the points as we all aim to adapt to this evolving journey as knowledge – and its distribution – get leveled up rapidly in new ways. Special thanks to Grant Watson and Keeyan Sanjasaz and BOND colleagues • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before • AI & Work Evolution = Real + Rapid 3 1 2 3 4 Operating Zone Market Share Source: YipitData (4/25) Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of Before 7 Leading USA-Based LLM App Users by Region Note:

same highest predicted probability, depending on how tiebreaking is implemented you may not always get the same output with temperature 0). Temperatures close to the max tend to create more random output specific techniques that take advantage of how LLMs are trained and how LLMs work will help you get the relevant results from LLMs Now that we understand what prompt engineering is and what it takes the simplest type of prompt. It only provides a description of a task and some text for the LLM to get started with. This input could be anything: a question, a start of a story, or instructions. The name

Embed AI into your products 9 Start now and invest early 11 Customize and fine-tune your models 13 Get AI in the hands of experts 16 Unblock your developers 18 Set bold automation goals 21 Conclusion early The sooner you get going, the more the value compounds. 04 Customize and 
 tune your models Tuning AI to the specifics of your use cases can dramatically increase value. 05 Get AI in the hands 
 or re-checking means your teams can focus on high-value tasks. 15 AI in the EnterpriseLesson 5 Get AI in the hands of experts BBVA takes an expert-led approach to AI Your employees are closest to

for it. After pre-training, we also perform long context extension and SFT for DeepSeek-V2-Lite and get a chat model called DeepSeek-V2-Lite Chat. Benchmark DeepSeek DeepSeekMoE DeepSeek-V2-Lite 7B Chat not True". Let’s evaluate A: A = not not True = not (not True) = not False = True. Plugging in A, we get: Z = not ( ( A ) ) = not ( ( True ) ) = not True = False. So the answer is False. Q: True and False evaluate B: B = not True and True = not (True and True) = not (True) = False. Plugging in A and B, we get: Z = A and B = False and False = False. So the answer is False. Q: not not ( not ( False ) ) is A:

our network, but also get a good performance gain by autotuning 3. TVM can support many kinds of hardware platform: Intel/arm CPU, Nividia/arm GPU, VTA…5 �������������� 1. Get a .log file from the py options = 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

VTA ON INTEL FPGA Step 1: Get DE10-Nano and download & install Quartus Prime 18.1 Lite Edition Step 2: Download SDCard Image from Terasic (Require Registration) Step 3: Get files from https://github vta/config/de10nano_config.json to vta_config.json Step 9: Go to vta/hardware/intel and run make command Step 10: Get the generated .sof file programmed into hardware Step 11: Evaluate the unit test script test_vta_insn

numpy as np from tvm import relay 2. Load a pretrained network mod, params = relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen mod = relay.build_extern(mod e>/graph_annotator.py ● Apply the annotator to a workload: mod, params = relay.testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod ata); } (*func_s)(packed_args, out); *rv = out; });}} Load the built shared library Get the corresponding subgraph function Execute the subgraph© 2019, Amazon Web Services, Inc. or its

register_shape_func("concatenate", False) def concatenate_shape_func(attrs, inputs, _): axis = get_const_int(attrs.axis) return [_concatenate_shape_func(inputs, convert(axis))] @script def _con Example input_name = "data" input_shape = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = get_model('resnet50_v1', pretrained=True) mod, params = relay.frontend.from_mxnet(block, shape={input_name:

directly from scratch. Python 1 2 3 4 5 6 weather_agent = Agent( name= instructions= tools=[get_weather], ) , "Weather agent" "You are a helpful agent who can talk to users about the weather prompts contain many conditional statements 
 (multiple if-then-else branches), and prompt templates get difficult to scale, consider dividing each logical segment across separate agents. Tool overload

an input matrix is matrix_aor matrix_b, row_major or col_moajor. 。 Visit the body of ComputeOp to get the indices of input matrices: inadexO, indexI 。 Compare the access indices with the axis/reduce_axis