undeniable that it’s ‘game on,’ especially with the USA and China and the tech powerhouses charging ahead. In this document, we share data / research / benchmarks from third parties that use methodologies Units ~300MM+ Units ~1B+ Units / Users ~4B+ Units Tens of Billions of Units MM Units in Log Scale Technology Compounding = Numbers Behind The Momentum13 AI Technology Compounding = Numbers gains. But payback periods are often long, especially for vertically-integrated players building ahead of demand. For newer entrants, monetization may lag build-out by quarters or even years. And then

1 − ?, 1 + ? � ?? � − ?D?? � ??||??? ? �� , (32) D?? � ??||??? ? � = ??? ? (??|?) ??(??|?) − log ??? ? (??|?) ??(??|?) − 1, (33) where ? and ? are hyper-parameters; and ?? is the advantage, computed language models. arXiv preprint arXiv:2402.03300, 2024. N. Shazeer. Fast transformer decoding: One write-head is all you need. CoRR, abs/1911.02150, 2019. URL http://arxiv.org/abs/1911.02150. N. Shazeer it is correct. Problem: Evaluate $\log_21$. Solution: Table 27 | An example of MATH. 45 PROMPT You are an expert Python programmer, and here is your task: Write a function to find the similar elements

out[i] += inputs[j][i] return out Shape function example Use hybrid script to write shape function Input shape tensors Type checking Data independent© 2019, Amazon Web Services, Inc exp_dispatcher) vmc = relay.backend.vm.VMCompiler() with tvm.autotvm.apply_graph_best("resnet50_v1_graph_opt.log"): vm = vmc.compile(mod, "llvm") vm.init(ctx) vm.load_params(params)

specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated. Many aspects of your prompt a machine learning engineer – everyone can write a prompt. Prompt Engineering February 2025 7 When you chat with the Gemini chatbot,1 you basically write prompts, however this whitepaper focuses on relationship between what’s in the previous tokens and what the LLM has seen during its training. When you write a prompt, you are attempting to set up the LLM to predict the right sequence of tokens. Prompt engineering

platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application options if options else [ “-shared”, “-fPIC”, “-m32”] b. python tensorflow_blur.py to get the .log c. Use the .log, with target=“llvm –mcpu=i686 –mtriple=i686-linux-gnu” then TVM_NDK_CC=“clang –m32” python

rows and columns in this dataset? 描述一下行和列 Can you make the graphs more beautiful? 把图美化一下 Can you write a one sentence recap of this data? 快速回顾一下 Create a visual chart, based on this data. 做一个视觉图表 What’s this dataset? 做一个整体展示 Can you create 10 graphs to present different data? 创作10个不同的图展示数据 Can you write me an article based on this dataset or statistic results? 根据结果写文章 Can you explain this dataset in

IRModule([te_add_one]) print(mod[”te_add_one”].args) Use hybrid script as an alternative text format Directly write pass, manipulate IR structures Accelerate innovation, 
 e.g. use (GA/RL/BayesOpt/your favorite

available to your agent by assigning a rating—low, medium, or high—based on factors like read-only vs. write access, reversibility, required account permissions, and financial impact. Use these risk ratings