Cloud Security Alliance News Clipping Site

Tag: parameter

  • AWS News Blog: Meet your training timelines and budgets with new Amazon SageMaker HyperPod flexible training plans

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    Source URL: https://aws.amazon.com/blogs/aws/meet-your-training-timelines-and-budgets-with-new-amazon-sagemaker-hyperpod-flexible-training-plans/ Source: AWS News Blog Title: Meet your training timelines and budgets with new Amazon SageMaker HyperPod flexible training plans Feedly Summary: Unlock efficient large model training with SageMaker HyperPod flexible training plans – find optimal compute resources and complete training within timelines and budgets. AI Summary and Description: Yes **Summary:** The announcement…

  • Cloud Blog: (QR) Coding My Way Out of Here: C2 in Browser Isolation Environments

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    Source URL: https://cloud.google.com/blog/topics/threat-intelligence/c2-browser-isolation-environments/ Source: Cloud Blog Title: (QR) Coding My Way Out of Here: C2 in Browser Isolation Environments Feedly Summary: Written by: Thibault Van Geluwe de Berlaere Executive Summary Browser isolation is a security technology where web browsing activity is separated from the user’s local device by running the browser in a secure environment,…

  • Hacker News: What happens if we remove 50 percent of Llama?

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    Source URL: https://neuralmagic.com/blog/24-sparse-llama-smaller-models-for-efficient-gpu-inference/ Source: Hacker News Title: What happens if we remove 50 percent of Llama? Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The document introduces Sparse Llama 3.1, a foundational model designed to improve efficiency in large language models (LLMs) through innovative sparsity and quantization techniques. The model offers significant benefits in…

  • Hacker News: Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

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    Source URL: https://arxiv.org/abs/2411.12580 Source: Hacker News Title: Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models Feedly Summary: Comments AI Summary and Description: Yes Summary: The paper discusses how procedural knowledge in pretraining influences the reasoning capabilities of Large Language Models (LLMs). It reveals that while LLMs demonstrate proficiency in problem-solving, their reasoning is…

  • Hacker News: Large Language Models as Markov Chains

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    Source URL: https://arxiv.org/abs/2410.02724 Source: Hacker News Title: Large Language Models as Markov Chains Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents a theoretical analysis of large language models (LLMs) by framing them as equivalent to Markov chains. This approach may unveil new insights into LLM performance, pre-training, and generalization, which are…

  • Hacker News: DeepThought-8B: A small, capable reasoning model

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    Source URL: https://www.ruliad.co/news/introducing-deepthought8b Source: Hacker News Title: DeepThought-8B: A small, capable reasoning model Feedly Summary: Comments AI Summary and Description: Yes Summary: The release of DeepThought-8B marks a significant advancement in AI reasoning capabilities, emphasizing transparency and control in how models process information. This AI reasoning model, built on the LLaMA-3.1 architecture, showcases how smaller,…

  • Simon Willison’s Weblog: 0xfreysa/agent

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    Source URL: https://simonwillison.net/2024/Nov/29/0xfreysaagent/#atom-everything Source: Simon Willison’s Weblog Title: 0xfreysa/agent Feedly Summary: 0xfreysa/agent Freysa describes itself as “the world’s first adversarial agent game". On 22nd November they released an LLM-driven application which people could pay to message (using Ethereum), with access to tools that could transfer a prize pool to the message sender, ending the game.…

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

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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,…