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← LLM Quantization and Compression

Section 0 · Lecture 1 · 1:06

welcome and course overview

section 1 intro →

0. Welcome

Free

welcome and course overview1:06

1. LLM Quantization GPTQ and AWQ

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section 1 intro0:12
why llm quantization and activation outliers9:42
gptq hessian error compensation9:24
awq activation aware scaling8:42
bit width group size and hub models10:30
summary and exercises hessian gptq10:24

2. GGUF bitsandbytes QLoRA and Serving

section 2 intro0:12
bitsandbytes llm int8 nf4 double quant12:30
gguf and the llama cpp ecosystem9:12
qlora fine tuning11:54
serving with vllm tgi and choosing a method10:12
exercises kv cache nf4 study and summary10:30

3. Pruning

section 3 intro0:12
pruning intuition lottery ticket three dimensions9:00
unstructured pruning random l1 weight orig mask10:48
structured pruning channels neurons compaction10:24
local vs global one shot vs iterative finetuning11:12
random vs l1 curves summary exercises9:06

4. Distillation Full Pipeline and Final Project

section 4 intro0:06
knowledge distillation10:42
pruning quantization combination and nas11:06
deployment pipeline and final project setup10:24
final project walkthrough and course wrapup11:48

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