Experimental News Clipping Site

Tag: parameter-efficient fine-tuning

  • AWS News Blog: Announcing Amazon Nova customization in Amazon SageMaker AI

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    Kurt Seifried
    in

    Source URL: https://aws.amazon.com/blogs/aws/announcing-amazon-nova-customization-in-amazon-sagemaker-ai/ Source: AWS News Blog Title: Announcing Amazon Nova customization in Amazon SageMaker AI Feedly Summary: AWS now enables extensive customization of Amazon Nova foundation models through SageMaker AI with techniques including continued pre-training, supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback and model distillation to better address domain-specific requirements across…

  • Cloud Blog: New GKE inference capabilities reduce costs, tail latency and increase throughput

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/understanding-new-gke-inference-capabilities/ Source: Cloud Blog Title: New GKE inference capabilities reduce costs, tail latency and increase throughput Feedly Summary: When it comes to AI, inference is where today’s generative AI models can solve real-world business problems. Google Kubernetes Engine (GKE) is seeing increasing adoption of gen AI inference. For example, customers like HubX run…

  • Cloud Blog: Google, Bytedance, and Red Hat make Kubernetes generative AI inference aware

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/google-bytedance-and-red-hat-improve-ai-on-kubernetes/ Source: Cloud Blog Title: Google, Bytedance, and Red Hat make Kubernetes generative AI inference aware Feedly Summary: Over the past ten years, Kubernetes has become the leading platform for deploying cloud-native applications and microservices, backed by an extensive community and boasting a comprehensive feature set for managing distributed systems. Today, we are…

  • Hacker News: CleaR: Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Labels

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    system automation
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    Source URL: https://arxiv.org/abs/2411.00873 Source: Hacker News Title: CleaR: Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Labels Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel approach to Parameter-Efficient Fine-Tuning (PEFT) designed to enhance model performance when working with noisy labeled data. This research is particularly relevant for professionals in AI,…

  • Cloud Blog: When to use supervised fine-tuning for Gemini

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/supervised-fine-tuning-for-gemini-llm/ Source: Cloud Blog Title: When to use supervised fine-tuning for Gemini Feedly Summary: Have you ever wished you could get a foundation model to respond in a particular style, exhibit domain-specific expertise, or excel at a specific task? While foundation models like Gemini demonstrate remarkable capabilities out-of-the-box, there can be a gap…