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Quantization

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GPTQ is a post-training quantization method to make the model smaller. It quantizes the layers by finding a compressed version of that weight, that will yield a minimum mean squared error like below 👇 Given a layer 𝑙 l with weight matrix 𝑊 𝑙 W l ​ and layer input 𝑋 𝑙 X l ​ , find quantized weight ℎ 𝑎 𝑡 𝑊 𝑙 hatW l ​ : ( 𝑊 ^ 𝑙 ∗ = 𝑎 𝑟 𝑔 𝑚 𝑖 𝑛 𝑊 𝑙 ^ ∣ ∣ 𝑊 𝑙 𝑋 − 𝑊 ^ 𝑙 𝑋 ∣ ∣ 2 2 ) ( W ^ l ​ ∗ =argmin W l ​ ^ ​ ​ ∣∣W l ​ X− W ^ l ​ X∣∣ 2 2 ​ ) TGI allows you to both run an already GPTQ quantized model (see available models here) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing —quantize like below 👇 Note that TGI’s GPTQ implementation doesn’t use AutoGPTQ under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI. To quantize a given model using GPTQ with a calibration dataset, simply run This will create a new directory with the quantized files whi

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