{
  "schema": "fable.research_map.receipt.v1",
  "createdAt": "2026-07-05T01:35:00.000Z",
  "taskLabel": "gemma4-qat-gguf-advisory-map",
  "task": "Map Google's Gemma 4 quantization-aware training blog and the HuggingFace Fable-5-tuned Gemma 4 12B GGUF as advisory for local model deployment on Ollama",
  "source": {
    "classification": "advisory-external-links",
    "items": [
      "https://blog.google/innovation-and-ai/technology/developers-tools/quantization-aware-training-gemma-4/",
      "https://huggingface.co/yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF"
    ],
    "contentVerified": false,
    "localProofExists": false,
    "liveVerification": "not fetched — links provided by user during Ollama setup session"
  },
  "extractedPrinciples": [
    "Gemma 4 uses quantization-aware training (QAT) — model was trained knowing it would be quantized, so quantized versions lose less quality than post-hoc quantization",
    "Google claims 4-bit QAT Gemma 4 approaches FP16 quality — significant for running on consumer/VPS hardware",
    "The HuggingFace model is a Fable 5 agentic composer fine-tune of Gemma 4 12B (not 27B), converted to GGUF format",
    "GGUF format is directly compatible with Ollama's llama.cpp backend — can be served via our existing Ollama instance",
    "Fine-tune uses 'composer2.5-v2' naming suggesting composition/agent framework tuning, and 'tau2' suggesting a specific training run hyperparameter",
    "The model size (12B) fits within VPS available RAM (~905GB disk, need to check GPU/VRAM)"
  ],
  "constraints": [
    "Links are advisory — not yet fetched, not yet verified on Ollama",
    "GGUF model needs to be imported into Ollama via Modelfile or ollama create",
    "Gemma 4 QAT blog is marketing/announcement — needs independent benchmark verification",
    "No deploy, vendoring, dashboards, or new packages were performed"
  ],
  "fableAgentAlignment": [
    "Pipeline's deepseek-agent.ts already supports configurable endpoint/model — can target gemma4:12b by changing model name",
    "RunAgent callback takes model config, so switching from deepseek-coder-v2 to gemma4 is a one-line change",
    "If Gemma 4 QAT delivers on its quality claims, it's the best candidate for the pipeline's agent runtime: smaller, faster, free, local"
  ],
  "skipped": [
    "actual model download/pull on Ollama (waiting on deepseek-coder-v2 to finish at 86%)",
    "benchmark comparisons between deepseek-coder-v2 and gemma4 on coding tasks"
  ],
  "decision": "advisory — explore after deepseek-coder-v2 finishes pulling",
  "deployAttempted": false,
  "autoPatchProven": false
}