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Computer Science, Computer Vision and Pattern Recognition
Bridging the gap between complex scientific research and the curious minds eager to explore it.
Computer Science, Computer Vision and Pattern Recognition
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LLaMA-2, the next generation of LLaMA. Meta trained and released LLaMA-2 in three model sizes: 7, 13, and 70 billion parameters. The model architecture remains largely unchanged from that of LLaMA-1 models, but 40% more data was used to train the foundational models. The accompanying preprint also mentions a model with 34B parameters that might be released in the future upon satisfying safety targets.
accuracy algorithm attention automation classification clustering communication complexity computer vision context data analysis dataset datasets deep learning efficiency evaluation fairness fine-tuning generalization generation graph theory image image synthesis language models learning machine learning models neural network neural networks optimization performance pre-training privacy pruning pytorch regularization robotics robustness security segmentation self-attention summarization training transformer transformers