Verified claim · AI-ML · 82% confidence
Megatron-LM introduced in paper: Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (Shoeybi et al., 2019).
Last verified 2026-06-19 · Methodology veritas-v0.1 · 5739ea06ced93de9
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Structured fields
- Subject
- Megatron-LM
- Predicate
introduced_in_paper- Object
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (Shoeybi et al., 2019)
- Confidence
- 82%
- Tags
- megatron-lm · model-parallelism · intra-layer-parallelism · large-language-models · distributed-training · 2019
Sources (2)
[1] preprint · arXiv (Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro) · 2019-09-17
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism“In this work, we present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters.”
[2] docs · Hugging Face
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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Yes — SourceScore verified this claim with 82% confidence as of 2026-06-19. 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 (Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, Bryan Catanzaro), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/5739ea06ced93de9.json includes an HMAC-SHA256 signature for audit verification.
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// "Megatron-LM introduced in paper: Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (Shoeybi et al., 2019)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/5739ea06ced93de9.json")
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# "Megatron-LM introduced in paper: Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (Shoeybi et al., 2019)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_megatron_lm_fact() -> dict:
"""Fetch the verified SourceScore claim for Megatron-LM."""
r = httpx.get("https://sourcescore.org/api/v1/claims/5739ea06ced93de9.json")
return r.json()