months means that the era of thinking machines is actually now upon us… …We at VAST believe that the path to the greatest potential gain is to simplify and reduce the fundamental challenges that need to companies – with aggressive cash burn – tested this premise hard, built large-scale data-driven network effects based on product excellence / constant improvement, developed technology-driven competitive advantage Each new modality forces models to align meaning across formats rather than optimize for one. The path to this capability unfolded stepwise: OpenAI’s CLIP paired vision and language in 2021; Meta followed

Nano© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Effects of Vision-specific Optimizations using TVM Speedup 0 1 2 3 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 Nano© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark Effects of Convolution operators using TVM AWS DeepLens Acer aiSage NVIDIA Jetson Nano Speedup 0

model becomes overly deterministic, sticking rigidly to the highest probability path, which can lead to a loop if that path revisits previously generated text. Conversely, at high temperatures, the model's from the user folder_name = input("Enter the folder name: ") # Check if the folder exists if not os.path.isdir(folder_name): print("Folder does not exist.") exit(1) Continues next page... Prompt Engineering new_file_name = f"draft_{file}" # Move the file to the new name shutil.move(os.path.join(folder_name, file), os.path.join(folder_name, new_file_name)) # Print a success message print("Files renamed

extern(outputs[0].shape, inputs, lambda ins, outs: tvm.call_packed('tvm.accel.accel_fused', attrs['path'], attrs['output_layout'], attrs['model_name'], outs[0], *ins ), name=name) return out >> 10© … @tvm.register_func("tvm.accel.accel_fused") def accel_fused(graph_path, output_layout, out, *ins ): path = c_char_p(graph_path.value).value layout = c_char_p(output_layout.value).value … >> 12©

● Implement a Python template to indicate if an op can be supported by your codegen ● Template path: python/tvm/relay/op/contrib/ /extern_op.py ● Boolean functions in the template Option 2: Graph-Level Annotation ● Implement a Relay IR visitor to annotate a subgraph ● Module path: python/tvm/relay/op/contrib//graph_annotator.py ● Apply the annotator to a workload: codegen class to accept subgraphs and build binary/library/engine for runtime dispatching ● Codegen path: src/relay/backend/contrib//codegen.cc ● Flow overview data weight1 weight3

01-of-00004.gguf DeepSeek-R1-UD-IQ1_S.gguf curl -fsSL https://ollama.com/install.sh | sh FROM /path/to/DeepSeek-R1-UD-IQ1_M.gguf PARAMETER num_gpu 28 # 每块RTX 4090加载7层（共4卡） PARAMETER num_ctx 2048

PreQuantized hosted models • MXNet Pre-quantized Models • Tested internally with MxNet + MKLDNN path • Will open RFC in a month© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved

human-in-the-loop intervention, helping ensure agents operate safely and predictably in production. The path to successful deployment isn’t all-or-nothing. Start small, validate with real users, and grow capabilities

上面的界面，找到 Volumes 输 入框，下图 35中 4 处，填入刚才的知识库路径，我的路径如下：/Users/zhen- guo/Documents/words 随后在 Container path 输入框中填入/var/lib/postgresql/data，下图 35中 5 处，这是固定不变的，直接复制过去！ 31 Figure 35: 配置 MaxKB 续 最后点击 Run 按钮，这样一个

mentioned in the paper. DeepSeek believes that innovation, novelty, and curiosity are essential in the path to AGI. 28 B. DeepSeek-V2-Lite: A 16B Model Equipped with MLA and DeepSeekMoE B.1. Model Description