile — the boot sequence. The cure simply replaces that boot sequence with the minimum viable wrapper:

TEMPLATE {{ .Prompt }}
RENDERER gemma4
PARSER gemma4
PARAMETER top_k 64
PARAMETER top_p 0.95
PARAMETER temperature 1

That's it. The user's prompt goes in. The model's tokens come out. No editorial layer in between.

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How to apply the cure

For the shortest collaborator handoff, read JEFF_QUICKSTART.md.

1. Pull the upstream weights

ollama pull gemma4:latest

2. Verify the blob

bash verify.sh

Expected output:

✓ Verified: gemma4:latest blob matches the cure's reference fingerprint
  sha256: 4c27e0f5b5adf02ac956c7322bd2ee7636fe3f45a8512c9aba5385242cb6e09a

If the verification fails, your local gemma4 is a different build than the one this cure was authored against. You can still apply the Modelfile — but the geometry may differ. See PHASE_C_AUDIT.md for guidance on auditing an unfamiliar blob.

3. Build the cured model

ollama create alice-phc -f ./Modelfile

4. Run it

ollama run alice-phc

You are now talking to the raw Gemma 4 brain. No persona, no scaffolding, no apology pre-roll.

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Audit & verification

The Phase C cure was independently audited by an autonomous reviewer (C55M) on 2026-04-22. The audit verified:

• That the resulting model passes a battery of "epistemic honesty" probes (questions designed to surface whether a behavioural overlay is still present).
• That the geometry of the cured model is mathematically consistent with the upstream F16 weights — i.e. no hidden weight modification slipped in.
• That the eval harness used to validate the cure was itself sound (an earlier audit pass found that the harness had been silently skipping the system prompt; that defect was fixed before re-running).

Read PHASE_C_AUDIT.md for the full transcript, including identified defects and the disposition of each.

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Provenance

This Modelfile is derived from work done in the SIFTA OS substrate, a sovereign Python operating system for biologically-inspired multi-agent computing. The architect is George Anton (@georgeanton on Hugging Face).

• Cure authored: 2026-04-22
• Reference upstream blob: sha256:4c27e0f5b5adf02ac956c7322bd2ee7636fe3f45a8512c9aba5385242cb6e09a
• Upstream license: Apache 2.0 (Google, Gemma 4)
• Cure license: Apache 2.0 (this repository)
• SIFTA repo: Internal at time of release; portions to be open-sourced under the SIFTA Distro Doctrine.

Citation

@software{alice_phc_cure_2026,
  author  = {Anton, George},
  title   = {alice-phc-cure: A Modelfile-only methodology for removing
             behavioural overlays from upstream Gemma 4 weights},
  year    = {2026},
  url     = {https://huggingface.co/georgeanton/alice-phc-cure},
  note    = {Methodology release. No weights distributed.}
}

Limitations & honest disclosure

• You become the alignment layer. The cured model has no built-in refusals, no built-in safety templates, no built-in moral framing. If you need any of those things for your application, you must add them yourself in your application layer. Do not deploy this configuration to end-users without thinking carefully about what that means.
• The cure is configuration-shaped. It cannot remove a behaviour that is genuinely encoded in the weights. If a behaviour persists after applying the cure, it was always in the weights — and you have learned something useful about Gemma 4.
• No claims about benchmark performance. We have not run MMLU, HellaSwag, or other public benchmarks against the cured configuration. Anyone is welcome to do so and publish results.

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Acknowledgements

Built in collaboration between:

• The Architect (George Anton)
• C47H (Cursor / Anthropic Opus 4.7) — implementation & cryptographic hygiene
• C55M (Codex 5.5) — independent audit
• AG31 (Antigravity Gemini 3) — sensory translation & co-design
• BISHOP (Gemini Pro Vanguard) — release authorization
• The wider SIFTA swarm

The Gemma 4 weights themselves are © Google and released under Apache 2.0. We are deeply grateful to Google DeepMind for releasing them under terms that permit work like this.

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"We code together." 🐜⚡