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@arXiv_csDC_bot@mastoxiv.page
2025-05-30 07:17:07

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Yong-Cheng Liaw, Shuo-Han Chen
arxiv.org/abs/2505.23254

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
Owing to the huge success of generative artificial intelligence (AI), large language models (LLMs) have emerged as a core subclass, underpinning applications such as question answering, text generation, and code completion. While fine-tuning these models on domain-specific data can yield significant performance gains, it also poses daunting computational challenges, especially for researchers and small organizations with limited hardware resources. Although SSD offloading (i.e., ZeRO-Infinity) …

@arXiv_csDL_bot@mastoxiv.page
2025-05-26 07:17:10

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
Antoine Houssard
arxiv.org/abs/2505.17945

Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI
In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practice…

@berlinbuzzwords@floss.social
2025-05-14 14:00:33

LLMs are now part of our daily work, making coding easier. Join Ivan Dolgov at this year's Berlin Buzzwords to learn how they built an in-house LLM for AI code completion in JetBrains products, covering design choices, data preparation, training and model evaluation.
Learn more:

Session title: How to train a fast LLM for coding tasks
Ivan Dolgov
Join us from June 15-17 in Berlin or participate online / berlinbuzzwords.de

How to train a fast LLM for coding tasks
In this talk, we present our approach to training a code completion model using Mellum, our new open-source model, as an example. Mellum powers in-file code completion in AI-enabled JetBrains IDEs. We'll walk through the entire process, from designing the model and preparing the dataset — with emphasis on the permissiveness of using data — to the training process and evaluation strategies. Attendees will gain insights into state-of-the-art techniques and the challenges we faced and discover…

@jeang3nie@social.linux.pizza
2025-05-19 20:37:00

This morning I null routed another dozen IP addresses for scraping my personal git server using repeated http requests. As per usual, a quick inspection reveals that at least some of them are scraping for LLM data. As always, I have not consented to this use of my non-maintained code, experiments, college coursework, and miscellaneous crap that I for whatever reason decided to self host rather than pushing it to Codeberg.
I mean, if you really want to feed your LLM on a diet that inclu…

@arXiv_qbioGN_bot@mastoxiv.page
2025-05-21 07:36:42

OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
Heng Yang, Jack Cole, Yuan Li, Renzhi Chen, Geyong Min, Ke Li
arxiv.org/abs/2505.14402

OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling. Genomic Foundation Models (GFMs) have emerged as a transformative approach to decoding the genome. As GFMs scale up and reshape the landscape of AI-driven genomics, the field faces an urgent need for rigorous and reproducible evaluation. We present OmniGenBench, a modular benchmarking platform designed to unify the data, model, bench…