4 advanced attention mechanisms you should know:
• Slim attention — 8× less memory, 5× faster generation by storing only K from KV pairs and recomputing V.
• XAttention — 13.5× speedup on long sequences via "looking" at the sum of values along diagonal lines in the attention matrix.
• Kolmogorov-Arnold Attention, KArAt — Adaptable attention with learnable activation functions using KANs instead of softmax.
• Multi-token attention (MTA) — Lets the model consider groups of nearby words together for smarter long-context handling.
Read the overview of them in our free article on https://huggingface.co/blog/Kseniase/attentions
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• Slim attention — 8× less memory, 5× faster generation by storing only K from KV pairs and recomputing V.
• XAttention — 13.5× speedup on long sequences via "looking" at the sum of values along diagonal lines in the attention matrix.
• Kolmogorov-Arnold Attention, KArAt — Adaptable attention with learnable activation functions using KANs instead of softmax.
• Multi-token attention (MTA) — Lets the model consider groups of nearby words together for smarter long-context handling.
Read the overview of them in our free article on https://huggingface.co/blog/Kseniase/attentions
https://t.iss.one/DataScienceM
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100 Important Data Science Interview Questions.pdf
11.7 MB
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Forwarded from Python | Machine Learning | Coding | R
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Datasets Guide 📚
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A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.
Importance:
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Link: https://docs.unsloth.ai/basics/datasets-guide
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A Complete Course to Learn Robotics and Perception
Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
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Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson
github.com/gtbook/robotics
roboticsbook.org/intro.html
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