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Tag: efficient fine

  • Cloud Blog: Supervised Fine Tuning for Gemini: A best practices guide

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/master-gemini-sft/ Source: Cloud Blog Title: Supervised Fine Tuning for Gemini: A best practices guide Feedly Summary: Foundation models such as Gemini have revolutionized how we work, but sometimes they need guidance to excel at specific business tasks. Perhaps their answers are too long, or their summaries miss the mark. That’s where supervised fine-tuning…

  • Cloud Blog: Orchestrating GPU-based distributed training workloads on AI Hypercomputer

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/gpu-orchestration-options-on-ai-hypercomputer/ Source: Cloud Blog Title: Orchestrating GPU-based distributed training workloads on AI Hypercomputer Feedly Summary: When it comes to AI, large language models (LLMs) and machine learning (ML) are taking entire industries to the next level. But with larger models and datasets, developers need distributed environments that span multiple AI accelerators (e.g. GPUs…

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

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    system automation
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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: 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,…