quantization-and-model-compression

SKILLWorkflowcommunauté
v0.0.0jpoindexterUnknownMis à jour il y a 19 jSource →

Reference-grade guide to shrinking and speeding up LLMs without retraining from scratch — numeric formats (FP8/INT8/INT4), PTQ methods (GPTQ, AWQ, SmoothQuant, bitsandbytes NF4, GGUF k-quants), KV-cache quantization, speculative decoding (Medusa/EAGLE/n-gram), and distillation — with concrete number

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il y a 19 jDernière mise à jour
Skill
Auteurjpoindexter
Version0.0.0
LicenceUnknown
CatégorieWorkflow
Formatsskill.md
PromptNon publié
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Gemini
À propos

Reference-grade guide to shrinking and speeding up LLMs without retraining from scratch — numeric formats (FP8/INT8/INT4), PTQ methods (GPTQ, AWQ, SmoothQuant, bitsandbytes NF4, GGUF k-quants), KV-cache quantization, speculative decoding (Medusa/EAGLE/n-gram), and distillation — with concrete numbers, when each fits, and the quality cliffs.

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