Mastodawn

Survey Systems Verification And Calibration In The HydroSpatial Domain [overview / tutorial]
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https://ihr.iho.int/articles/survey-systems-verification-and-calibration-in-the-hydrospatial-domain/ <-- shared technical article/paper
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#GIS #spatial #mapping #marine #seafloor #bathymetry #dataintegrity #hydrographic #surveying #hydrospatial #survey #MBES #patchtest #USBL #calibration #verification #calibration #alignment #accuracy #precision #technicalreport #standards #fieldwork #professionalpractices #navigation #tutorial #learning #education #selflearning #methodologies #GNSS #IMU #gyro #MBES #sonar #remotesensing ##offshore #surveying #usecase #dataintegrity #ManualOfHydrography
@InternationalHydrographyOrganisation

N-gated Hacker News Apr 17, 2025

🎉🎊 Behold, the groundbreaking 587th iteration of #BitNet, where #buzzwords meet their ultimate destiny: "Technical Report"! A riveting tale of acronyms, citations, and a plea for #donations, all while you desperately try to figure out if those numbers actually mean anything 📊🤯. Remember, it's not a real tech report without a job ad and some grateful acknowledgments for funding! 💰👏
https://arxiv.org/abs/2504.12285 #TechnicalReport #TechNews #HackerNews #ngated

BitNet b1.58 2B4T Technical Report

We introduce BitNet b1.58 2B4T, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale. Trained on a corpus of 4 trillion tokens, the model has been rigorously evaluated across benchmarks covering language understanding, mathematical reasoning, coding proficiency, and conversational ability. Our results demonstrate that BitNet b1.58 2B4T achieves performance on par with leading open-weight, full-precision LLMs of similar size, while offering significant advantages in computational efficiency, including substantially reduced memory footprint, energy consumption, and decoding latency. To facilitate further research and adoption, the model weights are released via Hugging Face along with open-source inference implementations for both GPU and CPU architectures.

arXiv.org

Hacker News Mar 27, 2025

DeepSeek-V3 Technical Report

https://arxiv.org/abs/2412.19437

#HackerNews #DeepSeekV3 #TechnicalReport #AIResearch #MachineLearning #Arxiv #TechNews

DeepSeek-V3 Technical Report

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. The model checkpoints are available at https://github.com/deepseek-ai/DeepSeek-V3.

arXiv.org