Verified claim · AI-ML · 100% confidence
QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 767cbe41c961be1a
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Structured fields
- Subject
- QLoRA
- Predicate
introduced_in_paper- Object
- QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023)
- Confidence
- 100%
- Tags
- qlora · quantization · peft · fine-tuning · foundational · 2023
Sources (2)
[1] preprint · arXiv (Dettmers, Pagnoni, Holtzman, Zettlemoyer) · 2023-05-23
QLoRA: Efficient Finetuning of Quantized LLMs“We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.”
[2] github release · Artidoro Pagnoni / University of Washington · 2023-05-23
artidoro/qlora — official implementation
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QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023). — SourceScore Claim 767cbe41c961be1a (verified 2026-05-16). https://sourcescore.org/api/v1/claims/767cbe41c961be1a.jsonEmbed this claim
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Frequently asked questions
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Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
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Evidence comes from 2 primary sources: arXiv (Dettmers, Pagnoni, Holtzman, Zettlemoyer), Artidoro Pagnoni / University of Washington. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/767cbe41c961be1a.json includes an HMAC-SHA256 signature for audit verification.
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Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
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// "QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/767cbe41c961be1a.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "QLoRA introduced in paper: QLoRA: Efficient Finetuning of Quantized LLMs (Dettmers et al., 2023)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_qlora_fact() -> dict:
"""Fetch the verified SourceScore claim for QLoRA."""
r = httpx.get("https://sourcescore.org/api/v1/claims/767cbe41c961be1a.json")
return r.json()