#IstioCon Moving large scale consumer e-commerce Infrastructure to Mesh Rajath Ramesh Principal Software Engineer @Carousell Harshad Rotithor Software Architect @Carousell #IstioCon About Carousell

#IstioCon Performance tuning and best practices in a Knative based, large-scale serverless platform with Istio 张龚, Gong Zhang, IBM China Development Lab 庄宇, Yu Zhuang, IBM China Development Lab #IstioCon The envoy mem release fix included in Istio 1.6.0+ resolved this issue. o Istiod MEM bumped with large numbers of Knative Services (#25532) Mem usage optimization of pilot resolved this issue. • Tune

the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded to Qwen1.5. Both language models and multimodal models are pretrained pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding, text generation, vision apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role":

对于使用 AI 辅助软件开发特别感兴趣。因此， 本期技术雷达讨论了许多代码辅助工具，如 GitHub Copilot、Tabnine 和 Codeium。我们兴奋于 open-source LLMs for coding 在工具领域可能带来的变革，并且我们看到了在编码之外的辅助领域中工具和能力的爆炸式增 长，如用户故事编写辅助、用户研究、电梯演讲和其他基于语言的任务。同时，我们希望开发人员能够负责任 积极的影响？虽然衡量标准可能变得更加细致入微，但真正的生产力衡量仍然难以捉摸。 本期主题 © Thoughtworks, Inc. All Rights Reserved. 7 众多大语言模型 大语言模型（LLMs）为现今人工智能的许多重要突破奠定了基础。目前的应用多使用类似聊天的界面进行交 互，例如 ChatGPT 或 Google Bard。生态中的主要竞争者（例如 OpenAI 的 ChatGPT，Google 合并队列 71. Google Bard 72. Google Cloud 工作站 73. Gradio 74. KWOK 75. Llama 2 76. Maestro 77. Open-source LLMs for coding 78. OpenCost 79. OpenRewrite 80. OrbStack 81. Pixie 82. Tabnine 暂缓 — 采纳 83. Playwright

self-supervised learning which has been instrumental in the success of natural language models like BERT. Self-Supervised learning helps models to quickly achieve impressive quality with a small number of labels data-efficient (specifically, label efficient) models. We will describe the general principles of Self-Supervised learning which are applicable to both language and vision. We will also demonstrate its efficacy performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks requires new models to be trained from scratch. For models that share the same domain, it is likely

seeking efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation, efficient models & layers, infrastructure). Our you just read this chapter, you would be able to appreciate why we need efficiency in deep learning models today, how to think about it in terms of metrics that you care about, and finally the tools at your practical projects. With that being said, let’s start off on our journey to more efficient deep learning models. Introduction to Deep Learning Machine learning is being used in countless applications today.

Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer adoption because chapter, our focus will be on the techniques that enable us to achieve our quality goals. High quality models have an additional benefit in footprint constrained environments like mobile and edge devices where concerned with samples and labels, distillation transfers knowledge from a large model or ensemble of models to smaller models. The obvious question at this point is: why are we talking about them in the

temporal data. These breakthroughs contributed to bigger and bigger models. Although they improved the quality of the solutions, the bigger models posed deployment challenges. What good is a model that cannot chapter, we will deepdive into their architectures and use them to transform large and complex models into smaller and efficient models capable of running on mobile and edge devices. We have also set up a couple our journey with learning about embeddings in the next section. Embeddings for Smaller and Faster Models We humans can intuitively grasp similarities between different objects. For instance, when we see

techniques in the last few chapters to improve efficiency and boost the quality of deep learning models. These techniques are just a small subset of the available techniques. It is often tedious to decide need to train a model with each of these four options to make an informed decision. Blessed with a large research community, the deep learning field is growing at a rapid pace. Over the past few years, we string produces. Unlike the guitar which has a few knobs, the hyperparameter search space can be quite large. Moreover, unlike a guitar knob which is associated with a single string, hyperparameters may influence

tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point (FP8) precision on Hopper GPUs which an 8X increase in computational throughput over FP32 arithmetic. APEX AMP is included to support models that currently rely on it, but torch.cuda.amp is the future-proof alternative and offers a number paper. This model script is available on GitHub. ‣ TransformerXL model: This transformer-based language model has a segment-level recurrence and a novel relative positional encoding. The enhancements