effect – found freedom with the November 2022 launch of OpenAI’s ChatGPT with its extremely easy-to-use / speedy user interface. In addition, relatively new AI company founders have been especially aggressive powerhouses charging ahead. In this document, we share data / research / benchmarks from third parties that use methodologies they deem to be effective – we are thankful for the hard work so many are doing to illustrate art improvement on a recognized benchmark, >1K citations, historically relevant, with significant use). Source: Epoch AI (5/25) Training Dataset Size (Number of Words) for Key AI Models – 1950-2025,

54 Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control the max token length 58 Use variables in prompts 58 Experiment with input formats and writing effective prompt can be complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure choosing a model. Prompts might need to be optimized for your specific model, regardless of whether you use Gemini language models in Vertex AI, GPT, Claude, or an open source model like Gemma or LLaMA. Besides

increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered systems known as agents. This guide is designed for product deployments into practical and actionable best practices. It includes frameworks for identifying promising use cases, clear patterns for designing agent logic and orchestration, and best practices to ensure your reservation, committing a code change, 
 or generating a report. Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are

products into companies to address their most pressing use cases. We use iterative deployment to learn quickly from customer use cases and use that information to accelerate product improvements. 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 customer 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 
 of experts The people closest to

1.3. Decoupled Rotary Position Embedding Following DeepSeek 67B (DeepSeek-AI, 2024), we intend to use the Rotary Position Embed- ding (RoPE) (Su et al., 2024) for DeepSeek-V2. However, RoPE is incompatible balance (LDevBal), and communication balance (LCommBal), respectively. Expert-Level Balance Loss. We use an expert-level balance loss (Fedus et al., 2021; Lepikhin et al., 2021) to mitigate the risk of routing maximum learning rate is set to 2.4 × 10−4, and the gradient clipping norm is set to 1.0. We also use a batch size scheduling strategy, where the batch size is gradually increased from 2304 to 9216 in

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 on 32bits, no 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

Runtime send program 较 ,we 人 Interace Optimize TVM operators on microcontrollers by making use of AutoTVM improve alLLoc_storage(40，64，f32) ; Tet outl = attoc_tensor(s，(19,)，f32); coalescing, memory re-use for invoke_tvn_op(add，(tl，t2)，(outl,))3 Out1l loops, and offloading dynamic } allocation

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 ML method)

its Affiliates. All rights reserved. Support dynamic model in TVM ● Support Any-dim in typing ● Use shape function to compute the type at runtime ● Virtual machine as a new runtime for Relay ● Dynamic const_range(len(inputs)): 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,

Reduce precision with int8/float16 - very helpful to maintain model in core-private L1 dcaches - Use rational approximations for transcendentals (exp, tanh, erf, etc) - very general technique, allows