Datasets:

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arrmlet

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reddit_dataset_test

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ERROR: type should be string, got "https://www.nature.com/articles/d41586-024-00478-x\n“one assessment suggests that ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes” for 180.5 million users (that’s 5470 users per household)\nBlackwell GPUs are 25x more energy efficient than H100s: https://www.theverge.com/2024/3/18/24105157/nvidia-blackwell-gpu-b200-ai \nSignificantly more energy efficient LLM variant: https://arxiv.org/abs/2402.17764 \nIn this work, we introduce a 1-bit LLM variant, namely BitNet b1.58, in which every single parameter (or weight) of the LLM is ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16) Transformer LLM with the same model size and training tokens in terms of both perplexity and end-task performance, while being significantly more cost-effective in terms of latency, memory, throughput, and energy consumption. More profoundly, the 1.58-bit LLM defines a new scaling law and recipe for training new generations of LLMs that are both high-performance and cost-effective. Furthermore, it enables a new computation paradigm and opens the door for designing specific hardware optimized for 1-bit LLMs.\nStudy on increasing energy efficiency of ML data centers: https://arxiv.org/abs/2104.10350\nLarge but sparsely activated DNNs can consume <1/10th the energy of large, dense DNNs without sacrificing accuracy despite using as many or even more parameters. Geographic location matters for ML workload scheduling since the fraction of carbon-free energy and resulting CO2e vary ~5X-10X, even within the same country and the same organization. We are now optimizing where and when large models are trained. Specific datacenter infrastructure matters, as Cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators inside them can be ~2-5X more effective than off-the-shelf systems. Remarkably, the choice of DNN, datacenter, and processor can reduce the carbon footprint up to ~100-1000X.\n\nIt also doesn’t do any plagiarism: \n\nA study found that it could extract training data from AI models using a CLIP-based attack: https://arxiv.org/abs/2301.13188 \n\nThe study identified 350,000 images in the training data to target for retrieval with 500 attempts each (totaling 175 million attempts), and of that managed to retrieve 107 images. A replication rate of nearly 0% in a set biased in favor of overfitting using the exact same labels as the training data and specifically targeting images they knew were duplicated many times in the dataset using a smaller model of Stable Diffusion (890 million parameters vs. the larger 2 billion parameter Stable Diffusion 3 releasing on June 12). This attack also relied on having access to the original training image labels\nDiffusion models can create human faces even when 90% of the pixels are removed in the training data https://arxiv.org/pdf/2305.19256 \n“if we corrupt the images by deleting 80% of the pixels prior to training and finetune, the memorization decreases sharply and there are distinct differences between the generated images and their nearest neighbors from the dataset. This is in spite of finetuning until convergence.”\n“As shown, the generations become slightly worse as we increase the level of corruption, but we can reasonably well learn the distribution even with 93% pixels missing (on average) from each training image.”"