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Source channel @lambdaexpression · Post #301 · 1月26日

DN42 access 本服务为那些无法轻松访问自身网络的用户以及希望体验 dn42 但又不想承担维护自有网络成本的用户提供 dn42 连接 默认情况下,地址从/96地址块中分配,如果您希望租用独立的/96前缀或更大的地址空间,请按照联系方式联系我 所有公开的PoP均已屏蔽来自中国境内的 IP 地址。如果您确实需要dn42 access,请与我联系并提供合理的理由 该服务由AS4242423377提供 - - - - - - - The service provides DN42 connectivity to members who cannot easily access their own networks, as well as to those who would like to explore DN42 without the overhead of maintaining their own network. By default, addresses are allocated from a /96 block. If you wish to lease a dedicated /96 prefix or a larger address space, please contact me using the methods provided in the contact information. All publicly accessible PoP are blocked for IPs originating from within China. DN42 access from within China is not publicly available. If you genuinely require access, please contact me and provide a valid justification. Hosted by AS4242423377. Policy 本服务需要花费时间和金钱才能运行,但为了您的利益,我们免费提供。使用本服务是一种特权,而非权利。您必须合理使用本服务,以确保其他用户也能继续享受同样的便利。任何滥用、误用或干扰服务或其他用户的行为都可能导致您的访问权限立即被暂停或终止。 滥用行为包括但不限于: - 过度使用资源 - 黑客攻击、病毒、木马等,或任何其他可能损害服务或对服务及其用户造成风险的干扰行为 - 传播可能导致民事或刑事责任的不良内容 - - - - - - - This service require real time and financial resources to operate, yet are provided free of charge for your benefit. Access to the services is a privilege, not a right. You must use the services responsibly and considerately to ensure that other users can continue to enjoy the same opportunities. Any misuse, abuse, or activities that disrupt the service or other users may result in immediate suspension or termination of access. Abuse could include, but is not limited to: - Excessive use of resources - Hacking, viruses, trojans etc or any other disruption that could harm or create risk to the services or its users - Distribution of objectional content that could create a civil or criminal liability PoP ## Toronto, Canada Prefix: fdb6:fc6a:e66c:724f:fad1:d2cf::/96 Zerotier: 4753cf475f65b0fb ## Los Angeles, USA coming soon #announcement#service

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Repositorio data science

@repo_science · Post #4131 · 2024/05/18 21:06

​​#DL 📱 Zeus New Pytorch Ecosystem Tool Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads. 🖥Github ----- Main channel: @repo_science Coupons: @freecoupons_reposcience -----

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Am Neumarkt 😱

@amneumarkt · Post #706 · 2025/11/21 07:53

#dl Introducing more symmetries in attention https://github.com/NVIDIA/torch-harmonics https://neurips.cc/virtual/2025/loc/san-diego/poster/117783

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@amneumarkt · Post #691 · 2025/10/05 07:41

#dl Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12. https://www.nature.com/articles/s41467-025-63790-8

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Am Neumarkt 😱

@amneumarkt · Post #683 · 2025/06/28 07:04

#dl A few cool ideas in this model. Introducing Gemma 3n: The developer guide - Google Developers Blog https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/

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Am Neumarkt 😱

@amneumarkt · Post #681 · 2025/06/14 08:43

#dl So tensorflow and jax are deprecated in the transformers package. https://github.com/huggingface/transformers/pull/38758

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@amneumarkt · Post #625 · 2024/10/03 21:31

#dl PyTorch Native Architecture Optimization: torchao | PyTorch https://pytorch.org/blog/pytorch-native-architecture-optimization/

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Am Neumarkt 😱

@amneumarkt · Post #602 · 2024/07/20 05:48

#dl There is this new lib called scale. One could compile CUDA code to use it on AMD GPU. https://docs.scale-lang.com/manual/how-to-use/ I don't know who is more pissed off, NVidia or AMD.

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Am Neumarkt 😱

@amneumarkt · Post #556 · 2024/03/16 09:09

#dl This repo is really nice. yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models https://github.com/yuanchenyang/smalldiffusion

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Am Neumarkt 😱

@amneumarkt · Post #506 · 2023/11/13 08:30

#dl Google & USC benchmarked a prompt based forecasting method, and the results are amazing. Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948

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