style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process. Inadequate prompts can lead to ambiguous, inaccurate responses, and can hinder the model’s ability right sequence of tokens. Prompt engineering is the process of designing high-quality prompts that guide LLMs to produce accurate outputs. This process involves tinkering to find the best prompt, optimizing creating a loop due to the vast number of available options. In both cases, the model's sampling process gets "stuck," resulting in monotonous and unhelpful output until the output window is filled. Solving

total KV cache containing (?? + ?? ℎ)? elements. In order to demonstrate the complete computation process of MLA, we also organize and provide its full formulas in Appendix C. 2.1.4. Comparison of Key-Value device-level balance loss to ensure balanced computation across different devices. In the training process of DeepSeek-V2, we partition all routed experts into ? groups {E1, E2, ..., E?}, and deploy each However, we also notice a phenomenon of “alignment tax” (Ouyang et al., 2022), i.e., the alignment process can negatively impact the performance on some standard benchmarks such as BBH. In order to alleviate

summary Seven lessons for enterprise AI adoption 01 Start with evals Use a systematic evaluation process to measure how 
 models perform against your use cases. 02 Embed AI in 
 your products Create new can dramatically increase value. 05 Get AI in the hands 
 of experts The people closest to a process are best-placed to improve 
 it with AI. 06 Unblock your
 developers Automating the software development conduct intensive evals for every proposed application. An eval is simply a rigorous, structured process for measuring how AI models actually perform against benchmarks 
 in a given use case. It’s also

), ], 19 A practical guide to building agents 24 25 26 27 28 29 30 32 32 33 ) main(): msg = input( ) orchestrator_output = await Runner.run( manager_agent,msg) 30 31 32 33 34 35 36 37 38 39 40 41 42 ), tools=[track_order_status, initiate_refund_process] ) triage_agent = Agent( name=Triage Agent", instructions= , handoffs=[technical_support_agent guardrails such as regex, and the OpenAI moderation API to vet our user inputs. Respond ‘we cannot process your message. Try again!’ Continue with function call Handoff to Refund agent Call initiate_


捕食是一个基本的生态过程，捕食的定义为：一种生物（捕食 者）捕食了另一种生物（猎物）（Begon等，1997）。 Predation is a fundamental ecological process,defined as one organism (predator) preying onanother organism (prey) (Begon et al., 1997). 在群 types of materials suffer from large volume expansion/shrinkage during the lithiation/delithiation process, leading to the formation of cracks, separation of active material from the current collector, alloy-based anodes have significant drawbacks. The main issue lies in their large volume expansion and shrinkage during the lithiation and delithiation process. This can result in the formation of cracks,

multi-process pipeline using shared memory Usually need >4 Pre-Process cores running to keep up with FPGA ˃ TVM pipeline needed. CPU/FPGA partitions ideally run in parallel >> 13 Post-Process (fc/softmax/nms) (fc/softmax/nms) FPGA Acceleration Pre-Process (resize)© Copyright 2018 Xilinx FPGA Pipeline report in MLSuite 1.5 (animated gif of ResNet-50, view in slideshow mode) >> 14© Copyright 2018 Xilinx Quantization

Launch of ChatGPT 2022* Knowledge Distribution Evolution = Over ~Six Centuries26 Knowledge is a process of piling up facts; wisdom lies in their simplification. Martin H. Fischer, German-born American Milestone Timeline – 2023-2025, per Stanford University *Multimodal = AI that can understand and process multiple data types (e.g., text, images, audio) together. **Open-source = AI models and tools made the next, compressing the time from idea to prototype and from prototype to product. In the process, the barrier to building with AI is collapsing – not just in cost, but in complexity. This is no

exe = relay.vm.compile(mod, target) vm = relay.vm.VirtualMachine(exe) vm.init(ctx) vm.invoke("main", *args) export© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. VM bytecode 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Relay virtual machine def @main(%i: int32) -> int32 { @sum_up(%i) /* ty=int32 */ } def @sum_up(%i1: int32) -> int32 { %0 = equal(%i1 alloc_tensor $8 $7 [] int32 invoke_packed PackedFunc[2] (in: $6, $0, out: $8) move $0 $8 ret $0 main: invoke $1 VMFunc[0]($0) ret $1© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved

a workload: mod, params = relay.testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod, “dnnl”)© 2019, Amazon Web Services, Inc. or its

float32]) { qnn.quantize(%input_data, out_dtype="uint8", output_zero_point=127, output_scale=0.5f) } def @main(%input_data: Tensor[(2, 5), float32]) -> Tensor[(2, 5), uint8] { %0 = divide(%input_data, 0.5f /* 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, 3, 2, 2), uint8]) -> Tensor[(1, 3, 1, 2),