and reviewing code 48 What about multimodal prompting? 54 Best Practices 54 Provide examples 54 Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control model temperature should be set to a low number, since no creativity is needed, and we use the gemini-pro default top-K and top-P values, which effectively disable both settings (see ‘LLM Output Configuration’ 1_1_movie_classification Goal Classify movie reviews as positive, neutral or negative. Model gemini-pro Temperature 0.1 Token Limit 5 Top-K N/A Top-P 1 Prompt Classify movie reviews as POSITIVE, NEUTRAL

toward specialized chips (GPUs, TPUs, AI accelerators…), liquid cooling, and frontier data center design. In 2019, AI was a research feature; by 2023, it was a capital expenditure line item. Microsoft natives – from Atomicwork, to Epic, Fujitsu, and Gainsight, to H&R Block and LG Electronics – to design, customize, and manage their AI apps and agents. We processed over 100 trillion tokens this quarter Development’ (2024); Anthropic; Katalon; AccelQ; Monday; Quill; Mintlify; Snyk; Ansible; UX Pilot; Ark Design AI AI Developer Use Cases – 2024, per IBM Code Generation Bug Detection & Fixing Testing

Network (FFN). However, for both the attention module and the FFN, we design and employ innovative archi- tectures. For attention, we design MLA, which utilizes low-rank key-value joint compression to eliminate not match MHA (we provide the ablation of MHA, GQA and MQA in Appendix D.1). For DeepSeek-V2, we design an innovative attention mechanism called Multi-head Latent Attention (MLA). Equipped with low-rank affinity scores calculated for the ?-th token and all routed experts. 2.2.2. Device-Limited Routing We design a device-limited routing mechanism to bound MoE-related communication costs. When expert parallelism

Target Hardware/Model Options Codegen CPU arm_cpu pixel2 (snapdragon 835), mate10/mate10pro (kirin 970), p20/p20pro (kirin 970) -target=arm64-linux-android -mattr=+neon llvm firefly rk3399, rock960, ultra96

告，允许开发者 自由使用、修改和分发其技术，促进了AI领域的创新和协作。 优势 挑战 测试评估：对标顶尖，能力出众  推理任务表现 • 教育类知识问答能力突出：在 MMLU、MMLU-Pro等测试中， DeepSeek R1成绩超越 OpenAI-4o等其他闭源模型。 • 数学推理能力对标顶尖模型：DeepSeek R1 在 AIME 2024 基准测试中得 分 79.8%（pass@1），略优于 向用户开 放其基础功能。o3-mini专注于数学、科学和工程等领域的复 杂推理任务，其性能和成本效益均优于之前的o1系列。 发布新一代Gemini 2.0系列模型，包括Gemini 2.0 Pro、 Gemini 2.0 Flash、Gemini 2.0 Flash-Lite和Gemini 2.0 Flash Thinking，旨在提升AI能力并提高性价比。 谷 歌  中美技术竟合

存储: 5GB 简单⽂本⽣成、基础代 码补全 7B - RAM: 8-10GB - GPU: GTX 1680（4-bit量 化） - 存储: 8GB - 内存: 16GB（M2 Pro/M3） - 存储: 8GB 中等复杂度问答、代码 调试 14B - RAM: 24GB - GPU: RTX 3090（24GB VRAM） - 存储: 20GB - 内存: 32GB（M3

hardware design full stack open source Current TVM Stack VTA Runtime & JIT CompilerTSIM: Support for Future Hardware Current TVM Stack New NPU Runtime TSIM Driver TSIM Binary New Hardware Design in Verilog

2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Outline • QNN Dialect • Design • Operators • Results on Intel Cascade Lake© 2019, Amazon Web Services, Inc. or its Affiliates extent)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. QNN Dialect • Design operators that satisfy many framework operators • qnn.quantize, qnn.dequantize, qnn.requantize

guide to 
 building agents Contents What is an agent? 4 When should you build an agent? 5 Agent design foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction Large Otherwise, a deterministic solution may suffice. 6 A practical guide to building agents Agent design foundations In its most fundamental form, an agent consists of three core components: 01 Model The

Pursued By A Bear - 3400us (baseline), 40us (target) - 85x speedup - Uh ohEnter, TVM and model co-design - PyTorch operator overhead makes interpreter infeasible - Reduce FLOPs with block-sparsified