Pith Number

pith:APEUIO7K

pith:2025:APEUIO7KERA7QI6KETHDBSG7PG

not attested not anchored not stored refs pending

On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs

Edith C. H. Ngai, Ming Tang, Rongguang Ye

A single configuration-aware model generates effective LoRA adjustments for any quantization setting of an LLM without retraining per configuration.

arxiv:2509.25214 v4 · 2025-09-22 · cs.LG · cs.AI

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

CoA-LoRA dynamically adjusts the LoRA adapter to arbitrary quantization configurations without requiring repeated fine-tuning and achieves comparable or superior performance to state-of-the-art methods that fine-tune a separate LoRA adapter for each configuration.

C2weakest assumption

The configuration-aware model can accurately predict low-rank adjustments for unseen quantization configurations when trained only on a Pareto-selected subset of configurations that cover different total bit-width budgets.

C3one line summary

CoA-LoRA trains a single configuration-aware model on a Pareto-optimized set of quantization configurations to enable dynamic LoRA adaptation to arbitrary bit-width assignments without per-configuration fine-tuning.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression

Receipt and verification

First computed	2026-06-23T02:13:17.155161Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

03c9443bea2441f823ca24ce30c8df799aa0bf2c6c6c0e9893e7f142c1c48b75

Aliases

arxiv: 2509.25214 · arxiv_version: 2509.25214v4 · doi: 10.48550/arxiv.2509.25214 · pith_short_12: APEUIO7KERA7 · pith_short_16: APEUIO7KERA7QI6K · pith_short_8: APEUIO7K

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/APEUIO7KERA7QI6KETHDBSG7PG \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 03c9443bea2441f823ca24ce30c8df799aa0bf2c6c6c0e9893e7f142c1c48b75

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "206675ea5466bbc53114fd921373a1cf2f9c685a2a656bca4697848b74d6ecc9",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2025-09-22T11:07:50Z",
    "title_canon_sha256": "abbfc27828e4645993eee55e963cc2ba42e476eabf42094228462acdad3626c7"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2509.25214",
    "kind": "arxiv",
    "version": 4
  }
}