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Quantization in modern LLMs - Advanced Quantization Techniques for Large Language Models Video Tutorial | LinkedIn Learning, formerly Lynda.com
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Have you used quantization with an open source machine learning library, and wondered how quantization works? How can you preserve model accuracy as you compress from 32 bits to 16, 8, or even 2 bits? In our new short course, Quantization in Depth, taught by Hugging Face's Marc Sun and Younes Belkada, you'll learn to implement variants of linear quantization, such as asymmetric and symmetric modes, from scratch. You'll also quantize at different granularities (per-tensor, per-channel, per-group)
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This is the clearest explanation of how LLM quantization works:It lets engineers compress a model by 4x and run it 2x faster without quality loss.I stumbled on this piece by Sam Rose and honestly wish I had it when I first tried to understand quantization.He breaks it down from absolute zero - bits, floats, how weights are stored - all the way to actually benchmarking quantized models. With interactive demos you can play with.Here's the core idea in 60 seconds:→ An LLM is billions of numbers (we
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