affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative Large Language Models where the model gets stuck in a cycle, repeatedly generating the same (filler) word, phrase, or sentence structure, often exacerbated by inappropriate temperature and top-k/ Prompt the model's output becomes excessively random, increasing the probability that a randomly chosen word or phrase will, by chance, lead back to a prior state, creating a loop due to the vast number of

== "NCHW": oc, ic, kh, kw = inputs[1].shape strategy.register_specialized_implement(wrap_compute_conv2d(topi.x86.conv2d_winograd), topi.x86 ment(wrap_compute_conv2d(topi.x86.conv2d_nchw), topi.x86.schedule_conv2d_nchw) elif layout == "NHWC": strategy.register_default_implement(wrap_compute_conv2d(topi topi.x86.schedule_conv2d_nhwc) elif layout == "NCHWc": strategy.register_default_implement(wrap_compute_conv2d(topi.nn.conv2d_nchwc), topi.x86.schedule_conv2d_nchwc)

Services, Inc. or its Affiliates. All rights reserved. Graph Partitioning Use external functions to wrap annotated subgraphs extern function data weight1 weight3 weight2 output data weight1 weight3

burn. *Cost-per-token = The expense incurred for processing or generating a single token (a word, sub-word, or character) during the operation of a language model. It is a key metric used to evaluate Richard Hirsh 0% 25% 50% 75% 100% 0 20 40 60 80 Electric Power Computer Memory ChatGPT: 75-Word Response % of Original Price By Year (Indexed to Year 0) AI Model Compute Costs High / Rising + cent… *Cost-per-token = The expense incurred for processing or generating a single token (a word, sub-word, or character) during the operation of a language model. It is a key metric used to evaluate

一种是 web 站点，用某些网站作为知识库构建的数据来源。在这里根据我的需 求，应该选择通用型。 然后点击进入选择文件夹这里，上传我的 Python 副业代码文件，说明文档， 数据格式可以是 txt、word、pdf、ppt 等，在确定需要上传的文档后，点击图 41中右下角的“创建并导入”按钮。 Figure 41: MaxKB 界面-知识库配置续 如下图 42所示，上传这里面的文件到本地 MaxKB

L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021. Y. Cui, T. Liu, W. Che, L. Xiao, Z. Chen, W. Ma