IEEE

Field Trial of Multi-Datacenter Distributed Training for LLM Based on Bandwidth Convergence and Two Parallel Strategies over 120km High-reliability 800Gbit/s C+L OTN

Abstract: We have conducted 175 billion parameters, 1024 GPUs large language model training with up to $99.41 \%$ (Pipeline parallel, PP) and $98.95 \%$ (Data parallel, DP) training efficiency in two ...
IEEE

PAHInA: Precision-Aware Hierarchical In-Network Aggregation for Edge Distributed Training

Abstract: The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, ...
Journal of Medical Internet Research

How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective

Moreover, we discuss strategies for metadata selection and human evaluation to ensure the quality and effectiveness of ITDs. By integrating these elements, this tutorial provides a structured ...
GitHub

SahilSharma1306/transformer-from-scratch

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GitHub

ROCM7.2 vector_quantize_pytorch: cannot import name 'group' from 'torch.distributed'

Click on 'start_gradio_ui_rocm.bat.' WARNING: 4B LM model is too large for 20GB GPU. Downgrading to 1.7B variant: acestep-5Hz-lm-1.7B Initializing service from command line... Using preferred download ...