Zeitpunkt Nutzer Delta Tröts TNR Titel Version maxTL Do 03.10.2024 00:00:23 62.140 0 3.759.060 60,5 Fosstodon 4.2.13 500 Mi 02.10.2024 00:00:06 62.140 +1 3.755.928 60,4 Fosstodon 4.2.13 500 Di 01.10.2024 00:01:13 62.139 +1 3.752.620 60,4 Fosstodon 4.2.13 500 Mo 30.09.2024 00:01:12 62.138 +3 3.749.484 60,3 Fosstodon 4.2.12 500 So 29.09.2024 00:01:07 62.135 -1 3.746.995 60,3 Fosstodon 4.2.12 500 Sa 28.09.2024 00:01:07 62.136 +2 3.745.047 60,3 Fosstodon 4.2.12 500 Fr 27.09.2024 00:01:07 62.134 +2 3.741.982 60,2 Fosstodon 4.2.12 500 Do 26.09.2024 00:00:18 62.132 +5 3.738.514 60,2 Fosstodon 4.2.12 500 Mi 25.09.2024 00:00:21 62.127 +15 3.744.145 60,3 Fosstodon 4.2.12 500 Di 24.09.2024 00:00:00 62.112 0 3.741.205 60,2 Fosstodon 4.2.12 500
Hal (@hlesesne) · 01/2021 · Tröts: 375 · Folger: 180
Do 03.10.2024 16:51
Summary: The article explores methods to efficiently deploy large language models (LLMs) on low-resource edge devices. The proposed approach reduces model size and memory consumption by leveraging knowledge distillation and quantization techniques, enabling the utilization of LLMs on devices with limited computational capacity.
Link: https://arxiv.org/abs/2410.00531
Comments: https://news.ycombinator.com/item?id=41730983
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