Gast ::
| Hauptseite > Publikationsdatenbank > Efficient Computation of Low-Rank Representations to Reduce Memory Requirements in LLM Training > print |
| Hauptseite > Publikationsdatenbank > Efficient Computation of Low-Rank Representations to Reduce Memory Requirements in LLM Training > print |
| 001 | 1034068 | ||
| 005 | 20250203103429.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06889 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06889 |
| 100 | 1 | _ | |a Penke, Carolin |0 P:(DE-Juel1)192254 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a LoRAINNe’24: workshop on LOw-Rank Approximations and their Interactions with Neural NEtworks |g LoRAINNe’24 |c Nancy |d 2024-11-26 - 2024-11-27 |w France |
| 245 | _ | _ | |a Efficient Computation of Low-Rank Representations to Reduce Memory Requirements in LLM Training |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1736500131_6151 |2 PUB:(DE-HGF) |x Invited |
| 520 | _ | _ | |a The OpenGPT-X project represents one of Europe’s pioneering publicly funded efforts in the domain of large language models (LLMs), covering the entire lifecycle from pre-training foundational models to fine-tuning and practical application development. To maximize the efficiency of training on High Performance Computing (HPC) resources, strategies aimed at reducing computational and memory demands are being explored. A promising avenue exploits the low-rank structure of gradients, as done in the LoRA or GaLore frameworks, the latter of which relies on the computation of dominant low-rank subspaces during training. The randomized range finder algorithm provides a more efficient alternative to computing a full singular value decomposition (SVD). We introduce a novel variant of the range finder, based on the blocked Householder QR decomposition, optimized for modern GPU accelerators. |
| 536 | _ | _ | |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5112 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a OpenGPT-X - Aufbau eines Gaia-X Knotens für große KI-Sprachmodelle und innovative Sprachapplikations-Services; Teilvorhaben: Optimierung und Skalierung auf großen HPC-Systemen (68GX21007F) |0 G:(DE-Juel-1)68GX21007F |c 68GX21007F |x 1 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1034068/files/Penke_LowRankRepresentationsToReduceMemoryInLLMs.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1034068 |p openaire |p open_access |p VDB |p driver |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)192254 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5112 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|