Verified claim · AI-ML · 100% confidence
PEFT (parameter-efficient fine-tuning) popularized in: Houlsby et al. 2019 — Adapter Modules + downstream PEFT library.
Last verified 2026-05-16 · Methodology veritas-v0.1 · fb9a06ffca4277c1
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Structured fields
- Subject
- PEFT (parameter-efficient fine-tuning)
- Predicate
popularized_in- Object
- Houlsby et al. 2019 — Adapter Modules + downstream PEFT library
- Confidence
- 100%
- Tags
- peft · adapters · fine-tuning · huggingface · foundational · icml · 2019 · introduced_in
Sources (2)
[1] preprint · arXiv / ICML 2019 (Houlsby, Giurgiu, Jastrzebski, Morrone, de Laroussilhe, Gesmundo, Attariyan, Gelly / Google + Jagiellonian) · 2019-02-02
Parameter-Efficient Transfer Learning for NLP“Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules.”
[2] github release · Hugging Face · 2023-02-10
Hugging Face PEFT libraryHugging Face is rated by SourceScore — see its reliability →
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PEFT (parameter-efficient fine-tuning) popularized in: Houlsby et al. 2019 — Adapter Modules + downstream PEFT library. — SourceScore Claim fb9a06ffca4277c1 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/fb9a06ffca4277c1.jsonEmbed this claim
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Evidence comes from 2 primary sources: arXiv / ICML 2019 (Houlsby, Giurgiu, Jastrzebski, Morrone, de Laroussilhe, Gesmundo, Attariyan, Gelly / Google + Jagiellonian), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/fb9a06ffca4277c1.json includes an HMAC-SHA256 signature for audit verification.
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const r = await fetch("https://sourcescore.org/api/v1/claims/fb9a06ffca4277c1.json");
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// "PEFT (parameter-efficient fine-tuning) popularized in: Houlsby et al. 2019 — Adapter Modules + downstream PEFT library."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/fb9a06ffca4277c1.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "PEFT (parameter-efficient fine-tuning) popularized in: Houlsby et al. 2019 — Adapter Modules + downstream PEFT library."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_peft_parameter_efficient_fine_tuning_fact() -> dict:
"""Fetch the verified SourceScore claim for PEFT (parameter-efficient fine-tuning)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/fb9a06ffca4277c1.json")
return r.json()