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Moore’s Law is Slowing Down MOTIVATION©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 3 Multi-Vendor Support MOTIVATION©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 4 Terasic DE10-Nano Setup Environment Variables Navigate to 3rdparty/cma and build kernel module Copy kernel module to DE10-Nano and Install Module CMA API Reference©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 7 INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 8 Hardware Configure Chisel VTA for DE10-Nano DEPLOY VTA ON INTEL FPGA©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 9 Hardware Datapath

among open-source models. The model checkpoints are available at h t t p s : / / g i t h u b . c o m / d e e p s e e k - a i / D e e p S e e k - V 2 . 0 20 40 60 80 100 Activated Parameters (Billions) 55 . . . , ??} corresponding to the outputs within each group: ?? = ?? − m???({?1, ?2, · · · , ??}) s??({?1, ?2, · · · , ??}) . (34) Training Strategy. In our preliminary experiments, we find that the reasoning capability of DeepSeek-V2 Chat (RL) still lags behind giant models, such as Erniebot-4.0 and GPT-4s. 19 Model Overall Reasoning 中文推理 Language 中文语言 Avg. Math. Logi. Avg. Fund. Chi. Open. Writ. Role

a year in the Internet business is like a dog year – equivalent to seven years in a regular person's life.’ At the time, the pace of change catalyzed by the internet was unprecedented. Consider now that large language models (LLMs) that – in 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 Growth = Unprecedented • AI Monetization Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from

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, and context process. Inadequate prompts can lead to ambiguous, inaccurate responses, and can hinder the model’s ability to provide meaningful output. You don’t need to be a data scientist or a machine learning challenges you can face while crafting prompts. Prompt engineering Remember how an LLM works; it’s a prediction engine. The model takes sequential text as an input and then predicts what the following

on your behalf. A workflow is a sequence of steps that must be executed to meet the user’s goal, whether that's resolving a customer service issue, booking a restaurant reservation, committing a code context and to take actions—and dynamically selects the appropriate tools depending on the workflow’s current state, always operating within clearly defined guardrails. 4 A practical guide to building agents agent’s reasoning and decision-making 02 Tools External functions or APIs the agent can use to take action 03 Instructions Explicit guidelines and guardrails defining how the 
 agent behaves Here’s what

automation goals Most processes involve a lot of rote work, ripe for automation. Aim high. Let’s drill down into each of these, with customer stories as examples. 5 AI in the EnterpriseLesson 1 Start benchmarks 
 in a given use case. It’s also a way to continuously improve the AI-enabled processes, with expert feedback at every step. How it started Morgan Stanley’s first eval focused on making their Stanley the confidence to start rolling the use cases 
 into production. 6 AI in the EnterpriseHow it’s going Today, 98% of Morgan Stanley advisors use OpenAI every day; access to documents has jumped from

以下是需要修正的 五个示例模板和参考文献： 原始文本 修正后文本 Boullis A, Fassotte B, Sarles L,LognayG, Heuskin S, Vanderplanck M.Bartram S, Haubruge E, Francis F,Verheggen F(2017 Elevated Carbon Dioxide Concentration Reduces in Aphids. J Chem Ecol 43:164-171. Boullis A, Fassotte B, Sarles L, Lognay G, Heuskin S, Vanderplanck M.Bartram S, Haubruge E, Francis F.Verheggen FJ (2017) Elevated CarbonDioxide Concentration Reduces 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 the main takeaway from this dataset? 找出最主要的信息 Can you explain this dataset like I’m 5 years old?

和os 全 W Open Source at OctoML ee We are big believers in the power of open source o 5S$ponsoring multiple employees to contribute to TVML. ee Today we'ltouch on a few of those contribution Let t2 3 memory planning,， storage Let s = alLLoc_storage(40，64，f32) ; Tet outl = attoc_tensor(s，(19,)，f32); coalescing, memory re-use for invoke_tvn_

indices of fragment registers Jorfintk_inner_inner=0K_inner_inner<16;++k inner_ innerf Jorfintjc c<S+fi cf compute _jocalli_ cj= fcompute_Jocalf_ cl+ flfoatfA_shared_ocallk_inner_innerj* B_sharea_locollffk train_op = loss_scale_optimizer.minimize(1oss) Loss Scaling in PAI-TF Loss Scaling the loss using S 了 Backward propagation in MP N 放gradients( Y ) Unscaled gradients Zero gr: adients Apply gradients Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA) cuUBLAS/VcuDNNVCUTL， Blade Kernel Lib S， ation 计算平台事业部 COMPUTING PLATFORM Weight Adjustment IHomogeneous 剂Function: f(cx) =cfGx) Conv/MatMu1l