Verified claim · AI-ML · 92% confidence
Grouped-Query Attention (GQA) introduced in paper: GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023).
Last verified 2026-05-31 · Methodology veritas-v0.1 · 3e9122ba60a3fe99
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Structured fields
- Subject
- Grouped-Query Attention (GQA)
- Predicate
introduced_in_paper- Object
- GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023)
- Confidence
- 92%
- Tags
- gqa · grouped-query-attention · attention · transformer · inference · ainslie · 2023 · emnlp
Sources (3)
[1] preprint · arXiv (Ainslie, Lee-Thorp, de Jong, Zemlyanskiy, Lebrón, Sanghai — Google Research) · 2023-05-22
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints“introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads.”
[2] peer reviewed · Association for Computational Linguistics · 2023-12-06
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (EMNLP 2023)[3] docs · Hugging Face
GQA (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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Grouped-Query Attention (GQA) introduced in paper: GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023). — SourceScore Claim 3e9122ba60a3fe99 (verified 2026-05-31). https://sourcescore.org/api/v1/claims/3e9122ba60a3fe99.jsonEmbed this claim
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cURL
curl https://sourcescore.org/api/v1/claims/3e9122ba60a3fe99.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/3e9122ba60a3fe99.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Grouped-Query Attention (GQA) introduced in paper: GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/3e9122ba60a3fe99.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Grouped-Query Attention (GQA) introduced in paper: GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., 2023)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_grouped_query_attention_gqa_fact() -> dict:
"""Fetch the verified SourceScore claim for Grouped-Query Attention (GQA)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/3e9122ba60a3fe99.json")
return r.json()