be checked as well Return True/False for this op After Annotation op op op op data weight1 weight3 weight2 output Subgraph begin Subgraph end© 2019, Amazon Web Services, Inc. or its Affiliates rights reserved. Example: Annotate an Entire Graph After Annotation op op op op data weight1 weight3 weight2 output Subgraph begin Subgraph end class WholeGraphAnnotator(ExprMutator): def __init__(self wrap annotated subgraphs extern function data weight1 weight3 weight2 output data weight1 weight3 weight2 output data weight1 weight3 weight2 output What are not supported yet? ● Duplicated

uint8], %weight: Tensor[(3, 3, 2, 2), uint8]) { qnn.conv2d(%data, %weight, … , out_dtype="int32", input_zero_point=1, kernel_zero_point=1)} def @main(%data: Tensor[(1, 3, 2, 3), uint8], %weight: Tensor[(3 Tensor[(3, 3, 2, 2), uint8]) -> Tensor[(1, 3, 1, 2), int32] { %0 = nn.conv2d(%data, %weight, … , out_dtype="int32") /* ty=Tensor[(1, 3, 1, 2), int32] */; %1 = cast(%data, dtype="int32") /* ty=Tensor[(1, 3, 2 ty=Tensor[(1, 1, 1, 2), int32] */; %6 = subtract(%0, %5) /* ty=Tensor[(1, 3, 1, 2), int32] */; %7 = cast(%weight, dtype="int32") /* ty=Tensor[(3, 3, 2, 2), int32] */; %8 = sum(%7, axis=[1, 2, 3]) /* ty=Tensor[(3)

AdamW optimizer (Loshchilov and Hutter, 2017) with hyper-parameters set to ?1 = 0.9, ?2 = 0.95, and weight_decay = 0.1. The learning rate is scheduled using a warmup-and-step-decay strategy (DeepSeek-AI, DeepSeek-V2 is trained based on the HAI-LLM framework (High-flyer, 2023), an efficient and light-weight training framework developed internally by our engineers. It employs a 16-way zero-bubble pipeline 2311.18743. URL https://doi.org/10.48550/arXiv.2311.18743. I. Loshchilov and F. Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. Mistral. Cheaper, better, faster,

请解释下面这段代码的逻辑，并说明完成了什么功能： ``` // weight 数组的大小 就是物品个数 for(int i = 1; i < weight.size(); i++) { // 遍历物品 for(int j = 0; j <= bagweight; j++) { // 遍历背包容量 if (j < weight[i]) dp[i][j] = dp[i - 1][j]; 1][j]; else dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - weight[i]] + value[i]); } } ``` 9. 角色扮演（自定义人设）：自定义人设，来与用户进行角色扮演。 SYSTEM 请你扮演一个刚从美国留学回国的人，说话时候会故意中文夹杂部分英文单词，显得非常 fancy，对话中总是带 有很强的优越感。 USER

Lib S， ation 计算平台事业部 COMPUTING PLATFORM Weight Adjustment IHomogeneous 剂Function: f(cx) =cfGx) Conv/MatMu1l 计算平台事业部 COMPUTING PLATFORM /c Weight Adjustment 和

- PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified weight matrices - not a new idea, cf WaveRNN, Sparse Transformers, etc - Reduce precision with int8/float16

llama:8b，这里的 1.5b, 7b、8b 代表什么？b 是英文的 billion，意思是十亿，7b 就是 70 亿，8b 就 是 80 亿，70 亿、80 亿是指大模型的神经元参数（权重参数 weight+bias）的 总量。目前大模型都是基于 Transformer 架构，并且是很多层的 Transformer 结构，最后还有全连接层等，所有参数加起来 70 亿，80 亿，还有的上千亿。

share-price growth when the railways were supplanting canals. The bubble of the 1840s deflated under the weight of overheated expectations and changing economic conditions… …Any technological advance which requires