{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A self-hosted model uses more compute or memory than your workload can justify.",
    "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.",
    "baseline": "The original unpruned model at the same precision, context, and load.",
    "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": "Task quality, actual GPU memory, serving throughput, and preparation cost.",
    "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": "2610.00426",
  "title": "IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs",
  "summary": "The method replaces one-shot structured pruning (SliceGPT-style PCA slicing that permanently removes width) with a single model retaining full projection matrices so nested subnetworks at multiple widths can be selected at inference time, with per-layer multi-ratio calibration and gradient-free ridge-regression correction of downstream linear layers. The cost is a calibration and regression pass per target ratio plus storing the full-width weights, and it does not reduce memory at rest the way quantization does; savings materialize only when a smaller width is actually deployed. It can fail because results are explicitly preliminary, per-width accuracy is not guaranteed to match separately pruned models, and serving efficiency depends on runtime support for dynamic width selection, which standard inference stacks do not provide out of the box.",
  "rationale": "On a single 24 GB GPU a slimmable model could let one checkpoint serve multiple latency budgets, but the preliminary evidence and the need for width-switching serving support make workload-specific validation necessary before adoption.",
  "technique": "Post-hoc slimmable LLMs via unpruned SliceGPT projections",
  "family": "esparsidade_e_poda",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00426",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "dimension": "compatibility",
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  "lab_reproductions": [],
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  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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