{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.",
    "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.",
    "baseline": "The same model at its original precision, on the same GPU.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Peak memory, task pass rate, latency, and total serving cost at the same context and load.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.00665",
  "title": "Analysis of Quantized and Efficiently Adapted Protein Language Models",
  "summary": "Replaces full-precision full fine-tuning and full-precision deployment of protein language models with 4-bit quantization combined with low-rank adapters (QLoRA), enabling large PLMs to fit on a single 24 GB GPU. Costs include variable training speed and power effects, plus model-, dataset-, and configuration-dependent performance; generative models can exhibit token-level distributional shifts even when structural and sequence-level metrics look preserved. Failure modes include unstable architectures or difficult tasks with low validation recovery, and quantization-induced output distribution changes that standard downstream metrics can miss.",
  "rationale": "QLoRA with 4-bit quantization directly fits the reader's single-GPU constraint and is a validated first-pass strategy, but the paper shows performance and distributional effects vary by model and task, so per-workload validation is required before production adoption.",
  "technique": "4-bit quantization and QLoRA for protein language models",
  "family": "quantizacao",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00665",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 4,
  "citations": null,
  "claims": [
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  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}